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Статті в журналах з теми "Optimization of reactive power"

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Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Liu, Bin, Ren Hui Kong, and Xiao Bing Xiao. "Reactive Power Optimization in District Power System." Advanced Materials Research 805-806 (September 2013): 751–55. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.751.

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Анотація:

Numerical optimization algorithms and heuristic optimization algorithms in reactive power optimization have been introduced. Analysis of the reactive power balance and the compensation, reactive power optimization mathematical model has been studied. Finally, it takes the least net loss as the object functions to optimize the regional power grid reactive power by genetic algorithm. Optimization results show that this method can effectively reduce network loss, and energy-saving effect is remarkable.

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2

Zhang, Xiao Hong, Han Zhang, and Jian Zhang. "Reactive Power Optimization in Regional Power Grid." Applied Mechanics and Materials 380-384 (August 2013): 3254–57. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3254.

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Анотація:

The paper analyzes reactive power optimization of regional power grid, and it establishes the mathematical model of power system reactive power optimization, taking minimizing annual operating cost as an object function, at the same time, considering the power factors and constraints of load bus voltage amplitude. It uses the MATLAB optimization toolbox quadratic function to optimize compute, and applies it to the reactive power optimization in actual regional power grid. Results show that the proposed mathematical model and algorithm are effective and practical.

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3

Zhao, Wen Qing, Li Wei Wang, Fei Fei Han, and De Wen Wang. "Reactive Power Optimization in Power System Based on Adaptive Particle Swarm Optimization." Advanced Materials Research 846-847 (November 2013): 1209–12. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1209.

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Анотація:

This paper summarizes the reactive power optimization of power system characteristics and requirements, proposed to target the active power loss of reactive power optimization mathematical model, And the traditional classical algorithm can not handle the limitations of discrete variables, using the adaptive particle swarm optimization algorithm to solve the problem of reactive power optimization. By testing on IEEE30 bus system simulation, comparing different algorithm optimization results show the effectiveness and superiority of APSO algorithm.

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4

Li, Yan Hong, and Zhi Rong Zhang. "Application of Reactive Power Optimization in Power Grid in AVC." Advanced Materials Research 971-973 (June 2014): 979–82. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.979.

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Анотація:

Automatic voltage control(AVC) is the highest form of current power grid voltage and reactive power control,during the implementation of AVC, the whole network reactive power optimization isthe core and foundation. Thispaper researches and discuses the application of reactive power optimization inpower grid AVC. In the traditional reactive power optimization, the reactivepower limits of synchronous generators are fixed. In this paper, thesynchronous generator PQ operating limits change with external conditions,thus establishes reactive power optimization model in accordance with therequirements of AVC. Thispaper presents reactive power optimization method based on the principle ofpartition. The method decomposes the system to several partitions. Eachpartition separately optimized, thus reduces the system scale.And the convergence of the algorithm, the calculation speed and the discretevariable processing etc. improve. At the same time, this method reflects theclassification, hierarchical, partition, characteristics of coordinated controlof AVC.

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5

Ethmane, I. A., M. Maaroufi, A. K. Mahmoud, and A. Yahfdhou. "Optimization for Electric Power Load Forecast." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3453. http://dx.doi.org/10.11591/ijece.v8i5.pp3453-3462.

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Load flow studies are one of the most important aspects of power system planning and operation. The main information obtained from this study comprises the magnitudes and phase angles of load bus voltages, reactive powers at generators buses, real and reactive power flow on transmission lines, other variables being known. To solve the problem of load flow, we use the iterative method, of Newton-Raphson. Analysis of the found results using numerical method programmed on the Matlab software and PSS/E Simulator lead us to seek means of controlling the reactive powers and the bus voltages of the Nouakchott power grid in 2030 year. In our case, we projected the demand forecast at 2015 to 2030 years. To solve the growing demand we injected the power plants in the system firstly and secondly when the production and energy demand are difficult to match due to lack of energy infrastructures in 2030.It is proposed to install a FACTS (Flexible Alternative Current Transmission Systems) system at these buses to compensate or provide reactive power in order to maintain a better voltage profile and transmit more power to customers.

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6

Su, Ai Ning, Hui Qiong Deng, and Tian Wei Xing. "Power System Reactive Power Optimization Based on Improved Genetic Agorithm." Advanced Materials Research 614-615 (December 2012): 1361–66. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1361.

