Efficient Operation of Power System with FACTS controllers using Evolutionary Techniques

Kar, Manoj Kumar; Kumar, Lalit; Kumar, Sanjay; Singh, Arun Kumar

doi:10.1109/spin48934.2020.9070909

Cited by 14 publications

(8 citation statements)

References 12 publications

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“…Since the IDE method is a modified version of DE, some variants of DE are chosen for comparison. As DE (Abou El Elaa et al, 2011), MFO (KAR et al, 2020;Kumar et al, 2021;Kumari et al, 2022), BSOA (Jordehi, 2015), PSO (Bhattacharyya & Kumar, 2016) T A B L E 3 WSRT results on unimodal and multimodal functions denoting the equivalency (≈), superiority (+), or inferiority (À) method when compared with the proposed method 96, 99, 133, 152, 158, 227, 268, 283 143, 145, 169, 210, 217, 219 parameters f and Cr set to 0.5 and 0.6, respectively. The MFO and BSOA control parameters are set in accordance with the parameters of the base papers.…”

Section: Resultsmentioning

confidence: 99%

“…This seeks to reduce processing time while improving the accuracy and practical ability of finding the best solution. Use of the moth-flame optimization (MFO) algorithm is described in (Kar et al, 2020;Kumar et al, 2021;Mei et al, 2017) as a nature-inspired heuristic strategy for solving the optimal RPM as well as ORPD problem. However, different test systems were considered for the study, still various loading condition has not been emphasized.…”

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confidence: 99%

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Statistical analysis based reactive power optimization using improved differential evolutionary algorithm

2022

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This study presents a novel improved differential evolutionary (IDE) algorithm for optimizing reactive power management (RPM) problems. The effectiveness of IDE algorithm is tested on different unimodal and multimodal benchmark functions. The objective function of the RPM is considered as the minimization of active power losses. Initially, the power flow analysis approach is employed to detect the optimal position of flexible AC transmission system (FACTS) devices. The proposed method is used to determine the optimal value of control variables such as generator's reactive power generation, transformer tap settings, and reactive power sources. Furthermore, the efficacy of the IDE approach is compared with other promising optimization methods such as variants of differential evolution algorithm, moth flame optimization (MFO), brainstorm-based optimization algorithm (BSOA), and particle swarm optimization (PSO) on various IEEE standard test bus (i.e., IEEE-30, -57, -118, and -300) systems with active and reactive loading incorporating FACTS devices. A Static VAR compensator (SVC) for shunt compensation and a thyristor-controlled series compensator (TCSC) for series compensation were used as FACTS devices.The proposed IDE method significantly reduces the active power loss, that is, 55.65% in IEEE 30, 39.68% in IEEE 57, 16.32% in IEEE 118, and 8.56% in IEEE 300 bus system at nominal loading. Finally, the statistical analysis such as Wilcoxon signed-rank test (WSRT) and ANOVA test were thoroughly analysed to demonstrate the firmness and accuracy of the proposed technique.active power loss, FACTS devices, improved differential evolution, load flow analysis, optimal placement, statistical analysisExisting power transmission lines operating under extreme stress and transmitting power close to their thermal limits which causes grid disruptions and violations of different operating constraints. In this scenario, reactive power management becomes a challenging task for power

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

Statistical analysis based reactive power optimization using improved differential evolutionary algorithm

2022

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“…As the proposed MDE algorithm is a variant of DE, we have considered DE as the comparative method. Since DE, 20 PSO, 7 SCA, 21 GWO, 22,23 MFO, 26,27 WOA 28,29 has been already used for ORPD problem, we have considered DE, PSO, SCA, GWO, MFO, and WOA as the comparative methods. The DE/best/1 scheme is chosen where the control parameters f and Cr are set to 0.5 and 0.6 respectively.…”

