Recognition Of Power Quality Disturbances Research Articles

Recognition of Multiple Power Quality Disturbances Based on Discrete Wavelet Transform and Improved Long Short-Term Memory Networks

In the past few years, distributed electricity generation from renewable sources, or microgrid systems, has been connected to the grid to increase power supply stability. This responds to government policy regarding commitment to using 100% renewable energy in operations (RE100) project efforts. This results in the entry of power electronic or non-linear equipment into the electrical system, making it more sensitive. Moreover, multiple power quality disturbances (PQDs) consist of a variety of single disturbances. Analysis of complex multi-label patterns is a challenging task. In this paper, we propose a methodology to address this challenge by leveraging Discrete Wavelet Transform (DWT) and Improved Long Short-Term Memory Networks (LSTM). Firstly, multiple PQDs are synthesized utilizing a mathematical model based on IEEE standards 1159-2019. Secondly, the obtained PQDs are decomposed into nine feature classes, yielding detailed (cDs) and approximation (cAs) coefficients through Five-Level DWT Decomposition. Furthermore, we conducted a comparative analysis of each component across five different wavelet functions: haar1, db4, bior1.3, coif2, and sym4. Thirdly, the cDs and cAs coefficients derived from each wavelet type undergo statistical analysis before being inputted into the LSTM model for classification of each feature class. Our results highlight that cD5 components obtained from the db4 wavelet exhibit the highest accuracy rate of 93.86%. This finding elucidates the significance of selecting appropriate wavelet types and compositions for the successful classification of multiple PQDs.

An intelligent complex power quality disturbance recognition method based on two dimension encoding conversion and machine vison

This study proposes an intelligent methodology for accurate identification of complex power quality disturbance (PQD) based on 2D encoding conversion and machine vision. Firstly, a 2D image encoding method as improved Markov transition field (IMTF) is proposed to convert 1D PQD signals to 2D images, which can realize the enhancement of disturbance feature display. It can solve the information loss and duplication problems of traditional Markov transition field (MTF). Then, a new classification method AFF-ResNetXt50 is proposed to realize the PQD accurate recognition. It can address the problem that traditional ResNetXt50 is susceptible to the interference from irrelevant information and thus severely degrades the classification accuracy. Compared with other popular methods, this proposed method can obtain higher accuracy as 98.5 % for PQD signals recognition. Finally, PQD experimental platform is established, and IEEE PES dataset is also used to certify the proposed method has the excellent performances and robustness.

Identification algorithm for power quality disturbance of inspection robot in converter station during operation

AbstractAs power quality disturbances become increasingly complex, it is imperative that the speed and accuracy of the inspection robot at the converter station be improved. For this purpose, this study designs a power quality disturbance feature extraction method based on the fast‐adaptive S‐transform. This method preserves the main feature information and eliminates redundant calculations on the basis of adaptive transform. On this basis, a power quality disturbance identification model built on a multi label lightweight gradient elevator is constructed. In the experimental results, compared to the generalized S‐transform, adaptive S‐transform, and S‐transform, the total extraction time of the proposed method was reduced by 96.09%, 91.56%, and 91.22%, respectively. The average accuracy of extracting features for a single disturbance was 99.56%, while for complex disturbance, it was 98.24%, both of which outperformed the comparison algorithms. It is verified that the proposed method can improve the extraction accuracy of power quality disturbance signal. In the recognition of a single disturbance signal, the constructed model exhibited a high accuracy of over 99%. In recognizing composite disturbance signals, the model demonstrated high accuracy and strong stability. Its effectiveness has been confirmed through experiments. The paper aims to enhance the speed and accuracy of power quality disturbance recognition algorithms. This will assist inspection robots working in converter stations, and ensure stable and safe operation of the power grid.