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Анотація:

Reactive power optimization is an effective method for improving the electricity quality and reducing the power loss in power system, and it is a mixed nonlinear optimization problem, so the optimization process becomes very complicated. Genetic algorithm is a kind of adaptive global optimization search algorithm based on simulating biological genetic in the natural environment and evolutionary processes, can be used to solve complex optimization problems such as reactive power optimization. Genetic algorithm is used to solve reactive power optimization problem in this study, improved the basic genetic algorithm, included the select, crossover and mutation strategy, and proposed a individual fitness function with penalty factor. The proposed algorithm is applied to the IEEE9-bus system to calculate reactive power. The results show the superiority of the proposed model and algorithm.

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7

V. "Reactive Power Optimization Using Quantum Particle Swarm Optimization." Journal of Computer Science 8, no. 10 (October 1, 2012): 1644–48. http://dx.doi.org/10.3844/jcssp.2012.1644.1648.

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8

Yehia, M., I. Ghandour, M. Saidy, and V. A. Stroev. "Reactive power optimization in large scale power systems." International Journal of Electrical Power & Energy Systems 14, no. 4 (August 1992): 276–83. http://dx.doi.org/10.1016/0142-0615(92)90056-f.

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9

DingPing, Li, Shen GuoLiang, Guo WenDong, Zhang ZHi, Hu BaoNing, and Gao Wei. "Power system reactive power optimization based on MIPSO." Energy Procedia 14 (2012): 788–93. http://dx.doi.org/10.1016/j.egypro.2011.12.1012.

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10

Taghavi, Reza, Ali Reza Seifi, and Meisam Pourahmadi-Nakhli. "Fuzzy reactive power optimization in hybrid power systems." International Journal of Electrical Power & Energy Systems 42, no. 1 (November 2012): 375–83. http://dx.doi.org/10.1016/j.ijepes.2012.04.002.

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11

Wang, Yan Yan, and Yan Song Li. "Research on the Reactive Power Optimization." Applied Mechanics and Materials 385-386 (August 2013): 991–94. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.991.

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Анотація:

The power system is facing line losses, low voltage level and some other issues, this article begin with the point of the reactive power optimization, and through with the improved PSO algorithm, we find a way to reduce the line network loss.

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12

Jabr, R. A. "Optimization of Reactive Power Expansion Planning." Electric Power Components and Systems 39, no. 12 (August 2, 2011): 1285–301. http://dx.doi.org/10.1080/15325008.2011.567220.

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13

Iba, K. "Reactive power optimization by genetic algorithm." IEEE Transactions on Power Systems 9, no. 2 (May 1994): 685–92. http://dx.doi.org/10.1109/59.317674.

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14

Lei, Lin, Yi Nan Ge, and Qin Yuan. "Improvement and Application of Genetic Algorithms in Reactive Power Optimization of Power Network." Advanced Materials Research 354-355 (October 2011): 1058–63. http://dx.doi.org/10.4028/www.scientific.net/amr.354-355.1058.

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Анотація:

Reactive power optimization that is optimized by Simple Genetic Algorithms has many limitations. According to the problem of reactive power optimization of high voltage system, the Simple Genetic Algorithms is improved. The improved algorithm is applied in reactive power optimization of IEEE-6 bus system, the results indicate that the improvement is effective and it can accelerate the convergence speed and enhance the ability of optimization.

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15

Liu, Shu Kui, Na Dong, Zhi Zheng, Li Cheng, and Qi Li. "Application of Modified Artificial Fish Swarm Algorithm in Power System Reactive Power Optimization." Applied Mechanics and Materials 321-324 (June 2013): 1361–64. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1361.

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Анотація:

Modified Artificial Fish Swarm Algorithm (MAFSA) based on the global search characteristic of Artificial Fish Swarm Algorithm (AFSA), and combined with the local search of chao optimization algorithm(COA), can avoid trapping into local minimal value and decrease the iteration numbers, which was a swarm intelligence optimization algorithm applied to continuous space. MAFSA was proposed to optimize the reactive power optimization, which applied for optimal reactive power is evaluated on an IEEE 30-bus power system. The modeling of reactive power optimization is established taking the minimum network losses as the objective. The simulation results and the comparison results with various optimization algorithms demonstrated that the MAFSA converges to better solutions than other approaches and the algorithm can make effectively use in reactive power optimization. Simultaneously, the validity and superiority of MAFSA was proved.

