Power Quality Disturbance Signals Research Articles

An Automatic Identification Framework for Complex Power Quality Disturbances Based on Ensemble CNN

A large number of electric vehicles (EVs) are connected to the grid, increasing the risk of power quality deterioration. Meanwhile, power quality disturbances (PQDs) directly affect EV charging safety. Intelligent identification of complex PQDs is the basis for solving the power quality problem, which is very meaningful for improving EV charging quality. This paper proposes an automatic recognition framework for complex PQDs based on ensemble convolution neural network (ECNN). Firstly, a multifusion structure on account of the time and frequency domain feature of PQDs signals is introduced. In addition, a composite convolution is proposed to reduce network complexity, which is using the standard convolution and depthwise separable convolution. Then, we design an adaptive-context mechanism to extend the versatility of ECNN. At the same time, the need to use batch normalization to accelerate training convergence and prevent training overfitting is verified. Furthermore, some visualization methods are performed to analyze the inner mode and illustrate the working mechanism of ECNN. Finally, we apply various experiments to prove the effectiveness of ECNN. Compared to other advanced deep neural networks and traditional methods, ECNN has better noise resistance, higher accuracy, and lower training cost.

A Hybrid Compression Method for Compound Power Quality Disturbance Signals in Active Distribution Networks

A Hybrid Compression Method for Compound Power Quality Disturbance Signals in Active Distribution Networks

Recognition of Hybrid PQ Disturbances Based on Multi-Resolution S-Transform and Decision Tree

Aiming at the problems of multiple types of power quality composite disturbances, strong feature correlation and high recognition error rate, a method of power quality composite disturbances identification based on multi-resolution S-transform and decision tree was proposed. Firstly, according to IEEE standard, the signal models of seven single power quality disturbances and 17 combined power quality disturbances are given, and the disturbance waveform samples are generated in batches. Then, in order to improve the recognition accuracy, the adjustment factor is introduced to obtain the controllable time-frequency resolution through multi-resolution S-transform time-frequency domain analysis. On this basis, five disturbance time-frequency domain features are extracted, which quantitatively reflect the characteristics of the analyzed power quality disturbance signal, which is less than the traditional method based on S-transform. Finally, three classifiers such as K-nearest neighbor, support vector machine and decision tree algorithm are used to effectively complete the identification of power quality composite disturbances. Simulation results show that the classification accuracy of decision tree algorithm is higher than that of K-nearest neighbor and support vector machine. Finally, the proposed method is compared with other commonly used recognition algorithms. Experimental results show that the proposed method is effective in terms of detection accuracy, especially for combined PQ interference.

A Conversion of Signal to Image Method for Two-Dimension Convolutional Neural Networks Implementation in Power Quality Disturbances Identification

The power quality is identified and monitored to prevent the worst effects arise on the electrical devices. These effects can be device failure, performance degradation, and replacement of some device parts. The deep convolutional neural networks (DCNNs) method can extract the complexity of image features. This method is adopted for the power quality disruption identification of the model. However, the power quality signal data is a time series. Therefore, this paper proposes an approach for the conversion of a power quality disturbance signal to an image. This research is conducted in several stages for constructing the approach proposed. Firstly, the size of a matrix is determined based on the sampling frequency values and cycle number of the signal. Secondly, a zero-cross algorithm is adopted to specify the number of signal sample points inserted into rows of the matrix. The matrix is then converted into a grayscale image. Furthermore, the resulting images are fed to the two-dimension (2D) CNNs model for the PQDs feature learning process. When the classification model is fit, then the model is tested for power quality data prediction. Finally, the model performance is evaluated by employing the confusion matrix method. The model testing result exhibits that the parameter values such as accuracy, recall, precision, and f1-score achieve at 99.81%, 98.95%, 98.84, and 98.87 %, respectively. In addition, the proposed method's performance is superior to the previous methods.Â

Novel method based on Teager Energy Operator for online tracking of power quality disturbances