Section: Resultsmentioning

confidence: 99%

“…18 A proper location for siting the FACTS controllers is obtained by a loss sensitivity and performance index sensitivity approach. 19 The solution to ORPD problem is addressed by different techniques such as DE, 20 sine cosine algorithm (SCA), 21 gray wolf optimization (GWO) 22,23 gravitational search algorithm (GSA) 24,25 moth-flame optimization (MFO) 26,27 whale optimization algorithm (WOA) 28,29 hybrid swarm intelligence 30 and so forth. However, the researchers [20][21][22][23][24][25][26][27][28][29][30][31] have not performed any statistical analysis on the obtained results while evaluating the effect of active and reactive loadings on IEEE30, IEEE57, and IEEE118 bus system.…”

Section: Introductionmentioning

confidence: 99%

Reactive power management by using a modified differential evolution algorithm

Kar

Kumar

Singh

et al. 2021

Optim Control Appl Methods

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In this article, a modified differential evolution (MDE) algorithm is proposed and applied to provide the solution for reactive power management by incorporating the flexible alternating current transmission systems (FACTS) controllers. The proper siting of FACTS controller has been achieved with an objective to minimize the losses and to improve the loading capability. The power flow analysis is performed to determine the optimal position for FACTS controllers. These controllers are incorporated in the most heavily loaded lines and hence controls the power flow in that particular line and allow more power to be transmitted in the remaining lines. The proposed MDE algorithm uses a novel DE/best3/1/bin mutation operator to produce three temporary mutant vectors which are averaged to obtain the mutant vector. Hence, the decision vectors of a generation simultaneously move toward the three best decision vectors of the population thereby maintains a better trade between exploration and exploitation. The proposed MDE algorithm is applied on different standard test bus (i.e., IEEE30, IEEE57, and IEEE118) systems with varying active and reactive loading (i.e., 100%, 110%, and 120%). The proposed method's performance is compared to those obtained from some well‐known meta‐heuristic algorithms. The proposed MDE algorithm optimized FACTS controllers reduce transmission loss by 60.90% in IEEE30 bus, 49.72% in IEEE57 bus and 8.37% in IEEE118 bus test system under base loading. The statistical analysis of the obtained results is carried out using the Wilcoxon signed rank test and the Friedman and Nemenyi hypothesis test, which ensures the reliability and robustness of the proposed method.

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“…FACTS devices mitigate the network overloading and ORPD problems when it is optimally placed [6]. Te ORPD problem was solved by using PSO [7], DE [8], BBO [9], and MFO [10,11]. Some researchers used distributed generation for minimizing loss [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Simplified Sine Cosine Algorithm for the Solution of Optimal Reactive Power Dispatch

Gupta

Kar

Kumar

et al. 2022

International Transactions on Electrical Energy Systems

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In this article, a simplified sine cosine algorithm (SSCA) is applied to solve the optimal reactive power dispatch (ORPD) issues by estimating the control variables. This algorithm uses sine cosine functions and generates number of random solutions to obtain the best solution by fluctuating inwards or outwards. The SSCA is implemented in the ORPD problem to find the best control variables to achieve minimum power loss and maximum net savings. Furthermore, the efficacy of SSCA is validated with other recently used algorithms considering three case studies, i.e., IEEE-30, -57, and -118 bus test system. The results show that the SSCA approach finds more precise and superior ORPD solutions. A comparison among SSCA and other methods proves the robustness of SSCA to attain the solution with faster convergence. The statistical analysis is performed to justify the effectiveness of SSCA by yielding minimum operating cost and maximum net savings as compared to other techniques considered in this study.

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Efficient Operation of Power System with FACTS controllers using Evolutionary Techniques

Cited by 14 publications

References 12 publications

Statistical analysis based reactive power optimization using improved differential evolutionary algorithm

Statistical analysis based reactive power optimization using improved differential evolutionary algorithm

Reactive power management by using a modified differential evolution algorithm

A Simplified Sine Cosine Algorithm for the Solution of Optimal Reactive Power Dispatch

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