Power Quality Signals Research Articles

Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector Machine

The widespread integration of renewable energy sources (RESs) into power systems using power electronics-based interface devices has led to a substantial rise in power quality (PQ) issues. There is an immediate requirement for effective monitoring, detection, and classification of power quality disturbances (PQDs) that is needed to take remedial measures and design planning of the system architecture. This study presents a hybrid approach with an objective for the feature extraction and classification of PQDs. The proposed hybrid approach is comprised of an optimized tunable-Q wavelet transform (OTQWT) for the feature extraction and incremental support vector machine (ISVM). A four-stage approach is suggested for the PQ detection and classification in this study. In the first stage, the various data are retrieved both in the form of synthetic data by mathematical formulations and real-time data with prototype design setup. In the second stage, regardless of the specified wavelet function, the PQD signals are decomposed into low-pass and high-pass sub-bands using the tunable-Q wavelet transform (TQWT). However, the utilization of default decomposition parameters to address nonstationary PQ signals may lead to information loss and reduced performance of the system. To avoid this limitation, an OTQWT as an enhanced technique to TQWT based on an Adaptive Particle Swarm Optimization (APSO) is suggested. A modified objective function based on the mean square error (MSE) is used to improve the decomposition process. In the third stage, an efficient classifier is suggested based on the ISVM. Lastly, to test and evaluate the performance of the proposed approach, twelve types of PQDs including noise and multiple occurrences are considered. The comparative analysis with other popular methods reflects the better performance of the proposed approach and justifies its use for PQ detection and classification purposes in real-time​ conditions.

Real-Time Implementation of Stockwell Transform in FPGA Platform Using Soft-core Processor Applied to Novelty Detection in Power Quality Signals

Real-Time Implementation of Stockwell Transform in FPGA Platform Using Soft-core Processor Applied to Novelty Detection in Power Quality Signals

Power Quality Signal Conditioning and Mitigation by Using LMSEXF-Based Control Strategy for Grid-Tied PV Assisted EV Charging Station

Power Quality Signal Conditioning and Mitigation by Using LMSEXF-Based Control Strategy for Grid-Tied PV Assisted EV Charging Station

A Compression Method for Power Quality Data

This paper presents a compression method for power quality (PQ) signals. This method consists of polynomial approximation algorithm and a lossless compression algorithm The proposed method uses the Deflate algorithm as lossless compression algorithm. The polynomial approximation is intended to decrease the entropy of the signal, thus increasing the compression ratio of the Deflate algorithm. The proposed method is characterized by high compression ratio and low computational cost, making feasible its implementation in embedded systems.

Classification Algorithm for DC Power Quality Disturbances Based on SABO-BP

To better address and improve the issues related to DC power quality, this paper proposes an identification method tailored for DC power quality disturbances. First, it explores the underlying mechanisms and waveform characteristics of common DC power disturbances. By integrating the results of time–frequency analysis obtained through the S-transform, five distinct features are designed and extracted to serve as classification indicators. The SABO algorithm is subsequently employed to optimize the BP neural network, assisting in determining the optimal input weights and hidden layer thresholds. This optimization technique helps prevent the network from becoming stuck in local minima, thereby enhancing its robustness and generalization capabilities. This paper presents a simulation system for AC/DC power systems to conduct experimental verification. The system simulates various DC power quality issues and monitors abnormal waveforms. According to the designated classification index, the features of simulated disturbance signals are extracted. The SABO-BP classification prediction model is then used to automatically classify and identify the samples. The experimental results demonstrate high accuracy in classification and identification using the proposed method. In comparison to the BP neural network method, the SABO-BP method demonstrates an 8.207% improvement in accurately identifying disturbance signals. It is capable of accurately identifying direct current power quality signals, thereby assisting in the evaluation and control of power quality issues.

