Classification Of Power Quality Disturbances Research Articles

A New Classification Scheme Based on Extended Kalman Filter and Support Vector Machine

In the field of power quality monitoring, due to the extensive quantity of measurements data, it is preferred that the detection, identification and classification of Power Quality Disturbances (PQD) can be performed automatically. This paper proposes a new efficient classification scheme based on two powerful techniques, the Extended Kalman Filter (EKF) and the Support Vector Machine (SVM). The feature extraction step plays the most important role, where, the EKF has a good advantage in reducing time computation by dispensing with the segmentation step. Moreover, only three suitable features are extracted from the parameters estimated based on the EKF, thus conserve memory space and time do without need for feature selection step. After the normalization of the extracted features, in the second step, the SVM classifier is applied to classify these disturbances into their specific categories. Simulation results are examined under different Gaussian white noises to prove the efficiency of the proposed classification diagram. Moreover, performance comparisons with other methods proposed in the literature are conducted, to show the effectiveness of the proposed scheme in term of, consumption of memory space, computation time, implementation complexity, and classification accuracy.

A new deep learning method for the classification of power quality disturbances in hybrid power system

A new deep learning method for the classification of power quality disturbances in hybrid power system

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.

Classification of Multiple Power Quality Disturbances by Tunable-Q Wavelet Transform with Parameter Selection

Identifying power quality (PQ) disturbances is an important prerequisite for developing mitigation measures to improve PQ. However, the coupling of multiple PQ disturbances in the noise condition makes it difficult to achieve effective feature extraction and classification. This article proposes a novel method to identify multiple PQ disturbances by integrating improved TQWT with XGBoost algorithm. The improved TQWT is proposed to automatically select the proper tuning parameters by screening the spectral information of PQ signals. Then, the improved TQWT is used to decompose PQ disturbances into sub-bands for further feature extraction. Optimum feature selection and classification are implemented in XGBoost. Classification accuracies of 26 categories of synthetic PQ disturbances under different noisy levels are tested and compared with existing methods. Results indicate that the proposed method is efficient and noise-resistant, and the classification accuracy can reach 97.63% under 20 dB noise, and keep above 99% under lower level noise.

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.

Classification of Power Quality Disturbances in Emerging Power System Using Discrete Wavelet Transform and K-Nearest Neighbor

This paper presents a wavelet and machine learning-based approach for the classification of power quality disturbances (PQDs) in emerging power systems. Renewable energy resources-based distributed generation (DG) is rapidly being used in the emerging power system to address the ever-increasing energy demand. PQDs are thought to be caused by the power electronic converters used in DG systems, DG operating conditions, and other common factors such as faults, switching activities, and non-linear loads. These PQDs must be detected and classified since they can create a variety of difficulties in end-user equipment. The proposed algorithm comprises the simulation of the emerging power system with a solar PV system, creating PQDs cases such as voltage sag, voltage swell, and voltage interruptions, capturing voltage signals which will be further processed using a discrete wavelet transform for feature extraction. The features extracted from DWT analysis are further used to develop the machine learning-based classifier for classification of PQDs. The proposed algorithm has been tested on a variety of PQDs. The simulation result shows that the proposed algorithm is efficient and it outperforms in the classification of PQDs.

Development of empirical mode decomposition based neural network for power quality disturbances classification

The complexity of the electric power network causes a lot of distortion, such as a decrease in power quality (PQ) in the form of voltage variations, harmonics, and frequency fluctuations. Monitoring the distortion source is important to ensure the availability of clean and quality electric power. Therefore, this study aims to classify power quality using a neural network with empirical mode decomposition-based feature extraction. The proposed method consists of 2 main steps, namely feature extraction, and classification. Empirical Mode Decomposition (EMD) was also applied to categorize the PQ disturbances into several intrinsic mode functions (IMF) components, which were extracted using statistical parameters and the Hilbert transformation. The statistical parameters consist of mean, root mean squared, range, standard deviation, kurtosis, crest factor, energy, and skewness, while the Hilbert transformation consists of instantaneous frequency and amplitude. The feature extraction results from both parameters were combined into a set of PQ disturbances and classified using Multi-Layer Feedforward Neural Networks (MLFNN). Training and testing were carried out on 3 feature datasets, namely statistical parameters, Hilbert transforms, and a combination of both as inputs from 3 different MLFNN architectures. The best results were obtained from the combined feature input on the network architecture with 2 layers of ten neurons, by 98.4 %, 97.75, and 97.4 % for precision, recall, and overall accuracy, respectively. The implemented method is used to classify PQ signals reliably for pure sinusoids, harmonics with sag and swell, as well as flicker with 100 % precision

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.

