Method For Classification Of Power Quality Disturbances Research Articles

A new deep learning method for classification of power quality disturbances using DWT-MRA in utility smart grid

Distributed generation sources (e.g. photovoltaic, wind power) are raised to fit the energy demands recently in proficient way. Further, adoption of renewable energy sources to the smart grid causes severe issues in quality of power signals. These problems can be overcome with correct identification and classification of power quality disturbances (PQDs) efficiently by the both producer and consumer. In this work, a new method for classification of PQDs based on signal processing technique and deep convolutional neural network (DCNN) is proposed. The proposed method compress the raw data of PQDs by using discrete wavelet transform (DWT) and multi-resolution analysis (MRA). Moreover, CNN has the ability to automatic feature selection and classification in a single stage instead of conventional methods i.e. artificial neural networks (ANNs). In this paper, DWT 1D-CNN is designed to extract and optimize multi-scale characteristics of disturbances. Further, 1-D convolutional, pooling and batch-normalization layers are added to facilitate normalize data, better generalization ability and, effective classification process against noisy data. A typical 11Kv distribution network is simulated in MATLAB to prove the validity of the proposed method in an essential part of smart grid.

Deep learning-based multi-model ensemble method for classification of PQDs in a hydrogen energy-based microgrid using modified weighted majority algorithm

In this study, new multiple deep classifiers with a modified Weighted Majority Voting (WMV)-based method are proposed to identify power quality disturbances (PQDs) in a hydrogen energy-based microgrid. In the proposed approach, closed-loop deep LSTM (Long Short Time Memory), deep CNN (Convolutional Neural Network), and hybrid (CNN-LSTM) models are used for automatic feature extraction and classification. Then, a modified WMV method is employed to ensemble the outputs of the three deep learning (DL) classifier models. The enhanced WMV mechanism performs an automatically updated weighting based on the validation results of the DL classification models, unlike voting methods in the literature. The improved WMV mechanism eliminates the challenges of using multiple DL classifiers in the voting system. The mathematical data results in LabVIEW, simulation results in Matlab/Simulink, and real data results in the laboratory show that the proposed method shows superior performance in accuracy and noise immunity to state-of-the-art methods.

Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

The accurate classification of power quality disturbance (PQD) signals is of great significance for the establishment of a real-time monitoring system of modern power grids, ensuring the safe and stable operation of the power system and ensuring the electricity safety of users. Traditional power quality disturbance signal classification methods are susceptible to noise interference, feature selection, etc. In order to further improve the accuracy of power quality disturbance signal classification methods, this paper proposes a power quality disturbance classification method based on S-transform and Convolutional Neural Network (CNN). Firstly, S-transform is used to extract disturbance signals to obtain the time-frequency matrix with characteristics of the disturbance signals. As an extension of wavelet transform and Fourier transform, S-transform can avoid the disadvantages of difficult window function selection and fixed window width. At the same time, the feature extracted by S-transform has better noise immunity. Secondly, CNN is used to perform secondary feature extraction on the obtained high-dimensional time-frequency modulus matrix to reduce data dimensions and obtain the main features of the disturbance signal, then the main features extracted are classified by using the SoftMax classifier. Finally, after a series of simulation experiments, the results show that the proposed algorithm can accurately classify single disturbance signals with different signal-to-noise ratios and composite disturbance signals composed of single disturbance signals, and it also has good noise immunity. Compared with other classification methods, the algorithm proposed in this paper has better timeliness and higher accuracy, and it is an efficient and feasible power quality disturbance signal classification method.

Power Quality Disturbance Classification Based on DWT and Multilayer Perceptron Extreme Learning Machine

In order to effectively identify complex power quality disturbances, a power quality disturbance classification method based on empirical wavelet transform and a multi-layer perceptron extreme learning machine (ELM) is proposed. The model uses the discrete wavelet transform (DWT) multi-resolution method to extract classification features. Combined with hierarchical ELM (H-ELM) characteristics, the particle swarm optimization (PSO) single-object feature selection method is used to select the optimal feature set. The hidden layer of the H-ELM classifier in the model is trained by forward training. Once the previous layer is established, the weight of the current layer can be fixed without fine-tuning. Therefore, the training speed can be accelerated, the recognition accuracy is almost independent of the parameter adjustment, and the model has strong robustness. In order to solve the problem of data imbalance in the actual power system, a data enhancement method is proposed to reduce the impact of data imbalance and enhance the generalization performance of the network. The simulation results showed that the proposed method can identify 16 disturbances efficiently and accurately under different noise conditions, and the robustness of the proposed method is verified by the measured data.

Power Quality Disturbance Classification Method Based on Wavelet Transform and SVM Multi-class Algorithms

The accurate identification and classification of various power quality disturbances are keys to ensuring high-quality electrical energy. In this study, the statistical characteristics of the disturbance signal of wavelet transform coefficients and wavelet transform energy distribution constitute feature vectors. These vectors are then trained and tested using SVM multi-class algorithms. Experimental results demonstrate that the SVM multi-class algorithms, which use the Gaussian radial basis function, exponential radial basis function, and hyperbolic tangent function as basis functions, are suitable methods for power quality disturbance classification.

Application of matching pursuits of power quality disturbance classification

To analyse power quality disturbance, the first thing is to classify disturbance. This paper presents a novel power quality disturbance classification method based on matching pursuits (MP) algorithm. The MP algorithm is used to decompose the atom dictionary. We design four coherent dictionaries by analysing characteristic of power quality disturbance, which simplifies the calculation of MP algorithm. Based on the coherent dictionaries, the fundamental frequency component (FFC) and disturbance component are extracted respectively. And the parameters of FFC and disturbance component are obtained. By analysing the relationship of energy in disturbance component and residual signal, the noise and the disturbance with little influence the FFC can be filtered. Simulation and real data results confirm the effectiveness of the proposed method, and obtaining accuracy with high signal-to-noise ratios.