Journal articles: 'Turbina a ga'

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Nikolić, Gojko. "Originalnost izuma Fausta Vrančića." Studia lexicographica 12, no. 23 (2019): 9–31. http://dx.doi.org/10.33604/sl.12.23.1.

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U radu je istražena originalnost triju izuma Fausta Vrančića sa stajališta današnjega poimanja izuma. Prvi izum koji je u radu istražen jest padobran, za koji je utvrđeno da Faust nije njegov izumitelj, već da ga je iznimno dobro tehnički unaprijedio, koncepcijski vrlo blisko suvremenim rješenjima. S obzirom na to da se uz padobran posvuda veže njegovo ime, mnoge će iznenaditi činjenica da Vrančić nije njegov izumitelj. Drugi izum koji je analiziran, a danas se nedovoljno ističe, odnosi se na mlin na plimu i oseku. Ideja nije nova, koristila se davno u povijesti, posebno u Irskoj i Engleskoj. Inovacija u Vrančićevu mlinu jest uporaba dvosmjernoga strujanja mora, pri čemu konstrukcija okretnoga pogonskoga vodeničnoga kola omogućuje njegovo okretanje uvijek u istom smjeru, neovisno o smjeru strujanja mora. Treća inovacija, koja se odnosi na Vrančićevo rješenje primjene statora i rotora kod vjetrenjače koje omogućuje korištenje vjetra iz bilo kojega smjera, danas se koristi kod turbina za razne fluide (vodu, paru, plinove).

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Mrzljak, Vedran, Nikola Anđelić, Ivan Lorencin, and Sandi Sandi Baressi Šegota. "The influence of various optimization algorithms on nuclear power plant steam turbine exergy efficiency and destruction." Pomorstvo 35, no. 1 (June 30, 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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Yan, Tao, Xian Min Lin, and You Ping Zhong. "A Load Allocation Optimization among Turbine-Generators Based on GA-ACO." Advanced Materials Research 614-615 (December 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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Liu, Zheng, Xin Liu, Kan Wang, Zhongwei Liang, José A. F. O. Correia, and Abílio De Jesus. "GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades." Energies 12, no. 6 (March 15, 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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Alawaad, Nasir Ahmed. "Steam turbine controllers design based on soft-computing techniques." IAES International Journal of Robotics and Automation (IJRA) 9, no. 4 (December 1, 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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Sun, Wei, and Le Shen. "Comprehensive Evaluation of Wind Turbine Type Selection Based on GA-SVR Model." Advanced Materials Research 468-471 (February 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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Nugroho, Herminarto, Nabilla Ananda Yusva, and 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, no. 1 (June 27, 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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Jiao, Bin, and Zhi Wei Gao. "The Fault Diagnosis of Wind Turbine Gearbox Based on QGA—LSSVM." Applied Mechanics and Materials 543-547 (March 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, and Jian Ding Zhang. "Application of Improved Genetic Algorithm in Maintenance Decision for Turbine-Generator Unit." Applied Mechanics and Materials 44-47 (December 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, and Chuan Hong Zhang. "Application of ANN Trained with GA in Energy Characteristics of Hydraulic Turbine." Advanced Materials Research 108-111 (May 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, and Göksu GÖREL. "Pitch Angle Control of Wind Turbine with PI, PID and GA-PID controller." Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi 14, no. 2 (July 31, 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, and Shi Feng-xia. "Optimal design of blade in pump as turbine based on multidisciplinary feasible method." Science Progress 103, no. 4 (October 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, and Fuaada Mohd Siam. "Sizing Optimization of Hybrid Photovoltaic-Wind-Battery System towards Zero Energy Building using Genetic Algorithm." MATEMATIKA 36, no. 3 (December 1, 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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Subramanian, Senthilkumar, Chandramohan Sankaralingam, Rajvikram Madurai Elavarasan, Raghavendra Rajan Vijayaraghavan, Kannadasan Raju, and Lucian Mihet-Popa. "An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III." Sustainability 13, no. 1 (January 5, 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, and Rui Zhu. "Optimization Design Study on a New Type Underwater Turbine Engine." Advanced Materials Research 850-851 (December 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, and Hamid Kherfane. "GA/PSO Robust Sliding Mode Control of Aerodynamics in Gas Turbine." Acta Universitatis Sapientiae Electrical and Mechanical Engineering 10, no. 1 (December 1, 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, and 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, and Yiguo Li. "GA-based nonlinear predictive switching control for a boiler-turbine system." Journal of Control Theory and Applications 10, no. 1 (December 28, 2011): 100–106. http://dx.doi.org/10.1007/s11768-012-0050-x.

