Research and Application of BP Neural Network in Specific Power Harmonic Detection

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APF (Active Power Filter) is widely used in power system harmonic control and reactive power compensation, has been proven as an effective method to overcome various power quality issues such as unbalanced source current, large reactive power harmonic and neutral currents due to the proliferation of nonlinear loads. Optimizing the performance of APF using conventional ip-iq detection method based on instantaneous reactive power theory is quite difficult because of the complex coordinate conversion, what’s more, the presence of low-pass filter will cause a certain delay. This paper proposes the implementation of BP neural network to extract specific harmonic, it can optimize the APF performance for load compensation under distorted supply voltage condition and sudden load fluctuation. Weight adjustment using the BFGS quasi-Newton algorithm, which can accurately detect the fundamental and harmonic component of the phase amplitude . Matlab simulation results demonstrate that the performance of BP neural network algorithm is superior compared to conventional method, in terms of both convergence rate and solution quality.

Info:

Periodical:

Advanced Materials Research (Volumes 588-589)

Edited by:

Lawrence Lim

Pages:

379-383

Citation:

X. D. Wang et al., "Research and Application of BP Neural Network in Specific Power Harmonic Detection", Advanced Materials Research, Vols. 588-589, pp. 379-383, 2012

Online since:

November 2012

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Price:

$38.00

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