Jing Li
Optimised ant colony algorithm, electrical equipment, status monitoring, predictive maintenance, optimisation of inspection path
With the increase of the power system equipment scale and complexity, condition monitoring, and maintenance of electrical equipment become more important. The traditional periodic maintenance strategy has limitations in resource utilisation and maintenance efficiency, which leads to necessary problem exploration. In this paper, a state monitoring and predictive maintenance strategy for electrical equipment based on the optimal ant colony algorithm (OACA) is proposed. The experimental results show that the optimised ant colony algorithm reaches the optimal value of 0.136 after 67 iterations. The efficiency is significantly higher than other methods. According to the optimised detection path, the path length for high failure rate devices is 203 km. The average failure rate devices is 26 km, while the low failure rate devices is 171 km. The total path length is shorter than other algorithms. When the fault increase rate is 20%–50%, the optimised ant colony algorithm achieves better results than the traditional maintenance strategies. In addition, the optimised path reduces the proportion of total cost and significantly improves resource utilisation. In general, this electrical equipment status monitoring and predictive maintenance strategy based on optimised ant colony algorithm can be widely applied in practice, thereby ensuring the safe and stable operation of the power system.
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