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16

Zheng, Kai Yuan. "Research on the Mathematical Model Construction and Algorithm of Dynamic Reactive Power Optimization about Distribution Network." Applied Mechanics and Materials 716-717 (December 2014): 1221–25. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.1221.

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Анотація:

Dynamic reactive power optimization of power system is an important means to ensure grid economic safety. Unlike static reactive power optimization, dynamic reactive power optimization has to consider the reactive power compensation devices switches and number of constraints of transformer tap stalls. The number of constraints undermine the independence of each period, each time reactive power optimization scheduling interrelated, so dynamic reactive power optimization must be considered from the entire period of time to compensate the number of constraints of the devices, and how to deal with action number of constraints is difficulty and focus of solving dynamic reactive power optimization of the power system.

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17

Hong, Hong, and Fang Liu. "Binary Adaptive Ant Colony Optimization in Reactive Power Optimization." Advanced Materials Research 616-618 (December 2012): 2091–96. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.2091.

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This article proposed an Adaptive Binary Ant Colony Optimization Algorithm, which is based on the dual network diagram, designed to state transition rules and information update rules, and then according to the algorithm processes adjust information volatilizing factor dynamically, Verify the validity and superiority of the algorithm.

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18

Lenin, K. "DIMENSIONED PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER OPTIMIZATION PROBLEM." International Journal of Research -GRANTHAALAYAH 6, no. 4 (April 30, 2018): 281–90. http://dx.doi.org/10.29121/granthaalayah.v6.i4.2018.1663.

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This paper present’s Dimensioned Particle Swarm Optimization (DPSO) algorithm for solving Reactive power optimization (RPO) problem. Dimensioned extension is introduced to particles in the particle swarm optimization (PSO) model in order to overcome premature convergence in interactive optimization. In the performance of basic PSO often flattens out with a loss of diversity in the search space as resulting in local optimal solution. Proposed algorithm has been tested in standard IEEE 57 test bus system and compared to other standard algorithms. Simulation results reveal about the best performance of the proposed algorithm in reducing the real power loss and voltage profiles are within the limits.

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19

Li, Xingmin, Hongwei Li, Shuaibing Li, Ziwei Jiang, and Xiping Ma. "Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads." Mathematical Problems in Engineering 2021 (November 23, 2021): 1–18. http://dx.doi.org/10.1155/2021/1196369.

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Анотація:

With a high proportion of renewable distributed generation and time-varying load connected to the distribution network, great challenges have appeared in the reactive power optimization control of the active distribution networks. This paper first introduces the characteristics of active distribution networks, the mechanism and research status of wind power, photovoltaic, and other renewable distributed generators, and time-varying loads participating in reactive power and voltage optimization. Then, the paper summarizes the methods of reactive power optimization and voltage regulation of active distribution network, including multi-timescale voltage optimization, coordinated optimization of network reconfiguration and reactive power optimization, coordinated optimization of active and reactive power optimization based on model predictive control, hierarchical and zoning control of reactive power, and voltage and power electronic switch voltage regulation. The pros and cons of the reactive power optimization algorithms mentioned above are summarized. Finally, combined with the development trend of the energy Internet, the future directions of reactive power and voltage control technology in the active distribution network are discussed.

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20

VARAPRASAD, JANAMALA, and KORITALA CHANDRA SEKHAR. "NEED FOR REACTIVE POWER OPTIMIZATION IN DEREGULATED POWER SYSTEM." i-manager's Journal on Power Systems Engineering 5, no. 2 (2017): 19. http://dx.doi.org/10.26634/jps.5.2.13619.

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21

Li, Zhihuan, Yinsheng Su, He Huang, Chunxiao Liu, and Jinfu Chen. "Power system robust reactive power optimization for load uncertainties." Journal of International Council on Electrical Engineering 5, no. 1 (January 2015): 51–54. http://dx.doi.org/10.1080/22348972.2015.1106082.

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22

Liu, Yan Wen, Ke Yin Jia, Hao Wang, and Yan Hua Wang. "Reactive Power Optimization Algorithm of Particle Swarm Optimization with Sensitivity Analysis." Advanced Materials Research 811 (September 2013): 666–71. http://dx.doi.org/10.4028/www.scientific.net/amr.811.666.