A new method for recognition and classification of power quality disturbances based on IAST and RF

A new method for recognition and classification of power quality disturbances based on IAST and RF

Complex Power Quality Disturbance Recognition Research Based on Deep Complementary Fusion of 2-D Coding Transition

Complex Power Quality Disturbance Recognition Research Based on Deep Complementary Fusion of 2-D Coding Transition

Power Quality Disturbance Recognition based on Deep Neural Network and Adaptive Feature Fusion

Aiming at the problems of low recognition accuracy and low noise resistance of current power quality disturbance recognition algorithms, a power quality disturbance recognition method based on the fusion of neural network and adaptive features is proposed. Firstly, one-dimensional features are extracted by 1D_CNN+GRU network. The GADF algorithm and 2D_CNN are used to extract 2D features, and then the extracted 1D features and 2D features are adaptively fused into a new feature. Finally, the new features are input into the channel attention mechanism and classified through the full connection layer. The experimental results show that the classification accuracy of the proposed method is above 92% for 40 disturbance types containing single, double, triple and quadruple disturbances, and above 96% for 20 disturbance types containing single and double disturbances.

A fused CNN‐LSTM model using FFT with application to real‐time power quality disturbances recognition

AbstractWith the progress of renewable energy generation and energy storage technologies, more and more renewable sources and devices are integrated into the power system. Due to the complexity of the power system, single and multiple power quality disturbances (PQDs) occur more frequently. Hence, real‐time detection of PQDs is the primary issue to mitigate the risk of distortions. This study presents the real‐time PQDs classification using fused convolutional neural networks (CNN) combined with long short‐term memory (fused CNN‐LSTM) architecture based on time and frequency domain features. The frequency‐domain features were obtained from time‐series data using fast Fourier transform. The original time‐domain and frequency‐domain features are extracted by respective CNN‐LSTM structures. The extracted time and frequency domain features are concatenated to classify the PQD through fully connected layers. Our proposed method was trained and tested using 16 types of synthetic noise PQDs data generated by mathematical models, in accordance with the standard IEEE‐1159. Moreover, to further verify the performance of our approach, a simulation distributed power system is carried out to detect various PQDs. We compared three advanced neural network approaches: Deep CNN, CNN‐LSTM, and multifusion CNN (MFCNN). The fused CNN‐LSTM model takes only 0.64 ms to classify each PQDs signal and achieves an accuracy of 98.95% and 98.89% in synthetic data and simulated power systems which indicates our proposed method outperformed compared methods.

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.

A Hybrid Signal Processing Technique for Recognition of Complex Power Quality Disturbances

This paper proposes an algorithm based on the Stockwell transform (ST) and Hilbert transform (HT) to classify complex power quality disturbances (CPQDs). Two indices are used for detection and locating the CPQDs. The first index is the hybrid power quality index (HPQI), which is used to classify CPQDs. The Stockwell maximum values factor (SMVF), Stockwell summation values factor (SSVF), and Stockwell variance values factor (SVVF) are computed by utilizing the ST to process a voltage signal having a superimposed PQ disturbance. To compute the Hilbert index (HI), the voltage signal is also processed using the HT. The HPQI index is calculated by element-to-element multiplication of the SMVF, SSVF, SVVF, HI, and a weight factor (WF). The second index is the hybrid power quality disturbance time localization index (HPLI), which is used to locate the complex PQ events in a given time frame. For classification of investigated CPQDs, seven different features extracted from various indices defined using ST and HT are taken as input to the rule-based decision tree (RBDT). RBDT classifies the disturbances using simple decision rules, which has the merit of low computational time. The algorithm is able to correctly detect CPQDs with a 98% accuracy rate. The proposed algorithm outperforms the literature's algorithm, which is based on HT and RBDT. Furthermore, it is demonstrated that the algorithm effectively recognizes CPQDs that occur in real-time on a practical distribution network in Rajasthan State, India.

Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection

With the growth of nonlinear electrical equipment, power quality disturbances (PQDs) often appear in electrical systems. To solve this, a practical heuristic methodology for PQD detection and classification based on empirical wavelet transform has been proposed. By using a multiresolution analysis tool, empirical wavelet transform, the voltage waveform signal is decomposed into several sub-signals, and some potential features are extracted in the statistical method. To reduce the feature vector dimensions, the ReliefF algorithm is used for feature selection and optimized for dimensionality reduction, which reduces the complexity of system calculation while ensuring accuracy. Finally, a classifier based on support vector machines (SVM) was built, and with the ranked feature vectors’ input, the PQD can be recognized. The experimental results verify that the classification results achieved high accuracy, which confirms the properties and robustness of the proposed approach in noisy environments.

