Classification Of Power Quality Events Research Articles

Evaluating the uncertainty of a virtual power quality disturbance generator and its use in power quality classifier evaluation

This paper presents the metrological evaluation of a proposed virtual power quality (PQ) disturbance generator. It explains in detail the calibration and measurement uncertainty estimation procedures for the RMS voltage and frequency of the generator, presents the results, and discusses their implications. The results indicate that the measurement uncertainty of the generator is satisfactory for all reference points, making it suitable for generation of PQ signals for research purposes, particularly in the development, testing, and improvement of PQ event classifiers. Additionally, the paper introduces a virtual power quality disturbance classifier for real-time classification of PQ events and verifies its accuracy through testing using the proposed PQ disturbance generator.

Exponential Smoothing of Quadrature Amplitude Modulation for Power Quality Disturbance Detecting and Classification

AbstractIn this study, exponential smoothing (ES)‐based quadrature amplitude modulation (QAM) is applied to detect and classify the power quality (PQ) events with high accuracy and low complexity. Considering the IEEE‐1159 standards, in the simulation environment, 14 different power quality distortion signals are produced. Each of these signals is modulated at the fundamental frequency by QAM. The high‐frequency constituents in the in‐phase and quadratic components resulting from the QAM are filtered by the ES. PQ events are detected and classified by extracting features from the filtered in phase and quadrature component. The performance of the proposed method is examined by taking randomly selected data samples for each PQ event, and its performance is and , respectively, at noiseless and dB SNR environments. The obtained results are compared with the others in the literature for the detection and classification of PQ events, and it is shown that the proposed method has better performance in noisy and noiseless conditions. In addition, to verify that the proposed method can be applied to real systems, oscillating transitions produced by capacitor banks in Simulink environment and voltage sag and swell events are generated in experimental environment. The proposed method successfully detected and classified PQ events. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Deep Learning for Power Quality Event Detection and Classification Based on Measured Grid Data

Deep Learning for Power Quality Event Detection and Classification Based on Measured Grid Data

Heterogeneous learning method of ensemble classifiers for identification and classification of power quality events and fault transients in wind power integrated microgrid

This paper proposes heterogeneous based ensemble Classifiers (voting and stacking method) to identify and classify different power system disturbances (power quality (PQ), faults, transients, and wind power variation) in wind integrated microgrid network. In the pre-processing stage of classification, a Discrete wavelet transform (DWT) technique is applied to extract the features from power system disturbance signals. The classification process for the proposed ensemble models involves two levels of classification. At the first level, the extracted features from the simulated power system events are used to learn the different individual base classifiers (logistic regression (LR), K-Nearest Neighbor (KNN), and J48 Decision tree (JDT)]. In second stage, a Meta-level classification is carried out based on predictions of base classifiers to get final predictions of class labels. First, the proposed ensemble models are utilized to discriminate the power system disturbances under random varying wind power condition and the predictive results (classification accuracy and performance indices) of ensemble models are compared with individual base classifiers (LR, KNN., and JDT). In addition, a sensitivity analysis is carried out under real time varying wind power condition and noisy environment of event signals to verify the effectiveness of ensemble models in further level. Furthermore, the robustness of proposed stacking ensemble model is verified with classification of single and combined PQ events of synthetic data, generated from the mathematical based PQ model software Predictions results under the different conditions show that stacking ensemble model offers substantial performance and discriminates the power disturbances with higher accuracy of classification than base classifiers and voting ensemble model.

A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines

Power quality has emerged as a sincere denominator in the planning and operation of a power system. Various events affect the quality of power at the distribution end of the system. Detection of these events has been a major thrust area in the last decade. This paper presents the application of Support Vector Machine (SVM) in classifying the power quality events. Well-known signal processing techniques, namely Hilbert transform and Wavelet transform, are employed to extract the potential features from the observation sets of voltages. Supervised architecture consisting of SVM has been constructed by tuning the parameters of SVM by various algorithms. It has been observed that Augmented Crow Search Algorithm (ACSA) yields the best accuracy compared to other contemporary optimizers. Further, Principal Component Analysis (PCA) is employed to choose the most significant features from the available features. On the basis of PCA, three different models of tuned SVMs are constructed. Comparative analysis of these three models, along with recently published approaches, is exhibited. Results are validated by the statistical one-way analysis of variance (ANOVA) method. It is observed that SVM, which contains attributes from both signal-processing techniques, gives satisfactory results.

