Adaptive Particle Swarm Optimization Approach for Optimal Reactive Power Planning

Rajkumar, P.; Devaraj, D.

doi:10.1109/icpst.2008.4745340

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…The selection of tuning parameter is based on the trial and error method. There are several optimization algorithms available to select the proper parameters to reduce the THD values such as GA, EA, and PSO . However, GA is more reliable than PSO, for continuous search problems like finding gain parameter, the PSO gives better results.…”

Section: Introductionmentioning

confidence: 99%

“…There are several optimization algorithms available to select the proper parameters to reduce the THD values such as GA, EA, and PSO. 15,16 However, GA is more reliable than PSO, for continuous search problems like finding gain parameter, the PSO gives better results. Compared with GA, the convergence time taken by the PSO is less, which is more comfortable for the dynamic load condition.…”

Section: Introductionmentioning

confidence: 99%

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Development and performance analysis of PSO‐optimized sliding mode controller–based dynamic voltage restorer for power quality enhancement

Jeyaraj

Devaraj

Velusamy

2019

Int Trans Electr Energ Syst

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Summary In the distribution power system, voltage sag and swell are the critical issues. The impact of this issue creates major economic loss and power loss in the distribution system. There are several detection methods available, mainly, connecting the FACTS devices such as Static Compensator (STATCOM), Unified Power Quality Conditioner (UPQC), and dynamic voltage restorer (DVR) at the point of sag or swell. DVR is one of the custom power devices to mitigate the voltage sag and swell. Sliding mode controller (SMC) is proposed in the paper, which is used to regulate the DVR to mitigate the voltage sag issue. Particle swarm optimization (PSO) algorithm is utilized in this work to find the optimum value of SMC parameters, namely, Kp and Ki, with the objective of minimizing the integral square error (ISE). A simple two feeder distribution network with DVR is developed in MATLAB‐SIMULINK to assess the performance of DVR under various faults and disturbed situations. The mitigation level and THD values of PSO‐optimized SMC‐based DVR results are compared with the PI controller–based DVR, PSO‐optimized PI controller DVR, and SMC‐based DVR. At last, in the proposed method, the trial is taken by adding a renewable energy resource, that is, PV farm at 400‐kW power rating. The solar energy is integrated with the existing distribution system, and the results are incorporated. Simulation results show that the PSO‐optimized SMC‐based DVR can compensate the voltage sag and swell and also reduce the load voltages efficiently.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and performance analysis of PSO‐optimized sliding mode controller–based dynamic voltage restorer for power quality enhancement

Jeyaraj

Devaraj

Velusamy

2019

Int Trans Electr Energ Syst

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“…Techniques based on combinatorial optimisation have been widely applied to solve reactive power planning problems; in particular, hybrid combinatorial algorithms [15], Tabu search [16], particle swarm optimisation [17] and evolutionary particle swarm optimisation [18].…”

Section: Introductionmentioning

confidence: 99%

Reactive power planning under conditional‐value‐at‐risk assessment using chance‐constrained optimisation

Lopez¹,

Contreras

Mantovani³

2015

IET Generation, Transmission & Distribution

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This study presents a risk-assessment approach to the reactive power planning problem. Chance-constrained programming is used to model the random equivalent availability of existing reactive power sources for a given confidence level. Load shedding because of random equivalent availability of those reactive power sources is implemented through conditional-value-at-risk. Tap settings of under-load tap-changing transformers are considered as integer variables. Active and reactive demands are considered as probability distribution functions. The proposed mathematical formulation is a two-stage stochastic, multi-period mixed-integer convex model. The tradeoff between risk mitigation and investment cost minimisation is analysed. The proposed methodology is applied to the CIGRE-32 electric power system, using the optimisation solver CPLEX in AMPL. NomenclatureThe following notations used throughout this paper are described below. The operators E, Pr, Var and Std are used as expectation, probability, variance and standard deviation measures, respectively.

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A review of meta-heuristic algorithms for reactive power planning problem

Shaheen

Spea

Farrag

et al. 2018

Ain Shams Engineering Journal

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Adaptive Particle Swarm Optimization Approach for Optimal Reactive Power Planning

Cited by 9 publications

References 19 publications

Development and performance analysis of PSO‐optimized sliding mode controller–based dynamic voltage restorer for power quality enhancement

Development and performance analysis of PSO‐optimized sliding mode controller–based dynamic voltage restorer for power quality enhancement

Reactive power planning under conditional‐value‐at‐risk assessment using chance‐constrained optimisation

A review of meta-heuristic algorithms for reactive power planning problem

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