Real-time frequency and harmonic evaluation using artificial neural networks

With increasing harmonic pollution in the power system, real-time monitoring and analysis of harmonic variations have become important. Because of limitations associated with conventional algorithms, particularly under supply-frequency drift and transient situations, a new approach based on nonlinea...

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Bibliographic Details
Published in:IEEE transactions on power delivery 1999-01, Vol.14 (1), p.52-59
Main Authors: Lai, L.L., Chan, W.L., Tse, C.T., So, A.T.P.
Format: Article
Language:English
Subjects:
Algorithms
Computation
Computer simulation
Demand
Feedback
Frequency
Harmonic analysis
Harmonics
Monitoring
Neural networks
Parameter estimation
Pollution
Power system analysis computing
Power system harmonics
Power system transients
Real time
Real time systems
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Summary:With increasing harmonic pollution in the power system, real-time monitoring and analysis of harmonic variations have become important. Because of limitations associated with conventional algorithms, particularly under supply-frequency drift and transient situations, a new approach based on nonlinear least-squares parameter estimation has been proposed as an alternative solution for high-accuracy evaluation. However, the computational demand of the algorithm is very high and it is more appropriate to use Hopfield type feedback neural networks for real-time harmonic evaluation. The proposed neural network implementation determines simultaneously the supply-frequency variation, the fundamental-amplitude/phase variation as well as the harmonics-amplitude/phase variation. The distinctive feature is that the supply-frequency variation is handled separately from the amplitude/phase variations, thus ensuring high computational speed and high convergence rate. Examples by computer simulation are used to demonstrate the effectiveness of the implementation. A set of data taken on site was used as a real application of the system.
ISSN:0885-8977
1937-4208
DOI:10.1109/61.736681