Review on detection and classification of underlying causes of power quality disturbances using signal processing and soft computing technique

The new age power system is highly subjected to PQ disturbances that require proper attention and address. The research in this field is mainly categorized into different parts such as mathematical modeling, basic PQ principles, standards, impact and solutions, sources, and analysis. There are several underlying causes behind the occurrence of the PQ disturbance. Therefore, it is important to address the exact underlying cause for proper mitigation of the PQ disturbance. There are several methods available in the literature, which concentrated on to detection and classification of power quality events rather than the root cause of the PQ events. An effective method for root cause identification of PQ events is the need of the day. This article covers a broad review of signal processing and soft computing techniques used for the detection & recognition of the underlying cause of it. This will help the researcher, engineers, designers working in the field of detection, recognition, and monitoring of power quality. The comparative study of existing methods used in the literature is tabulated. The major concerns and obstacles in categorizing the recognition of power quality disturbances are thoroughly examined and discussed. The potential for new researchers in the field of power quality disturbance detection and recognition of underlying causes is further explored in this review.

Research on electrical power quality disturbance recognition method based on edge computing and LightGBM

In conventional cloud computing, method that data is transmitted and calculated on the cloud cannot satisfy the real-time demand for energy quality disturbance recognition. This paper proposed a power quality disturbance recognition method based on edge computing and LightGBM classification algorithm. Our main idea is that the feature of disturbance is extracted on the edge sides and used to classified on the cloud. Firstly, a multi-group feature set was extracted at the edge side intelligent fusion terminal by wavelet transform. Secondly, we used feature training accuracy to select the optimal feature collection. Finally, the optimal feature set was selected to determine the disturbance recognition method of this paper. Experiments had shown that the proposed method meets demand on data transmission by 99.5%, and achieves 97.53% recognition accuracy. Our method not only guarantees high accuracy of the power quality disturbance recognition but also alleviates the bandwidth load pressure brought by large amounts of data transmission.

Power Quality Disturbance Identification and Optimization Based on Machine Learning

In order to improve the electrical quality disturbance recognition ability of theneural network, this paper studies a depth learning-based power quality dis-turbance recognition and classification method: constructing a power qualityperturbation model, generating training set; construct depth neural network;profit training set to depth neural network training; verify the performance ofthe depth neural network; the results show that the training set is randomlyadded 20DB-50DB noise, even in the most serious 20dB noise conditions,it can reach more than 99% identification, this is a tradition. The methodis impossible to implement. Conclusion: the deepest learning-based powerquality disturbance identification and classification method overcomes thedisadvantage of the selection steps of artificial characteristics, poor robust-ness, which is beneficial to more accurately and quickly discover the categoryof power quality issues.

Power Quality Disturbances Recognition Using Modified S-Transform Based on Optimally Concentrated Window with Integration of Renewable Energy

To meet power quality requirements, it is necessary to classify and identify the power quality of the power grid connected with renewable energy generation. S-transform (ST) is an effective method to analyze power quality in time and frequency domains. ST is widely used to detect and classify various kinds of non-stationary power quality disturbances. However, the long taper and scaling criteria of the Gaussian window in standard ST (SST) will lead to poor time domain resolution at low frequency and poor frequency resolution at high frequency. To solve the discrete side effects, it is necessary to select the optimal window function to locate the time frequency accurately. This paper proposes a modified ST (MST) method. In this method, an improved window function of energy concentration in time-frequency distribution is introduced to optimize the shape of each window function. This method determines the parameters of Gaussian window to maximize the product of energy concentration in a time-frequency domain within a given time and frequency interval, so as to improve the energy concentration. The result shows that compared with the SST with Gaussian window, ST based on the optimally concentrated window proposed in this paper has better energy concentration in time-frequency distribution.

Signal-piloted processing and machine learning based efficient power quality disturbances recognition.

Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless processing activities, power consumption and latency can occur. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. The signal-piloted acquisition and processing brings real-time compression. Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. Additionally, a reduced computational cost and latency of classifier is promised. The classification is accomplished by using robust machine learning algorithms. A comparison is made among the k-Nearest Neighbor, Naïve Bayes, Artificial Neural Network and Support Vector Machine. Multiple metrics are used to test the success of classification. It permits to avoid any biasness of findings. The applicability of the suggested approach is studied for automated recognition of the power signal’s major voltage and transient disturbances. Results show that the system attains a 6.75-fold reduction in the collected information and the processing load and secures the 98.05% accuracy of classification.