• A TEO -based PQ disturbance tracking algorithm is proposed for the voltage fluctuations with sag disturbance. • The inverter-based DG, nonlinear load and fault event influence on the voltage flicker and sag levels. • The new approach can be used to track multiple PQ disturbance with a complex structure. • This method with proper accuracy can track the voltage amplitude variations in the presence of notch and harmonics. This paper presents an optimal algorithm for simultaneous tracking of power quality (PQ) events in distribution systems in the presence of the inverter-based distributed generation and nonlinear load. The algorithm is based on Teager Energy Operator (TEO). The main specification of the Teager Energy Operator, compared with the commonly used methods in the literature, is the simplicity of its mathematical. Using a proposed method, a suitable combination of the TEO and a relevant filter set is defined as a criterion to reduce envelope ripples and improve a suitable tracking for the power quality disturbances. This approach is able to accurately detect the distortions and sudden changes of voltage under different conditions. The paper considers the voltage fluctuations and voltage sag at the point of common coupling (PCC) and nearby points of the PCC as these voltage disturbances. Extensive simulations for the several power quality disturbance signals and the distribution network based on nonlinear elements using the new approach are performed and performance evaluation is clearly distinguished from the results. Simulation results for single and multiple power quality disturbances present that the proposed method is precise and suitable in detecting voltage disturbances.

Ensemble deep learning for automated classification of power quality disturbances signals

• A deep learning framework for intelligent classification of PQD signals is proposed. • The improved Bagging algorithm has better accuracy and generalization performance. • The active learning strategy selects the most representative samples from a dataset. • The active learning strategy uses less labeled data thus saving manual labeling cost. The automatic classification of power quality disturbances (PQD) is of great significance for solving power quality problems. In this study, we propose an ensemble deep learning framework to realize intelligent classification of PQ disturbances. Specifically, based on the characteristics of the sequence of disturbance signals, the Long Short Term Memory (LSTM) network is used to classify the signals. In addition, the Bagging theory is used to integrate the training results of multiple LSTM networks to improve the generalization of the network. Our contribution lies in the combination of deep learning and ensemble learning to extract the classification representation of PQD signals. In view of the large number of unlabeled power quality disturbance samples in the power grid, the active learning strategy is adopted to select the most representative samples from the data set, which can enhance the model performance with less labeled data. Finally, experiments were conducted in different noise environments. Compared with the existing multi-label learning models, this method achieves better classification performance with good calculation speed. Furthermore, the proposed active learning strategy is able to train the classification model with fewer labeled samples, reducing the manual labeling costs.

Combined ST/MST and radial basis function neural networks for power quality disturbance signal classification

In this paper, the classification of power quality disturbances using combined ST/MST (S-Transform/Modified S-Transform) and Radial Basis Function Neural Network (RBFNN) is proposed. The extraction of significant features from the power quality disturbance signals is one of the challenging tasks in recognizing different disturbances. The Stockwell Transform/Modified Stockwell Transform (ST/MST) based features are distinct, understandable and more immune to noise. The important attributes present in the signals are retrieved from the ST/MST contours, MST 3D plots and MST based statistical curves. The relevant features are also extracted from the statistical curves. The extracted features are given as input to the RBFNN for further classification. This method is evaluated under both noisy and noiseless conditions. The performance of the proposed approach is compared with other conventional approaches in the literature. The simulation results demonstrate that the proposed MST based RFNN technique is more effective for the detection and classification of power quality disturbances.

Recognition of Power Quality Disturbance Based on RCNN

With the development of the smart grid, the problem of power quality disturbance is becoming more and more serious. To improve the classification ability of power quality disturbance signals, this paper designs a recurrent convolutional neural network model to realize the classification and identification of power quality disturbance signals. The model first uses the bidirectional long short-term memory neural network module to encode the disturbance signal. Then it uses the convolutional layer to perform spatial feature extraction and enforces the disturbance classification through the fully connected layer. The model is verified and analyzed by simulation data, which shows that the model has both good generalization ability and higher accuracy.

Generation of Power Quality Disturbances Required for the Performance Assessment of Different Power Quality Monitoring Algorithms using Multiple Approaches

This paper presents multiple approaches for the generation of Power Quality Disturbances (PQDs) and the development of the dataset required for evaluating the effectiveness of various PQDs detection and classification algorithms. In today's power networks, power quality is an important issue. Three PQDs, namely the voltage sags, swells, and interruptions, are considered under study in this research paper. In today's electricity grid, these PQDs are thought to be more prevalent and common. Today's end-users demand high-quality supply for the operation of their sensitive equipment. The utility must keep an eye on PQ and take the required actions to maintain it in order to deliver a supply of high quality. The creation of reliable and efficient PQ monitoring algorithms is a major goal for many budding researchers. The PQD signals or dataset of PQDs are required for the development and performance assessment of these PQ monitoring algorithms. The different approaches available for the generation of PQDs and dataset are discussed in detail in this research paper. This research work focuses on generating PQDs through the use of MATLAB programming, simulation of PQD models in the MATLAB Simulink environment, and experimental methodology. This paper will serve as a useful resource for budding researchers working in the PQ domain.

