Optimal Reactive Power Generation for Transmission Power Systems Considering Discrete Values of Capacitors and Tap Changers

Abstract

In this paper, an improved coyote optimization algorithm (ICOA) is developed for determining control parameters of transmission power networks to deal with an optimal reactive power dispatch (ORPD) problem. The performance of ICOA method is superior to its conventional coyote optimization algorithm (COA) thanks to modifications of two new solution generations of COA. COA uses a center solution to generate an update step size in the first solution generation and produced one new solution by using random factors to diversify the search space in the second solution generation. By tackling the drawbacks of COA, ICOA can reduce control parameters and computation steps, shorten execution time, and provide better results. ICOA is compared to its conventional COA for three standard IEEE systems of 30-, 57-, and 118-buses with continuous and discrete control variables. Moreover, three other algorithms such as water cycle algorithm (WCA), salp swarm algorithm (SSA), and sunflower optimization algorithm (SFOA) have been also implemented for further investigation of the real performance of the proposed method. All the applied methods are metaheuristic algorithms based on population and randomization. The result comparison from the test systems has indicated that ICOA can provide higher solution quality than other methods with reasonable execution time. Therefore, ICOA is a reliable tool for finding optimal solutions of the ORPD problem.

Optimal placement and operation of soft open points, capacitors, and renewable distributed generators in distribution power networks to reduce total one-year energy loss

“…However, other remaining hybrid systems with SOPs devices on Lines 4, 6, 7, and 8 could reach better profiles. Using Equation (2) in the study [ 58 ] to calculate the total voltage deviation (TVD), Table 7 is established to rank the voltage profile improvement of different cases, from the base system to the eight hybrid systems with SOPs at Lines L1-L8. The hybrid system with SOPs on Line L6 (HS-L6) can reach the best voltage profile with the smallest TVD, 0.587 Pu.…”

Novel Extreme Learning Machine and Chaotic in-Built Opposition Based – Quantum Ruddy Turnstone Optimization Algorithms for Real Power Loss Dwindling and Voltage Consistency Enhancement

“…Table 9 shows the loss appraisal, Table 10 shows the voltage aberration evaluation and Table 11 gives the power constancy assessment. ICOA [48] 22.376 ICOA1 [48] 22.383 WCA [48] 26.0402 SSA [48] 25.3854 SFOA [48] 26.6541 COA [48] 24.5358 LISA-I [51] 26.88 LISA-II [51] 26.92 ISA [51] 26.97 MOPSO [49] 27.83 MOEPSO [49] 27.42 MFO [52] 24.25 MOGWA [53] 21.171 SGA [13] 25.64 PSO [13] 25.03 HAS [13] 24 ICOA [48] 0.6051 ICOA1 [48] 0.6155 WCA [48] 0.7309 SSA [48] 0.94 SFOA [48] 0.7913 COA [48] 0.6711 LISA-I [51] 1.0642 LISA-II [51] 1.072 ISA [51] 1.0912 MOPSO [49] 1.10 MOEPSO [49] 0 ICOA [48] 0.25169 ICOA1 [48] 0.2583 WCA [48] 0.2789 SSA [48] 0.29 SFOA [48] 0.2831 COA [48] 0 12 shows the loss appraisal and Table 13 shows the voltage aberration evaluation. Figures 18 and 19 give the graphical appraisal between the methods.…”

“…Comparison of real power loss done between the standard methods and proposed Ruddy turnstone optimization In Table 14 shows the loss appraisal and Table 15 shows ICOA [48] 114.8036 ICOA1 [48] 114.8623 WCA [48] 118.3207 SSA [48] 125.7288 SFOA [48] 125.6801 COA [48] 132.3341 COA1 [48] 123.6867 COA2 [48] 126.0426 LISA-I [51] 119.79 LISA-II [51] 120.15 ISA [51] 120.67 ALCPSO [54] 121.53 CLPSO [54] 130 ICOA [48] 0.1605 ICOA1 [48] 0.1608 WCA [48] 0.2315 SSA [48] 0.4883 SFOA [48] 0.6061 COA [48] 0.2034 COA1 [48] 0.1928 COA2 [48] 0.1936 LISA-I [51] 0.2819 LISA-II [51] 0.2876 ISA [51] 0 ICOA [48] 0.06061 ICOA1 [48] 0.06064 WCA [48] 0.060731 SSA [48] 0.0639 SFOA [48] 0.0619 COA [48] 0.06123 COA1 [48] 0.06072 COA2 [48] 0 • In Ruddy turnstone optimization algorithm the constellation of Ruddy turnstone, which mobile from one place to alternate in the sequence of repositioning and the fresh investigation agent position is to evade the smash amongst their contiguous Ruddy turnstone. [51] 338.715 ISA [51] 339.325 FAHCLSO [49] 341.001 PSO [49] 341…”

“…Juneja Kapil [47] used a Fuzzy-Controlled Differential Evolution to solve the problem. Kien et al [48] used Discrete Values of Capacitors and Tap Changers. Souhil et al [49] applied ant lion optimization algorithm.…”

Optimal Reactive Power Dispatch Using a Chaotic Turbulent Flow of Water-Based Optimization Algorithm

“…Recently, different optimization methods have been studied to solve the ORPD problem; various optimization methodologies are recommended, such as deterministic and metaheuristic algorithms [8]. These algorithms include original, modified deterministic, original, modified metaheuristic, and crossbreed heuristic algorithms [9]. Deterministic algorithms are the earliest methods, and these involve minimizing real power losses using the interior point method, Newton method, quadratic programming method, and an ANN-based memory model [10][11][12][13].…”