Optimal Network Reconfiguration to Reduce Power Loss Using an Initial Searching Point for Continuous Genetic Algorithm

Abstract

In this paper, an effective method to determine an initial searching point (ISP) of the network reconfiguration (NR) problem for power loss reduction is proposed for improving the efficiency of the continuous genetic algorithm (CGA) to the NR problem. The idea of the method is to close each initial open switch in turn and solve power flow for the distribution system with the presence of a closed loop to choose a switch with the smallest current in the closed loop for opening. If the radial topology constraint of the distribution system is satisfied, the switch opened is considered as a control variable of the ISP. Then, ISP is attached to the initial population of CGA. The calculated results from the different distribution systems show that the proposed CGA using ISP could reach the optimal radial topology with better successful rate and obtained solution quality than the method based on CGA using the initial population generated randomly and the method based on CGA using the initial radial configuration attached to the initial population. As a result, CGA using ISP can be a favorable method for finding a more effective radial topology in operating distribution systems.

Optimization of distributed generation units in reactive power compensated reconfigured distribution network

“…After optimal NR (case A), the real and reactive power losses have been reduced by 40.4% and 22%, respectively consequent to the opening of five sectionalizing switches 7, 14, 10, 32 and 28 (maintaining radial arrangement) which is better when compared to [3][4][5][6]26]. The bus voltage improvement after case A is around 0.04 p.u compared to BC.…”

“…The bus voltage improvement after case A is around 0.04031 p.u compared to BC. It is obvious that the performance of the proposed method is effective in achieving better power loss minimization compared with FPEO [3], BPSO [4], CGA and ICSA [5] and PSO with H-matrix [6].…”

“…NR has been recognized with great importance from 1988 onwards. Hence, many researchers are focussing their researches on optimal NR-based optimization problems [3][4][5][6]. By using NR, the advantages, such as reduction in power loss, improvement in bus voltage, load congestion management and reliability of the DN, get improved and this is reflected in the performance improvement of the DN.…”

Forecasting Water Quality Index in Groundwater Using Artificial Neural Network

“…The networks included one hidden layer. According to the literature, networks with a higher number of hidden layers are better fitting for higher complexity cases [27]. The number of neurons in the hidden layer (2 ÷ 10) was selected experimentally, as well as reflected the mean square error and the regression value R scores.…”

Investigating the actual performance of recent meta-heuristic algorithms in solving different optimization problems

“…Due to the high effectiveness as mentioned while dealing with optimization problems, a vast number of meta-heuristic methods have been introduced and developed, such as evolutionary programming (EP) [8], genetic algorithm (GA) [9], particle swarm optimization (PSO) [10], bat algorithm (BA) [11], ant colony optimization (ACO) [12], Social Learning Optimization (SLO) [13], Quantum-inspired Algorithm for Resource Optimization (QARO) [14], Chaotic Harmony Search (CHS) [15], Fruit Fly Optimization Algorithm (FOA) [16], Ant Lion Optimizer (ALO) [17], Archimedes Optimization Algorithm (AOA) [18], Sine Cosine Algorithm (SCA) [19], Pathfinder algorithm (PFA) [20], Gravitational Search Algorithm (GSA) [21], Bacterial Foraging Optimization (BFO) [22], whale optimization algorithm (WOA) [23], water cycle algorithm (WCA) [24], Walrus Optimization Algorithm (WaOA) [25], Lyrebird Optimization Algorithm (LOA) [26], Green Anaconda Optimization (GAO) [27], and Harris hawks optimization (HHO) [28].…”