Abstract
Identification of flicker sources is necessary to find who is responsible for the measured flicker and improve power quality. This paper puts forward a new method for identifying flicker sources with minimum measurement units. Contrary to the previous works where flicker sources are considered a single-frequency signal, the autoregressive moving average (ARMA) is used to model active and reactive power variations. First, the envelope of the network voltage at the considered busbars is derived by the Hilbert transform. Then, appropriate flicker indices are extracted from the power spectral density (PSD) of the voltage envelope. A novel two-level structure of a set of ANNs is proposed, which needs a low number of voltage measurement units to locate the flicker sources. Using the captured data from different simulations of various scenarios, the Artificial Neural Networks (ANNs) are trained to categorize flicker sources.
Highlights
Nowadays, penetration of time-varying loads and some distributed generation sources like wind turbines in power systems lead to power quality degradation of the power systems
autoregressive moving average (ARMA) model of the two flicker sources are considered (10%, 2%) in the first case and (5%, 1%) in the second case of the average powers
For collecting the training data, dominant flicker source (DFS) is moved from bus 15 to 2, and each time weak flicker source (WFS) is placed at three busses in different regions
Summary
Penetration of time-varying loads and some distributed generation sources like wind turbines in power systems lead to power quality degradation of the power systems. The methods are based on the extraction of low-frequency components of the voltage and current signals to calculate the flickering power. In [14], some features from voltage were extracted using S-transform to train an ANN, by which the location of flicker sources could be detected with fewer measurement units. As a result, employing three frequency ranges, the PSD of the voltage magnitude variations is extracted as a candidate corresponding to each frequency range These candidates are used as the index for flicker source location.
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