Knowledge-based Fault Analytics Strategies of Smart Grid in a Green Cloud Environment

Abstract

Green cloud represents an energy efficient paradigm that processes tasks in ubiquitous workplace and stores dataset using optimization techniques in the distributed computing environment. There are several computing platforms that acquire green cloud attributes and perform cost-effective processing that uses energy optimization procedures. The smart grid is one of the platforms that applies intelligent methods for producing and consuming energy resources effectively and works with principles of the green cloud environment. It uses IoT devices to generate sensory data and uses semantic technologies to process and store this data into a large-scale dataset storage ecosystem. Currently, smart grid does not have an effective system to address faults that resolves around sensory data generation, transmission, integration and storage problems efficiently. One of the main reasons is the use of decentralized approach that resolves individual IoT device issues and does not consider the whole sensory data generation and storage perspective of the smart grid. The smart grid relies on individual edge nodes for reporting any exception and issue. Moreover, the analytics obtained through storage ecosystem merely gives information about generated data. The smart grid returns only analytics summary of the dataset without evaluating device status, their functional performance and health statistics. In this dissertation, we present novel strategies that focus on the collective and individual analytics of device through the generated sensory dataset. The knowledge-based techniques address fault analytics by avoiding hardware communication and retrieve accurate results of IoT devices. The IoT devices are analyzed through customized semantic graphs for health and performance statistics, i.e., hot socket tendency, remaining lifespan, and energy consumption. The experiments involved in evaluating proposed strategies consisting of functional findings that strengthened the novel knowledge-based strategies and delivered accurate fault analytics in the smart grid.