Identification Of Power Quality Disturbances Research Articles

Identification of Power Quality Disturbances in Electrical Distribution System using Fast Fourier Transforms and Super Learner Ensembles

Currently, the use of non-linear loads and equipment, as well as renewable energy sources injected into the power system, tends to increase. As a result, the waveform of the electrical signal changes, and distortion occurs in the distribution system, which affects the quality and reliability of the electrical system. Importantly, sometimes this leads to malfunctions in protection equipment. This paper presents the algorithm for power quality disturbance (PQD) identification in electrical distribution systems, which involves three main steps: (1) Generating simulated waveforms using a signal processing approach; (2) extracting features using the Fast Fourier Transforms (FFT) technique; and (3) identifying the type of PQD using Super Learner Ensembles (SLE), which employs cross-validation to assess the performance of multiple machine learning models. Subsequently, the model’s efficiency is verified and tested using data from electronic energy meters installed in the distribution system of the Provincial Electricity Authority (PEA). The accuracy resulting from synthetic and experimental data sets is 99.90% and 99.69%, respectively. The results indicate that the model performs well in identifying power quality disturbances and achieves high accuracy.

An automatic recognition method for power quality disturbances based on CAB-Net

Abstract The accurate identification of power quality disturbances (PQDs) is crucial for maintaining the stability and reliability of power systems. In this paper, an automatic identification method for PQDs based on CNN-BiGRU-Attention Network (CAB-Net) is proposed. To handle the temporal characteristics and complexity of composite PQDs signals, the proposed model leverages the bidirectional information transmission of the BiGRU model to efficiently extract temporal features. Furthermore, to boost recognition accuracy, an attention mechanism is incorporated, the key information can be intelligently locked in the feature extraction stage, and the information can be efficiently screened and utilized, thereby enhancing its ability to identify power quality disturbance signals. To confirm the validity of the method, we use the simulation data for experimental proof. Experimental results demonstrate that compared to traditional power quality disturbance identification methods, the method has excellent recognition ability and stronger robustness.

A Review of Microgrid Power Quality Disturbance Identification Studies

A Review of Microgrid Power Quality Disturbance Identification Studies

Power quality disturbance identification based on MD-EfficientNet

Power quality disturbance identification based on MD-EfficientNet

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

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

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.

Multi-strategy active learning for power quality disturbance identification

As clean energy facilities are increasingly integrated into power distribution networks, the incidence of multiple power quality disturbances (MPQDs) is on the rise. Currently, most MPQDs classification models rely on supervised learning, which demands a substantial amount of labeled data. However, obtaining labeled MPQDs data from the real world is often a time-consuming and labor-intensive process, requiring annotation by experienced engineers, particularly for events involving MPQDs. To tackle this challenge, we introduce an innovative active learning (AL) approach known as Adaptive Weighting Multi-Strategy AL (AWMSAL) for MPQDs classification. In our framework, AWMSAL simultaneously considers multiple strategies using rank aggregation to select MPQDs data that is both full-information and highly representative for labeling. In addition, our method employs a unique weight calculation function to dynamically adjust weights based on the differences in data values observed with various strategies during the AL process. To enhance the classifier performance, we incorporate the Laplacian manifold regularizer into the Extreme Learning Machine in both supervised and unsupervised settings. We compared the performance of the AWMSAL method against several state-of-the-art active learning algorithms using synthetic and hardware-generated data sets. The results demonstrated that AWMSAL achieved recognition rates exceeding 99% in a shorter number of iterations. When the same termination condition was met, AWMSAL outperformed other active learning algorithms by 2.45% to 18.33% in terms of accuracy.

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.

Identification of composite power quality disturbances based on relative position matrix

With the integration of large-scale nonlinear loads and distributed power sources into the grid, composite power quality disturbances (PQDs) events are becoming increasingly common, which significantly degrade the quality of power supply. Therefore, this paper focuses on studying the accurate classification of composite PQDs to mitigate the risk of power quality deterioration. However, traditional classification methods perform barely satisfactory in terms of accuracy and robustness in the classification of PQDs. To address these issues, this paper proposes a method for recognizing composite PQDs based on relative position matrix (RPM). Initially, utilizing the RPM method, the initial one-dimensional PQD time series data is transformed into two-dimensional image data while preserving its high-frequency characteristics. This process results in the creation of an informative and feature-rich image training set. Subsequently, an end-to-end framework for PQDs classification was developed. The framework utilizes convolutional neural networks to automatically extract multi-scale spatial and temporal features from image data. This design aims to automate the classification of composite PQDs, eliminating the need for labor-intensive manual signal processing and feature extraction. This integration ensures a more accurate and robust classification. Finally, the proposed method is tested on a case involving 30 types of PQDs at varying noise levels and compared with existing power quality disturbance classification methods, and results show that the proposed method has better performance than the previously established methods.

