The transformer is a vital component of the power system. Continuous stress on the transformer due to overload, transient and faults will lead to physical damages. The isolation of the transformer causes significant revenue loss and inconvenience to the consumers at the distribution level. This invites the need to achieve a reliable power supply to the consumers and to perform maintenance activity appropriately. Optimized and predictive maintenance strategies are evolved to improve power availability for consumers. The model considers dispersive generation at the customer end, namely solar photovoltaics standalone system, diesel generation, and vehicle to load capabilities. Incipient or critical status of transformers’ functional parameters are observed through the transformer terminal unit and sent to the internet of things platform. The remote processing unit acquires the information from all the distribution transformer and generates the optimized and reliability-centered maintenance schedule. In the proposed work, new reliability indices concerning the consumer dispersive generation are defined. The maximization of the reliability problem is solved using the coconut tree optimization technique. The highest reliability of power supply to the consumer and maintenance schedule are obtained. Economic facet of the estimated maintenance schedule exhibit benefit for both utility and consumer as it encapsulate time of use tariff. The heuristic dataset is used to synthesize the trained model by the machine learning algorithm and future maintenance schedule is predicted. The comparative study is made for the outcome of time-based optimized and predicted maintenance schedules against reliability.

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With the growth of wireless sensor networks (WSN), new technologies like the Internet-of-Things (IoT) are being created. There may be challenges that come because when implementing these application areas in practice. The primary issue is energy utilization while data transmission between these resource restricted sensors. In this work, we present a cluster-based routing protocol for IoT to anticipate energy utilization. Furthermore, for cluster head selection and cluster updation, we presented a multi-population ensemble particle swarm optimizer. The simulation was carried out using the MATLAB platform and demonstrates its superiority over different approaches.

A large range of applications in different areas are available in wireless sensor networks (WSNs). The Internet of Things (IoT), which allows connections between various objects or devices through the internet, was one of the most recent emerging applications. However, as opposed to mobile ad-hoc networks, WSNs have a greater concern for battery capacity, which affects the network’s durability. Therefore, numerous studies have focused on reducing the WSN’s energy use. One of the many approaches to minimizing the energy of the WSNs is to design a hierarchical clustering algorithm. In this paper, an algorithm for IoT applications is presented that is based on energy-efficient clustering and the cluster head election process