A Type 2 Fuzzy Logic–Based Maintenance Solution for Power System in Renewable Energy Applications

Abstract

Power systems are crucial for low-carbon energy applications. Condition maintenance plays a vital role in reducing the maintenance cost of renewable power systems without sacrificing system reliability. This paper proposes a hybrid method to effectively deal with the operational changes and uncertainties of state maintenance within the power system of renewable energy applications. Specifically, a multi-objective evolutionary algorithm is first adopted to maintain key components when only considering system variables and overall performance. During operation, numerous variations in offshore substations are detected from power grids and other equipment, such as continuous aging, weather, load factors, measurement, and human-judgment factors. Then, the advisor implements a system optimization maintenance plan in the substation, which can predict changes in load reliability based on the type 2 fuzzy logic and hidden Markov model technology. The reliability of the load point of each substation would also be obtained. Illustrative results indicate that these serious deteriorations would cause substation for the re-optimization maintenance and optimization activities to meet expected reliability. Through connecting an offshore substation to a medium-sized offshore substation, the uncertainties in condition-based maintenance of renewable energy applications can be well handled.