Articles de revues : « Turbune a ga »

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Alawaad, Nasir Ahmed. « Steam turbine controllers design based on soft-computing techniques ». IAES International Journal of Robotics and Automation (IJRA) 9, n^o 4 (1 décembre 2020) : 281. http://dx.doi.org/10.11591/ijra.v9i4.pp281-291.

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Steam turbine is viewed as a standout among hotspots for control age in the most recent decades, its elements examination end up being dynamically more basic. For this investigation, the model chose is of turbine speed control framework. The purpose behind this is that model is regularly experienced in refineries in a type of steam turbine that utilization hydraulic governor to control the speed of the turbine. To suit plan prerequisites, a mathematical model for the turbine was determined in light of transfer function and state space definition. There are two sorts of controllers for steam turbines which are traditional and modern controllers. Internal mode control with proportional integral derivative (IMC-PID) and linear quadratic controller (LQR) are classical type. Fuzzy logic controller (FLC) and intelligent optimization techniques like, ant colony algorithm (ACOA) and genetic algorithm (GA) are modern type. The proposed work centers on classical verses modern controllers. Results got demonstrate that embracing such a controller (GA) improves the design requirements and transient stability. The system control was actualized in simulation utilizing MATLAB/Simulink.

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Fakhri, Eyman, Salvy Bourguet, Jérôme Thiébot, Mohamed Machmoum et Hamid Gualous. « Optimization of the electrical connection topology of a tidal farm network ». E3S Web of Conferences 191 (2020) : 04002. http://dx.doi.org/10.1051/e3sconf/202019104002.

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This article presents an approach to optimize the electrical connection topology of tidal energy converters in a tidal farm. The methodology is based on a genetic algorithm (GA). The main purpose is to present a technique of coding to find the best electrical connection topology of the tidal farm network. The optimization model takes into account the energy loss in the submarine cables. The model gives as its output the optimal number of turbine clusters connected to each offshore substation, the number of turbines in each cluster, the cross-section of MV and HV cables, the connection design for each cluster of turbines as well as the number and the locations of the offshore substations. A particle swarm optimization algorithm (PSO) is used to confirm the results obtained with the GA. The optimization approach is applied to the Fromveur Strait (France).

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Mrzljak, Vedran, Nikola Anđelić, Ivan Lorencin et Sandi Sandi Baressi Šegota. « The influence of various optimization algorithms on nuclear power plant steam turbine exergy efficiency and destruction ». Pomorstvo 35, n^o 1 (30 juin 2021) : 69–86. http://dx.doi.org/10.31217/p.35.1.8.

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This paper presents an exergy analysis of the whole turbine, turbine cylinders and cylinder parts in four different operating regimes. Analyzed turbine operates in nuclear power plant while three of four operating regimes are obtained by using optimization algorithms – SA (Simplex Algorithm), GA (Genetic Algorithm) and IGSA (Improved Genetic-Simplex Algorithm). IGSA operating regime gives the highest developed mechanical power of the whole turbine equal to 1022.48 MW, followed by GA (1020.06 MW) and SA (1017.16 MW), while in Original operating regime whole turbine develop mechanical power equal to 996.29 MW. In addition, IGSA causes the highest increase in developed mechanical power of almost all cylinders and cylinder parts in comparison to the Original operating regime. All observed optimization algorithms increases the exergy destruction of the whole turbine in comparison to Original operating regime - the lowest increase causes IGSA, followed by GA and finally SA. The highest exergy efficiency of the whole turbine, equal to 85.92% is obtained by IGSA, followed by GA (85.89%) and SA (85.82%), while the lowest exergy efficiency is obtained in Original operating regime (85.70%). Analyzed turbine, which operates by using wet steam is low influenced by the ambient temperature change. IGSA, which shows dominant performance in exergy analysis parameters of the analyzed turbine, in certain situations is overpowered by GA. Therefore, in optimization of steam turbine performance, IGSA and GA can be recommended.

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Bin Ali, Muhammad, Zeshan Ahmad, Saad Alshahrani, Muhammad Rizwan Younis, Irsa Talib et Muhammad Imran. « A Case Study : Layout Optimization of Three Gorges Wind Farm Pakistan, Using Genetic Algorithm ». Sustainability 14, n^o 24 (17 décembre 2022) : 16960. http://dx.doi.org/10.3390/su142416960.

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Wind is an important renewable energy source. The majority of wind farms in Pakistan are installed in Jhimpir, Sindh Wind Corridor. At this location, downstream turbines encounter upstream turbines’, wake, decreasing power output. To maximize the power output, there is a need to minimize these wakes. In this research, a method is proposed to maximize the power output using a Genetic Algorithm (GA). Hub heights and inter-turbine spacing are considered variables in this method. Two wind farms located at Jhimpir, Sindh, namely, Second and Third Three Gorges Wind Farms (TGWFs), have been analyzed. Three different cases are considered to maximize the power output. In Case 1, thesame hub heights and inter-turbine spacing without wake effects are considered. In Case 2, the same hub heights and inter-turbine spacing with wake effects are considered. In Case 3, variable hub heights and inter-turbine spacing with wake effects are considered. The results revealed that TGWFs, with variable hub heights and inter-turbine spacing, produce more power output. It is also revealed that the increase in power output, in the case of two different hub heights, is greater in comparison to three different hub heights. Eventually, the proposed method may help in the layout optimization of a wind farm.

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Ye, Jiawei, Wei Zeng, Zhigao Zhao, Jiebin Yang et Jiandong Yang. « Optimization of Pump Turbine Closing Operation to Minimize Water Hammer and Pulsating Pressures During Load Rejection ». Energies 13, n^o 4 (23 février 2020) : 1000. http://dx.doi.org/10.3390/en13041000.

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In load rejection transitional processes in pumped-storage plants (PSPs), the process of closing pump turbines, including guide vane (GVCS) and ball valve closing schemes (BVCS), is crucial for controlling pulsating pressures and water hammer. Extreme pressures generated during the load rejection process may result in fatigue damage to turbines, and cracks or even bursts in the penstocks. In this study, the closing schemes for pump turbine guide vanes and ball valves are optimized to minimize water hammer and pulsating pressures. A model is first developed to simulate water hammer pressures and to estimate pulsating pressures at the spiral case and draft tube of a pump turbine. This is combined with genetic algorithms (GA) or non-dominated sorting genetic algorithm II (NSGA-II) to realize single- or multi-objective optimizations. To increase the applicability of the optimized result to different scenarios, the optimization model is further extended by considering two different load-rejection scenarios: full load-rejection of one pump versus two pump turbines, simultaneously. The fuzzy membership degree method provides the best compromise solution for the attained Pareto solutions set in the multi-objective optimization. Employing these optimization models, robust closing schemes can be developed for guide vanes and ball valves under various design requirements.

