A novel decision optimization for thermal power unit based on condition-based predictive maintenance and equilibrium optimizer

Abstract

A novel maintenance decision method for thermal power unit is proposed in paper. The proposed method tackles the issues of inflexibility and high cost in current thermal power unit maintenance strategies. It takes into account both intelligent fault prediction results and the current unit condition to make predictive maintenance decisions. It constructs a unit state model and a power generation prediction model based on DCS data from the thermal power unit. Constraints such as unit constraints, unit failure constraints, and unit power generation constraints are utilized in the construction of the objective function. The objective function incorporates start-stop cost, maintenance cost and downtime loss, and the Equilibrium Optimizer is used to determine the optimal maintenance strategy. Operational data from a power plant is used to verify the superiority of the method in enhancing unit power generation efficiency and the economic benefits of the power plant. The results show that the proposed method can reduce the cost by 34.9 % in case of minor failures.