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Sule, Aliyu Hamza, Ahmad Safawi Mokhtar, Jasrul Jamani Bin Jamian, Attaullah Khidrani, and 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, no. 5 (October 1, 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, and Wen Tao Sun. "Hydro Turbine Nonlinear Model Parameter Identification Based on Improved Biogeography-Based Optimization." Applied Mechanics and Materials 672-674 (October 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, no. 3 (September 1, 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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Li, Yi-Guang. "Diagnostics of power setting sensor fault of gas turbine engines using genetic algorithm." Aeronautical Journal 121, no. 1242 (July 3, 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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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, no. 2 (January 1, 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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N A, Prashanth, and 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, no. 3.3 (June 21, 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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Hamdan, Mohammad, and Mohammad Hassan Abderrazzaq. "Optimization of Small Wind Turbines using Genetic Algorithms." International Journal of Applied Metaheuristic Computing 7, no. 4 (October 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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Tang, Lingdi, Shouqi Yuan, Yue Tang, and Zhipeng Qiu. "Optimization of impulse water turbine based on GA-BP neural network arithmetic." Journal of Mechanical Science and Technology 33, no. 1 (January 2019): 241–53. http://dx.doi.org/10.1007/s12206-018-1224-3.

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Kim, Jin-sung, Jong-hyun Jeon, and Hoon Heo. "Hybrid DSO-GA-based sensorless optimal control strategy for wind turbine generators." Journal of Mechanical Science and Technology 27, no. 2 (February 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, and Žarko Ćojbašić. "CASTING IMPROVEMENT BASED ON METAHEURISTIC OPTIMIZATION AND NUMERICAL SIMULATION." Facta Universitatis, Series: Mechanical Engineering 15, no. 3 (December 9, 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, and 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, no. 2 (June 1, 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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Shekar, Chandra, and M. R. Shivakumar. "Multi-objective wind farm layout optimization using evolutionary computations." International Journal of Advances in Applied Sciences 8, no. 4 (December 1, 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, and Zhiwei Liu. "Thermodynamic Optimization of Aircraft Environmental Control System Using Modified Genetic Algorithm." Processes 10, no. 4 (April 8, 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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Nugroho, Herminarto, Carolus Aditya, and Septiyan Nungsizu. "Penerapan Metode Genetic Alghorithm untuk Meminimalkan Biaya Perawatan Sistem Pembangkit Energi Hibrid Solar Panel dan Turbin Angin." ENERGI & KELISTRIKAN 13, no. 2 (December 27, 2021): 172–77. http://dx.doi.org/10.33322/energi.v13i2.1329.

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Sebagai renewable energy, Teknologi hybrida solar panel dan wind tubine menjadi salah satu sistem yang dapat dipergunakan di masa mendatang, pada sistem ini ketika siang maupun malam alat dapat bekerja sehingga menghasilkan sumber energi secara berkesinambungan. Untuk dapat menyediakan cukup energi listrik untuk pemakaian seharian, perlu menentukan jumlah panel surya dan turbin angin yang cukup. Masing-masing panel surya dan turbin angin menghasilkan energi listrik yang berbeda, serta biaya perawatan yang berbeda pula. Oleh sebab itu, maintenance system dari alat ini harus diperhatikan agar tidak memakan banyak biaya, namun tetap dapat menyediakan energi listrik yang cukup. Pada metode yang disimulasikan pada matlab, Genetic Alghorithm(GA) dan Fmincon dipilih karena pada 2 metode ini fungsi global minimum bisa didapatkan dengan berbagai iterasi maupun generasinya. Kedua metode ini dapat menentukan jumlah panel surya dan turbin angin yang tepat agar kebutuhan listrik dapat terpenuhi serta dengan biaya perawatan yang minimal.

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Bin Ali, Muhammad, Zeshan Ahmad, Saad Alshahrani, Muhammad Rizwan Younis, Irsa Talib, and Muhammad Imran. "A Case Study: Layout Optimization of Three Gorges Wind Farm Pakistan, Using Genetic Algorithm." Sustainability 14, no. 24 (December 17, 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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Zhao, M., X. F. Ge, Q. F. Jiang, J. Li, Y. S. Ding, and J. Q. Du. "Application of GA-BP model to fault diagnosis of hydro-turbine generating units." IOP Conference Series: Earth and Environmental Science 163 (July 30, 2018): 012125. http://dx.doi.org/10.1088/1755-1315/163/1/012125.

