Disturbance Ratio for Optimal Multi-Event Classification in Power Distribution Networks

Abstract

This paper presents an effective approach to identify power quality events based on IEEE Std 1159-2009 caused by intermittent power sources like those of renewable energy. An efficient characterization of these disturbances is granted by the use of two useful wavelet based indices. For this purpose, a wavelet-based Global Disturbance Ratio index (GDR), defined through its instantaneous precursor (Instantaneous Transient Disturbance index ITD(t)), is used in power distribution networks (PDN) under steady-state and/or transient conditions. An intelligent disturbance classification is done using a Support Vector Machine (SVM) with a minimum input vector based on the GDR index. The effectiveness of the proposed technique is validated using a real-time experimental system with single events and multi-events signals.

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

“…Literature addressed research work on PQ disturbances such as sag, swell, transient, flicker, harmonics, and combinations of two or more. Different types of noisy conditions are considered in [3], [50], [80], [170], [184], [216] whereas [78], [132], [134], [206] consider PQ monitoring with and without noise. Also, DSP and FPGA processors with data acquisition equipment have been found capable of handling This work is licensed under a Creative Commons Attribution 4.0 License.…”

“…Therefore, it is essential to diagnose these PQDs according to international standards, and suitable preventive techniques should be implemented. In this regard, detection and classification of features are the essential tasks of PQ monitoring systems in smart grid [3]. In a PQ monitoring system, a set of features are optimized, and the best feature for the detection and classification process is selected to make the analysis more effective.…”

Identification of Combined Power Quality Disturbances Using Singular Value Decomposition (SVD) and Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT)

“…where α is the number of cycles, value range is [0. 1,10], f samp is the sampling frequency, value range is [1000, 6400], w(n) is the white noise, and SNR = 30 dB. Besides the relative error, reconstruction SNR is proposed to evaluate the similarities between fitting result and the actual signal:…”

“…With the switching-in of lots of non-linear and impact loads and the mass application of power electronic equipment, the power quality of distribution networks is becoming increasingly serious in recent years [1]. A single disturbance source could trigger a series of complex disturbances because of the increasing complexity of the grid [2].…”

Classification of multiple power quality events via compressed deep learning

“…Most of the research work focused on only two kinds of multiple PQD such as sag with harmonics and swell plus harmonics . In Borras et al, harmonics plus sag, harmonics plus swell, oscillatory transient plus sag, oscillatory transient plus swell, and oscillatory transient with harmonics were classified using support vector machine (SVM). In, however, six kinds of combined PQD waveforms were classified by rank wavelet and SVM.…”