Reactive power and Voltage Control in Kerala Grid and Optimization of Control Variables Using Genetic Algorithm

Sreejaya, P.; Rejitha, R. S.

doi:10.1109/icpst.2008.4745156

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…To build the VSOR, it is necessary to employ proper optimization algorithm to control power, voltage and frequency [20] to search its stability region boundary accurately and quickly. Recently, many traditional and nature-inspired optimization algorithms are different aspects of the voltage stability problems, such as genetic algorithm (GA) [21]- [23], particle swarm optimization (PSO) algorithm [24] - [28], artificial neural network (ANN) [29], [30], Tabu search [31], dynamic programming [32], [33], differential evolution [34], gravity search algorithm (GSA) [35]- [37], etc. Some of these methodologies show excellent performance in reaching a near-global optimum while greatly prevailing over the difficulty arises due to the nonlinearity nature of such problems.…”

Section: Introductionmentioning

confidence: 99%

Voltage Security Operation Region Calculation Based on Improved Particle Swarm Optimization and Recursive Least Square Hybrid Algorithm

Maihemuti

Wang

et al. 2021

Journal of Modern Power Systems and Clean Energy

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Large-scale voltage collapse incidences, which result in power outages over large regions and extensive economic losses, are presently common occurrences worldwide. To avoid voltage collapse and operate more safely and reliably, it is necessary to analyze the voltage security operation region (VSOR) of power systems, which has become a topic of increasing interest lately. In this paper, a novel improved particle swarm optimization and recursive least square (IPSO-RLS) hybrid algorithm is proposed to determine the VSOR of a power system. Also, stability analysis on the proposed algorithm is carried out by analyzing the errors and convergence accuracy of the obtained results. Firstly, the voltage stability and VSOR-surface of a power system are analyzed in this paper. Secondly, the two algorithms, namely IPSO and RLS algorithms, are studied individually. Based on this understanding, a novel IPSO-RLS hybrid algorithm is proposed to optimize the active and reactive power, and the voltage allowed to identify the VSOR-surface accurately. Finally, the proposed algorithm is validated by using a simulation case study on three wind farm regions of actual Hami Power Grid of China in DIgSILENT/Power Factory software. The error and accuracy of the obtained simulation results are analyzed and compared with those of the particle swarm optimization (PSO), IPSO and IPSO-RLS hybrid algorithms.

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Section: Introductionmentioning

confidence: 99%

Voltage Security Operation Region Calculation Based on Improved Particle Swarm Optimization and Recursive Least Square Hybrid Algorithm

Maihemuti

Wang

et al. 2021

Journal of Modern Power Systems and Clean Energy

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“…OPF is a nonlinear, non-convex, large-scale, static optimization problem with both continuous and discrete control variables. Even in the absence of discrete control variables, the OPF problem is non-convex due to the existence of the nonlinear (AC) power flow equality constraints [12].…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm base reactive power dispatch and voltage control

2014

IJAERD

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Reactive power control becomes extremely important after deregulation because costumers request high quality power supply with reasonable price. Also the Voltage profile at different nodes in the power system is affected greatly by the variations in load and generation profiles during normal and abnormal operating states. The reactive power control devices such as generators, tap positions of on-load tap changer of transformers, shunt reactors are used to correct voltage limits violations while simultaneously reducing the system real power losses. Genetic algorithms (GAs) are well-known global search techniques anchored on the mechanisms of natural selection and genetics. Hear i present the work on genetic algorithm for reactive power optimization and voltage control. The objective of genetic algorithm base reactive power optimization is minimizing active power losses while maintaining the quality of voltages.

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“…Recently, Genetic Algorithm (GA) has been widely used to RPO [4][5][6][7]. GA has been theoretically and empirically proven to provide robust search in complex spaces.…”

Section: Introductionmentioning

confidence: 99%

An Improved Catastrophic Genetic Algorithm and Its Application in Reactive Power Optimization

Ouyang¹

2010

EPE

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This paper presents an Improved Catastrophic Genetic Algorithm (ICGA) for optimal reactive power optimization. Firstly, a new catastrophic operator to enhance the genetic algorithms’ convergence stability is proposed. Then, a new probability algorithm of crossover depending on the number of generations, and a new probability algorithm of mutation depending on the fitness value are designed to solving the main conflict of the convergent speed with the global astringency. In these ways, the ICGA can prevent premature convergence and instability of genetic-catastrophic algorithms (GCA). Finally, the ICGA is applied for power system reactive power optimization and evaluated on the IEEE 14-bus power system, and the application results show that the proposed method is suitable for reactive power optimization in power system

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Reactive power and Voltage Control in Kerala Grid and Optimization of Control Variables Using Genetic Algorithm

Cited by 12 publications

References 11 publications

Voltage Security Operation Region Calculation Based on Improved Particle Swarm Optimization and Recursive Least Square Hybrid Algorithm

Voltage Security Operation Region Calculation Based on Improved Particle Swarm Optimization and Recursive Least Square Hybrid Algorithm

Genetic algorithm base reactive power dispatch and voltage control

An Improved Catastrophic Genetic Algorithm and Its Application in Reactive Power Optimization

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