Capacitor Allocation Using Multiobjective Water Cycle Algorithm and Fuzzy Logic

Abstract

Radial distribution systems are susceptible to a lack of voltage profile and increase system losses, particularly at the distant ends of the distribution feeder. This manuscript proposes an approach to solve the optimal capacitor placement problem in radial distribution networks to minimize system losses, improve the voltage profile of all buses, promote total voltage stability, and improve net savings. The optimal capacitor placement problem is solved in two stages. Firstly, normalized loss sensitivity factor and voltage magnitude are used as inputs to build fuzzy expert rules to arrange the most candidate buses for capacitor placement. Secondly, a multiobjective water cycle algorithm is applied to determine the optimal sizes and locations of capacitors within the predefined search space using fuzzy expert rules. The multiobjective function is formulated with operational constraints considering fixed and switched capacitors. To validate the effectiveness of this methodology, it is demonstrated on IEEE 33 and IEEE 94-bus radial distribution networks. Clearly, the findings show the improvement in the voltage profile and static voltage stability, the significant reduction in system losses, as well as the enhancement in overall savings. Furthermore, a comprehensive evaluation is also carried out by comparing the numerical results with other methods such as interior point algorithm, a combination fuzzy real coded genetic algorithm method, water cycle algorithm for IEEE 33-bus system and artificial bee colony algorithm for IEEE 94-bus system which prove the viability and effectiveness of the proposed methodology.

Metaheuristic‐based graded objective function strategy for optimal thyristor‐controlled series capacitors estimation in weak distribution systems with uncertain distributed generation

“…One of the most effective algorithms is the HSSA [2], which surpasses other powerful algorithms such as the Multiobjective Water Cycle Algorithm [39], Multiobjective Grey Wolf Optimizer [40], and Sorting Genetic Algorithm [38]. In this study, we utilize the Hyper-Spherical Search Algorithm in conjunction with the graded objective functions management strategy (HSSA-GOFMS) to address the proposed problem effectively.…”

“…In order to choose the best algorithm for the radial distribution systems problem effectively, we conducted a thorough comparison of recent algorithms [2,[37][38][39][40] to find the optimal solution for the shunt capacitor of IEEE 33-bus. Our comparison revealed that the Hyper-Spherical Search Algorithm (HSSA) [2] demonstrates greater strength and applicability compared to the Multi-objective Water Cycle Algorithm [39], Multi-objective Gray Wolf Optimizer [40], Non-dominated Sorting Genetic Algorithm [38], and other optimizers used in the referenced papers. The HSSA achieved the best value for the maximum percentage of minimization in real power loss (34.27%), making it the ideal choice for optimizing the thyristor-controlled series capacitor (TCSC) to enhance the quality of weak electrical power distribution systems.…”

“…These optimization problems always involve constraints, and extensive research has been conducted on both single‐objective and multi‐objective constrained optimization [18]. Various heuristic algorithms have been utilized to enhance the performance of distribution power systems, including genetic algorithms [19], cuckoo search [20], the Salp Swarm algorithm [21], Artificial bee colony [22], chaotic stochastic fractal search algorithm [23], Particle Swarm Optimization [24], Water Cycle Algorithm [25], harmony search algorithm [26], Improved harmony search algorithm [27], polar bear optimization algorithm [28], particle swarm optimization and Dragonfly algorithm [29], fuzzy‐dragonfly method [30], Hybrid Local Search‐Genetic Algorithm [31], Dragonfly algorithm [32], thief and police algorithm [33], a robust data clustering method [34], Improved Tunicate Swarm Intelligent Algorithm [35], fuzzy‐differential evolution [36], improved atom search optimization [37], Non‐dominated Sorting Genetic Algorithm [38], Multi‐objective Water Cycle Algorithm [39], Multi‐objective Grey Wolf Optimizer [40], Shark Optimization [41], and Mine Blast Algorithm [42].…”

“…In order to choose the best algorithm for the radial distribution systems problem effectively, we conducted a thorough comparison of recent algorithms [2, 37–40] to find the optimal solution for the shunt capacitor of IEEE 33‐bus. Our comparison revealed that the Hyper‐Spherical Search Algorithm (HSSA) [2] demonstrates greater strength and applicability compared to the Multi‐objective Water Cycle Algorithm [39], Multi‐objective Gray Wolf Optimizer [40], Non‐dominated Sorting Genetic Algorithm [38], and other optimizers used in the referenced papers. The HSSA achieved the best value for the maximum percentage of minimization in real power loss (34.27%), making it the ideal choice for optimizing the thyristor‐controlled series capacitor (TCSC) to enhance the quality of weak electrical power distribution systems.…”

“…This inspires the authors to tackle these issues through an improved metaheuristic algorithm to optimize TCSC in distribution systems interfaced with uncertain distributed generation. ✓Consideration of uncertainty in renewable distribution energies: With the expansion of renewable energy sources in distribution networks, there is a need to develop methods that account for the uncertainty because of its impotence according to Reference [7]. ✓Challenges with existing optimization methods [17–40]: Despite significant progress in mathematical optimization methods, simultaneous achievement of the objective function and constraints remains problematic, leading to convergence issues in some cases. This motivates the authors to devise an algorithm to address this limitation. ✓Drawbacks of fixed series capacitors: While fixed series capacitors have shown improvement in the performance of electrical distribution networks, they lack flexibility and often bring abrupt changes, akin to steps on a ladder [11–16].…”