Marvellous significance performance analysis of PQ events prediction and classification

This paper compares various significant research techniques concerning the power quality (PQ) events prediction and classification system presented by the authors previously and examines its viability scale as far as the research gap. This paper examines some of the frequently exercised PQ classification techniques named as Feedforward Neural Network (FNN), Sequential Ant Lion Optimizer and Recurrent Neural Network (SALRNN), dual-layer Feedforward network and Short-Time Fourier Transform (STFT) from the most significant literature in order to have more insights of the techniques. The research work has presented a simple framework that retains a balance between higher accuracy in the detection of disturbances as well as also maintains an effective computational performance for a large number of the power signals. The principle aim of the paper is research and comparative analysis of above-mentioned algorithms for searching the best impressive technique to detect and classify the PQ events. The simulation results of this research can be reasoned that the SALRNN technique performed very well to detect and classify the PQ disturbances when compared with the other two techniques such as FNN and STFT. The SALRNN technique is more optimal than the other existing techniques in terms of RMSE, MAPE, MBE, sensitivity, specificity and consumption time is analyzed.

A Synchronized Lissajous-Based Method to Detect and Classify Events in Synchro-Waveform Measurements in Power Distribution Networks

Waveform measurement units (WMUs) are a new class of smart grid sensors. They capture <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">synchro-waveforms</i> , i.e., time-synchronized high-resolution voltage waveform and current waveform measurements. In this paper, we propose new methods to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">detect</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">classify</i> power quality events in power distribution systems by using synchro-waveform measurements. The methods are built upon a novel graphical concept, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">synchronized Lissajous curve</i> . The proposed event detection and event classification methods work by analyzing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">shape</i> of the synchronized Lissajous curves during disturbances and events. The impact of challenging factors, such as the angle, the location, and other parameters of the event are discussed. We show that these challenges can be addressed if we treat the synchronized Lissajous curves as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">images</i> , instead of as time series as in the raw synchronized waveform measurements. Hence, we can take advantage of the recent advancements in the field of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">image processing</i> so as to capture the overall characterizing patterns in the shapes of the synchronized Lissajous curves. We develop a Convolutional Neural Network (CNN) method to classify the events, where the input is the synchronized Lissajous images. The effectiveness of the proposed event detection and classification methods is demonstrated through computer simulations, including hardware-in-the-loop simulations, and real-world field data. Multiple case studies verify the performance of the proposed methods. The proposed event detection method can accurately detect events, and identify the start time and the end time of each event. The proposed event classification method can classify power quality events with high accuracy. The proposed detection and classification methods do <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</i> require any prior knowledge about the network. They use data from as few as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">only two</i> WMUs.

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.

Classification of Stockwell Transform Based Power Quality Disturbance with Support Vector Machine and Artificial Neural Networks

The detection and classification of power quality events that disturb the voltage and/or current waveforms in the electrical power distribution networks is very important to generate electrical energy and to deliver this energy to the end-user equipment at an acceptable voltage. Various property extraction methods are used to determine the type of disturbances in the electrical signal. In this study, seven power distortions including voltage sag, voltage swell, voltage harmonics, voltage sag with harmonics, voltage swell with harmonics, flicker, transient signals and pure sine as a reference signal is used. Synthetic data are produced in MATLAB using parametric equations based on TS EN 50160 standard. Four kinds of feature extraction as frequency-amplitude, time-amplitude, geometric mean and standard deviation is made with Stockwell Transform (ST), which is one of the methods used for the feature extraction of the determined GKB. Detection of voltage distortions is interpreted through these properties. 640 simulation data is entered into the classifier by using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) and their classification performance is compared.

Fast TT transform and optimized probabilistic neural network-based power quality event detection and classification

Fast TT transform and optimized probabilistic neural network-based power quality event detection and classification

Power quality event classification using complex wavelets phasor models and customized convolution neural network

Origin and triggers of power quality (PQ) events must be identified in prior, in order to take preventive steps to enhance power quality. However it is important to identify, localize and classify the PQ events to determine the causes and origins of PQ disturbances. In this paper a novel algorithm is presented to classify voltage variations into six different PQ events considering the space phasor model (SPM) diagrams, dual tree complex wavelet transforms (DTCWT) sub bands and the convolution neural network (CNN) model. The input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands which are simultaneously processed by the 2D CNN model to perform classification of PQ events. In the proposed method CNN model based on Google Net is trained to perform classification of PQ events with default configuration as in deep neural network designer in MATLAB environment. The proposed algorithm achieve higher accuracy with reduced training time in classification of events than compared with reported PQ event classification methods.

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.

Multiple power quality events recognition and classification based on adaptive Eigen-based type-2 fuzzy logic algorithm

Multiple power quality events recognition and classification based on adaptive Eigen-based type-2 fuzzy logic algorithm

Power Quality Classification of disturbances using Discrete Wavelet Packet Transform (DWPT) with Adaptive Neuro-Fuzzy System

With the development of the technologies, the demand for good quality of electric power is increasing day by day. In Distributed Generation Systems (DGs), the quality of power can cause serious problems such as sensitive equipment's malfunction, the temperature riseof machines. Therefore, detection of power quality events in the power system is more important to take further actions. The existing power quality events classification methods have high computational time with low accuracy. In order to overcome this problem, this paper presents Discrete Packet Wavelet Transform-Kalman filter based Adaptive Neuro-Fuzzy approach for identification and classification of PQ events. The proposed method classifies the events with better classification accuracy, less convergence time and low in error prediction. The results show that the proposed method has better performance compared with the existing classification methods. The proposed method is Implemented and tested using MATLAB and it provides more accuracy when compared to the existing systems such as Discrete Wavelet Transform based Fuzzy Logic Adaptive System and Fourier Transform based Artificial neural networks etc..