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Chen, Sihan, Yongguang Ma, and 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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Fakhri, Eyman, Salvy Bourguet, Jérôme Thiébot, Mohamed Machmoum, and 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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Ruane, Kieran, Marios Soutsos, An Huynh, Zoe Zhang, Angela Nagle, Kenny McDonald, T. Russell Gentry, Paul Leahy, and Lawrence C. Bank. "Construction and Cost Analysis of BladeBridges Made from Decommissioned FRP Wind Turbine Blades." Sustainability 15, no. 4 (February 12, 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, and 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, no. 2 (June 23, 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, and Hung Manh Tran. "Дослідження цифровий адаптивної системи повороту лопаток направляючого апаратів осьового компресора." Aerospace technic and technology, no. 4sup2 (August 27, 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, and 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, and Jawad Ahmad. "Optimal Pitch Angle Controller for DFIG-Based Wind Turbine System Using Computational Optimization Techniques." Electronics 11, no. 8 (April 18, 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, and Shuncong Zhong. "Nondestructive Evaluation of Thermal Barrier Coatings Thickness Using Terahertz Technique Combined with PCA–GA–ELM Algorithm." Coatings 12, no. 3 (March 15, 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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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, no. 1 (February 1, 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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AZZOUZ, Said. "Innovative PID-GA MPPT Controller for Extraction of Maximum Power From Variable Wind Turbine." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 8 (August 5, 2019): 117–22. http://dx.doi.org/10.15199/48.2019.08.26.

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Nicholas, P. Emmanuel, K. P. Padmanaban, D. Vasudevan, and T. Ramachandran. "Stacking sequence optimization of horizontal axis wind turbine blade using FEA, ANN and GA." Structural and Multidisciplinary Optimization 52, no. 4 (June 19, 2015): 791–801. http://dx.doi.org/10.1007/s00158-015-1269-1.

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SHIMOYAMA, Koji, Shu YOSHIMIZU, Shinkyu JEONG, Shigeru OBAYASHI, and Yasuyuki YOKONO. "Multi-Objective Design Optimization for a Steam Turbine Stator Blade Using LES and GA." Journal of Computational Science and Technology 5, no. 3 (2011): 134–47. http://dx.doi.org/10.1299/jcst.5.134.

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Liu, Yong Xiao, Ying Liu, Ya Song Du, Rui Qi Wang, and Wei Huang. "Optimal Allocation of Distributed Generation in Micro-Grid Based on the Theory of Life Cycle Cost." Advanced Materials Research 614-615 (December 2012): 1903–7. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1903.

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In order to solve the problems of the optimal locations, types and sizes of DG (distributed generation) in an islanded micro-grid, which contains wind turbine, photovoltaic, diesel engine, energy storage device and micro gas turbine. The economic model of optimal allocation was built with the objective function that took LCC (life cycle cost) of DGs and active power loss cost into account. The GA (genetic algorithm) was used to optimize the planning of DG and the optimal scheme was attained. The model comprehensively evaluates the economy of the five kinds of DG in the view of LCC theory, and simultaneously realizes the optimal locations, types and sizes considering the differences of DGs and active power loss of the network. Through the case analysis, the results show this model and method can solve the problem of optimal allocation of DG effectively. It is very practical to guide investing and constructing micro-grid for the investors.

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Machmudah, Affiani, Tamiru Alemu Lemma, Mahmud Iwan Solihin, Yusron Feriadi, Armin Rajabi, Mohamad Imam Afandi, and Aijaz Abbasi. "Design Optimization of a Gas Turbine Engine for Marine Applications: Off-Design Performance and Control System Considerations." Entropy 24, no. 12 (November 25, 2022): 1729. http://dx.doi.org/10.3390/e24121729.

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This paper addresses a design optimization of a gas turbine (GT) for marine applications. A gain-scheduling method incorporating a meta-heuristic optimization is proposed to optimize a thermodynamics-based model of a small GT engine. A comprehensive control system consisting of a proportional integral (PI) controller with additional proportional gains, gain scheduling, and a min-max controller is developed. The modeling of gains as a function of plant variables is presented. Meta-heuristic optimizations, namely a genetic algorithm (GA) and a whale optimization algorithm (WOA), are applied to optimize the designed control system. The results show that the WOA has better performance than that of the GA, where the WOA exhibits the minimum fitness value. Compared to the unoptimized gain, the time to reach the target of the power lever angle is significantly reduced. Optimal gain scheduling shows a stable response compared with a fixed gain, which can have oscillation effects as a controller responds. An effect of using bioethanol as a fuel has been observed. It shows that for the same input parameters of the GT dynamics model, the fuel flow increases significantly, as compared with diesel fuel, because of its low bioethanol heating value. Thus, a significant increase occurs only at the gain that depends on the fuel flow.

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Rahul Malhotra, Narinder Singh, and Yaduvir Singh. "An Efficient Fuzzy-GA Flow Control of Turbine Compressor System: A Process Control Case Study." International Journal of Advancements in Computing Technology 2, no. 4 (October 31, 2010): 128–39. http://dx.doi.org/10.4156/ijact.vol2.issue4.14.

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Hwang, Hyun-Joon. "A Design of GA-Based Model-Following Boiler-Turbine H∞ Control System Having Robust Performance." Journal of the Korean Society of Marine Engineering 36, no. 1 (January 31, 2012): 126–32. http://dx.doi.org/10.5916/jkosme.2012.36.1.126.

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Journal articles on the topic 'Turbina a ga'

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