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Low-Order Dominant Harmonic Estimation Using Adaptive Wavelet Neural Network.
- Source :
-
IEEE Transactions on Industrial Electronics . Jan2014, Vol. 61 Issue 1, p428-435. 8p. - Publication Year :
- 2014
-
Abstract
- In recent years, harmonic pollution has worried the power engineers considerably due to the increased penetration of power-electronics-based devices in the utility grid. Monitoring of certain low-order harmonics in the power supply is more important than monitoring of the entire spectrum because, usually, these are the most significant ones. In this paper, a technique based on an adaptive wavelet neural network that is the most suitable for dominant low-order harmonic estimation is presented. The proposed method works with only half-cycle data point inputs, compared to the requirement of at least one-complete-cycle data for other estimation techniques. A simple, fast converging, and reliable learning algorithm based on back propagation is used for training of the network parameters. The proposed method is examined with a number of simulated and experimental signals. The test results confirm that the proposed method accurately estimates the dominant low-order harmonics in pragmatic situations of fundamental frequency deviation, presence of interharmonics, low signal-to-noise ratio, etc. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 02780046
- Volume :
- 61
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Industrial Electronics
- Publication Type :
- Academic Journal
- Accession number :
- 89267710
- Full Text :
- https://doi.org/10.1109/TIE.2013.2242414