Classification of Power Quality Events - An Inclusive Review

Power Quality is major issue that has led to large amount of research by the industry and academic experts. The increased uses of the electronics in electrical utilities have created a need of monitoring the power quality and provide the mitigation to enhance the performance of the electrical equipment. With advancement in technologies and availability of various online monitoring tools, it is very important to assess and review these technologies. The increased rate of research in this field is proven to be beneficial to the consumers. Customers are now able to request for better quality of service and making the Power Quality issue more important. This paper provide an inclusive review of various application of signal processing on power quality events, analysis of artificial intelligent techniques to classify the signals and optimization techniques for decision making. The paper also explores the upcoming opportunities for power quality classification and further improvement in terms of higher performance of the electrical equipment.

A novel hybrid downsampling and optimized random forest approach for islanding detection and non‐islanding power quality events classification in distributed generation integrated system

A novel hybrid downsampling and optimized random forest approach for islanding detection and non‐islanding power quality events classification in distributed generation integrated system

Feature extraction and power quality event classification using Curvelet transform and optimized extreme learning machine

This article presents an efficient method for power quality events (PQEs) detection and classification based on Curvelet transform (CT) and optimized extreme learning machine (OELM). Initially, various PQEs signal data are extracted even under noisy and simultaneously occurred multi-event conditions to reflect the real-time conditions. Relevant features of all these extracted signals are computed by using a multi-resolution and multidirectional fast discrete CT (FDCT) approach. These features are used to classify the events accurately by the proposed OELM. In this classification strategy, a modified differential evolution (MDE) is introduced to optimize the conventional ELM to enhance its precision rate. The proposed novel approach is tested and analyzed under various noisy conditions with 20 dB, 30 dB, and 50 dB with single and multi PQEs conditions. Comparative results with other recently proposed approaches by various authors reveal that the proposed CT- and OELM-based classifier titled CT-OELM can be considered as a competitive choice for implementing in the real-time monitoring system.

Detection and classification of power quality disturbances using GWO ELM

Many industies have equipments sensitive to bad power quality that affects their production and product quality. Therefore, it is important to automatically monitor the quality of power with minimum human intervention. It is possible to analyze and interprete raw data from the industrial equipments to useful information with the help of signal processing and artificial intelligence system. This paper presents the automatic classification of power quality events using Extreme Learning Machine (ELM) in combination with optimization techniques. S transform is used for extraction of useful features of the disturbance signal. The features are used to train the ELM for classifying PQ events. Further the parameters of ELM are tuned through Grey Wolf Optimization (GWO) approach to improve the classification accuracy. Seventeen different categories of PQ events are used for the classification purpose. The efficiency of GWO-ELM is compared with other widely used classifiers such as K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and ELM. The simulation results reveal that the proposed approach can accurately detect and classify the PQ events.

Feature Extraction and Classification of Power Quality Events Based on Fast Fourier Transformation and Artificial Neural Network

This paper presents an effective method for detection and classification of Power Quality Events (PQE), based on Fast Fourier Transformation (FFT) for event identification and Artificial Neural Network (ANN) technique for classifying of these events. Firstly, synthetic data such as pure sine as a reference, voltage sag, voltage swell, flicker, transient, voltage with harmonics are created in MATLAB based on TS EN 50160 standard. Database with 480 PQE waveforms is generated with 80 samples for each of the 6 types of the waveform with randomly different event amplitude, beginning occurrence time, time duration, frequency component and angle according to a type of event. FFT is used to extract features of the events by decomposing the signal. Then, 16384×480 data are reduced to 480×480 data by applying Principal Component Analysis (PCA) that is prevent over-learning, obtain less runtime using less computing power and reduce data and storage space. Finally, a total of 480 PQE are classified by using ANN. 336 of these PQE are used for training cluster, 72 of PQE are used for verification and the remaining 72 are used for testing. Firstly, the ANN has been trained correctly. The classification performance of the ANN in PQE has been examined by inserting the test into ANN. The performance of ANN is 99.8% for these PQE. The purpose of this research is to provide an artificial intelligence assistant that can fast and accurately advise the power system operators for the networks, and the results also show that the goal has been achieved.

Multiple power quality event detection and classification using modified S-transform and WOA tuned SVM classifier

In this paper, a novel method for the classification of power quality events is illustrated. Fifteen types of power quality events consisting of single and multi-stage disturbances are considered f...