A Novel Label-Guided Attention Method for Multilabel Classification of Multiple Power Quality Disturbances

Multiple power quality disturbance (PQD) contains various single disturbances, so its classification is essentially a multilabel classification task. Due to the complexity of multilabel tasks, the performance of existing multilabel methods for PQD is hard to meet practical needs, which severely restricts the engineering application of multilabel methods. Therefore, in this article, we propose a novel multilabel method named LGAN, which incorporates deep learning and explores the correlations between PQD labels to improve performance. First, 1-D convolutional neural network extracts features automatically from PQD signals. Then, a label-guided attention module is adopted to learn the specific feature representation of each PQD category. Finally, the bidirectional recurrent neural network models the label correlations from the label-related features, and then predicts the final PQD category. Various comparative experiments show that the performance of LGAN is much better than the existing multilabel methods. Additionally, the test using real-time detection platform further verifies the availability of the proposed method.

Classification of Single and Combined Power Quality Disturbances Using Stockwell Transform, ReliefF Feature Selection Method and Multilayer Perceptron Algorithm

: In this study, a method based on Stockwell transform (ST), ReliefF feature selection method and Multilayer Perceptron Algorithm (MPA) algorithm was developed for classification of Power Quality (PQ) disturbance signals. In the method, firstly, ST was applied to different PQ signals to obtain classification features. A total of 30 different classification features were obtained by taking different entropy values of the matrix obtained after ST and different entropy values of the PQ signals. The use of all of the classification features obtained causes the method to be complicated and the training/testing times to be prolonged. Therefore, so as to determine the effective ones among the classification features and to ensure high classification success with less classification features, ReliefF feature selection method was used in this study. PQ disturbances were classified by using 8 different classification features determined by ReliefF feature selection method and MPA. The simulation results show that the method provides a high classification success in a shorter training/testing time. At the same time, simulation results have shown that the method was successful on testing data with noise levels of 35 dB and above after only one training.

The effects on classifier performance of 2D discrete wavelet transform analysis and whale optimization algorithm for recognition of power quality disturbances

Power Quality (PQ) is becoming more and more important day by day in the electric network. Signal processing, pattern recognition and machine learning are increasingly being studied for the automatic recognition of any disturbances that may occur during the generation, transmission, and distribution of electricity. There are three main steps to identify the PQ disturbances. These include the use of signal processing methods to calculate the features representing the disturbances, the selection of those that are more useful than these feature sets to prevent the creation of a complex classification model, the creating a classification model that recognizes multiple classes using the selected feature subsets. In this study, one-dimensional (1D) PQ disturbances signals are transformed into two-dimensional (2D) signals, 2D discrete wavelet transforms (2D-DWT) are used to extract the features. The features are extracted by using the wavelet families such as Daubechies, Biorthogonal, Symlets, Coiflets and Fejer-Korovkin in 2D-DWT to analyze PQ disturbances. Whale Optimization Algorithm (WOA) and k-nearest neighbor (KNN) classifier determine the feature subsets. Then, WOA and k nearest neighbor (KNN) classifier are used to determine the feature group. By using KNN and Support Vector Machines (SVM) classification methods, Classifier models that distinguish PQ disturbances are formed. The main aim of the study is to determine the features derived from 2D wavelet coefficients for different wavelet families and to determine which of them has a better classification performance to distinguish PQ disturbances signals. At the same time, different classification methods are simulated and a model which can classify PQ disturbances signals with high performance is created. Also, the generated models are analysed for their performance in terms of different noise levels (40 dB, 30 dB, 20 dB). The result of this simulation study shows that the model developed to classify PQ disturbances is superior to conventional models and other 2D signal processing methods in the literature. In addition, it was concluded that the proposed method can cope better with noisy signals by low computational complexity and higher classification rate.