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Zhou, Ling, Qiancheng Zhao, Xian Wang et Anfeng Zhu. « Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN ». Energies 14, n^o 21 (21 octobre 2021) : 6905. http://dx.doi.org/10.3390/en14216905.

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When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA).

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Kirchner-Bossi, Nicolas, et Fernando Porté-Agel. « Wind farm layout and unconstrained hub height optimization using genetic algorithms applied to different power densities ». Journal of Physics : Conference Series 2265, n^o 4 (1 mai 2022) : 042049. http://dx.doi.org/10.1088/1742-6596/2265/4/042049.

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Abstract LES and wind tunnel studies have shown significant benefit when allowing turbines (T) in a wind farm to adopt different heights. This work presents two new genetic algorithms (GA) that perform wind farm layout optimization (WFLO) involving continuous and top-unconstrained Z-coordinate (XYZ-WFLO), applied to different power densities (PD) and using Horns Rev 1 as case study. One provides each turbine the possibility to adopt any height (XYZInd ). The other is a self-adaptive GA allowing turbines to automatically cluster into a fixed number of maximum heights (XYZClus). When considering 80T, compared to the baseline the levelized cost of energy (LCOE) is reduced up to 2.3% (XYZInd ), vs. a 0.88% improvement obtained through XY-WFLO. XYZClus shows performances close to XYZInd even with just 2 Z-clusters (2%), which can entail a more feasible solution for the industry. The allowance for different heights exerts the main role in the performance improvement, in contrast to merely allowing turbines to increase their height. Results considering different PD yield the optimum XYZ-WFLO performance through 70T (2.5% LCOE decrease), while XY-WFLO provides best results considering 60T (1.5%). This indicates that the most efficient XYZ-WFLO solution also allows for bigger power productions. The benefit of XYZ-WFLO against XY-WFLO increases with PD. The optimized solutions arrange turbines into very few different heights, whose amount is positively related to PD. Finally, it is verified that the solutions attained reproduce the vertically staggered patterns proposed in conceptual studies (LES, wind tunnel).

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Yang, Wanqing. « Comparison of SAGA and GA in Wind Turbine Layout Optimization Based on Modified Two-dimensional Wake Model or Jensen Model ». Journal of Physics : Conference Series 2418, n^o 1 (1 février 2023) : 012108. http://dx.doi.org/10.1088/1742-6596/2418/1/012108.

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Abstract In this paper, Simulated Annealing Genetic Algorithm (SAGA) and a new two-dimensional wake model called 2D_k Jensen model are adopted for optimal wind turbine layout (WTL) in the wind farms and are compared with Genetic Algorithm (GA) and Jensen model, respectively, aiming to minimize the investment cost and maximize the wind power generation as much as possible. The influence of the radial distribution of wake on the equivalent wind speed in the wake superposition region is considered. In the case of single wind direction and single speed, total output power and energy extraction efficiency are both improved when SAGA is applied to the two model conditions respectively, especially for the WTL using the 2D_k Jensen model, these two aspects are significantly improved by 13.75% and 24.10%, respectively, and the objective function is reduced by 19.05%. The results demonstrate that SAGA is more conducive to solving the practical configuration optimization of wind turbines, compared with the original GA.

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Yan, Tao, Xian Min Lin et You Ping Zhong. « A Load Allocation Optimization among Turbine-Generators Based on GA-ACO ». Advanced Materials Research 614-615 (décembre 2012) : 1049–54. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1049.

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With regard to the slow-convergence disadvantage in the latter generations of GA, ACO is combined with GA in this paper to solve the optimal load allocation problem among thermal power plant units. By comparison with GA method, results show that GA-ACO has faster convergence than the GA method.

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Asfour, R., T. Brahimi et MF El-Amin. « Wind Farm Layout : Modeling and Optimization Using Genetic Algorithm ». IOP Conference Series : Earth and Environmental Science 1008, n^o 1 (1 avril 2022) : 012004. http://dx.doi.org/10.1088/1755-1315/1008/1/012004.

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Abstract Wind Farm Layout Optimization (WFLO) is a complex multidisciplinary topic that requires a lot of expertise and is becoming an essential part of today’s wind farm planning. Yet, selecting optimum wind farm locations is complex, time-consuming, and influenced by environmental factors and upstream turbines inflow wind. The present study attempts to develop an optimization approach based on the Genetic Approach (GA) to determine the most suitable wind turbine locations that maximize the net energy production while minimizing the Cost of Energy (COE) ($/kWh). The WFLO for the optimized objective function was performed for 500, 1000, and 1500 iterations. The best output was obtained for 1500 iterations with the lowest value for the objective function.

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Li, Yi-Guang. « Diagnostics of power setting sensor fault of gas turbine engines using genetic algorithm ». Aeronautical Journal 121, n^o 1242 (3 juillet 2017) : 1109–30. http://dx.doi.org/10.1017/aer.2017.49.

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ABSTRACTGas path diagnostics is one of the most effective condition monitoring techniques in supporting condition-based maintenance of gas turbines and improving availability and reducing maintenance costs of the engines. The techniques can be applied to the health monitoring of different gas path components and also gas path measurement sensors. One of the most important measurement sensors is that for the engine control, also called the power setting sensor, which is used by the engine control system to control the operation of gas turbine engines. In most of the published research so far, it is rarely mentioned that faults in such sensors have been tackled in either engine control or condition monitoring. The reality is that if such a sensor degrades and has a noticeable bias, it will result in a shift in engine operating condition and misleading diagnostic results.In this paper, the phenomenon of a power-setting sensor fault has been discussed and a gas path diagnostic method based on a Genetic Algorithm (GA) has been proposed for the detection of power-setting sensor fault with and without the existence of engine component degradation and other gas path sensor faults. The developed method has been applied to the diagnostic analysis of a model aero turbofan engine in several case studies. The results show that the GA-based diagnostic method is able to detect and quantify the power-setting sensor fault effectively with the existence of single engine component degradation and single gas path sensor fault. An exceptional situation is that the power-setting sensor fault may not be distinguished from a component fault if both faults have the same fault signature. In addition, the measurement noise has small impact on prediction accuracy. As the GA-based method is computationally slow, it is only recommended for off-line applications. The introduced GA-based diagnostic method is generic so it can be applied to different gas turbine engines.

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Liu, Zheng, Xin Liu, Kan Wang, Zhongwei Liang, José A. F. O. Correia et Abílio De Jesus. « GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades ». Energies 12, n^o 6 (15 mars 2019) : 1026. http://dx.doi.org/10.3390/en12061026.

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This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades.

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Sun, Wei, et Le Shen. « Comprehensive Evaluation of Wind Turbine Type Selection Based on GA-SVR Model ». Advanced Materials Research 468-471 (février 2012) : 579–82. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.579.

