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Electrical insulation materials play a key role in stable and uninterrupted operation of electrical power system. Researchers working in the field of electrical insulation have turned their focus to nano composite insulation materials for better performance and reliability. A typical nano composite insulation material is a combination of nano particles and base materials. Since last 25 years silicon rubber and epoxy are two widely used electrical insulation materials for outdoor and indoor applications respectively. To further enhance the electrical insulation performance of these materials addition of nano particles has proven to be of practical value. This study presents an investigation on zepoxy (a military grade epoxy) incorporated with three different nano particles at varying percentages. The main aim was to find the nature of relationship between leakage current (LC) and partial discharge (PD). In addition to that optimum composition which results in lowest values of PD and LC was also investigated for each of three nano composites. The results of this study help to understand the interdependence between LC and PD for indoor electrical insulation based on zepoxy. LC and PD play a vital role in condition assessment of any insulation material.

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With an ever increasing electrical load demand and the associated fuel price to generate it, researchers are compelled to find alternate, cheap and environment friendly ways for power generation to cater to this techno-economic conundrum. Renewable Energy Sources (RES) are now being integrated in Distributed Generation (DG) based environment as a solution to this problem, hence resulting in Microgrids, with multiple sources and loads demarcating their footprint. Control and management of a diverse generation profile within a single microgrid is arduous and computationally intensive for a single centralized controller. This paper addresses this inherent problem and proposes a decentralized Multiagent based intelligent control technique to efficiently encompass the heterogeneous generation profile of Microgrids. Artificial Neural Network (ANN) based intelligent agents are deployed at the planning stage for each component of the Microgrid. These agents are responsible for maintaining individual local control parameters within the prescribed control margins, hence creating a multi-agent environment in a Microgrid structure. The proposed intelligent multi-agent control scheme is tested on a test system to furnish its merits over its traditionally employed counterparts. Simulation results show that ANN integration not only reduced the computational burden but also reduced the overall operating cost (around 10.2 %) by reducing the thermal generation.