Post-Outage Reactive Power Flow Calculations by Genetic Algorithms: Constrained Optimization Approach

Optimal power flow in multi‐terminal HVDC networks with embedded high‐power DC–DC converters for voltage matching and flexible DC transmission

“…7 shows a flowchart for the overall GA optimisation procedure. The optimisation performance of GA is governed by a set of parameters such as the population size, the crossover rate, the mutation rate, and the stopping criteria, which can influence the solution accuracy and computation time [24,27]. If the choice of the population size is underestimated, then it is possible to achieve a local optimal solution, due to improper population evolution.…”

“…GA in Matlab) with extensive programming background reduces the application complication [26]. The GA is based on natural biological evolution [27]. The main features of GA are code-based solution solver (i.e.…”

“…The main features of GA are code-based solution solver (i.e. not using solutions in their original forms), good exploration with population-based search, use of fitness function rather than derivatives, and search area based on probabilistic transition behavior [27]. The GA approach is popular due to its capabilities to handle several types of systems and constraints.…”

Double branch outage modeling and simulation: Bounded network approach

“…Since, the model used only limited number of network variables, it was fast enough for real time applications providing better accuracy than the traditional methods [10]. Single branch outage problem was later solved by genetic algorithms [11], by particle swarm optimization method [12], by differential evolution method and by harmony search method [13].…”

Post-Outage State Estimations for Outage Management

“…This approach brought some advantages in computational efficiency. Later, the problem was solved by genetic algorithms (See Ozdemir et al (2005)).…”