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A.M. Purohit

Bio: A.M. Purohit is an academic researcher from College of Engineering, Pune. The author has contributed to research in topics: Fault (power engineering) & Electric power transmission. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Papers
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Proceedings ArticleDOI
16 Apr 2015
TL;DR: The merits and demerits of different methodologies to enhance distance protection of compensated transmission lines are highlighted.
Abstract: Growing dependence of modern society on electrical energy has resulted into increasingly complex nature of electrical power systems. It requires operation of the network closer to their stability limits. This leads to compensated transmission lines. Installing capacitors of fixed or controllable value provides a viable solution to improve power system stability and also to optimize power flow transmission capability of electrical grids transmission capability of power grid, optimize power flow and to improve system stability. However, installations of capacitors results into changing directions and values of fault currents and voltages in the networks. It causes problems for protective relays and fault locators. Distance relay is prone to maloperate due to large variations in voltage and current. Since fault location and classification are important tasks to safe guard electric power systems, as per the literature survey various practical or theoretical schemes are used to attain the reliable protection system for transmission lines equipped with compensation capacitors. This paper highlights merits and demerits of different methodologies to enhance distance protection of compensated transmission lines.

3 citations


Cited by
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Proceedings ArticleDOI
01 Jan 2017
TL;DR: This survey presents the importance of artificial neural networks for fault location, because it has many advantages compared to other soft computing techniques namely fuzzy logic approach, wavelet technique, support vector machine and other conventional methods.
Abstract: This paper presents a survey on location of faults in overhead transmission lines using artificial neural networks. The demand for electricity is raising day by day, with proportional to increase of population, we need to generate more power to meet out the demand. It's a challenging task for power engineers to provide good quality of power to the consumers as per their requirement. In electrical power system maximum number of faults occurs in overhead transmission lines. Fault location is one of the major tasks in fault analysis. So the upgradation of old protective relaying systems is necessary due to the unexpected changes in electrical power systems. This survey presents the importance of artificial neural networks for fault location, because it has many advantages compared to other soft computing techniques namely fuzzy logic approach, wavelet technique, support vector machine and other conventional methods.

20 citations

Journal ArticleDOI
TL;DR: A thorough literature survey of distance protection of series compensated lines, including a clear background of the problems that need to be solved, is conducted, limited to distance protection schemes that work with local measurements only.

10 citations

Journal ArticleDOI
TL;DR: The proposed technique is able to generate both the topology and the weighting of neural networks and indicates high accuracy rate to fault diagnosis in double-circuit transmission lines compared to other ANN-based approaches found in the literature.
Abstract: This paper presents a new approach to address both fault classification and location in double-circuit power transmission lines Fault diagnosis is achieved by using an algorithm based on the successive geometric segmentation approach The proposed technique is able to generate both the topology and the weighting of neural networks The input parameters are the magnitudes of phase voltages and currents measured in only one bus of a double-end fed transmission line In order to validate the methodology, a comprehensive dataset of cross-country faults was simulated using a mathematical model The results indicate high accuracy rate to fault diagnosis in double-circuit transmission lines compared to other ANN-based approaches found in the literature

2 citations