Power Quality Improvement and Signal Conditioning of PV Array and Grid Interfaced Off-Board Charger for Electric Vehicles with V2G and G2V Capabilities

Power Quality Improvement and Signal Conditioning of PV Array and Grid Interfaced Off-Board Charger for Electric Vehicles with V2G and G2V Capabilities

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.

XPQRS: Expert power quality recognition system for sensitive load applications

Power Quality (PQ) disturbances of a few milliseconds duration may lead to malfunctioning the sensitive devices connected to the network. This necessitates detecting these PQ disturbances in a minimum time before affecting the sensitive load and adopting countermeasures. An expert PQ recognition system (XPQRS) is proposed to identify seventeen types of PQ events using only one power cycle acquired at 5 kHz. In XPQRS, the input PQ signal is first concatenated with its four derivatives to highlight the deviations. Log energy, Shannon energy, and mobility parameters, extracted from the resultant vectors, are then fed to Quadratic Support Vector Machine. XPQRS offers 96.5% accuracy and provides fast decisions in 34.99 ms which makes it suitable for its implementation on an embedded system. XPQRS shows noise immunity and outperforms the previous studies in terms of decision time, accuracy, and computational complexity.

Modeling of power numerical relay digitizer harmonic testing in Wavelet transform

In today’s modern power devices and rapid growth power demands, the need for precise and accurate protection relays is a must for the power distribution system. That is, to segregate faulty sectors within fewer cycles, power relays should perform at the highest level of accuracy to detect abnormal conditions in power distribution. Therefore, this work will investigate the enhancement of the numerical relay testing in terms of harmonic distortions effect on the digitized output waveform as direct causes of relay failures. However, as it is an expensive process of testing the digitizing element of the numerical relay, this paper proposes a new algorithm of Wavelet transforms in power quality signal processing testing using MATLAB simulation. As this newly proposed method of advanced waveform analysis algorithm will enhance the testing process of digitizing elements, and reduce data compiling complexity, a comparison between conventional Fourier Transforms testing and Wavelet algorithm under abnormal conditions will be simulated based on inserting multi harmonics effect. As a result, based on the Wavelet bank of filters, de-noising, and decomposition structure filters, Wavelet has provided promising results in defining the effect of waveform distortion tripping time, fault location, total harmonic distortion, signal-to-noise ratio, and spurious-free dynamic range.

Bipolar alternate mark inversion encoding (BAMI) scheme for performance signature of optical transceiver systems under thermal effects

Abstract This study has clarified the bipolar alternate mark inversion encoding scheme for performance signature of optical transceiver systems under thermal effects. The light power through optical fiber channel variations with spectral wavelength at various temperature variations are clarified. The total electrical power through PIN receiver at various temperature variations are demonstrated. The electrical power amplitude, max Q Factor and min BER through PIN receiver at different temperature variations are clarified in details. Electrical and optical signal per noise ratio with laser bias current at various ambient temperatures are demonstrated. The total optical/electrical power and maximum quality signal with laser bias current at various temperature variations.

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.

Research on reconstruction algorithm of power quality data based on self-encoding network

Taking on the problems of unstable reconstruction performance and high reconstruction method computational cost induced by the unpredictability of the measurement matrix during power quality signal reconstruction, this research provides a power quality reconstruction model based on a self-encoding network and compressed sensing for the first time. The model includes a noise-adding module, an encoder module and a decoder module. The noise-adding module adds a specific amount of white noise in relation to the original signal to simulate the disturbance signal collected in the real scene. The encoder module uses a nonlinear measurement method to observe the power quality signal, and the decoder module completes the signal through a compressed sampling matching tracking algorithm. Refactoring. The outcomes of the experiment reveal that the compressed reconstruction model proposed in this paper significantly improves the efficiency and stability of power quality signal reconstruction.