Power quality events recognition using enhanced empirical mode decomposition and optimized extreme learning machine

In this paper, a novel approach based on Empirical Mode Decomposition (EMD) and an Extreme Learning Machine (ELM) for the detection and classification of Power Quality Events (PQEs) is proposed. The EMD technique is used for computing the prominent features required to characterize the PQE signals. A down-sampled Kriging Interpolation (KI) based EMD is suggested to enhance the performance of the EMD operation in terms of accuracy and speed. The ELM is applied for the classification of Power Quality Disturbances (PQDs), considering all the derived features through the KI-EMD approach. Symbiotic Organism Search (SOS) optimization technique is applied to enhance the performance and robustness of ELM by optimally computing the values of the system parameters. The performance of the proposed approach is justified with test cases under diverse noise conditions. Comparative results and analysis are provided to show an improvement of 2-5% in terms of accuracy, speed, and robustness compared to other conventional methods. Experimental results validate the efficacy of the proposed approach under real-time conditions.

Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review

The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.

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.

FFNet: An automated identification framework for complex power quality disturbances

Effective identification of complex power quality (PQ) disturbances is the premise and key to improve power quality issues in the current complex power grid environment. However, the influx of nonlinear loads and impact loads makes power system disturbance signals distorted and complex, which increases the difficulty of PQ disturbances identification. To address this issue, this work presents a novel feature fusion network (FFNet) for the automated detection and classification of complex PQ disturbances. Firstly, an adaptive double-resolution S-transform (ADRST) algorithm is proposed for PQ disturbance time-frequency analysis. The adaptive window parameters optimization method is developed in ADRST to improve time-frequency resolution based on energy concentration maximization. Next, an improved convolutional neural network (CNN) method is presented for feature automatical extraction and multiple disturbances classification. Integrating ADRST and improved CNN, a classification framework called FFNet is further proposed to identify various complex PQ disturbances. Finally, experiment cases based on simulation and experimental PQ signals are conducted, where the results demonstrate that the classification accuracy of proposed framework can reach 99.47% even in 20 dB noise level.

Classification of complex power quality disturbances based on modified empirical wavelet transform and light gradient boosting machine

Accurate detection and classification of power quality (PQ) disturbances is an essential prerequisite for PQ mitigation. To address this issue, a new PQ assessment framework based on modified empirical wavelet transform (MEWT) and light gradient boosting machine (LightGBM) is proposed here. First, the frequency estimation mechanism and band segmentation rule of empirical wavelet transform (EWT) is modified, which makes EWT suitable for analysing complex PQ signals. Second, based on the PQ disturbance analysis results of MEWT, 8 characteristic curves are defined and statistical features are extracted in both the time and frequency domain. Third, binary relevance-based LightGBM (BR-LightGBM) is designed for the multilabel classification of massive PQ events. Considering the impact of input features and model structure, feature selection and hyper parameter optimization are conducted for achieving better classification performance. Finally, extensive experiments based on synthetic data and two groups of measured data show the effectiveness of the proposed method on 48 types of complex PQ disturbances. Compared with other algorithms for multiple PQ signals, the proposed method is fast for computation and performs better in classification accuracy and robustness, which is a prospective alternative for the PQ monitoring system.