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Aiming at the current situation of wind turbine type selection in China, this paper has built a more scientific and systematic index system for comprehensive evaluation of wind turbine type selection, and also applied the Support Vector Regression machine evaluation model with parameters optimized by Genetic Algorithm. Through automatic global optimization for parameters, this model has reached an extremely high accuracy required for evaluation of type selection. Empirical analysis shows that the application of this model has a realistic popularized significance for improving the method of the wind turbine type selection and enhancing its efficiency.

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Tamizifar, Mohammad, Masoud Mosayebi et Saeid Ziaei-Rad. « Optimal sensor placement and model updating applied to the operational modal analysis of a nonuniform wind turbine tower ». Mechanical Sciences 13, n^o 1 (6 avril 2022) : 331–40. http://dx.doi.org/10.5194/ms-13-331-2022.

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Abstract. Test planning is a crucial step in the operational modal analysis (OMA) of wind turbines (WT), and it is an essential part of choosing the best positions for installing the sensors of the structures. On the other hand, updating the finite element model (FEM) with the OMA results implies a better prediction of the real structure's dynamic and vibrational behavior. This paper aims to show how the OMA of a nonuniform and two-section wind turbine tower can be performed more effectively, using the required test planning and optimal sensor placement. Then, accordingly, the OMA is used in operating and parked conditions to find the objective bending mode characteristics. Moreover, the updating of the applicable FEM of the multi-sectional wind turbine tower will be described. The tailor-made genetic algorithm (GA) is used to find the MEMS (micro electro-mechanical system) sensors' optimal positions of the WT under study. The OMA was performed and the acquired data analyzed using the stochastic subspace identification (SSI) method. Based on the OMA results, the FEM is updated by applying the sensitivity method. The results show that a tailor-made GA is a practical and quick approach to finding the optimal position of the sensors to obtain the best results for the objective modes of the WT. The OMA results, under operating and parked conditions, prove some modal characteristics of WTs. Based on the sensitivity analysis and engineering judgment, the modulus of elasticity was selected as a parameter for updating. Finally, we found that the updated FEM had less than 1 % error compared to the obtained frequencies from the test.

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Jiao, Bin, et Zhi Wei Gao. « The Fault Diagnosis of Wind Turbine Gearbox Based on QGA—LSSVM ». Applied Mechanics and Materials 543-547 (mars 2014) : 950–55. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.950.

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A quantum genetic algorithm (QGA) with good global optimization ability and fast convergence speed is proposed to solve the parameter selection problems of least squares support vector machine (LSSVM) on wind turbine gearbox fault diagnosis model. The method can convert the LSSVM model parameter selection into optimization. It overcomes the problem that GA is easy to fall into local optimum in the optimization process and it also improves the optimization performance. A series experiments are carried out on the data sets of UCI database. Compared with GA—LSSVM and CV—LSSVM, the classification accuracy is improved. Finally QGA—LSSVM model is applied to the wind turbine gearbox diagnosis and a good result is achieved.

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He, Qing, et Jian Ding Zhang. « Application of Improved Genetic Algorithm in Maintenance Decision for Turbine-Generator Unit ». Applied Mechanics and Materials 44-47 (décembre 2010) : 2940–44. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2940.

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The complicated function relations are more prone to appear in the maintenance scheduling of steam-turbine generator unit. Many constrained conditions are often attendant with these function relations. In these situations, the traditional method often can not obtain the exact value. The genetic algorithm (GA), a kind of the heuristic algorithms, does not need the function own good analytic properties. In addition, as the operating unit of GA is the group, so it applies to the parallel computing process. In GA executive process, the offspring continually inherit the genes from the parents, so it is more prone to be involved in the local convergence. An improved genetic algorithm is proposed and used in the model of maintenance decision of turbine-generator unit under. The goal of the model is to seek to the rational maintenance scheduling of the generator unit, so as to minimize the sum of the maintenance expense, the loss of the profit on the generated energy, and the loss of the penalty. It is proved by the example that IGA is highly efficient.

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Wang, Li Ying, Wei Guo Zhao et Chuan Hong Zhang. « Application of ANN Trained with GA in Energy Characteristics of Hydraulic Turbine ». Advanced Materials Research 108-111 (mai 2010) : 692–95. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.692.

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The learning algorithm of artificial neural network (ANN) trained with genetic algorithm (GA) are introduced, based on the operation data of hydropower station, the network model of energy characteristics is established based on GA-ANN, the relationship curve between head H and output N is gained under some efficiency. The results show that the algorithm is better than BP neural network and avoid the limitations of BP neural network, the results can be used in the optimal operation of hydropower, and it has a practical significance. The results show the new model has a great importance in hydraulic unit study. It could be generalized into other all efficiency prediction, and it offers a new way in water conservancy and at the meantime a new method for the study of ANN and GA.

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SOUGUEH, Ismail Mohamed, et Göksu GÖREL. « Pitch Angle Control of Wind Turbine with PI, PID and GA-PID controller ». Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi 14, n^o 2 (31 juillet 2022) : 502–13. http://dx.doi.org/10.29137/umagd.1036461.

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Günümüzde yenilenebilir enerji kaynaklarının enerji piyasasındaki payı yükseliş içerisindedir. Bu kaynakların en önemlisi ise rüzgâr enerjisidir. Rüzgâr hızına bağlı olarak rüzgâr türbin hız kontrolü önemli kontrol parametresidir. Bu hız parametresinin yüksek rüzgâr hızlarında türbin ve diğer önemli parçalara zarar vermemesi için denetim altında tutulması gerekmektedir. Bu çalışmada, PI, PID ve GA-PID kontrolör ile bir rüzgâr türbinin hatve açısını kontrol etmek için bir modelleme yapılmıştır. Yüksek rüzgâr hızlarında enerji sistemine zarar verilmemesi ve değişken rüzgâr hızının çıkış gücüne çıkış gücüne etkisinin kabul edilebilir ölçülerde kalmasını sağlamak PI, PID ve GA-PID kontrolör yöntemi kullanılmıştır. Çıkış gücünün sabit tutulması hedeflenmiştir. Matlab/Simulink programında simülasyon olarak rüzgâr türbini modellenmiştir. Bu çalışmada çıkış gücünü noktası (500 KW) GA-PID kontrolörün uygulanmasıyla daha optimum seviyede kalması sağlanmıştır.

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Sen-chun, Miao, Zhang Hong-biao, Wang Ting-ting, Wang Xiao-hui et Shi Feng-xia. « Optimal design of blade in pump as turbine based on multidisciplinary feasible method ». Science Progress 103, n^o 4 (octobre 2020) : 003685042098210. http://dx.doi.org/10.1177/0036850420982105.

