Orthogonal genetic algorithm based power system restoration path optimization

Abstract

Summary Optimizing the power system restoration path is a key issue for the system restoration after a blackout. Because the optimization is a complex nonlinear programming problem, artificial intelligent algorithms are widely employed to solve this problem due to its modeling flexibility and strong optimization capability. However, because the dimension of restoration path optimization is very high especially for large‐scale systems, artificial intelligent algorithms in current works are easy to be trapped in the local optima. To improve the optimal solution from the artificial intelligence algorithms, an orthogonal genetic algorithm is employed in this paper to optimize the restoration path, which can search the solution space in a statistically sound manner. First, the experimental design method was employed to generate orthogonal array as the initial population which was scattered uniformly over the feasible solution space. Then, the orthogonal crossover operator based on the orthogonal experimental design was employed to generate a small but representative feasible solution as the potential offspring. Finally, the proposed method is validated by using the IEEE 118‐bus test system and part of the Jiangsu power grid in China.

A MILP-based restoration planning method for generator start-up considering flexible re-energizing times of transmission lines

“…In existing studies, several meta-heuristic algorithms have been employed to solve nonlinear models for GSUS optimization. Here, we take the artificial bee colony (ABC) algorithm [33] and the orthogonal genetic algorithm (OGA) [34] as examples to solve the original nonlinear model and compare the results with those of the proposed MILP model. The two-stage solution strategy in [15] was employed to implement both methods.…”

Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience

“…In Reference 96, a fast and elitist non‐dominated sorting GA (NSGA‐II) is proposed, and the main objectives of the proposed method are to maximize restoration generating capacity in limited time as shown in Figure 2, parallel restoration through power sub‐area, and minimize the time for the reconstruction of the skeleton network. In References 22,104, an orthogonal GA proposed to optimize the lines (restoration paths) as an objective for network reconfiguration, which plays a vital for power system restoration after a major blackout. The objective of the proposed algorithm is to minimize the weight of the power transmission path from the vertex set run through the targeted vertex as shown in Figure 2.…”

Optimal Electric Arc Furnace Model’s Characteristics Using Genetic Algorithm and Particle Swarm Optimization and Comparison of Various Optimal Characteristics in DIgSILENT and EMTP-RV

“…en, the selection operator selects some of the best chromosomes using a stochastic process [39]. e crossover and mutation operators are applied to the selected chromosomes, producing a new generation of chromosomes [40,41]. is process continues until a certain number of iterations or convergence criteria is reached.…”