Reliability analysis and power quality improvement model using enthalpy based grey wolf optimizer
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Reliability analysis and power quality improvement model using enthalpy based grey wolf optimizer Niharika Thakur1 · Y. K. Awasthi1,2 · A. S. Siddiqui3 Received: 15 February 2020 / Accepted: 1 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract With the modern era of a restructured power system environment, there are various power quality issues faced when it is being transferred within different utilities. To overcome these issues and deliver maximum effective power maintaining its quality in terms of Voltage Stability, Losses, distortion in the signal received etc. is the major challenge. This paper presents the enthalpy based Grey Wolf Optimisation (GWO) model and aims to solve the issues of the node voltage deviation power system, feeder power losses, power factor, and total harmonic distortion in a power system. The results of the model presented is compared with different conventional algorithms and found to be most suitable due to its simplicity, faster convergence and high search precision. The analysis of the presented method is performed on IEEE 30 and 57 test bus systems in MATLAB/SIMULINK environment. When compared with the conventional models, our proposed method shows 0.49, 0.69 and 1.41% better fitness accuracy for IEEE 30 and 3.23, 2.37 and 1.45% better for IEEE 57 than PSO, ABC and GA respectively. The proposed method has proved to be effective in terms of all the objectives stated above and attaining the maximum quality of power. Keyword FACTS · Feeder power loss · System’s node voltage deviation · Power factor · Voltage sag · Total harmonic distortion Abbreviations GWO Grey wolf optimization TS Tabulated search * Y. K. Awasthi [email protected] 1
High Power Electrical Laboratory, Department of Electronics and Communication Engineering, Manav Rachna University, Faridabad, HR 121004, India
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Department of Electronics and Communication Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, HR 121004, India
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Department of Electrical Engineering, Jamia Millia Islamia (Central University), New Delhi 110025, India
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FACTS Flexible alternating current transmission system GA Genetic algorithm SA Simulated annealing PSO Particle swarm optimization VSM Voltage stability margin EA Evolutionary algorithm TCSC Thyristor controlled series capacitor SVC Static var compensator STATCOM Static synchronous compensator UPFC Unified power flow controller THD Total harmonic distortion LOA Local optima avoidance AWOA Adaptive whale optimisation algorithm VSI Voltage stability index
1 Introduction In a deregulated and profoundly aggressive power system network with expanding load requirements, transmission lines are stranded against being over-burden past their points of confinement leading to congestion in the system and therefore the issues linked with the quality of power being delivered have become imperative these days [1]. Over-loading transmission lines won’t just build ins
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