PQ Event Detection After Noise Removal Using Fuzzy Transform and Hilbert Spectral Analysis on EMD

In this paper, a noise removal technique from Power Quality (PQ) signals is proposed which is based on a soft thresholding method. Thresholding is accomplished by using Fuzzy Membership Function (MF) and Empirical Mode Decomposition (EMD). To assess the denoising performance, the proposed technique is applied to detect PQ disturbances from the denoised power system signals. The EMD is applied to decompose the signal into different components. Next, the Intrinsic Mode Functions (IMFs) obtained from the EMD are utilized to determine the IMF dependent thresholding parameters, which are used into the modified fuzzy MF for the noise removal. For improved performance, several random versions of the first IMF are created, independently soft thresholded, and averaged to yield the final denoised version of the first IMF. A separate measurement is used to decide how many IMFs are required to be thresholded. For the evaluation of the noise removal performance, 1) the proposed method is compared with a wavelet-transform-based state of art technique, and 2) an application is presented on the denoised signal for the detection of PQ events from real and simulated power system disturbance data. The denoised and processed IMFs are subjected to Hilbert spectral analysis for the identification of the localized features, which are then utilized for the detection of PQ events in smart grid signal.

Detection and Classification of Power Quality Disturbances Using Stock Well Transform and Improved Grey Wolf Optimization-Based Kernel Extreme Learning Machine

Power quality disturbances (PQDs) in modern electrical power systems, caused by the integration of nonlinear power electronic devices and erratic distributed generation (DG), lead to interruptions and significant energy losses for end users. However, conventional methods for PQDs classification face challenges in dealing with noise interference and feature selection. To address these challenges, this research paper proposes a novel approach that combines the stockwell transform (ST) with an improved grey wolf optimization-based kernel extreme learning machine to enhance classification accuracy. The stockwell transform is utilized to extract meaningful features from the power quality (PQ) signals, which are subsequently input into the kernel extreme learning machine (KELM). Furthermore, the parameters of the KELM model are optimized using the improved grey wolf optimization (IGWO) approach to improve the accuracy of classification. To evaluate the performance of the proposed method, real-time implementation is considered by incorporating PQDs data with signal-to-noise ratios (SNR) of 20 dB, 30 dB, and 40 dB into the original synthetic signals. Multiple noise conditions are simulated to assess the proposed model ability to identify and classify disturbance signals. The results demonstrate a detection accuracy of 99.76% under noiseless conditions, indicating the model’s high accuracy. Moreover, the proposed method exhibits robustness to noise, achieving accuracies of 98.86%, 98.32%, and 97.3% at SNR levels of 40 dB, 30 dB, and 20 dB, respectively. In the end, this work has performed a comparative study with other previously published work. Compared with other classification methods, the algorithm proposed in this paper has higher accuracy, and it is an efficient and feasible classification method.

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

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

A Novel Data Compression Methodology Focused on Power Quality Signals Using Compressive Sampling Matching Pursuit

In this research a new data compression technique for electrical signals was proposed. The methodology combined wavelets and compressed sensing techniques. Two algorithms were proposed; the first one was designed to find specific characteristics of any type of energy quality signal such as the number of samples per cycle, zero-crossing indices, and signal amplitude. With the data obtained, the second algorithm was designed to apply a biorthogonal wavelet transform resulting in a shifted signal, and its amplitude was modified with respect to the original. The errors were rectified with the attributes found in the early stage, and the application of filters was conducted to reduce the ripple attached. Then, the third algorithm was designed to apply Compressive Sampling Matching Pursuit, which is a greedy algorithm that creates a dictionary with orthogonal bases representing the original signal in a sparse vector. The results exhibited excellent features of quality and were accomplished by the suggested compression and reconstruction technique. These results were a compression ratio of 1020:1, that is, the signal was compressed by 99.90% with respect to the original one. The quality indicators achieved were RTE = 0.9938, NMSE = 0.0098, and COR = 0.99, exceeding the results of the most relevant research papers published in Q1 high-impact journals that were further discussed in the introduction section.

