scholarly journals Enhanced Fractional Chaotic Whale Optimization Algorithm for Parameter Identification of Isolated Wind-Diesel Power Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 140862-140875
Author(s):  
Yashar Mousavi ◽  
Alireza Alfi ◽  
Ibrahim Beklan Kucukdemiral
Keyword(s):  
Power Systems ◽  
Parameter Identification ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Whale Optimization

scholarly journals Nature-Inspired Whale Optimization Algorithm for Optimal Coordination of Directional Overcurrent Relays in Power Systems

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2297 ◽  
Author(s):  
Wadood ◽  
Khurshaid ◽  
Farkoush ◽  
Yu ◽  
Kim ◽  
...  
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Operating Time ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Test Systems ◽  
Whale Optimization ◽  
Overcurrent Relays ◽  
Optimal Coordination ◽  
Hunting Strategy

In power systems protection, the optimal coordination of directional overcurrent relays (DOCRs) is of paramount importance. The coordination of DOCRs in a multi-loop power system is formulated as an optimization problem. The main objective of this paper is to develop the whale optimization algorithm (WOA) for the optimal coordination of DOCRs and minimize the sum of the operating times of all primary relays. The WOA is inspired by the bubble-net hunting strategy of humpback whales which leads toward global minima. The proposed algorithm has been applied to six IEEE test systems including the IEEE three-bus, eight-bus, nine-bus, 14-bus, 15-bus, and 30-bus test systems. Furthermore, the results obtained using the proposed WOA are compared with those obtained by other up-to-date algorithms. The obtained results show the effectiveness of the proposed WOA to minimize the relay operating time for the optimal coordination of DOCRs.

scholarly journals Power system voltage instability risk mitigation via emergency demand response-based whale optimization algorithm

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Mohammed Amroune ◽  
Tarek Bouktir ◽  
Ismail Musirin
Keyword(s):  
Power Systems ◽  
Power System ◽  
Demand Response ◽  
Optimization Algorithm ◽  
Voltage Stability ◽  
Risk Mitigation ◽  
Stability Margin ◽  
Whale Optimization Algorithm ◽  
Voltage Instability ◽  
Whale Optimization

AbstractIn recent years, due to the economic and environmental issues, modern power systems often operate proximately to the technical restraints enlarging the probable level of instability risks. Hence, efficient methods for voltage instability prevention are of great importance to power system companies to avoid the risk of large blackouts. In this paper, an event-driven emergency demand response (EEDR) strategy based on whale optimization algorithm (WOA) is proposed to effectively improve system voltage stability. The main objective of the proposed EEDR approach is to maintain voltage stability margin (VSM) in an acceptable range during emergency situations by driving the operating condition of the power system away from the insecure points. The optimal locations and amounts of load reductions have been determined using WOA algorithm. To test the feasibility and the efficiency of the proposed method, simulation studies are carried out on the IEEE 14-bus and real Algerian 114-bus power systems.

State Estimation of Power Using the Whale Optimization Algorithm

2020 ◽  
Vol 11 (4) ◽  
pp. 194-213
Author(s):  
Suvabrata Mukherjee ◽  
Provas Kumar Roy
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Optimization Algorithm ◽  
Measurement Errors ◽  
Population Based ◽  
Whale Optimization Algorithm ◽  
State Variables ◽  
Optimal Point ◽  
Whale Optimization ◽  
Population Based Algorithm

In power systems, the process of attaining a better prediction from a set of variables from state variables is called state estimation (SE). These variables consist of magnitudes of bus voltage and the corresponding angles of all the buses. Because of the non-linearity and intricacy of ever-developing power systems, it has become important to apply upgraded techniques for the dissolution and supervision in practical environments. The discussed analysis evaluates the appositeness of a new metaheuristic technique called the whale optimization algorithm (WOA) which is a population-based algorithm, to reduce the measurement errors so as to gauge the optimal point of the power system when some susceptible values are inadequate. WOA displays admirable attainment in global optimization. It employs a bubble-net hunting approach and it mimics the social behaviour of humpback whales to get the best candidate solution. The approach is tested on IEEE-14, IEEE-30, and IEEE-57 bus test systems and the potency is validated by comparison with the biogeography based optimization algorithm (BBO).

scholarly journals Whale Optimization Algorithm-Based DG Allotment for Loss Minimization of Distribution Networks

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Power Systems ◽  
Radial Distribution ◽  
Optimization Algorithm ◽  
Distribution Networks ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Loss Minimization ◽  
Optimum Placement ◽  
Suggested Technique ◽  
Whale Optimization

For optimum placement of distributed generation (DG) units in balanced radial distribution network for loss minimization, implementation of whale optimization algorithm (WOA), a state-of-the-art meta-heuristic optimization algorithm is proposed in this paper. Encouraged by bubble-net hunting strategy of whales, WOA mimes the collective practice of humpback whales. For validating performance in solving the mentioned problem, the suggested technique is implemented on IEEE 33-bus and IEEE 69-bus balanced radial distribution test networks. The obtained results demonstrate that feasible and effective solutions are obtained using the proposed approach and can be used as a propitious substitute in practical power systems to overcome the optimum DG siting and sizing issue. Also concerning the best knowledge of the authors, it is the first report on the application of WOA in solving optimum DG siting and sizing issue.

scholarly journals A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2367 ◽  
Author(s):  
Tan Ding ◽  
Li Chang ◽  
Chaoshun Li ◽  
Chen Feng ◽  
Nan Zhang
Keyword(s):  
Parameter Identification ◽  
Optimization Algorithm ◽  
Mixed Strategy ◽  
Hydraulic Turbine ◽  
Water Hammer ◽  
Whale Optimization Algorithm ◽  
Local Optimum ◽  
Movement Strategies ◽  
Mutation Strategy ◽  
Whale Optimization

For solving the parameter optimization problem of a hydraulic turbine governing system (HTGS) with a delayed water hammer (DWH) effect, a Mixed-Strategy-based Whale Optimization Algorithm (MSWOA) is proposed in this paper, in which three improved strategies are designed and integrated to promote the optimization ability. Firstly, the movement strategies of WOA have been improved to balance the exploration and exploitation. In the improved movement strategies, a dynamic ratio based on improved JAYA algorithm is applied on the strategy of searching for prey and a chaotic dynamic weight is designed for improving the strategies of bubble-net attacking and encircling prey. Secondly, a guidance of the elite’s memory inspired by Particle swarm optimization (PSO) is proposed to lead the movement of the population to accelerate the convergence speed. Thirdly, the mutation strategy based on the sinusoidal chaotic map is employed to avoid prematurity and local optimum points. The proposed MSWOA are compared with six popular meta-heuristic optimization algorithms on 23 benchmark functions in numerical experiments and the results show that the MSWOA has achieved significantly better performance than others. Finally, the MSWOA is applied on parameter identification problem of HTGS with a DWH effect, and the comparative results confirm the effectiveness and identification accuracy of the proposed method.

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