High-Precision Intelligent Identification of Complex Power Quality Disturbances Based on Improved KST and CNNs

High-Precision Intelligent Identification of Complex Power Quality Disturbances Based on Improved KST and CNNs

Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

Wasserstein Adversarial Learning for Identification of Power Quality Disturbances With Incomplete Data

Power quality disturbances (PQDs) have adverse impacts on safe operation and reliability of modern integrated power system so it is of great necessity to identify them. Existence of missing measurement data hinders accurate identification of potential PQDs and the inevitable discrepancy after data recovery vitiates the current detection methods. Besides, the related research is lacked. In this study, a novel unified framework of Wasserstein adversarial learning (WAL) is proposed on identifying PQDs with incomplete data for the first time. It consists of Wasserstein adversarial imputation (WAI) and Wasserstein adversarial domain adaptation (WADA). WAI minimizes the improved Wasserstein distance between the data distributions of observed and generated PQD parts to impute missing values. During this process, PQD characteristics can be well recovered. Then, WADA leverages the Wasserstein domain discrepancy between the feature distributions of source labeled complete and target unlabeled imputed PQDs to capture domain-invariant features. Thus, labels of target imputed PQDs can be predicted accurately. Experimental verification demonstrates that the proposed WAI and WADA outperform other typical methods with better imputation results and higher classification accuracy. Constrained Wasserstein loss empowers the proposed deep learning models with excellent convergence and gradient stability.

Multivariable signal processing algorithm for identification of power quality disturbances

This paper investigated a multi-variable power quality disturbance identification algorithm (MPQDIA) applying the Stockwell transform (ST), Hilbert transform (HT) and rule based decision tree (RBDT). Voltage waveform with associated power quality disturbances (PQDs) are realized by the mathematical formulation in compliance with standard IEEE-1159. These voltage signals with PQDs are processed applying the ST and HT for computing the index for power quality identification (IPI) and index for power quality location (IPL). IPI and IPL plots effectively identify and locate the PQDs of simple nature and multiple nature. Four features are extracted from data sets of IPI and IPL. These features are considered as input for the RBDT to classify the PQDs. MPQDIA is effectively tested for identification, to locate and to classify the total 21 PQDs of simple as well as multiple natures. Performance of MPQDIA is analysed in terms of rightly and wrongly categorized PQDs in noise free environment and considering noise of 20 dB SNR (signal to noise ratio) level. Efficiency of MPQDIA is compared with the ST and RBDT based algorithm, WT based fast Kurtogram and decision tree powered algorithm, and ST and RBDT powered method in terms of classification accuracy, noise level for which performance is not affected, effectiveness to detect the simple nature and multiple nature PQDs. It is established that MPQDIA effectively recognizes the PQDs on a real time power system network. Study is performed with the help of MATLAB software.

Power quality disturbances identification based on adaptive symplectic geometric mode decomposition and improved marine predators algorithm

Power quality disturbances identification based on adaptive symplectic geometric mode decomposition and improved marine predators algorithm

Identification of Power Quality Disturbances in Electrical Systems - A Signal Processing Approach

Abstract: The Detection and Classification of Power Quality Disturbances (PQD) is important for quick diagnosis and mitigation disturbances. Poor power quality could have serious effects on sensitive electric devices. Consumers face difficulty to quantify the cost of failure equipment. There is a need to recognize and mitigate PQD to supply clean power to the consumer. In this Project PQDs simulated with MATLAB R2022b to be validated experimentally on a test bench using stepdown transformer