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Анотація:

Reactive power optimization is very important to power systems economic operation and nowadays, the research about it gets more and more popular. The paper presents a reactive power optimization algorithm of particle swarm optimization combined with sensitivity analysis. The paper first builds the mathematic model of reactive power optimization and introduces particle swarm optimization. Then, presents the sensitivity method in detail and talks about the process of computing the sensitivity. Finally, take the algorithm into practical application and the results proves that sensitivity analysis could improve the particle swarm optimization algorithm.

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23

Wei, Wei, Li Jie Ding, and Yan Jiao Liu. "A Review of Regional Reactive Power Optimization Techniques." Advanced Materials Research 986-987 (July 2014): 1360–64. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1360.

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Анотація:

With increasingly complex of the power grid structure and increasingly user requirements of power quality, Power grid voltage reactive power optimization is still the difficult points in power system operation control. This paper introduces the general optimization methods of multi-objective reactive power optimization, intelligent algorithm, the development of the hybrid method, and their respective advantages, disadvantages and improvement; It also analyzes and summarizes the simplification of search space for reactive power optimization and the key issues and research development trend of the real-time reactive voltage control system.

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24

Fikri Ruslan, Nabil, Ismail Musirin, Mohamad Khairuzzaman Mohamad Zamani, Muhammad Murtadha Othman, Zulkiffli Abdul Hamid, Zikri Abadi Baharuddin, and Nor Azura Md Ghani. "Power Tracing Monitoring incorporating Optimal Reactive Power Dispatch." International Journal of Engineering & Technology 7, no. 3.15 (August 13, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i3.15.17394.

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General power flow studies do not manage to trace the contributors by generators on power losses in the whole power transmission system. Thus, power tracing approach is utilized to address this issue. Power tracing is a termed used to describe the contributors for the power losses dissipated on the transmission line. The traditional technique made use the knowledge of circuit analysis such as cut set theory. However, there was no element of optimization which can help to achieve the optimal solution. This paper presents the power tracing monitoring during voltage stability improvement process, implemented by optimal reactive power dispatch. In this study, the impact of power tracing on voltage stability variation was investigated. Evolutionary Programming (EP) was developed and utilized to incorporate power tracing, along with voltage stability improvement. A pre-developed scalar voltage stability index was incorporated to indicate the voltage stability condition. On the other hand, the voltage stability initiative was conducted via the optimal reactive power dispatch. The power tracing was monitored for both; the pre-optimization and post-optimization scenarios. Small system model was tested to realize the power tracing phenomenon, which is rather rare study in power system community. Results on power tracing obtained during the pre- and post-optimal reactive power dispatch revealed that not all generators will involve in the contribution on the total transmission loss in the system. This can be beneficial to power system operators for allocating the cost without discrimination in the long run.

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25

Muhammed Neda, Omar, and Alfian Ma'arif. "Chaotic Particle Swarm Optimization for Solving Reactive Power Optimization Problem." International Journal of Robotics and Control Systems 1, no. 4 (January 28, 2022): 523–33. http://dx.doi.org/10.31763/ijrcs.v1i4.539.

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Анотація:

The losses in electrical power systems are a great problem. Multiple methods have been utilized to decrease power losses in transmission lines. The proper adjusting of reactive power resources is one way to minimize the losses in any power system. Reactive Power Optimization (RPO) problem is a nonlinear and complex optimization problem and contains equality and inequality constraints. The RPO is highly essential in the operation and control of power systems. Therefore, the study concentrates on the Optimal Load Flow calculation in solving RPO problems. The Simple Particle Swarm Optimization (PSO) often falls into the local optima solution. To prevent this limitation and speed up the convergence for the Simple PSO algorithm, this study employed an improved hybrid algorithm based on Chaotic theory with PSO, called Chaotic PSO (CPSO) algorithm. Undeniably, this merging of chaotic theory in PSO algorithm can be an efficient method to slip very easily from local optima compared to Simple PSO algorithm due to remarkable behavior and high ability of the chaos. In this study, the CPSO algorithm was utilized as an optimization tool for solving the RPO problem; the main objective in this study is to decrease the power loss and enhance the voltage profile in the power system. The presented algorithm was tested on IEEE Node-14 system. The simulation implications for this system reveal that the CPSO algorithm provides the best results. It had a high ability to minimize transmission line losses and improve the system's voltage profile compared to the Simple PSO and other approaches in the literature.