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In order to make the pump as turbine (PAT) run efficiently and safely, a multidisciplinary optimization design method for PAT blade, which gives consideration to both the hydraulic and intensity performances, is proposed based on multidisciplinary feasibility (MDF) optimization strategy. This method includes blade parametric design, Latin Hypercube Sampling (LHS) experimental design, CFD technology, FEA technology, GA-BP neural network and NSGA-II algorithm. Specifically, a parameterized PAT blade with cubic non-uniform B-spline curve is adopted, and the control point of blade geometry is taken as the design variable. The LHS experimental design method obtains the sample points of training GA-BP neural network in the design space of variables. The hydraulic performance of each sample point (including the hydraulic pressure load on the blade surface) and the strength performance analysis of blades are completed by CFD and FEA technology respectively. In order to save calculation time of the whole optimization design, the multi-disciplinary performance analysis of each sample in the optimization process is completed by single-coupling method. Then, GA-BP neural network is trained. Finally, the multi-disciplinary optimization design problem of PAT blade is solved by the optimization technology combining GA-BP neural network and NSGA-II algorithm. Based on this optimization method, the PAT blade is optimized and improved. The efficiency of the optimized PAT is improved by 1.71% and the maximum static stress on the blade is reduced by 7.98%, which shows that this method is feasible.

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Johar, Farhana, Julies Bong Shu Ai et Fuaada Mohd Siam. « Sizing Optimization of Hybrid Photovoltaic-Wind-Battery System towards Zero Energy Building using Genetic Algorithm ». MATEMATIKA 36, n^o 3 (1 décembre 2020) : 235–50. http://dx.doi.org/10.11113/matematika.v36.n3.1237.

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A new topic of Zero Energy Building (ZEB) is getting famous in research areabecause of its goal of reaching zero carbon emission and low building cost. Renewableenergy system is one of the ideas to achieve the objective of ZEB. Genetic Algorithm (GA)is widely used in many research areas due to its capability to escape from a local minimalto obtain a better solution. In our study, GA is chosen in sizing optimization of thenumber of photovoltaic, wind turbine and battery of a hybrid photovoltaic-wind-batterysystem. The aim is to minimize the total annual cost (TAC) of the hybrid energy systemtowards the low cost concept of ZEB. Two GA parameters, which are generation numberand population size, have been analysed and optimized in order to meet the minimumTAC. The results show that the GA is efficient in minimizing cost function of a hybridphotovoltaic-wind-battery system with its robustness property

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Hamdan, Mohammad, et Mohammad Hassan Abderrazzaq. « Optimization of Small Wind Turbines using Genetic Algorithms ». International Journal of Applied Metaheuristic Computing 7, n^o 4 (octobre 2016) : 50–65. http://dx.doi.org/10.4018/ijamc.2016100104.

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This paper presents a detailed optimization analysis of tower height and rotor diameter for a wide range of small wind turbines using Genetic Algorithm (GA). In comparison with classical, calculus-based optimization techniques, the GA approach is known by its reasonable flexibilities and capability to solve complex optimization problems. Here, the values of rotor diameter and tower height are considered the main parts of the Wind Energy Conversion System (WECS), which are necessary to maximize the output power. To give the current study a practical sense, a set of manufacturer's data was used for small wind turbines with different design alternatives. The specific cost and geometry of tower and rotor are selected to be the constraints in this optimization process. The results are presented for two classes of small wind turbines, namely 1.5kW and 10kW turbines. The results are analyzed for different roughness classes and for two height-wind speed relationships given by power and logarithmic laws. Finally, the results and their practical implementation are discussed.

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Subramanian, Senthilkumar, Chandramohan Sankaralingam, Rajvikram Madurai Elavarasan, Raghavendra Rajan Vijayaraghavan, Kannadasan Raju et Lucian Mihet-Popa. « An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III ». Sustainability 13, n^o 1 (5 janvier 2021) : 410. http://dx.doi.org/10.3390/su13010410.

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Wind energy is an abundant renewable energy resource that has been extensively used worldwide in recent years. The present work proposes a new Multi-Objective Optimization (MOO) based genetic algorithm (GA) model for a wind energy system. The proposed algorithm consists of non-dominated sorting which focuses to maximize the power extraction of the wind turbine, minimize the cost of generating energy, and the lifetime of the battery. Additionally, the performance characteristics of the wind turbine and battery energy storage system (BESS) are analyzed specifically torque, current, voltage, state of charge (SOC), and internal resistance. The complete analysis is carried out in the MATLAB/Simulink platform. The simulated results are compared with existing optimization techniques such as single-objective, multi-objective, and non-dominating sorting GA II (Genetic Algorithm-II). From the observed results, the non-dominated sorting genetic algorithm (NSGA III) optimization algorithm offers superior performance notably higher turbine power output with higher torque rate, lower speed variation, reduced energy cost, and lesser degradation rate of the battery. This result attested to the fact that the proposed optimization tool can extract a higher rate of power from a self-excited induction generator (SEIG) when compared with a conventional optimization tool.

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Zhang, Fang Fang, Zhen Shan Zhang et Rui Zhu. « Optimization Design Study on a New Type Underwater Turbine Engine ». Advanced Materials Research 850-851 (décembre 2013) : 292–95. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.292.

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In order to improve economic performance of a new type underwater turbine engine, objective function with maximizing inner efficiency in design condition is constructed based on establishing design calculation model of single stage impulse turbine engine. By using GA algorithm, optimization match study is finished among four parameters, such as working back pressure of turbine engine, mean diameter of turbine cascade, air-in inclination angle and diffuse angle of nozzle, and whose effect on inner efficiency is studied respectively. Results show that, for the engine studied, inner efficiency has sensitivity to working back pressure, while not to diffuse angle of nozzle. Which has been improved by 6.24% after optimization design in design condition, and working substance consumption per second has been decreased by 5.87% correspondingly, so economic performance of the studied engine has been improved obviously. The established model and its calculation results provide initial model and parameters for thermodynamic simulation for turbine engine in off-design condition.

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Debbah, Abdesselam, et Hamid Kherfane. « GA/PSO Robust Sliding Mode Control of Aerodynamics in Gas Turbine ». Acta Universitatis Sapientiae Electrical and Mechanical Engineering 10, n^o 1 (1 décembre 2018) : 42–66. http://dx.doi.org/10.2478/auseme-2018-0003.

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Abstract In gas turbine process, the axial compressor is subjected to aerodynamic instabilities because of rotating stall and surge associated with bifurcation nonlinear behaviour. This paper presents a Genetic Algorithm and Particle Swarm Optimization (GA/PSO) of robust sliding mode controller in order to deal with this transaction between compressor characteristics, uncertainties and bifurcation behaviour. Firstly, robust theory based equivalent sliding mode control is developed via linear matrix inequality approach to achieve a robust sliding surface, then the GA/PSO optimization is introduced to find the optimal switching controller parameters with the aim of driving the variable speed axial compressor (VSAC) to the optimal operating point with minimum control effort. Since the impossibility of finding the model uncertainties and system characteristics, the adaptive design widely considered to be the most used strategy to deal with these problems. Simulation tests were conducted to confirm the effectiveness of the proposed controllers.