Power Quality Disturbance Classification Based on Parallel Fusion of CNN and GRU

Effective identification of complex power quality disturbances (PQDs) is the premise and key to improving power quality issues in the current complex power grid environment. However, with the increasing application of solid-state switches, nonlinear devices, and multi-energy system generation, the power grid disturbance signals are distorted and complicated. This increases the difficulty of PQDs identification. To address this issue, this paper presents a novel method for power quality disturbance classification using a convolutional neural network (CNN) and gated recurrent unit (GRU). The CNN consists of convolutional blocks, some of which come with a squeeze-and-excitation block (SE), and is used to extract the short-term features from PQDs, where the convolutional block is used to capture the spatial information from PQDs and the SE is used to enhance the feature extraction capability of the convolutional neural network. The GRU network is designed to capture the long-term feature from PQDs, and an attention mechanism connected to GRU’s hidden states at different times is proposed to improve the GRU’s feature capture ability in long-term sequences. The CNN and GRU are parallelly arranged to perceive the same PQDs in two different views, and the feature information extracted from them is fused and transmitted to the Softmax activation layer for classification. Based on MATLAB-Simulink, a typical multi-energy-source system is constructed to analyze PQDs, and twelve PQDs are simulated to validate the proposed method. The simulation results show that the proposed method has higher classification accuracy in both single and hybrid disturbances and significant advantages in noise immunity.

Identification of Power Quality Disturbances Based on Fast Fourier Transform and Artificial Neural Network

This paper presents the proposed algorithms for the identification of Short Duration RMS Variations and Long Duration RMS Variations combined with harmonic. The proposed algorithms are Fast Fourier Transform (FFT) and Artificial Neural Network (ANN). The Algorithms identify nine types of Power Quality (PQ) disturbances such as normal signal, voltage sag, voltage swell, under voltage, over voltage, voltage sag combined harmonic, voltage swell combined harmonic, undervoltage combined harmonic, and over voltage combined harmonic. FFT is used to obtain the frequency spectrum of each PQ disturbance with frequency sampling of 1000 Hz, data length of 200. Output FFT is used to input data for ANN. Output ANN is a type of nine PQ disturbances. The result shows that proposed algorithms (FFT combined ANN) are effective for identification, which ANN with 20 neurons in the hidden layer has an accuracy of approximately 99.95 %

Research on power quality disturbance classification algorithm based on edge computing

Power quality analysis and governance need the identification of power quality issues. With the use of smart meters and various smart collection devices, more and more power quality data are collected, and the massive data collection brings pressure on communication, storage and computation to the conventional algorithm for identifying and classifying power quality disturbances based on cloud computing. In the paper, a classification algorithm for power quality disturbance identification based on edge computing and fusion model is proposed. The algorithm’s key concept is to compress and sense the power quality signals at the edge side, and then transmit the compressed power quality data to the cloud, which uses an improved Dense-Net and LSTM fusion model to identify and classify the compressed power quality data. Through experiments, it is proved that the method can compress the power quality signal to 70% of the original signal size while satisfying the recognition and data on power quality disturbance categorization accuracy, reducing the communication cost of data transmission, lowering the computational pressure and caching pressure on the cloud, and having certain robustness.

High-Precision Identification of Power Quality Disturbances Based on Discrete Orthogonal S-Transforms and Compressed Neural Network Methods

High-Precision Identification of Power Quality Disturbances Based on Discrete Orthogonal S-Transforms and Compressed Neural Network Methods

Identification of Power Quality Disturbances Using S-Transform and Multi-Class Support Vector Machine

Abstract: An essential issue in power quality disturbances is identifying and classifying power quality disturbances from anywhere and at any time. This article proposed a new approach to identify and classify power quality disturbances over the web using S-transform, Multi-Class Support vector machine (SVM), and Matlab framework. S-Transform is used as an extraction feature to obtain the temporal frequency characteristics of power quality events. The development of the multi-class SVM classifier, in which the system classifies various power quality disturbances. Finally, the Matlab framework integrated the graphical and computational processes with remote access via the web. The test result indicated the suggested method's effectiveness and robustness for identifying and classifying power quality disturbances through the web.Abstrak: Masalah penting dalam gangguan kualitas daya adalah mengidentifikasi dan mengklasifikasikan gangguan kualitas daya dari mana saja dan kapan saja. Artikel ini mengusulkan pendekatan baru untuk mengidentifikasi dan mengklasifikasikan gangguan kualitas daya melalui web menggunakan S-transform, Multi-Class Support vector machine (SVM), dan Matlab. S-Transform digunakan sebagai fitur ekstraksi untuk mendapatkan karakteristik frekuensi temporal dari peristiwa kualitas daya. Multi class SVM classifier dikembangkan dimana sistem mengklasifikasikan berbagai gangguan kualitas daya. Akhirnya, Matlab framework mengintegrasikan proses grafis dan komputasi sehingga dapat diakses jarak jauh melalui web. Hasil pengujian menunjukkan efektivitas dan robustnes metode yang usulkan untuk mengidentifikasi dan mengklasifikasikan gangguan kualitas daya melalui web.