Abstract
The introduction of various intelligent electronic devices (IEDs), sensors and other network controls for smarter operation of the electric grid has resulted in massive data explosion. With an exponential growth in volume and diversity of data sources, developing an effective data management system is challenging and also imperative. This paper explores the current state of data analysis and associated problems in power sector with reference to Indian scenario and narrates how Big Data Analysis and statistics analysis tools could be adapted to improve power system data analysis and load research by acting on the deluge of big data and leveraging various statistical algorithms. We leverage Apache Hadoop BigData ecosystem for large volume of load research data and ‘R’ for pattern recognition and load forecasting. The paper also suggests an approachable roadmap to the power utility for sub-station data analysis embracing identification of peaks and valleys in sub-station demand, detection of anomalies in the data and also conducting short term load forecasting of the sub-station peak demand using these technologies.
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Acknowledgment
The first author gratefully acknowledges the guidance and motivation of Dr. K.T. Veeramanju, Professor & HOD E&E, Sri Jayachamarajendra College of Engineering, Mysuru, India, Dr. R. Nagaraja, Managing Director, Power Research & Development Consultants Pvt. Ltd., Bengaluru, India and Mr. Ganapathi Devappa, Consultant, Power Research & Development Consultants Pvt. Ltd., Bengaluru, India for her work.
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Shankarlinga, S.S., Veeramanju, K.T., Nagaraja, R. (2018). Big Data Analysis - An Approach to Improve Power System Data Analysis and Load Research. In: Nagabhushan, T., Aradhya, V.N.M., Jagadeesh, P., Shukla, S., M.L., C. (eds) Cognitive Computing and Information Processing. CCIP 2017. Communications in Computer and Information Science, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-10-9059-2_36
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DOI: https://doi.org/10.1007/978-981-10-9059-2_36
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