A Comparative Study on Condition Monitoring of HVDC Transmission System using Different Artificial Neural Network Techniques
| Main Authors: | Khushboo Nagar, Manish Shah |
|---|---|
| Format: | Article eJournal |
| Bahasa: | eng |
| Terbitan: |
, 2019
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/3336095 |
Daftar Isi:
- High Voltage Direct Current Technology has been a most attractive and beneficial transmission technology not only when power has to be transmitted over long distances but for Grid Interconnection & Synchronization. The healthy condition of HVDC system not only secure uninterrupted supply of electricity but manages challenges of electrical power network. faults on power system transmission lines are supposed to be first detected and then be classified correctly and should be cleared in least possible time for that fulfillment ANN is used and utilized by their different training algorithm. This paper present a comparative study on a fault finding in HVDC transmission system using Artificial Neural Network (ANN) with three different learning algorithms. ANN is used because it is a nonlinear data driven, adaptive and very powerful tool for forecasting purposes. The various approaches available for fault analysis of HVDC system ANN is one of them with accuracy and easiness as key parameters. The performance of the proposed method is investigated using MATLAB/Simulink environment. This paper present a comparative study of a fault finding techniques in HVDC transmission system using Artificial Neural Network (ANN) with three different learning algorithm. ANN is used because it is a non linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to finding the fault using ANN with Levenberg-Marquard (LM) training algorithm, Scaled Conjugte Gradient (SCG) and Bayesian Regularization (BR) training algorithm and their results are compared besed on their of Mean Square Error (MSE) and regressive curve. Here are the 4000 samples per fault based on different value of current and voltages at ac and dc side which are the input data, feed in ANN with target i.e. at normal condition ac fault value and dc fault value is 0 and 0 respectively, at dc side fault value of dc fault is 1 and ac fault is 0 and all for the rest of the two conditions and then results comparison occur individually based on MSE at some number of hidden layers then after there will be comparison of all three training algorithm for fault finding at high speed, because there should be fast switching in circuit breaker during fault so that it can trip easily and can keep whole system healthy.