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https://doi.org/10.3390/en11071808
Title: | An improved algorithm for optimal load shedding in power systems | Authors: | Larik, R.M Mustafa, M.W Aman, M.N Jumani, T.A Sajid, S Panjwani, M.K |
Keywords: | Electric power plant loads Electric power transmission Genetic algorithms Outages Particle swarm optimization (PSO) Standby power systems Voltage control Blackouts Bus voltage magnitude Fast voltage stability indices Load-shedding Optimal load shedding Transmission capacities Under voltage load shedding Voltage collapse Electric load shedding |
Issue Date: | 2018 | Publisher: | MDPI AG | Citation: | Larik, R.M, Mustafa, M.W, Aman, M.N, Jumani, T.A, Sajid, S, Panjwani, M.K (2018). An improved algorithm for optimal load shedding in power systems. Energies 11 (7) : 1808. ScholarBank@NUS Repository. https://doi.org/10.3390/en11071808 | Rights: | Attribution 4.0 International | Abstract: | 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. © 2018 by the authors. Licensee MDPI, Basel, Switzerland. | Source Title: | Energies | URI: | https://scholarbank.nus.edu.sg/handle/10635/178545 | ISSN: | 1996-1073 | DOI: | 10.3390/en11071808 | Rights: | Attribution 4.0 International |
Appears in Collections: | Staff Publications Elements |
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