Power Quality Data Compression and Disturbances Recognition Based on Deep CS-BiLSTM Algorithm With Cloud-Edge Collaboration

The current disturbance classification of power quality data often has the problem of low disturbance recognition accuracy due to its large volume and difficult feature extraction. This paper proposes a hybrid model based on distributed compressive sensing and a bi-directional long-short memory network to classify power quality disturbances. A cloud-edge collaborative framework is first established with distributed compressed sensing as an edge-computing algorithm. With the uploading of dictionary atoms of compressed sensing, the data transmission and feature extraction of power quality is achieved to compress power quality measurements. In terms of data transmission and feature extraction, the dictionary atoms and measurements uploaded at the edge are analyzed in the cloud by building a cloud-edge collaborative framework with distributed compressed sensing as the edge algorithm so as to achieve compressed storage of power quality data. For power disturbance identification, a new network structure is designed to improve the classification accuracy and reduce the training time, and the training parameters are optimized using the Deep Deterministic Policy Gradient algorithm in reinforcement learning to analyze the noise immunity of the model under different scenarios. Finally, the simulation analysis of 10 common power quality disturbance signals and 13 complex composite disturbance signals with random noise shows that the proposed method solves the problem of inadequate feature selection by traditional classification algorithms, improves the robustness of the model, and reduces the training time to a certain extent.

Denoising Transient Power Quality Disturbances Using an Improved Adaptive Wavelet Threshold Method Based on Energy Optimization

Noise significantly reduces the detection accuracy of transient power quality disturbances. It is critical to denoise the disturbance. The purpose of this research is to present an improved wavelet threshold denoising method and an adaptive parameter selection strategy based on energy optimization to address the issue of unclear parameter values in existing improved wavelet threshold methods. To begin, we introduce the peak-to-sum ratio and combine it with an adaptive correction factor to modify the general threshold. After calculating the energy of each layer of wavelet coefficient, the scale with the lowest energy is chosen as the optimal critical scale, and the correction factor is adaptively adjusted according to the critical scale. Following that, an improved threshold function with a variable factor is proposed, with the variable factor being controlled by the critical scale in order to adapt to different disturbance types’ denoising. The simulation results show that the proposed method outperforms existing methods for denoising various types of power quality disturbance signals, significantly improving SNR and minimizing MSE, while retaining critical information during disturbance mutation. Meanwhile, the effective location of the denoised signal based on the proposed method is realized by singular value decomposition. The minimum location error is 0%, and the maximum is three disturbance points.

Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid

Due to the penetration of renewable energy and load variation in the microgrid, the diagnosis of power quality disturbances (PQD) is important to the operation stability and safety of the microgrid system. Once the power imbalance is present between the generation and the load demand, the fundamental frequency would deviate from the nominal value. As a result, the performance of the power quality classifier based on the neural network would be deteriorated since the deviation of fundamental frequency is not taken into account. In this paper, the regulated two-dimensional (2D) deep convolutional neural network (CNN)-based approach for PQD classification is proposed. In the data preprocessing stage, the IEC-based synchronizer is introduced to detect the deviation of fundamental frequency. In this way, the 2D grayscale image serving as the input of the deep CNN classifier can be accurately regulated. The obtained 2D image can effectively preserve information and waveform characteristics of the PQD signal. The experiment is implemented with datasets containing 14 different categories of PQD. According to this result, it is revealed that the regulated 2D deep CNN can improve the effectiveness of PQD classification in a real-time manner. Furthermore, the proposed method outperforms the methods in previous studies according to the field verification.

Automated classification of power quality disturbances in a SOFC&PV-based distributed generator using a hybrid machine learning method with high noise immunity

In this study, a new hybrid machine learning (ML) method is developed to classify the power quality disturbances (PQDs) for a hydrogen energy-based distributed generator (DG) system. The proposed hybrid ML method uses a new approach for the feature extraction by using a pyramidal algorithm with an un-decimated wavelet transform (UWT). The pyramidal UWT method is used and investigated with the Stochastic Gradient Boosting Trees (SGBT) classifier to classify PQD signals for a Solid Oxide Fuel Cell & Photovoltaic (SOFC&PV)-based DG. The overfitting problem of SGBT in noisy signals is eliminated with the features extracted by pyramidal UWT. Mathematical, simulative and real data results confirm that the developed UWT-SGBT method can classify PQDs with high accuracy of up to 99.59%. The proposed method is also tested under noisy conditions, and the pyramidal UWT-SGBT method outperformed other ML with wavelet transform (WT)-based methods in the literature in terms of noise immunity.