Wavelet spectral analysis and attribute ranking applied to automatic classification of power quality disturbances

The increasing use of non-linear loads and devices in power systems has increased the incidence of waveform changes in electrical energy signals. The intensity and frequency of these disturbances define energy quality being delivered to the consumers. This work explored such issue and proposed two new approaches to improve the effectiveness of automatic classification of these disorders. The first concerns the extraction of attributes. Initially, the best correlation between the Discrete Wavelet Transform (DWT) decomposition levels and the original signals was investigated, pointing out the 8th level as the most representative. Regarding the disturbances and their respective 8th level detail coefficients, 7 traditional metrics were applied and a new feature was proposed: the weighted center frequency (wcf). Additionally, a spectral analysis of the 8th level detail coefficients was performed and thus 4 new features completed a vector with 12 attributes. The second novelty consisted of a multiple indexes analysis to define a global marker for ranking and selecting attributes. To the best of our knowledge, this is the first time that a multi-parametric ranking technique has been used to support automatic classification of electrical disturbances. These features were extracted from a synthetic database of 10,000 signals and subjected to different classifiers: an Artificial Neural Network (ANN) and a Support Vector Machine (SVM). The results showed that two proposed attributes related to the spectral analysis of the DWT decomposition had a relevant impact on the performance of the classifiers. Furthermore, the results of the classifiers confirmed the correctness of the ranking method by finding a difference of about 30% when comparing the classification performances considering half of the highest ranked attributes against the lowest ranked ones. Finally, the results obtained were compared with those of other prominent works in the literature, such that its relevance was verified by pointing to effective solutions for the automatic classification of electrical disturbances.

Detection and Classification of Multiple Power Quality Disturbances Using Stockwell Transform and Deep Learning

With the widespread access of nonlinear loads, enterprises and consumers are faced with the problem of power quality disturbance (PQD) in the grid. The detection and classification of multiple power quality disturbances is considered to be a challenging task. In this paper, a novel hybrid method based on Stockwell Transform (ST) and Deep Learning is proposed to detect and classify multiple power quality disturbances (MPQDs). Compared with previous cases where only single or dual PQDs are considered, this paper fully considers the existence of MPQDs compounds in real situations and designs a more general and automated approach based on Deep Learning for automated feature selection and classification. Firstly, the S-transform is used to extract the features of the one-dimensional signal and take the modulus of the result to obtain the ST-matrix of the signal. In order to improve the anti-noise performance of the system, the parameters for generating the time-frequency domain contour are optimized, so that contour images with less noise can be drawn. Secondly, the above images are fed into the designed Convolutional Neural Network (CNN) for training. A total of 37 PQDs detection and classification tasks including single and multiple disturbances were completed. It is compared with other existing methods to demonstrate its robustness under noisy environments. Finally, an experimental platform for MPQDs was built to verify the proposed method, and the results demonstrated that the method can effectively detect and classify MPQDS.

Pqeventcog: Classification of Power Quality Disturbances Based on Optimized S-Transform and CNNs with Noisy Labeled Datasets

Pqeventcog: Classification of Power Quality Disturbances Based on Optimized S-Transform and CNNs with Noisy Labeled Datasets

A novel learning method for the classification of power quality disturbances using the deep convolutional neural network

Din momentul în care sunt integrate o multitudine de sisteme electroenergetice într-un singur sistem interconectat, din acel moment apare un risc ce crește în amplitudine legat de deteriorarea calității energiei electrice în toate etapele și stadiile în care se găsește energia electrică (producție, transport, distribuție și în etapa finală cea de utilizare a sa. Clasificarea […]

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.

Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

A multiple power quality (MPQ) disturbance has two or more power quality (PQ) disturbances superimposed on a voltage signal. A compact and robust technique is required to identify and classify the MPQ disturbances. This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality (MPQ) disturbance using stockwell transform (ST) and hilbert transform (HT). This will reduce the computational time to identify the MPQ disturbances, which makes the algorithm fast. A MPQ identification index (IPI) is computed using statistical features extracted from the voltage signal using the ST and HT. IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances. A MPQ time location index (IPL) is computed using the features extracted from the voltage signal using ST and HT. IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time. Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio (SNR). The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree (RBDT) is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances. MATLAB software is used to perform the study.

Classification of Multiple Power Quality Disturbances Based on Continuous Wavelet Transform and Lightweight Convolutional Neural Network

Classification of Multiple Power Quality Disturbances Based on Continuous Wavelet Transform and Lightweight Convolutional Neural Network