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Summary The paper presents a new, noninvasive method for the measurement of power grid harmonic impedance. The method is a robust variant of transfer function estimation method. Its novelty lays in the way of selecting local harmonic impedance estimates for averaging, based on input–output coherence estimates, which immunizes it against background voltage variations. The method is validated by simulation including varying background voltage harmonics, both for fixed and slowly varying harmonic impedance. It has also been tested with noisy and nonsynchronously sampled signals. The method is compared with another noninvasive method with data selection. Simulation results clearly show that the proposed method returns lower estimation errors. Also, its computational complexity is significantly reduced compared with the reference one. The presented results of a field test conducted at medium‐voltage/low‐voltage substation prove that the method works well in real conditions. Copyright © 2015 John Wiley & Sons, Ltd.

The paper presents a new method for simultaneous tracking of varying grid impedance and its uncertainty bounds. Impedance tracking consists of two stages. In the first stage, the actual noise estimate is obtained from least squares (LS) residua. In the second stage, the noise covariance matrix is approximated with the use of residual information. Then weighted least squares (WLS) method is applied in order to estimate impedance and background voltage. Finally uncertainty bounds for impedance estimation are computed. The robustness of the method has been verified using simulated signals. The proposed method has been compared to sliding LS. The results have shown, that the method performs much better than the LS for all considered cases, even in the presence of significant background voltage variations.

This paper presents a method for contactless measurement of instantaneous voltage and waveform reconstruction for use in a medium voltage (MV) indoor substation. Voltage waveforms are reconstructed by artificial neural network (ANN) using the signals originating from electric field sensors located under MV bus. The method is validated by the experiment in a typical indoor MV substation. Depending on the selection of ANN architecture and data used for training the root mean squared error of waveforms, reconstruction as low as 0.3% to 2.1% can be achieved in a steady state. The practical advice on the application of the proposed measurement method in a production environment is also given. Index Terms-Artificial neural networks (ANN), contactless voltage measurement, electric field (EF) sensors, waveform reconstruction.