Highly accurate detection of power quality disturbance using segmented and modified S-transform

Power quality (PQ) is important for the normal operation of modern power grids. The accurate detection of PQ disturbances is the basis for dealing with PQ events in power systems. This paper proposes an algorithm based on the segmented and modified S–transform (SMST) to accurately detect PQ disturbances. The SMST uses a modified Gaussian window with two parameters that can be adaptively adjusted according to the intrinsic characteristics of the PQ signals. Furthermore, the idea of segmentation is used in the modified S–transform, whereby the range of frequency of the signals is divided into three parts, i.e., low-, medium-, and high-frequency bands. The two adjustable parameters can then be accordingly selected in different frequency bands. This enables the SMST to adaptively satisfy different requirements of detection at different frequency bands. Finally, the results of detection of the proposed model were compared with those of prevalent algorithms in the area through simulations. They showed that it can significantly increase the time-frequency resolution in different frequency bands, and has superior accuracy of detection.

Black-box attacks and defense for DNN-based power quality classification in smart grid

Machine learning (ML) models are widely used in smart grid, but they are vulnerable to adversarial examples that are maliciously crafted using the input data. Therefore, the use of these models in smart grid can cause significant damage. Although there are many studies on ML models attack and defense, there are few studies based on smart grid environment. In this paper, we first utilize deep neural network (DNN) to classify power quality (PQ) signals. Then an algorithm based on the Projected Gradient Descent (PGD) is proposed to attack the classification model, which employs multiple iterations to find the optimal perturbation. Finally, a defense model based on Multi-Level-Denoising Autoencoder (MLD-AE) is proposed to defend against the black-box attacks. The experimental results show that, compared with the Fast Gradient Sign Method (FGSM), the proposed black-box attack algorithm can significantly reduce the classification ability of the model with a smaller disturbance. Furthermore, compared with adversarial training defense methods, the proposed defense model can improve the power system’s ability to resist black-box attacks without reducing the ability of the classification model to classify clean PQ signals, and has excellent scalability.

A new signal processing approach/method for classification of power quality disturbances

The disadvantage of conventional signal processing methods used for Power Quality (PQ) classification is that their mathematical calculations are complex. Therefore, it is necessary to develop simple, effective, and fast signal processing tools to analyze the PQ signal. In this study, a new signal processing method with simple mathematical operations based on the Integer Factor (IF) down sampling/Approximation Derivatives (AD) was developed to analyze PQ signals. IF was employed to analyze the signals at various sampling frequencies, while the AD method was employed to obtain different degrees of detail and approximation coefficients for the signals. In order to prove that the IF/AD signal processing method can perform a fast and detailed signal processing analysis, the IF/AD method and the Discrete Wavelet Transforms (DWT)/Multiresolution method, which is one of the basic signal processing methods, were compared. It was determined that the developed signal processing method has analyzed PQ disturbances in a shorter time and in more detail than DWT/Multiresolution method. After it was determined that the IF/AD method was effective in the analysis of PQ signals, a new classification method based on the IF-AD signal processing approach, the Slime Mould feature selection algorithm, and Support Vector Machine was developed. The classification method was applied to synthetic signals with noise, real-time data generated in the laboratory and PQ online data. The results proved that the developed classification method was successful. Therefore, this study will provide a different approach to PQ classification methods and non-stationary signal analysis studies.

Power Quality Indices Online Prediction Based on VMD-LSTM Residual Analysis

In order to ensure the provision of high-quality power quality and the smooth operation of the power system, aiming at the problem of low prediction accuracy of power quality indices, this paper proposes an online prediction method of power quality indices based on VMD-LSTM residual analysis. Firstly, the VMD-LSTM is used to simulate and predict the normal power quality signal; then the predicted signal data is different from the actual power quality signal data to obtain the residual signal, and the residual signal is input into the one-dimensional convolutional neural network for training, and finally the power quality indices prediction model is obtained. Finally, taking voltage flicker as an example, simulation experiments compared with other prediction models verify the better effectiveness and accuracy of the proposed method.