Abstract
This paper proposes a machine-learning-based framework for voltage quality analytics, where the space phasor model (SPM) of the three-phase voltages before, during, and after the event is applied as input data. The framework proceeds along with three main steps: (a) event extraction, (b) event characterization, and (c) additional information extraction. During the first step, it utilizes a Gaussian-based anomaly detection (GAD) technique to extract the event data from the recording. Principal component analysis (PCA) is adopted during the second step, where it is shown that the principal components correspond to the semi-minor and semi-major axis of the ellipse formed by the SPM. During the third step, these characteristics are interpreted to extract additional information about the underlying cause of the event. The performance of the framework was verified through experiments conducted on datasets containing synthetic and measured power quality events. The results show that the combination of semi-major axis, semi-minor axis, and direction of the major axis forms a sufficient base to characterize, classify, and eventually extract additional information from recorded event data.
Highlights
Among power quality (PQ) disturbances, three of the main voltage events are voltage dip, voltage swell, and interruption
Event characterization aims to obtain single event characteristics (SECs) that can be used to interpret the corresponding voltage event and its impact on the grid and equipment connected to the grid [6,7,8,9]
The results presented in this paper show that the Gaussian-based anomaly detection (GAD) method is a suitable method for extracting event data from recorded voltage waveforms in a three-phase system and that the Principal component analysis (PCA) facilitates calculating efficient single value characteristics for the voltage event
Summary
Among power quality (PQ) disturbances, three of the main voltage events are voltage dip, voltage swell, and (voltage or supply) interruption. This paper further extends the earlier works and proposes a complete framework that receives the SPM complex values as an input and proceeds with three successive steps: (a) event data extraction and classification, (b) characterization, and (c) additional information extraction. These three steps are essential for the framework, but they can be filled in using different algorithms. The proposed SPM-based framework enables instantaneous event detection since it does not use any low-pass filter such as the RMS-based model It provides the same basic characteristics (semi-minor, semi-major axes, ellipse rotating angle) for dip, swell, and interruption events; 2.
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