Abstract
Preventive maintenance plan of generators is essential to ensure the availability of electricity supply in a power system. There are several types of preventive plans of which the periodic preventive maintenance (PPM) and reliability-centred maintenance (RCM) are most popular in the power system industry. Nevertheless, these plans do not consider the operational factors and optimum maintenance efforts that could maximize the net benefit. This paper proposes an innovative Smart Maintenance (SM) model that leads to an effective maintenance plan. The proposed approach is advanced through three main concepts: 1. Markov chains to describe the component’s reliability; 2. Fuzzy logic to determine the component’s operational risk; and 3. Maintenance exertion degree to define the impact of maintenance over the component’s failure rate. The smart maintenance problem is solved using the Accelerated Quantum Particle Swarm Optimization (AQPSO) in conjunction with the Sequential Median Latin Hypercube (SMLH) sampling. The case studies justify the effectiveness of the proposal and its superiority over the PPM and RCM in realises extended benefits.
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