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Amine, Haraoubia Mohamed, Hamzaoui Abdelaziz et Essounbouli Najib. « Wind Turbine Maximum Power Point Tracking Using FLC Tuned with GA ». Energy Procedia 62 (2014) : 364–73. http://dx.doi.org/10.1016/j.egypro.2014.12.398.

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Wu, Jie, Jiong Shen, Mattias Krug, Sing Kiong Nguang et Yiguo Li. « GA-based nonlinear predictive switching control for a boiler-turbine system ». Journal of Control Theory and Applications 10, n^o 1 (28 décembre 2011) : 100–106. http://dx.doi.org/10.1007/s11768-012-0050-x.

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Nugroho, Herminarto, Nabilla Ananda Yusva et Ortega Incon Marama Pandiangan. « Penerapan Metode Particle Swarm Optimization dan Genetic Algorithm pada Optimisasi Sudut Kelengkungan Turbin Air Banki Untuk Mendapatkan Efisiensi Daya Optimal ». ENERGI & ; KELISTRIKAN 14, n^o 1 (27 juin 2022) : 82–89. http://dx.doi.org/10.33322/energi.v14i1.1636.

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Indonesia memiliki banyak sumber daya seperti panas bumi, surya, angin, air, dan lain sebagainya. Diantara banyaknya sumber daya, air merupakan sumber daya yang paling dominan bagi Indonesia. Karena jumlah ketersediaan air di Indonesia mencapai 694 milyar meter kubik pertahunnya. Sehingga, pembangkit listrik tenaga air banyak digunakan sebagai pemasok energi. Pada jurnal ini membahas efisiensi pembangkit listrik tenaga air menggunakan model turbin air Banki yang ditulis oleh Mockmore dan Merryfield pada tahun 1949. Permasalahan yang akan dibahas adalah bagaimana memaksimalkan daya keluaran turbin dengan menggunakan metode optimasi Particle Swarm Optimization (PSO) dan Genetic Algorithm (GA). Perbedaan yang didapat dengan menggunakan kedua metode ini adalah nilai maksimum daya keluaran, efisiensi dan jumlah iterasi maksimum yang dilakukan. Berdasarkan perbedaan tersebut maka dapat dikatakan bahwa metode PSO lebih baik daripada GA dalam memaksimalkan daya keluaran dan efisiensi dari turbin

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Sule, Aliyu Hamza, Ahmad Safawi Mokhtar, Jasrul Jamani Bin Jamian, Attaullah Khidrani et Raja Masood Larik. « Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer ». International Journal of Electrical and Computer Engineering (IJECE) 10, n^o 5 (1 octobre 2020) : 5251. http://dx.doi.org/10.11591/ijece.v10i5.pp5251-5261.

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The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the PSO and GA tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and the controller step input response. The GWO, the Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA) tuning methods were implemented in the Matlab 2016b to search the optimal gains of the Proportional and Integral controller through minimization of the objective function. A comparison was made between the results obtained from the GWO tuning method against PSO and GA tuning techniques. The GWO computed the smallest value of the objective function minimized. It exhibited faster convergence and better time response specification compared to other methods. These and more performance indicators show the superiority of the GWO tuning method.

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Zhao, Jie, Li Wang, Yu Heng Tang, Di Chen Liu et Wen Tao Sun. « Hydro Turbine Nonlinear Model Parameter Identification Based on Improved Biogeography-Based Optimization ». Applied Mechanics and Materials 672-674 (octobre 2014) : 1617–21. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1617.

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The nonlinear model of hydro turbine considering the elastic water column was proposed, and the improved biogeography-based optimization (IBBO) algorithm was applied to identify parameters of this model. The cosine species migration model and the elitist reservation strategy were introduced into the algorithm to improve operating efficiency. Based on measured data, comparison of the effects of different identification methods was presented, including the IBBO algorithm, genetic algorithm (GA) and particle swarm optimization (PSO). The results demonstrated that the IBBO algorithm can be applied in parameters identification of hydro turbine nonlinear model, and it has the advantages of faster convergence and higher precision.

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Benbouhenni, Habib. « Application of DPC and DPC-GA to the dual-rotor wind turbine system with DFIG ». IAES International Journal of Robotics and Automation (IJRA) 10, n^o 3 (1 septembre 2021) : 224. http://dx.doi.org/10.11591/ijra.v10i3.pp224-234.

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<p>The work presents the dual-rotor wind energy conversion system (DRWECS) with a direct driven doubly-fed induction generator (DFIG). The system consists of a dual-rotor wind turbine (DRWT) with a DFIG, the grid side converter (GSC), and the machine side converter (MSC). To command the MSC, the direct power command (DPC) based on genetic algorithm (GA) and classical pulse width modulation (PWM) has been applied. To achieve the maximum power from the DRWT, the maximum powe point tracking (MPPT) technique has been used. The performed simulation studies confirmed the high performances of the DPC-GA contro method.</p>

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Behera, Sasmita, et Subham Sahoo. « Design of a Pitch Controller for a Wind Turbine Using Hybrid Mean-Variance Mapping Optimization ». ECTI Transactions on Electrical Engineering, Electronics, and Communications 19, n^o 3 (31 octobre 2021) : 298–311. http://dx.doi.org/10.37936/ecti-eec.2021193.222600.

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A variable-speed wind energy conversion system (WECS) has the advantage of extracting more power from the time-varying wind. To achieve this, the pitch angle is controlled to maintain the speed of the turbine and hence the generated power at a constant level, while reducing mechanical stress on the turbines. In this work, a proportional-integral (PI) controller is used for pitch angle control. The optimal PI control gains and are tuned by the hybrid mean-variance mapping optimization (MVMO-SH) technique, particle swarm optimization (PSO), and a genetic algorithm (GA). Different fitness evaluation criteria and optimization techniques are compared, and the performances of optimal controllers presented in the time domain. The results reveal that MVMO-SH achieves the minimum error criteria within a shorter time. The optimal controller design gives an error of less than in the region for which it is tuned. The performance of the optimal PI controller is designed for one operating condition in different cases of wind gust, random variation of wind, and disturbance from the grid side to mitigate line to ground fault. The performance of the controller is shown to be satisfactory in all the cases studied.

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Li, Y. G. « A gas turbine diagnostic approach with transient measurements ». Proceedings of the Institution of Mechanical Engineers, Part A : Journal of Power and Energy 217, n^o 2 (1 janvier 2003) : 169–77. http://dx.doi.org/10.1243/09576500360611317.