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26

Wang, XM, HB Wang, J. Zhang, SC Liu, YF Ma, and W. Yan. "Reactive Power Optimization Based on AVC Time-division Control Strategy." Journal of Physics: Conference Series 2158, no. 1 (January 1, 2022): 012011. http://dx.doi.org/10.1088/1742-6596/2158/1/012011.

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Abstract In order to avoid frequent actions of transformer taps and capacitor banks caused by reactive power optimization, this paper proposes a reactive power optimization based on AVC time division control strategy. The time division control strategy is used to segment the load curve of the next day, and the reactive power optimization process of each period is calculated by genetic algorithm. The strategy and algorithm are applied to the reactive power and voltage optimization of IEEE 30 bus system. The simulation results show that the method can realize reactive power optimization more efficiently.

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27

Chen, Li Jun, Ran Ran Hai, Ya Hong Zhang, and Gang Gang Xu. "Reactive Power Optimization Based on CAGA Algorithm." Advanced Materials Research 616-618 (December 2012): 2210–13. http://dx.doi.org/10.4028/www.scientific.net/amr.616-618.2210.

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Reactive power optimization is a typical high-dimensional, nonlinear, discontinuous problem. Traditional Genetic algorithm(GA) exists precocious phenomenon and is easy to be trapped in local minima. To overcome this shortcoming, this article will introduce cloud model into Adaptive Genetic Algorithm (AGA), adaptively adjust crossover and mutation probability according to the X-condition cloud generator to use the randomness and stable tendency of droplets in cloud model. The article proposes the cloud adaptive genetic algorithm(CAGA) ,according to the theory, which probability values have both stability and randomness, so, the algorithm have both rapidity and population diversity. Considering minimum network loss as the objective function, make the simulation in standard IEEE 14 node system. The results show that the improved CAGA can achieve a better global optimal solution compared with GA and AGA.

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28

El-Sayed, Mohamed A. H. "Artificial neural network for reactive power optimization." Neurocomputing 23, no. 1-3 (December 1998): 255–63. http://dx.doi.org/10.1016/s0925-2312(98)00081-2.

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29

Van Cutsem, Th. "Network Optimization-Based Reactive Power Margin Calculation." IFAC Proceedings Volumes 21, no. 11 (September 1988): 195–201. http://dx.doi.org/10.1016/s1474-6670(17)53744-1.

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30

Abdul-Rahman, K. H., and S. M. Shahidehpour. "Reactive power optimization using fuzzy load representation." IEEE Transactions on Power Systems 9, no. 2 (May 1994): 898–905. http://dx.doi.org/10.1109/59.317657.

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31

Katuri, Rayudu, A. Jayalaxmi, G. Yesuratnam, and Dedeepya Yeddanapaalli. "Genetic Algorithm Optimization of Generator Reactive Power." AASRI Procedia 2 (2012): 192–98. http://dx.doi.org/10.1016/j.aasri.2012.09.034.

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32

Bhongade, Sandeep, and Aakash Tomar. "Optimal Reactive Power Dispatch Optimization Using STATCOM." Journal of The Institution of Engineers (India): Series B 102, no. 2 (February 12, 2021): 277–93. http://dx.doi.org/10.1007/s40031-021-00537-1.

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33

Rugthaicharoencheep, Nattachote, Manat Boonthienthong, and Aroon Charlangsut. "Optimal Reactive Power Control in Power System with Particle Swarm Optimization Technique." Applied Mechanics and Materials 891 (May 2019): 246–52. http://dx.doi.org/10.4028/www.scientific.net/amm.891.246.

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Анотація:

This paper considers an application of Newton's optimal power flow to the solution of the secondary voltage/reactive power control in power system. This procedure is based on the sensitivity theory applied to the determination of zones for the secondary voltage/ reactive power control and corresponding reduced set of regulating sources, whose reactive outputs represent control variables in the optimal power flow program. PSO is applied to solve the OPF problem for optimal power flow the optimal power flow program output becomes a schedule to be used by operators in the process of OPF-PSO (Optimal Power Flow - Particle swarm optimization) PSO applied to optimal reactive power dispatch is evaluated on an IEEE 30-bus power system. The optimization strategy is general and can be used to solve other power system optimization problems as well.

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34

Geng, Shu Chao, and Jian Sheng Zhang. "Reactive Power Optimization in Power Supply System for Industrial Enterprise." Applied Mechanics and Materials 150 (January 2012): 170–73. http://dx.doi.org/10.4028/www.scientific.net/amm.150.170.

