Statistical analysis based reactive power optimization using improved differential evolutionary algorithm

Abstract

This study presents a novel improved differential evolutionary (IDE) algorithm for optimizing reactive power management (RPM) problems. The effectiveness of IDE algorithm is tested on different unimodal and multimodal benchmark functions. The objective function of the RPM is considered as the minimization of active power losses. Initially, the power flow analysis approach is employed to detect the optimal position of flexible AC transmission system (FACTS) devices. The proposed method is used to determine the optimal value of control variables such as generator's reactive power generation, transformer tap settings, and reactive power sources. Furthermore, the efficacy of the IDE approach is compared with other promising optimization methods such as variants of differential evolution algorithm, moth flame optimization (MFO), brainstorm-based optimization algorithm (BSOA), and particle swarm optimization (PSO) on various IEEE standard test bus (i.e., IEEE-30, -57, -118, and -300) systems with active and reactive loading incorporating FACTS devices. A Static VAR compensator (SVC) for shunt compensation and a thyristor-controlled series compensator (TCSC) for series compensation were used as FACTS devices.The proposed IDE method significantly reduces the active power loss, that is, 55.65% in IEEE 30, 39.68% in IEEE 57, 16.32% in IEEE 118, and 8.56% in IEEE 300 bus system at nominal loading. Finally, the statistical analysis such as Wilcoxon signed-rank test (WSRT) and ANOVA test were thoroughly analysed to demonstrate the firmness and accuracy of the proposed technique.active power loss, FACTS devices, improved differential evolution, load flow analysis, optimal placement, statistical analysisExisting power transmission lines operating under extreme stress and transmitting power close to their thermal limits which causes grid disruptions and violations of different operating constraints. In this scenario, reactive power management becomes a challenging task for power

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“…Figure 5 shows that the bus voltages are within the specifed limits when FACTS devices are incorporated, thus satisfying the equality constraints. Tis system has seven generators, eighty numbers of transmission lines, ffteen numbers of tap changing transformers, four numbers of SVC, and four numbers of TCSC [30]. Te total demand of real and reactive powers is 1250.8 MW and 336.4 MVAR, respectively, at 100 MVA.…”

“…Tis system has ffty-three generators, one hundred eighty-six numbers of transmission lines, nine numbers of tap changing transformers, fve numbers of SVC, and fve numbers of TCSC [30]. Te total demand of real and reactive powers is 4242 MW and 1438 MVAR, respectively, at 100 MVA.…”

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“…Because metaheuristic methods are stochastic in nature, therefore, statistical analysis on the generated data is required to draw definitive conclusions. As a result, WSRT [41,42] is applied to the obtained results (at a 95% confidence level) to identify the inferiority (−), superiority (+), or equivalency ( ≈ ) of a technique in contrast to the suggested MGWOA approach. Table 3 presents the WSRT results.…”