Hybrid PSO-tabu search for the optimal reactive power dispatch problem

Abstract

This paper presents a new approach to solve the optimal reactive power dispatch (ORPD) problem based on hybridizing Particle Swarm Optimization (PSO) and TabuSearch (TS) meta-heuristics (PSO-TS). The ORPD problem is formulated as a nonlinear constrained single-objective optimization problem where the real power loss is to be minimized. The proposed approach is used to find the settings of the control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive compensation devices, to optimize power transmission loss. The study was implemented on IEEE 30-bus systems, and the results were compared with non-hybridized PSO and TS and other evolutionary algorithms reported in the literature.

Optimal Reactive Power Flow for Large-Scale Power Systems Using an Effective Metaheuristic Algorithm

“…Besides, saving percentage (%) of the SFS method compared with each one is also calculated and reported in the tables for further comparisons. Saving percentage from these tables can see that SFS outperforms most methods excluding PSO-TS [16] and ISFS [37] for power loss objective and QOTLBO [28] for voltage deviation objective. Saving values show that these methods get improvement over SFS by 0.137%, 0.295%, and 2.45%; however, only ISFS [37] has found better solution than SFS, meanwhile PSO-TS [16] has not reported MI and N pop for comparison of convergence speed and recalculated minimum of QOTLBO is 0.1031, which is much higher than reported value of 0.0856.…”

“…where G ij is the conductance of conductor ij; β i and β j are the phases of voltage at buses i and j, respectively; V loadi is the voltage of load bus i; V ref is expected to be the voltage equaling 1.0 pu; and L j is called L-index of-bus j [16]. Basically, L-index is within the range from 0 to 1 in which 0 is the best value and 1 is the worst value.…”

“…ese methods have been successfully and widely applied to solving the ORPF problem, consisting many original methods, improve/modified methods, or combined/hybrid methods. ey have been constantly developed and have become a big method family such as particle swarm optimization (PSO) family [13][14][15][16][17], differential evolution (DE) family [18][19][20][21], and genetic algorithm (GA) family [22][23][24][25], while many standard methods have been also applied in [26][27][28][29][30][31][32][33][34]. Sahli et al [16] presented a combination between particle swarm optimization and tabu search (PSO-TS) by incorporating the best search function of PSO and TS.…”

Metaheuristic research: a comprehensive survey

“…Simulated Annealing (SA) [21], Genetic Algorithms (GAs) [22],Particle Swarm Optimization (PSO) [23], etc.). There are also many hybrid methods which combine local search capability of local search algorithms as an improvement mechanism in global search/population based metaheuristics [24][25][26].…”

Flujo Óptimo Reactivo para Minimización de Pérdidas: una Nueva Propuesta de Función de Adaptación y Manejo de Restricciones

“…Otras adaptaciones de los algoritmos genéticos para abordar el ORPD son presentadas en (Xuexia et al, 2012) y (Cheng et al, 2004. Técnicas como la búsqueda Tabú (Sahli et al, 2014) y MVMO (Rueda y Erlich, 2013) también han sido aplicados al problema de ORPD. En (Yan et al, 2004) y (Chao-Rong et al, 2013 se aborda el problema usando técnicas metaheurísticas híbridas.…”