Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm

Abstract

This paper applies a proposed modified stochastic fractal search algorithm (MSFS) for dealing with all constraints of optimal reactive power dispatch (ORPD) and finding optimal solutions for three different cases including power loss optimization, voltage deviation optimization, and L-index optimization. The proposed MSFS method is newly constructed in the paper by modifying three new solution update mechanisms on standard stochastic fractal search algorithm (SSFS). The first modification is to keep only one formula and abandon one formula in the diffusion process while the second modification and the third modification are used in the first update and the second update. In two updates of SSFS, solutions with low quality are updated with high probability while other solutions with high quality do not get chances to be updated. This manner results in the fact that some promising solutions around the high quality solutions can be missed. In order to tackle this restriction, the second modification of MSFS is to newly update the worst solutions in the first update and the best solutions in the second update. In the third modification, all existing formulas of SSFS in the two updates are abandoned and the same new proposed technique is used for updating such solutions in two updates. Compared to SSFS, the three modifications can bring advantages to MSFS such as using smaller number of produced solutions per iteration, spending shorter execution time, finding better optimal solutions, and owning more stable search ability. Furthermore, the proposed method also sees its effectiveness and robustness over SSFS by testing on IEEE 30-bus system and IEEE 118-bus system with three different single objectives for each system. The proposed method can find less minimum, average, and maximum for all the cases in addition to faster search speed. Besides, the proposed method is also compared to other methods such as PSO-based method group, GA-based method group, DE-based method group, and other recent methods. Result comparisons also indicate that the proposed method can be more efficient than almost all these methods with respect to less minimum and smaller values of control parameters. As a result, evaluation of the performance of the proposed method is that it should be used for seeking solutions of ORPD problem.

Impact of the penetration of distributed generation on optimal reactive power dispatch

“…It shows that the proposed algorithm Fig. 4 Convergence characteristics of the algorithms for the IEEE 57 bus system without DG injection TLBO [20] FPA [20] CSA [20] SSA [20] MSSA [20] HSSSA [20] SSO [20] ISSO [20] MSFS [50] 116 is far superior and consistent in obtaining better results than the other well-established techniques. The results also depict the ability of the proposed technique in obtaining better results for all the functions under any constraints.…”

Fractional PSOGSA Algorithm Approach to Solve Optimal Reactive Power Dispatch Problems With Uncertainty of Renewable Energy Resources

“…In case of minimizing the power losses for 13 control variables, the by using FPSOGSA is reduced from 5.811 MW (base case) to 4.5308 MW. Judging from Table IX and Table X, the results computed by FPSOGSA are better than MFO [44], C-PSO [37], GWO [40], PSO-CF [23], QOTLBO [41], IGA [43], MICA-IWO [38], PSOGSA [45], FODPSO-EE [50], LAPO [28], ALO [42], MSFS [46], BBO [48] and OGSA [49]. According to Figs.…”

Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems

“…e obtained results from two studied systems showed that the COA reached a better solution than GA and PSO, but there was no demonstration about the faster speed of COA since settings of population and iterations were ignored. In [35], COA was applied for more complicated problem than previous applications, which was the problem of optimizing the reactive power flow for transmission power systems. However, the solution proposed by COA was not as good as other methods.…”