The proposed algorithm is developed by the combined application of Gaussian and Cauchy probability distributions in ABC algorithm and hence termed as swarm-inspired artificial bee colony (SIABC) algorithm. The performance of the proposed SIABC algorithm is compared with particle swarm optimization (PSO...
Particle swarm optimizationArtificial bee colony algorithmGaussian and cauchy distributionsIn this paper, an enhanced artificial bee colony (ABC)-based algorithm is proposed for solving optimal power flow (OPF) with wind farm. The OPF calculations determine optimal values of control variables and system...
The paper considers the operation, maintenance costs, and peak cut of each device in the system, utilizes the adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm to optimize the power generation scheduling of each device, and suggests a solution for the day-ahead optimization...
Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information. Renew. Energy 2015, 75, 301–307. [Google Scholar] [CrossRef] Fazelpour, F.; Tarashkar, N.; Rosen, M.A. Short-term ...
Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine. Int. J. Electr. Power Energy Syst. 2015, 73, 625–631. [Google Scholar] [CrossRef] An, X.; Jiang, D.; Liu, C.; Zhao, M. Wind farm power prediction...
The system operators use rewards and penalties to create revenue; thus, the positive imbalance cost increases the system profit, while the negative imbalance cost decreases it. The maximization and minimization problems are the two objective functions in this study, respectively. The objective functions...
Harris hawk and particle swarm optimization (HHO–PSO) was applied to PSS and STATCOM in [159] to damp power system oscillations. The combination of algorithms may successfully address some limitations. However, it increases the computational complexities of the optimization process. 4. Discussion ...