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Most gas turbine performance analysis based diagnostic methods use the information from steady state measurements. Unfortunately, steady state measurement may not be obtained easily in some situations, and some types of gas turbine fault contribute little to performance deviation at steady state operating conditions but significantly during transient processes. Therefore, gas turbine diagnostics with transient measurement is superior to that with steady state measurement. In this paper, an accumulated deviation is defined for gas turbine performance parameters in order to measure the level of performance deviation during transient processes. The features of the accumulated deviation are analysed and compared with traditionally defined performance deviation at a steady state condition. A non-linear model based diagnostic method, combined with a genetic algorithm (GA), is developed and applied to a model gas turbine engine to diagnose engine faults by using the accumulated deviation obtained from transient measurement. Typical transient measurable parameters of gas turbine engines are used for fault diagnostics, and a typical slam acceleration process from idle to maximum power is chosen in the analysis. The developed diagnostic approach is applied to the model engine implanted with three typical single-component faults and is shown to be very successful.

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Reddy, Narender Kangari, et Somnath Baidya Roy. « Layout optimization for offshore wind farms in India using the genetic algorithm technique ». Advances in Geosciences 54 (17 octobre 2020) : 79–87. http://dx.doi.org/10.5194/adgeo-54-79-2020.

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Abstract. Wind Farm Layout Optimization Problem (WFLOP) is a critical issue when installing a large wind farm. Many studies have focused on the WFLOP but only for a limited number of turbines and idealized wind speed distributions. In this study, we apply the Genetic Algorithm (GA) to solve the WFLOP for large hypothetical offshore wind farms using real wind data. GA mimics the natural selection process observed in nature, which is the survival of the fittest. The study site is the Palk Strait, located between India and Sri Lanka. This site is a potential hotspot of offshore wind in India. A modified Jensen wake model is used to calculate the wake losses. GA is used to produce optimal layouts for four different wind farms at the specified site. We use two different optimization approaches: one where the number of turbines is kept the same as the thumb rule layout and another where the number of turbines is allowed to vary. The results show that layout optimization leads to large improvements in power generation (up to 28 %), efficiency (up to 34 %), and cost (up to 25 %) compared to the thumb rule due to the reduction in wake losses. Optimized layouts where both the number and locations of turbines are allowed to vary produce better results in terms of efficiency and cost but also leads to lower installed capacity and power generation. Wind energy is growing at an unprecedented rate in India. Easily accessible terrestrial wind resources are almost saturated, and offshore wind is the new frontier. This study can play an important role while taking the first steps towards the expansion of offshore wind in India.

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N A, Prashanth, et P. Sujatha. « Comparison Between PSO and Genetic Algorithms and for Optimizing of Permanent Magnet Synchronous Generator (PMSG) Machine Design ». International Journal of Engineering & ; Technology 7, n^o 3.3 (21 juin 2018) : 77. http://dx.doi.org/10.14419/ijet.v7i3.3.14490.

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This paper proposes application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the design of direct-driven permanent magnet synchronous generator machine (PMSGs) for wind turbine applications. The power rating of these machines is in the mega watt (MW) level. The constraints and requirements of the generator are outlined. The proposed design scheme optimizes various PMSG parameters like Pole pair number, Linear current density, Air gap thickness, Rotor outer diameter, Relative width of the permanent magnet etc to achieve certain objectives like maximizing efficiency, increasing Torque, improving power factor etc. The results obtained by GA algorithm and those by PSO algorithm are compared. The performance of Particle Swarm Optimization is found to be better than the Genetic Algorithm, as the PSO carries out global search and local searches simultaneously, whereas the Genetic Algorithm concentrates mainly on the global search. Results show that the proposed PSO optimization algorithm is easy to develop and apply and produced competitive designs compared to the GA algorithm.

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Tang, Lingdi, Shouqi Yuan, Yue Tang et Zhipeng Qiu. « Optimization of impulse water turbine based on GA-BP neural network arithmetic ». Journal of Mechanical Science and Technology 33, n^o 1 (janvier 2019) : 241–53. http://dx.doi.org/10.1007/s12206-018-1224-3.

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Kim, Jin-sung, Jong-hyun Jeon et Hoon Heo. « Hybrid DSO-GA-based sensorless optimal control strategy for wind turbine generators ». Journal of Mechanical Science and Technology 27, n^o 2 (février 2013) : 549–56. http://dx.doi.org/10.1007/s12206-012-1239-0.

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Radiša, Radomir, Nedeljko Dučić, Srećko Manasijević, Nemanja Marković et Žarko Ćojbašić. « CASTING IMPROVEMENT BASED ON METAHEURISTIC OPTIMIZATION AND NUMERICAL SIMULATION ». Facta Universitatis, Series : Mechanical Engineering 15, n^o 3 (9 décembre 2017) : 397. http://dx.doi.org/10.22190/fume170505022r.

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This paper presents the use of metaheuristic optimization techniques to support the improvement of casting process. Genetic algorithm (GA), Ant Colony Optimization (ACO), Simulated annealing (SA) and Particle Swarm Optimization (PSO) have been considered as optimization tools to define the geometry of the casting part’s feeder. The proposed methodology has been demonstrated in the design of the feeder for casting Pelton turbine bucket. The results of the optimization are dimensional characteristics of the feeder, and the best result from all the implemented optimization processes has been adopted. Numerical simulation has been used to verify the validity of the presented design methodology and the feeding system optimization in the casting system of the Pelton turbine bucket.

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Izadbakhsh, Maziar, Alireza Rezvani et Majid Gandomkar. « Dynamic response improvement of hybrid system by implementing ANN-GA for fast variation of photovoltaic irradiation and FLC for wind turbine ». Archives of Electrical Engineering 64, n^o 2 (1 juin 2015) : 291–314. http://dx.doi.org/10.1515/aee-2015-0024.

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Abstract In this paper, dynamic response improvement of the grid connected hybrid system comprising of the wind power generation system (WPGS) and the photovoltaic (PV) are investigated under some critical circumstances. In order to maximize the output of solar arrays, a maximum power point tracking (MPPT) technique is presented. In this paper, an intelligent control technique using the artificial neural network (ANN) and the genetic algorithm (GA) are proposed to control the MPPT for a PV system under varying irradiation and temperature conditions. The ANN-GA control method is compared with the perturb and observe (P&O), the incremental conductance (IC) and the fuzzy logic methods. In other words, the data is optimized by GA and then, these optimum values are used in ANN. The results are indicated the ANN-GA is better and more reliable method in comparison with the conventional algorithms. The allocation of a pitch angle strategy based on the fuzzy logic controller (FLC) and comparison with conventional PI controller in high rated wind speed areas are carried out. Moreover, the pitch angle based on FLC with the wind speed and active power as the inputs can have faster response that lead to smoother power curves, improving the dynamic performance of the wind turbine and prevent the mechanical fatigues of the generator

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Madan, Charan Jeet, et Naresh Kumar. « Fuzzy grey wolf optimization for controlled low-voltage ride-through conditions in grid-connected wind turbine with doubly fed induction generator ». SIMULATION 95, n^o 4 (18 juin 2018) : 327–38. http://dx.doi.org/10.1177/0037549718777607.