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Анотація:

Improving power factor will enhance the efficiency of power-using and the economization on energy. This article analyzes reactive power compensation in several methods, and does a detailed technical comparison. With actual situation of enterprises, some views are put forward for reactive power optimization.

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35

WANG, Hong, and Zhijie WANG. "Multi-Objective Reactive Power Optimization Including Distributed Generation." Advances in Sciences and Engineering 11, no. 1 (June 15, 2019): 46–52. http://dx.doi.org/10.32732/ase.2019.11.1.46.

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Анотація:

In order to solve the problem of reactive power optimization of distribution network with distributed power supply, the multi-objective reactive power optimization function is established from multiple perspectives, and the equation constraint and inequality constraint equation of power system are considered. Secondly, taking IEEE33 node distribution system with distributed power supply as an example, reactive power optimization of single objective function is carried out to verify that the proposed algorithm has a global convergence and a great advantage in convergence speed. Finally, multi-objective reactive power optimization of distribution network with distributed power supply is carried out. Simulation results demonstrate the effectiveness of the proposed algorithm.

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36

Sun, Peng, Ming Wu Luo, and Chao Xia Sun. "Optimization Method of Reactive Power Generation in Wind Plant Based on DE Algorithm." Advanced Materials Research 953-954 (June 2014): 543–51. http://dx.doi.org/10.4028/www.scientific.net/amr.953-954.543.

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Анотація:

Reactive power optimization scheduling problem of wind plant including capacitor and fan is researched in this paper. According to the structure of the wind plant to set different scenarios , aiming at minimizing the reactive power loss , establish the reactive power optimization mathematical model of the wind plant, calculate the optimal reactive power of wind turbine in different positions , minimize the reactive power loss inside wind plant under the constraints of the offset range of node voltage and the reactive power demand of grid. Through the analysis of examples,clear whether a reasonable reactive scheduling scheme can be getted, and present the optimization result . Keywords: Wind plant,Reactive power optimization scheduling,DE algorithm

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37

Gong, Yu. "Research on Reactive Power Optimization of Voltage Control of Power Grid." Applied Mechanics and Materials 347-350 (August 2013): 1288–92. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1288.

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Анотація:

The reactive power optimization control of grid voltage is the important measures of lowering the net loss and improving the voltage quality. The paper reviews the situation of voltage reactive power control in our country, putting forward implementation program of AVC system. The system can collect and dispatch the real-time data of automotive nodal voltage, active power and reactive power and so on. The objection is minimum net loss and voltage excursion. After optimization of the system we can form a set of commands about the position of the shunt capacitor and adjust the tap of load and OLTC.These commands can be carried out through dispatching automatic four remote control system.The control system can effectively improve the voltage quality, reduce grid power loss, and make reactive power compensation equipment fully utilized, so have a strong broad application value.

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38

Zhou, Yu, Zhengshuo Li, and Guangrui Wang. "Study on leveraging wind farms' robust reactive power range for uncertain power system reactive power optimization." Applied Energy 298 (September 2021): 117130. http://dx.doi.org/10.1016/j.apenergy.2021.117130.

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39

SHEKHAPPA G., ANKALIKI, KULKARNI POOJA, and PUTHRAN VRUTHA A. "REACTIVE POWER OPTIMIZATION AND LINE LOSS REDUCTION IN POWER SYSTEM." i-manager's Journal on Power Systems Engineering 4, no. 4 (2017): 41. http://dx.doi.org/10.26634/jps.4.4.11396.

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40

Li, Juan, Jing Chen, You Xin Yuan, Xue Song Zhou, and Yi Fei Wang. "A Novel Parameter Optimization Platform of Dynamic Reactive Power Compensator for Composite Power Load." Applied Mechanics and Materials 742 (March 2015): 737–40. http://dx.doi.org/10.4028/www.scientific.net/amm.742.737.

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Анотація:

Reactive power compensation is one of the effective means to improve the quality of voltage, reduce loss and save energy. How to make full use of the inductance converter and capacitance banks and to make them cooperative run, is the key of industrialization for the dynamic reactive power compensator for the composite power load. Therefore, a novel parameter optimization platform of the dynamic reactive power compensator for the composite power load is designed in this paper. The following works have been done in the study: reactive power variation characteristic of the composite power load and power factor requirements, capacity calculation and parameter adjusting method of the dynamic reactive power compensator, and a novel parameter optimization platform of the dynamic reactive power compensator for the composite power load. The research has laid a theoretical foundation for this dynamic reactive power compensator in industrial applications.

