Optimal power flow under both normal and contingent operation conditions using the hybrid fuzzy particle swarm optimisation and Nelder–Mead algorithm (HFPSO–NM)

Optimal Power Flow by Black Hole Optimization Algorithm

“…The total fuel cost convergence curve is shown in Fig(1) and the corresponding optimum control variable settings are shown in Table (II). The total fuel cost obtained is 800.8882 $/h, which is higher than the published best result in [43] of 794.9545 $/h by 0.7464% as shown in Table (III). 2) Case 2: Voltage profile improvement: Minimizing the fuel cost may produce inappropriate voltage profile which means that the solution is not feasible because of the poor voltage profile. Hence, the objective function in (17) is modified by introducing the voltage deviation as new term as follows…”

“…The control variable is shown in Table (II) while a comparison of the results with other techniques are shown in Table (IV). Table(IV) shows that the results are generally closed and the obtained total fuel cost using the BH algorithm is higher than the best result proposed [43] by 0.1221% and the voltage deviation obtained is higher by 58.55%.…”

Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems

“…Lifetime optimisation in wireless networks [27] PSO with fuzzy acceleration coefficients is hybridised with Nelder Mead algorithm to utilise its strong exploitative capability.…”

Security optimal power flow considering loading margin stability using hybrid FFA–PS assisted with brainstorming rules

“…Hybrid methods considered as an alternative and robust solution to combine different methods. In the recent literature various hybrid methods have been proposed and applied with success for solving many complex and combined problems related to power system planning, operation and control, some of these techniques are, Evolving ant direction differential evolution [29], A modified teaching-learning based optimization [30], hybrid differential evolution (DE) with particle swarm optimization (PSO) [31], hybrid fuzzy particle swarm optimization and Nedler-Mead algorithm (HFPSO-NM) [32], chaotic improved PSO [33], hybrid imperialist competitive-sequential quadratic programming (HIC-SQP) algorithm [34], A modified shuffle frog leaping algorithm [35], new modified and hybrid modified imperialist competitive algorithms [36], adaptive biogeography based predator-prey optimization technique [37], self-evolving brain-storming inclusive teaching-learning-based algorithm [38], A hybrid GA-PS-SQP [39] and hierarchical adaptive PSO [40]. As well described in the literature review, the structure of these methods is based on how adjusting dynamically their parameters and on combination between various methods to exploit efficiently the best performances of each method to achieve the global solution at a reduced time.…”