NDLI: Soft computing tools for protection of compensated network

Content Provider	IEEE Xplore Digital Library
Author	Dash, P.K. Chilukuri, M.V.
Copyright Year	2003
Description	Author affiliation: Silicon Inst. of Technol., Bhubaneshwar, India (Dash, P.K.)
Abstract	This paper presents a soft computing tool namely the fuzzy neural network (FNN) based distance relaying of a transmission line operating with a thyristor controlled series capacitor (TCSC) protected by MOVs. The FNN structure is seen as a neural network for training and the fuzzy viewpoint is utilized to gain insight into the system and to simplify the model. The number of rules is determined by the data itself and therefore, a smaller number of rules is produced. The network is trained with back propagation algorithm with a pruning strategy to eliminate the redundant rules and fuzzification neurons resulting in a compact network structure. The classification and location tasks of the relay are accomplished using different FNNs. Once the fault type is identified by the classifier the selected FNN-locator estimates the location of the fault accurately. The networks make use of fundamental currents and voltages at the relay end, sequence components of current, system frequency and the firing angle of the TCSC to derive the trip decision. The superior capability of the strategy is adjudged through test results for different situations of power system including high resistance in the fault path.
Starting Page	52
Ending Page	61
File Size	1923814
Page Count	10
File Format	PDF
ISBN	0780382080
DOI	10.1109/PECON.2003.1437416
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-12-15
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer networks Protection Fuzzy neural networks Fuzzy control Thyristors Power capacitors Power system relaying Protective relaying Power transmission lines Neural networks
Content Type	Text
Resource Type	Article

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