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• W1973597469 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." @default.
• W1973597469 created "2016-06-24" @default.
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• W1973597469 date "2014-01-01" @default.
• W1973597469 modified "2023-10-05" @default.
• W1973597469 title "Low-Order Dominant Harmonic Estimation Using Adaptive Wavelet Neural Network" @default.
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• W1973597469 doi "https://doi.org/10.1109/tie.2013.2242414" @default.
• W1973597469 hasPublicationYear "2014" @default.
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