Big Data Analytics for Predictive Maintenance in Modern Power Systems
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Abstract
The complexity of power systems has accelerated and this has emphasized the use of sophisticated maintenance strategies that would help in enhancing reliability in power systems, minimize down-time as well as reduce operational costs. Conventional corrective and preventive maintenance strategies have been found inadequate in managing the dynamism of large power grids that are increasingly incorporating smart grids, renewable energy sources and smart-incorporated devices. Predictive maintenance has become an even better replacement since it allows one to predict failing equipment to handle before they fail and conduct it proactively. The analysis of Big Data is fundamentally relevant in meeting this paradigm since it offers the ability to collect, analyze and extract meaning of large amounts of heterogenous data collected using a variety of sources including IoT, SCADA systems, and smart meters. This paper explores the ways by which predictive maintenance in the new power systems can be reinforced through Big Data Analytics. It presents the framework that combines superior data collection, scalable storage systems, and machine learning model to help detect faults and predict system health. Analyzing the impacts that Big Data-based predictive maintenance has to offer, the paper focuses on three positive dimensions of the change, including increased resilience of systems, economic feasibility, and sustainability of operations. By overcoming the problems of extensibility, instant response, and accuracy in the decision making, the suggested solution allows Big Data Analytics to become one of the main pillars of smart future power system management.