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With its enormous environmental and monetary benefits, the wind turbine has become an acceptable alternative to the generation of electricity by fossil fuel or nuclear power plants. Research remains focused on improving the performance of wind turbines with maximum flexibility and gains. The main objective of the paper is to simulate a low-voltage ride-through (LVRT) control system that is convenient for the development of a controller that should have the ability to rectify fault signals. This paper proposes a novel method called grey wolf optimization with fuzzified error (GWFE) model to simulate the optimized control system. Further, it compares the GWFE-based LVRT system with the standard LVRT system, systems with minimum and maximum gain, and conventional methods like genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), ant bee colony (ABC), and grey wolf optimization (GWO) algorithms. Accordingly, it analyses the simulation results regarding qualitative analysis like active power, [Formula: see text] comparison, gain, pitch degree, reactive power, rotor current, stator current, and [Formula: see text] and [Formula: see text] measurements; and quantitative analysis like RMSE computation of [Formula: see text] with varying speed. Hence, the proposed GWFE algorithm is beneficial for simulating the LVRT system compared to other conventional methods.

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Shekar, Chandra, et M. R. Shivakumar. « Multi-objective wind farm layout optimization using evolutionary computations ». International Journal of Advances in Applied Sciences 8, n^o 4 (1 décembre 2019) : 293. http://dx.doi.org/10.11591/ijaas.v8.i4.pp293-306.

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<p class="Abstract">The usage of fossil fuels is actually not good for living nature and in future, this limited source of energy will vanish. Therefore, we need to go with the clean and renewable source of energy such as wind power, solar energy etc. In this paper, we are concentrating in wind power through optimizing the wind turbine placement in wind farm. The area-of-convex hull, maximize ‘output power’ and minimum spanning tree distance are our main objective topics, due to their effect in wind farm design. An implementation of modified version of the wind turbine (WT) placement model is uses to estimate the yields of the (wind farm) WF layouts and for simplifying the behavior of wind field, in this paper we uses a simple wake approach. Moreover, to resolve the multi-objective problem here we proposed (Modified Genetic Algorithm) MGA, which is considerably better than the (Genetic Algorithm) GA and for evaluate the performance of MGA we use the multi-objective (EA) evolutionary algorithms such as; Genetic algorithm (GA) and SPEA2 and, produce different number of WT layouts. These methodologies are consider with various ‘problematic specific operators’ that are present in this paper.</p>

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Liu, Qihang, Laihe Zhuang, Jie Wen, Bensi Dong et Zhiwei Liu. « Thermodynamic Optimization of Aircraft Environmental Control System Using Modified Genetic Algorithm ». Processes 10, n^o 4 (8 avril 2022) : 721. http://dx.doi.org/10.3390/pr10040721.

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This paper presents an optimization method for the civil aircraft environmental control system (ECS) mainly involving two airstreams: the ram airstream for cooling and the bleed airstream for supplying the cabin. The minimum total fuel energy consumption rate (FECR), defined as the weighted sum of the shaft power extraction and propulsive power loss, is obtained under the precondition of the constant outputs in the cooling capacity and outlet pressure. A modified genetic algorithm (GA) is proposed to acquire the optimal values of the heat transfer areas, temperature ratio of bleed air, mass flow rate of ram air, and pressure ratios of the turbine, compressor, and fan. The statistical results show that the multipoint crossover and continuity improvement implemented in the modified GA improve convergence and distribution performance. The probability of reaching a satisfactory result using modified GA is 62.4% higher than standard GA. Due to the decrease of inlet parameters of bleed air and the elimination of power input in the compressor, the FECR of the optimization case can be lowered by 11.0%. In general, the evaluation method considering energy quality together with the modified optimization technique is proved effective in energy-saving design for such energy systems such as ECS with multiple inputs and outputs.

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Zhao, M., X. F. Ge, Q. F. Jiang, J. Li, Y. S. Ding et J. Q. Du. « Application of GA-BP model to fault diagnosis of hydro-turbine generating units ». IOP Conference Series : Earth and Environmental Science 163 (30 juillet 2018) : 012125. http://dx.doi.org/10.1088/1755-1315/163/1/012125.

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Chen, Sihan, Yongguang Ma et Liangyu Ma. « Fault early warning of pitch system of wind turbine based on GA-BP neural network model ». E3S Web of Conferences 194 (2020) : 03005. http://dx.doi.org/10.1051/e3sconf/202019403005.

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A fault early warning method based on genetic algorithm to optimize the BP neural network for the wind turbine pitch system is proposed. According to the parameters monitored by SCADA system, using correlation analysis to screen out the parameters of the pitch system with strong power correlation. The BP neural network optimized by genetic algorithm is used to establish the model of the pitch system under normal working conditions. The verification results show that the input parameters of the pitch system model determined by the correlation coefficient are more reasonable, and the accuracy of the pitch system model established by the genetic algorithm-optimized BP neural network is higher than that of the unoptimized model. Based on the above model, a sliding window model is established, and the early warning threshold is determined through the statistics of the residuals of the sliding window to realize the fault early warning of the pitch system of the wind turbine. The example shows that the method can give early warning in the event of failure, and verifies the effectiveness of the method.

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Ruane, Kieran, Marios Soutsos, An Huynh, Zoe Zhang, Angela Nagle, Kenny McDonald, T. Russell Gentry, Paul Leahy et Lawrence C. Bank. « Construction and Cost Analysis of BladeBridges Made from Decommissioned FRP Wind Turbine Blades ». Sustainability 15, n^o 4 (12 février 2023) : 3366. http://dx.doi.org/10.3390/su15043366.

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This paper describes repurposing projects using decommissioned wind turbine blades in bridges conducted under a multinational research project entitled “Re-Wind”. Repurposing is defined by the Re-Wind Network as the re-engineering, redesigning, and remanufacturing of a wind blade that has reached the end of its life on a turbine and taken out of service and then reused as a load-bearing structural element in a new structure (e.g., bridge, transmission pole, sound barrier, seawall, shelter). The issue of end-of-life of wind turbine blades is becoming a significant sustainability concern for wind turbine manufacturers, many of whom have committed to the 2030 or 2040 sustainability goals that include zero-waste for their products. Repurposing is the most sustainable end-of-life solution for wind turbine blades from an environmental, economic, and social perspective. The Network has designed and constructed two full-size pedestrian/cycle bridges—one on a greenway in Cork, Ireland and the other in a quarry in Draperstown, Northern Ireland, UK. The paper describes the design, testing, and construction of the two bridges and provides cost data for the bridges. Two additional bridges that are currently being designed for construction in Atlanta, GA, USA are also described. The paper also presents a step-by-step procedure for designing and building civil structures using decommissioned wind turbine blades. The steps are: project planning and funding, blade sourcing, blade geometric characterization, material testing, structural testing, designing, cost estimating, and construction.

