Harmonic source monitoring and identification using neural networks

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Abstract
Neural networks are applied to make initial estimates of harmonic sources in a power system with nonlinear loads. The initial estimates are then used as pseudomeasurements for harmonic state estimation, which further improves the measurements. This approach permits measurement of harmonics with relatively few permanent harmonic measuring instruments. Simulation tests show that the trained neural networks are able to produce acceptable estimates for varying harmonic sources and that the state estimator will generally pull these estimates closer to the correct values. The process successfully identified and monitored a suspected harmonic source that had not previously been measured.