In this modern era, the power system is stigmatized by a conglomeration of ultra-high voltage AC and DC, multi-terminal DC, and extra-high voltage AC transmission networks. It consists of a large distribution network beyond the country along with traditional generation having Ultra Mega Power Plants (UMPP) and growing ingress of Renewable Energy Sources (RES). The System Integrity and Protection Schemes (SIPS) play a crucial role in preserving a secure and reliable grid and facilitate efficient grid control during severe power system contingencies. In this paper, the operational experiences of three important SIPS in India are presented. Based on a thorough analysis, the drawbacks of these SIPS are identified and an algorithm is proposed to overcome the drawbacks using synchrophasor technology. The performance and capability of the proposed algorithm are evaluated by simulation studies on the 39-bus New England system embedded with HVDC link in MATLAB. Simulation results consummated confirm the effectiveness of the scheme in preserving system integrity. Results are validated using Electrical Transient Analyzer Program (ETAP), which is a very powerful design and analysis tool and has an extremely user-friendly interface.
The advances in Wide Area Measurement Systems (WAMS) and deployment of a huge number of phasor measurement units (PMUs) in the grid are generating big data volume. This data can be used for a variety of applications related to grid monitoring, management, operation, protection, and control. With the increase in this data size, the respective storage capacity needs to be enhanced. Also, communication infrastructure readiness remains bottleneck to transfer this big data. One of the probable solutions could be transmitting compressed data. This paper presents techniques for data compression in the smart transmission system using singular values decomposition (SVD) and the eigenvalues decomposition (EVD). The SVD and EVD based principal component analysis (PCA) techniques are applied to the real-time PMU data collected from extra-high voltage (EHV) substations of transmission utility in the western regional grid of India. Adequacy of data is checked by Kaiser-Meyer-Olkin (KMO) test in order to have the satisfactory performance of these techniques towards achieving the objective of efficient data compression. Results are found satisfactory gives compression more than 80% using real time data.
Synchrophasor technology improves power grid visibility by installing phasor measurement units (PMUs) over a wide area in the power system. Big data received from PMUs contains important information about grid behavior. This information is useful in monitoring the safety and security of the grid. An extensive state-of-the-art review of big data analytics and its prime applications in power systems are expressed in this paper. It presents, a general background in data analysis techniques such as exploratory data analysis, statistical data analysis, and unsupervised data mining techniques like clustering. Two 400 kV transmission line tripping events are analyzed from the data recorded by the PMUs installed in the western part of the Indian grid i.e., Maharashtra State Electricity Transmission Company Limited (MSETCL) grid. The box plots, Correlogram, and the formation of clusters carried out for the PMU data recorded under ambient and disturbance events. This provides insights on how effective big data helps to make the right decision at right time for effective management of the power grid under normal and contingency conditions.
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