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41

Yang, Xu. "Research on Agriculture Reactive Power Optimization Control Based on DSP." Applied Mechanics and Materials 484-485 (January 2014): 650–54. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.650.

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Article systematic analysis of rural power grid situation, use of existing data resources and distribution lines to achieve the level of automation for reactive power optimization, operation optimization and concrete implementation. Papers able to give a complete, scientific and reasonable reactive power optimization. While article takes DSP2812 as core to developed reactive power compensation controller, to achieve the reactive power compensation, power distribution network detection information, and send messages via GPRS, remote control compensation capacitors. Configuration parameter has a flexible, reliable, convenient and practical features.

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42

Yao, Jianhong, Lingyu Zhang, Daxing Sun, and Jianbai Song. "Reactive Power Optimization in Power System Based on Improved Multi-agent Ant Colony Optimization." Information Technology Journal 13, no. 8 (April 1, 2014): 1561–66. http://dx.doi.org/10.3923/itj.2014.1561.1566.

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43

Li, Chuang, Min You Chen, Yong Wei Zhen, Ang Fu, and Jun Jie Li. "Reactive Power Optimization in Distribution Network with Wind Farm." Advanced Materials Research 614-615 (December 2012): 1372–76. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1372.

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Анотація:

The traditional methods to adjust voltage in distribution network reactive power optimization is discretization，and it is difficult to realize the continuous voltage adjustment. A reactive power optimization model and algorithm in distribution network with wind farm is proposed. The network loss，deviation of voltage and stability of voltage are taken into account in the multi-objective reactive power optimization model. The quantum particle swarm optimization（QPSO）algorithm has been used to solve the reactive power optimization problem. The algorithm described particle state by wave function, not only increase the diversity of population，but also avoid premature convergence. The comparison of the simulation result between QPSO and PSO on the modified IEEE 33-bus system demonstrated the effectiveness and advantage of quantum particle swarm optimization.

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44

Tao, Yanhui, and Weijia Yue. "Multi Objective Reactive Power Optimization of Distribution Network with Distributed Generation Power Uncertainty." Journal of Physics: Conference Series 2023, no. 1 (September 1, 2021): 012041. http://dx.doi.org/10.1088/1742-6596/2023/1/012041.

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Abstract Distributed generation will have a lot of adverse effects on the steadiness of the power system, resulting in the power wastage of the Distribution Network (DN) and the destruction of the reactive power balance of the power grid. It is essential to establish a dynamic reactive power optimization model to ameliorate the security, steadiness and economy of the DN. Based on this, this paper first analyses the connotation and typical characteristics of Distributed Generation (DG), then studies the connotation and typical characteristics of DG, and finally gives the multi-objective reactive power optimization strategy of DG DN.

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45

Wang, Bao Yi, Hao Yin, and Shao Min Zhang. "Research on Algorithm of Distributed Reactive Power Optimization Based on Cloud Computing and Improved NSGA-II." Applied Mechanics and Materials 644-650 (September 2014): 1927–30. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1927.

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A distributed reactive power optimization algorithm is put forward based on cloud computing and improved NSGA-II (fast non-dominated sorting genetic algorithm) in this paper. It is designed to solve problem of multi-objective reactive power optimization with huge amounts of data in power grid, whose difficulties lie in local optimum and slow processing speed. First, NSGA-II's crossover and mutation operator are improved based on Cloud Model, so as to satisfy the adaptive characteristics. In this way, we improved global optimization ability and convergence speed when dealing with large-scale reactive power optimization. Second, we introduced cloud computing, parallelized the proposed algorithm based on MapReduce programming framework. In this way, we achieved distributed improved NSGA-II algorithm, effectively improved the calculation speed of handling massive high-dimensional reactive power optimization. Through theoretical study demonstrated the superiority of the algorithm to solve the Multi-Objective reactive power optimization.

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46

Ramirez, Juan M., Javier Vargas-Marín, and Rosa E. Correa-Gutiérrez. "Power Systems Decentralized Optimization." International Journal of Emerging Electric Power Systems 15, no. 6 (December 1, 2014): 545–56. http://dx.doi.org/10.1515/ijeeps-2013-0156.