On recognition of multiple power quality disturbance events in electricity networks

Effective power quality disturbance (PQD) events recognition is important for electricity network operation. This paper proposes an improved methodology for detecting and classifying multiple PQD events, which may simultaneously occur in the electricity network. It employs an adaptive dual resolution Stockwell-transform for signal processing and a short-term Renyi entropy estimation for feature extraction. Also, a multi-class support vector machine is employed to classify the types of PQD events. A variety of power quality disturbance signals are synthesized to evaluate the effectiveness and efficiency of the proposed method. These signals include both single fault/abnormal event and mixed events (e.g. a mixture of several PQD events occurring simultaneously). Real-life disturbance recordings from a transmission network service provider are also collected and used to verify the proposed method. The results show that the proposed method can achieve high classification accuracy at noisy environment. Especially, it is capable of identifying the disturbance signals comprising both harmonics with low total harmonic distortion levels and transients. Moreover, the algorithms developed in this paper can achieve a high computational efficiency.

An efficient algorithm for atomic decomposition of power quality disturbance signals using convolutional neural network

The atomic decomposition (AD) algorithm for Power Quality Disturbance (PQD) signals can obtain sparser and physically clearer results than the conventional fixed basis decomposition method. However, the conventional atomic decomposition (CAD) algorithm for PQD signals suffers from excessive computational resource consumption and low accuracy of sub-dictionaries selection. In this paper, the CAD algorithm for PQD signals is optimized by introducing a Convolutional Neural Networks based (CNN) sub-dictionary predictor inspired by the PQD signal classification technique. In the optimized algorithm, by using a sub-dictionary predictor to select sub-dictionaries directly, the range of each atomic search is significantly reduced. In addition, the undesirable effects of the intelligent optimization algorithm are mitigated, which match the non-global optimal results in search of sub-dictionaries. Besides, the goal of reducing computation resources is achieved, and the accuracy of sub-dictionaries selection is improved. Finally, the CAD algorithm and optimized CNN-based atomic decomposition (CNN-AD) algorithm are compared and analyzed on the synthetic dataset and the measured dataset from IEEE 1159.2 Working Group on power quality disturbances. Consequently, it is verified that the CNN-AD can reduce the requirement of computation resources and improve the accuracy of sub-dictionaries selection in the decomposition for PQD signals.

Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm

In this paper, an adaptive multiscale enhanced average combination morphological filter is proposed to analyze voltage signals captured at the point of common coupling of the photovoltaic integrated direct current microgrid for diagnosing several power quality disturbances. This method performs multiscale operation on the power quality disturbance signals from which the optimized structuring element scales are chosen by using maximum value of sparse envelope kurtosis index. Further, statistical features are extracted by using optimized scales. The extracted features are used as inputs to a deep stacked extreme leaning machine autoencoder to retrieve the most distinguishable unsupervised features and these features are processed through kernel random vector functional link network based supervised classifier to classify the power quality disturbances. Further, to improve the classification performance of the proposed technique, a meta heuristic chaotic particle swarm optimization integrated with sine cosine optimization algorithm is used. This optimization algorithm optimizes the kernel parameters by minimizing the mean absolute error. The efficacy of the proposed technique is tested in terms of classification accuracy and confusion matrix and is verified by multifunction high-speed national instrument device for monitoring of power quality disturbances in the MATLAB/Simulink platform. The simulations and hardware results demonstrate that the proposed technique exhibits remarkable accuracy, faster training time and low computational complexity as compared with other existing methods.

A Comprehensive Overview on Modified Versions of Stockwell Transform For Power Quality Monitoring

The increasing trends toward the accurate identification of power quality disturbances (PQD) via power quality (PQ) monitoring require an appropriate digital signal processing (DSP) technique and a robust classifier. To this end, Stockwell transform (ST), one of the most efficient feature extraction DSP tools, and its several variants play an utmost role in PQ assessment framework. Its time-varying spectral characteristics generally extract the local instantaneous frequency spectrum from the global temporary behavior of PQD signal. However, the Standard ST suffers from thepoor time-frequency resolution because of its frequency-dependent Gaussian window (GW). While the analysis of the statistically time-varying signals requires a suitable balance between time and frequency resolution. To this end, this paper provides a comprehensive literature review on several modified versions of Standard ST for the first time to reduce the computational complexity of the algorithm as well as maximize the energy concentration of the time-frequency plane. A comparative analysis of all the modified STs has been presented in tabular form to provide the key characteristics of each technique. Additionally, a case study has been presented to substantiate the highest accuracy of the proposed algorithm over the other ST variants. Apart from the PQD classification, miscellaneous applications of Standard ST and its modified variants have been indicated. This review paper may provide a valuable resource to the researchers for further improvement of the time-frequency resolution of ST not only in classifying PQD but also for its other wide applications.