Recognition of complex and multiple power quality disturbances using wavelet packet-based fast kurtogram and ruled decision tree algorithm

This paper introduces an algorithm based on wavelet packet supported fast kurtogram and decision rules for the identification and classification of complex power quality (PQ) disturbances. Features are extracted from the signals using fast kurtogram, envelope of filtered voltage signal and amplitude spectrum of squared envelop. Proposed algorithm can be implemented for the recognition of the complex PQ disturbances, which include the combination of voltage sag and harmonics, voltage momentary interruption (MI) and oscillatory transient (OT), voltage MI and harmonics, voltage sag and impulsive transient (IT), voltage sag, OT, IT and harmonics. Proposed work has been performed using the MATLAB software. Performance of the algorithm is compared with performance of algorithm supported by discrete wavelet transform (DWT) and fuzzy C-means clustering (FCM).

A Generalized Approach for Power Quality Disturbances Recognition Based on Kalman Filter

This paper presents a new automatic detection and classification approach of power quality (PQ) problems using Kalman filter. Kalman filter is used as an estimator to calculate the fundamental frequency and harmonic components amplitudes of the voltage or current signals. Then the instantaneous total harmonic distortion (iTHD) and the energy are calculated. For each half cycle of the processed signal, five decision quantities are calculated based on iTHD and energy and these quantities are the three consecutive maximum values of iTHD, standard deviation and energy difference between distorted signal and its fundamental frequency component. Decision rules based on these decision quantities are applied to identify and classify the PQ events in this captured signal. The proposed approach is tested on single and combined PQ events that are generated using the MATLAB with the help of mathematical models that are conformity with standard IEEE-1159. The performance is assessed using more than 100 dataset of every PQ event and the results show that the accuracy is 100 and 98.8 for noiseless and high-level of noise, respectively. In addition, the proposed approach performance is validated through comparisons with other classification. Several practical PQ events are generated by lab experiments to validate the proposed approach. The simulation and experimental results show that the proposed approach is efficient and robust and can be implemented to design PQ monitoring device.

An Advanced Genetic Algorithm with Improved Support Vector Machine for Multi-Class Classification of Real Power Quality Events

This article proposes an application of non-dominated sorting genetic algorithm III (NSGA III) and directed acyclic graph support vector machine (DAG-SVM) based combined approach for recognition and classification of power quality disturbances. Power disturbances are non-stationary and non-linear by nature and require a large number of features vectors for detection, which results in high computation time and error. The proposed method first employs NSGA III algorithm for an effective features extraction from power signals. The numbers of features required for detection are less in case of proposed NSGA III algorithm. The NSGA III algorithm generates optimal solutions based on multi objective optimization and then fitness function is generated with the help of Pareto front to obtain unique features set from power signals. The NSGA III not only has exact localization of power quality events and stronger robustness but also reduces the computational time in comparison with the traditional NSGA II. The obtained unique feature vectors are used for training of DAG-SVM to classify the power quality disturbances. In case of N-classes, the proposed DAG-SVM generates N(N-1)/2 classifiers, one for each pair of classes and makes the decision accurate and faster. The short event detection, lesser computational timing, superior classification accuracy, and high anti-noise performance are the main advantages of the proposed method. Furthermore, Virtex-5 FPGA based processor is used to test and validate the feasibility of the proposed method for real time analysis of power quality events.

Power quality disturbances recognition using adaptive chirp mode pursuit and grasshopper optimized support vector machines

This paper presents a new method for identifying different types of power quality disturbances (PQDs). In this article, adaptive chirp mode pursuit (ACMP) is used to extract useful features and the greedy algorithm that uses the similar principles of the matching pursuit method is adopted in the ACMP. Besides, the ACMP takes advantage of the sparse matrices, which reduce the computational cost. The infinite feature selection as the filter-based algorithm is applied for the elimination of improper features. Also, the grasshopper optimization algorithm (GOA) is used to optimize the parameters of the SVM as the classifier. The obtained results of the simulations and real disturbances show the high accuracy and speed of the proposed algorithm, which makes it possible to use it in power quality measuring and analysis devices. Also, the proposed method has noise rejection capability, which is another advantage that justifies its use in measuring and analyzing PQDs.