Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network

Abstract

This paper proposes a novel, two-stage and hybrid approach based on variational mode decomposition (VMD) and the deep stochastic configuration network (DSCN) for power quality (PQ) disturbances detection and classification in power systems. Firstly, a VMD technique is applied to discriminate between stationary and non-stationary PQ events. Secondly, the key parameters of VMD are determined as per different types of disturbance. Three statistical features (mean, variance, and kurtosis) are extracted from the instantaneous amplitude (IA) of the decomposed modes. The DSCN model is then developed to classify PQ disturbances based on these features. The proposed approach is validated by analytical results and actual measurements. Moreover, it is also compared with existing methods including wavelet network, fuzzy and S-transform (ST), adaptive linear neuron (ADALINE) and feedforward neural network (FFNN). Test results have proved that the proposed method is capable of providing necessary and accurate information for PQ disturbances in order to plan PQ remedy actions accordingly.

Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network

“…The results demonstrate that the proposed method based on the PSR and CNN can achieve an excellent classification rate for PQDs. Additionally, the accuracy comparison of the proposed PQ assessment framework with other methods was illustrated in Table 2 with five methods, including EMD with balanced neural tree [41], ST with NN and DT [25], Hybrid ST with DT [42], ADALINE with FNN [43], and VMD with DSCN [22]. In Table 2, the proposed method is shown to be as good as the current best methods in terms of accuracy, as the 99.67-99.90% accuracy is well within the measurement uncertainty.…”

“…The conventional methods of PQD recognition and classification contain two steps: feature extraction and classification. The feature extraction methods are mostly based on signal processing techniques, such as Fourier transform (FT) [8], short time Fourier transform (STFT) [9,10], wavelet transform (WT) [11][12][13], S-transform (ST) [14,15], empirical mode decomposition (EMD) [16][17][18], independent component analysis (ICA) [19], and variational mode decomposition (VMD) [20][21][22]. These methods are used to extract features from different types of PQDs.…”

“…For PQD classification, it is difficult to know what features should be extracted. In current research, the process of feature extraction relies heavily on various handcrafted features with different number and type, for example, relative mode energy ratio (RMER), mode's center frequency, number of zero-crossings, and instantaneous amplitude (IA) are used in [21]; mean, variance, and kurtosis are used in [22]. Taking inappropriate features decreases the accuracy of the classifier; (3) These methods all belong to the shallow model in nature.…”

One Cycle Control of A PWM Rectifier a New Approach

“…The study and analysis of harmonics, energy consumption, and power quality of light-emitting diode (LED) lamps equipped in building lighting systems [6]. A novel, two-stage and hybrid approach based on variational mode decomposition (VMD) and the deep stochastic configuration network (DSCN) for power quality (PQ) disturbances detection and classification in power systems [7]. In power quality, two of the most significant concerns are harmonic currents and power factor, PF, caused by nonlinear loads.…”

Adaptive Damping Control Strategy of Wind Integrated Power System

“…Therefore, machine study methods using statistical analysis may be more suitable for this particular application. For example, the classification and regression tree (CART) [14], neural network method [15,16] and so on. Therefore, this paper presents an adaptive control scheme based on CART for a wind integrated power system to accommodate the stochastic variation of wind generation.…”