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Huang, Rui, Yubo Wang, Chi-Cheng Chu, Rajit Gadh et Yu-jin Song. « Optimal Configuration of Distributed Generation on Jeju Island Power Grid Using Genetic Algorithm : A Case Study ». Journal of Communications Software and Systems 10, n^o 2 (23 juin 2014) : 135. http://dx.doi.org/10.24138/jcomss.v10i2.134.

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With the rapid development of wind turbine, photovoltaicand battery technologies, renewable energy resources such as wind and solar become the most common distributedgenerations (DG) that are being integrated into microgrids. One key impediment is to determine the sizes and placements of DGs within which the microgrid can achieve its maximum potential benefits. The objective of the paper is to study and propose an approach to find the optimal sizes and placements of DGs in a microgrid. The authors propose a comprehensive objective function with practical constraints which take all the important factors that will impact the reliability of the power grid into account. To solve the optimization problem, genetic algorithm (GA) is used and compared with a mathematical optimization method nonlinear programming. The proposed model is tested on a real microgrid, i.e. Jeju Island, to evaluate and validate the performances of the approach. The simulation results present the optimal configuration of DGs for Jeju Island power grid. The analysis on results shows that GA maintains a delicate balance between performance and complexity. It is concluded that GA performs better not only in accuracy, stability, but also in computation time.

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Гольцов, Анатолий Сергеевич, et Hung Manh Tran. « Дослідження цифровий адаптивної системи повороту лопаток направляючого апаратів осьового компресора ». Aerospace technic and technology, n^o 4sup2 (27 août 2021) : 79–86. http://dx.doi.org/10.32620/aktt.2021.4sup2.10.

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In the simplest cases, P-, PI- and PID-controllers with rigid (unchanged) parameter settings are used to control technological processes. If the mathematical model of the control object contains unknown disturbing influences and parameters that change during the control process, then a digital control system with a learning model should be used. The tasks of control, analysis and modeling of various processes and systems are solved using their mathematical models. The choice of a model is dictated by the conditions of implementation and the requirement of adequacy. The problem of developing control algorithms under uncertainty occupies one of the central places in modern control theory. To solve the arising problems of structural and parametric identification, as a rule, methods, and algorithms of the theory of adaptive control systems are used. The application of the principles of adaptation allows to ensure high accuracy of modeling with a significant change in the dynamic properties of the system under study, to unify individual subsystems and their blocks; reduce the development and debugging time of the system. This article discusses the problem of studying a digital adaptive system for turning the blades of guide apparatus (GA) of an axial compressor of gas turbine engines (GTE). The aim of the study is to increase the efficiency of the control system for turning the blades of the GA using a digital adaptive system. The gas-dynamic stability of the GTE axial compressor under changes in external conditions and engine throttling is provided by air bypass and turning the blades of the first and last compressor stages. The statement of the problem of synthesis of the system of adaptive control of the rotation of the blades of the GA compressor of the gas turbine engine is carried out. A mathematical model of the ACS in the canonical form “model of the system in the state space” and an algorithm for turning the blades of the GA using an adaptive PI-controller have been developed. Simulation modeling of ACS was carried out using the Matlab / Simulink software package. When implementing an adaptive PI-controller, the pressure deviation downstream of the compressor stage from the required value is reduced to 0.4 %.

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YOSHIMURA, Shinobu, et Dennis Brian. « Thermo-elastic Design Optimization of Turbine Blade Using ADVENTURE System with Parallel GAs ». Proceedings of The Computational Mechanics Conference 2002.15 (2002) : 399–400. http://dx.doi.org/10.1299/jsmecmd.2002.15.399.

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Khurshid, Arsalan, Muhammad Ali Mughal, Achraf Othman, Tawfik Al-Hadhrami, Harish Kumar, Imtinan Khurshid, Arshad et Jawad Ahmad. « Optimal Pitch Angle Controller for DFIG-Based Wind Turbine System Using Computational Optimization Techniques ». Electronics 11, n^o 8 (18 avril 2022) : 1290. http://dx.doi.org/10.3390/electronics11081290.

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With the advent of high-speed and parallel computing, the applicability of computational optimization in engineering problems has increased, with greater validation than conventional methods. Pitch angle is an effective variable in extracting maximum wind power in a wind turbine system (WTS). The pitch angle controller contributes to improve the output power at different wind speeds. In this paper, the pitch angle controller with proportional (P) and proportional-integral (PI) controllers is used. The parameters of the controllers are tuned by computational optimization techniques for a doubly-fed induction generator (DFIG)-based WTS. The study is carried out on a 9 MW DFIG based WTS model in MATLAB/SIMULINK. Two computational optimization techniques: particle swarm optimization (PSO), a swarm intelligence algorithm, and a genetic algorithm (GA), an evolutionary algorithm, are applied. A multi-objective, multi-dimensional error function is defined and minimized by selecting an appropriate error criterion for each objective of the function which depicts the relative magnitude of each objective in the error function. The results of the output power flow and the dynamic response of the optimized P and PI controllers are compared with the conventional P and PI controller in three different cases. It is revealed that the PSO-based controllers performed better in comparison with both the conventional controllers and the GA-based controllers.

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Yuan, Baohan, Weize Wang, Dongdong Ye, Zhenghao Zhang, Huanjie Fang, Ting Yang, Yihao Wang et Shuncong Zhong. « Nondestructive Evaluation of Thermal Barrier Coatings Thickness Using Terahertz Technique Combined with PCA–GA–ELM Algorithm ». Coatings 12, n^o 3 (15 mars 2022) : 390. http://dx.doi.org/10.3390/coatings12030390.

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Thermal barrier coatings (TBCs) are usually used in high temperature and harsh environment, resulting in thinning or even spalling off. Hence, it is vital to detect the thickness of the TBCs. In this study, a hybrid machine learning model combined with terahertz time-domain spectroscopy technology was designed to predict the thickness of TBCs. The terahertz signals were obtained from the samples prepared in laboratory and actual turbine blade. The principal component analysis (PCA) method was used to decrease the data dimensions. Finally, an extreme learning machine (ELM) was proposed to establish the thickness of TBCs prediction model. Genetic algorithm (GA) was selected to optimize the model to make it more accurate. The results showed that the root correlation coefficient (R2) exceeded 0.97 and the errors (root mean square error and mean absolute percentage error) were less than 2.57. This study proposes that terahertz time-domain technology combined with PCA–GA–ELM model is accurate and feasible for evaluating the thickness of the TBCs.

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AZZOUZ, Said. « Innovative PID-GA MPPT Controller for Extraction of Maximum Power From Variable Wind Turbine ». PRZEGLĄD ELEKTROTECHNICZNY 1, n^o 8 (5 août 2019) : 117–22. http://dx.doi.org/10.15199/48.2019.08.26.

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Articles de revues sur le sujet « Turbune a ga »

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