Improved NSGA-Ⅲ Algorithm and BP Fuel-cost Prediction Network for Many-objective Optimal Power Flow Problems.

To effectively handle the many-objective optimal power flow (MOOPF) problems considering the simultaneous reduction of power loss, emission and fuel cost, an improved NSGA-Ⅲ (INSGA-Ⅲ) algorithm is put forward in this paper. In detail, the proposed INSGA-Ⅲ algorithm adopts the competitive solutions preliminarily optimized by traditional NSGA-Ⅲ method as the initial population and integrates the novel adaptive dominant (NAD) strategy. Comparing with the original NSGA-Ⅲ algorithm, INSGA-Ⅲ obtains the more preferable Pareto front (PF) with uniform distribution. More significantly, an entirely new BP fuel-cost prediction network is proposed to explore the potential elite power flow (EPL) solutions. These EPL solutions determined around the best compromise solution (BCS) of INSGA-Ⅲ algorithm provide decision-makers with more and better scheduling schemes. The effectiveness and superiorities of proposed INSGA-Ⅲ algorithm and BP fuel-cost prediction model are verified by both dual-objective and triple-objective MOOPF simulation experiments. In general, this paper presents an innovative way to solve the complex engineering problems by computer technologies represented by intelligent algorithms and neural networks. [ABSTRACT FROM AUTHOR]