2020
DOI: 10.3390/app10186318
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A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems

Abstract: Conventional protection schemes in the distribution system are liable to suffer from high penetration of renewable energy source-based distributed generation (RES-DG). The characteristics of RES-DG, such as wind turbine generators (WTGs), are stochastic due to the intermittent behavior of wind dynamics (WD). It can fluctuate the fault current level, which in turn creates the overcurrent relay coordination (ORC) problem. In this paper, the effects of WD such as wind speed and direction on the short-circuit curr… Show more

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Cited by 30 publications
(16 citation statements)
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“…This review article emphasizes the technical issues (transient overvoltage issues) related to the transmission network reconfiguration. Thus, the key issue is to handle transient issues in network reconfiguration for grid resilience and protection against the influence of weather events 110,111 …”
Section: Practical Challenges Issues and Industry Practicesmentioning
confidence: 99%
“…This review article emphasizes the technical issues (transient overvoltage issues) related to the transmission network reconfiguration. Thus, the key issue is to handle transient issues in network reconfiguration for grid resilience and protection against the influence of weather events 110,111 …”
Section: Practical Challenges Issues and Industry Practicesmentioning
confidence: 99%
“…The outcomes of these problems are beneficial to initiate different demand response actions and demand side flexibility assessment [7][8][9][10]. Several prominent optimizations algorithms that tried to solve these problems include: Genetic algorithm (GA) [11], simulated annealing (SA) [12], differential evolution (DE) [13,14], moth swarm optimization algorithm (MSA) [15], spider monkey optimization (SMO) [16], particle swarm optimization (PSO) [17,18], grey wolf optimizer (GWO) [19], gravitational search algorithm (GSA), fire fly algorithm (FFA) [20,21], harmony search algorithm (HSA) [22,23], spiral optimization algorithm (SOA) [24], squirrel search algorithm (SSA) [25], harris hawks optimization (HHO) [26], sine-cosine algorithm (SCA) [27], artificial bee colony (ABC) [28], bacterial forging algorithm (BFA) [29], flower pollination algorithm (FPA) [30], differential evolution (DE) [31], modified flower pollination algorithm (FPA) [32], , Fluid search optimization (FSO) [33], improved ABC (IABC) [34], modified BFA (MBFA) [35], whale optimization algorithm (WOA) [36], hybrid hierarchical evolution (HHE) [37], hybrid particle swarm gravitational search algorithm (PSOGSA) [38], chaos turbo PSO (CTPSO) [39], new global PSO (NGPSO) [40], multiobjective PSO (MOPSO) [41], multi-objective DE based PSO (MODE/PSO) [42] quantum inspired glowworm swarm optimization (QGSO) [43], combination of cont...…”
Section: Introductionmentioning
confidence: 99%
“…The development of technologies related to the internet of things (IoT), artificial intelligence (AI), blockchain and big data encourages power system operators to modernize power grid and smart city development [10][11][12]. It is always significant to have an efficient power system as greater losses distress the overall economy [13][14][15]. An efficient power system that meets demand uncertainties is helpful to generate deferent flexible options to initiate demand response (DR) actions [16,17].…”
Section: Introductionmentioning
confidence: 99%