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Abstract The paper’s aim is to explore both active and reactive power losses minimization and voltage stability by a decentralized strategy. The approach assumes that the grid is split into sub-systems; each sub-system is in charge of its own optimal solution using equivalents for the neighbors, so that information exchange is not required. Schedule interchanges among sub-systems have been added as constraints into the optimal formulation. Once the individual solutions are available, the corresponding settings are allocated to the corresponding equipment and a full power flow study is carried out to assure the steady-state preservation. Results are exhibited on two test power systems.

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47

Lou, Yu Cheng, Da Xie, Jun Qi Feng, Min Xia Yang, and Yu Zhang. "Control Strategy of the Charging-Discharging-Storage Integrated Station on Reactive Power/Voltage of Power System." Advanced Materials Research 860-863 (December 2013): 1120–28. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.1120.

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Compared to reactive power/voltage index and standard operation procedure (SOP) of power system, the reactive power/voltage index of charging-discharging-storage integrated station is defined, which includes voltage margin and voltage adjustable capability of integrated station ((ISVM & ISVAC). And then the reactive power compensation control strategy of integrated station is put forward and proved to be lossless in power. According to the reactive power regulation characteristic, a reasonable reactive power distribution between shunt integrated stations can be realized by the reactive power/voltage optimization algorithm. And the synthesis optimization can be achieved when AVC applied into integrated station, which will bring value-added benefit to power quality and greatly improve the grid reliability.

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48

Li, Yun Zhi, Quan Yuan, Yang Zhao, and Qian Hui Gang. "A Novel Reactive Power Compensation Approach Based on Particle Swarm Optimization." Applied Mechanics and Materials 740 (March 2015): 401–4. http://dx.doi.org/10.4028/www.scientific.net/amm.740.401.

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Анотація:

The particle swarm optimization (PSO) algorithm as a stochastic search algorithm for solving reactive power optimization problem. The PSO algorithm converges too fast, easy access to local convergence, leading to convergence accuracy is not high, to study the particle swarm algorithm improvements. The establishment of a comprehensive consideration of the practical constraints and reactive power regulation means no power optimization mathematical model, a method using improved particle swarm algorithm for reactive power optimization problem, the algorithm weighting coefficients and inactive particles are two aspects to improve. Meanwhile segmented approach to particle swarm algorithm improved effectively address the shortcomings evolution into local optimum and search accuracy is poor, in order to determine the optimal reactive power optimization program.

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49

Liu, Hongying. "Research on the Application of Artificial Intelligence Technology in Power System Intelligent Dispatching Automation." Journal of Physics: Conference Series 2083, no. 4 (November 1, 2021): 042047. http://dx.doi.org/10.1088/1742-6596/2083/4/042047.

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Abstract From the perspective of meeting the power quality requirements of users, the article analyses the characteristics of traditional voltage and reactive power control mode and the regional power grid reactive voltage optimization centralized closed-loop control mode (AVC system) based on the dispatch automation system (SCADA/EMS) from the perspective of technical management. Combining the reactive power/voltage real-time optimization control model, a real-time optimization control method of the regional power grid based on the improved differential evolution algorithm is proposed. The particle swarm algorithm is combined with the characteristics of reactive power/voltage control to improve the initial particle quality, reduce the optimization space, and introduce a crossover operator to improve the calculation speed and efficiency of the algorithm. Taking an actual regional power grid as an example, the simulation calculation of reactive power/voltage real-time optimization is carried out. The results show that the proposed algorithm and control strategy are feasible and effective.

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50

Li, Kang, and Guige Gao. "Research on Reactive Power Optimization of Power System Based on Improved Particle Swarm Algorith." Journal of Physics: Conference Series 2136, no. 1 (December 1, 2021): 012045. http://dx.doi.org/10.1088/1742-6596/2136/1/012045.

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Анотація:

Abstract Artificial intelligence algorithms are widely used to optimize problems in power systems, and reactive power optimization in power systems has achieved good results in particle swarm optimization, but there are also problems. This paper optimizes the particle swarm algorithm. The particle swarm algorithm is improved mainly by increasing the inertia weight and improving the convergence parameters. This algorithm overcomes the blindness of local optimization solution and particle swarm algorithm, and improves the calculation speed. At the same time, MATLAB is used to compile the calculation program, and the simulation results are used to verify the feasibility of the reactive power optimization algorithm used in the research of power system.

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