Electricity Distribution Network: Seasonality and the Dynamics of Equipment Failures Related Network Faults

Power systems are inclined to frequent failures due to equipment malfunctions in the network. Equipment malfunctions can occur in any of the equipment in the network such as transformers, switchgear, overground cables or underground cables. Any failures in a distribution network directly affect the network stability, availability and reliability. Therefore, quick elimination and prevention of network faults is paramount importance for the DNOs. It is challenging to predict equipment failure accurately for a given period due to the uncertain nature of the fault forecasting process. This study aims to predict the monthly distribution network faults caused by equipment failures with the highest possible accuracy using the three different time-series algorithms. Those three models were implemented in each category of data sets to find the most efficient algorithm based on Mean Absolute Percentage Error as the selected accuracy metrics. Also, to make better business decisions, the DNO community needs to understand the role of the seasonality of the network faults. This study will investigate seasonality using the time-series seasonal decomposition method. Accurate fault prediction at the distribution level and correct understanding of seasonality will help distributed network operators manage and plan network maintenance work. This research also may influence the DNOs in engineering staff management, setting up asset investment priorities and future design strategy,