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Energies 2018, 11(7), 1808;

An Improved Algorithm for Optimal Load Shedding in Power Systems

School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bharu Skudai 81310, Malaysia
Department of Electrical Engineering, NED University of Engineering and Technology Karachi, Sindh 75270, Pakistan
Department of Computer Science, National University of Singapore, Singapore 119077, Singapore
Mehran University of Engineering and Technology SZAB campus Khairpur Mirs, Sindh 66020, Pakistan
School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Department of Energy Systems Engineering, Sukkur IBA University Pakistan, Sindh 65200, Pakistan
Author to whom correspondence should be addressed.
Received: 10 June 2018 / Revised: 30 June 2018 / Accepted: 3 July 2018 / Published: 10 July 2018
(This article belongs to the Section Electrical Power and Energy System)
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A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA. View Full-Text
Keywords: under voltage loadshedding; power systems; blackouts; voltage collapse; genetic algorithms (GA); particle swarmoptimization (PSO) under voltage loadshedding; power systems; blackouts; voltage collapse; genetic algorithms (GA); particle swarmoptimization (PSO)

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Larik, R.M.; Mustafa, M.W.; Aman, M.N.; Jumani, T.A.; Sajid, S.; Panjwani, M.K. An Improved Algorithm for Optimal Load Shedding in Power Systems. Energies 2018, 11, 1808.

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