Detection Of Power Quality Disturbances Research Articles

Utilization of Stockwell Transform and Random Forest Algorithm for Efficient Detection and Classification of Power Quality Disturbances

Power quality disturbances (PQDs) can lead to significant operational and financial losses in power systems. Accurate detection and classification of PQDs are essential for maintaining power quality and preventing power system failures. This research article introduces an innovative approach for the precise detection and classification of single- and multiple-state power quality disturbances (PQDs) using the Stockwell transform (ST) and a random forest classifier. To create realistic PQD signals, seventeen distinct classes are generated in accordance with IEEE Standard 1159, employing mathematical equations implemented in MATLAB software. The ST is employed to extract relevant features from the PQD signals, which are subsequently utilized as input for the random forest classifier. The classifier employs bootstrapping sampling to generate multiple training sets from the original dataset. Each training set is used to construct a decision tree by recursively partitioning the data based on significant features. To mitigate overfitting and enhance robustness, a random subset of features is selected at each node of the decision tree, thereby reducing tree correlation. The performance of the random forest classifier is compared with other widely utilized machine learning classifiers. The results exhibit the efficacy of the proposed approach in accurately detecting and classifying PQ events, highlighting its superiority over alternative methods.

A New Approach to Detect Power Quality Disturbances in Smart Cities Using Scaling-Based Chirplet Transform with Strategically Placed Smart Meters

The growth of Internet of Things (IoT)-enabled devices has increased the amount of data created by the distribution network’s periphery nodes, requiring more data transfer capacity. Recent applications’ real-time requirements have strained standard computing paradigms, and data processing has struggled to keep up. Edge computing is employed in this research to detect distribution network faults, allowing for instant sensing and real-time reaction to the control room for faster investigation of distribution problems and power outages, making the system more reliable. Moreover, to overcome the challenges of fault detection, advanced signal processing methods need to be integrated with the Adaboost classifier. An Adaboost-based edge device, suitable for installation on top of a power pole, is proposed in this research as a means of real-time fault detection. To increase throughput, decrease latency and offload network traffic, data collecting, feature extraction and Adaboost-based problem identification are all performed in an integrated edge node. Enhanced detection accuracy (98.67%) and decreased latency (115.2 ms) verify the effectiveness of the suggested approach. In this research, we enhance the classical chirplets transform to create the scaling-basis chirplet transform (SBCT) for time–frequency (TF) analysis. This approach modulates the TF basis around the relevant time function to modify the chirp rate with frequency and time. By carefully selecting the sampling frequency, it is possible to discriminate between short circuit fault and high-impedance fault (HIF) by calculating spectral entropy. The TF representation obtained with the SBCT provides considerably higher energy concentrations, even for signals with numerous components, closely spaced frequencies and heavy background noise.

Simulation and FPGA Implementation of Transient Power Quality Disturbance Detection

With the access to new energy in the smart grids and the massive use of nonlinear and unbalanced large loads, the problem of power quality becomes more and more serious. At present, the transient power quality disturbance problem has been paid more attention to. The research on transient power quality is relatively late, and various detection methods and technologies are not mature. It is necessary to use more efficient methods to detect and identify transient power quality to improve the safety of electricity use. This paper proposes to detect transient power quality disturbance by wavelet transform. In this paper, the original signal modelling of five kinds of power quality problems such as voltage sag and voltage swell. The 9/7 wavelet lifting coefficient is optimized in this paper and is carried out on the MATLAB software platform to improve the processing speed of the algorithm and realize parallel processing. The simulation results show that the improved lifting wavelet processing can effectively detect the transient power quality disturbance.

The Power Quality Monitoring Method of Intelligent Power Distribution Cabinet

To improve the authenticity of reconstructed signals in power quality disturbance detection, an improved wavelet denoising method based on a coquantum-particle swarm is proposed to solve the problem that parameters in the traditional wavelet function are not easy to obtain, and a power quality disturbance detection framework is constructed. Simulation analysis verifies that the proposed method can effectively improve the disturbance detection performance compared with the traditional method. The reconstructed signal is closer to the original signal, which verifies the effectiveness of the proposed method.

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 detection based on IEWT

With the continuous development of distributed generation, power quality problems are more complex. In order to ensure that the power quality of the active distribution network meets the requirements of users, it is of great significance to accurately obtain the power quality disturbance parameters. For the problem that the traditional empirical wavelet transform needs to manually select the number of frequency bands when analyzing unknown power quality disturbances, this paper proposes an improved empirical wavelet transform (IEWT) algorithm. The algorithm uses the Piecewise Cubic Hermite Interpolation (PCHIP) method and the improvement of the transition region to achieve adaptive frequency band division and noise interference reduction, and different amplitude (AM) and pitch (FM) components can be obtained. Then the Normalized Direct Quadrature (NDQ) algorithm and Singular Value Decomposition (SVD) algorithm are used to extract frequency, amplitude and time parameters of the AM-FM components. Simulation and experimental results show that the power quality disturbance parameters extracted by the proposed method are more accurate and less affected by noise.

Islanding detection and power quality disturbance classification in multi DG based microgrid using down sampling empirical mode decomposition and multilayer neural network

Abstract Power Quality, Equipment and Personnel safety of any distributed generation (DG) system connected to utility Grid merely depends on accurate detection of Islanding and non-islanding Power quality disturbances. The main objective of the proposed research is to detect islanding events with very narrow non-detection zone (NDZ) and classification of power quality disturbances with higher accuracy using signal processing and intelligent method together. A noise robust down sampling empirical mode decomposition (DEMD) is used to extract signature of islanding and power quality (PQ) disturbance features from the collected voltage signals and multilayer perceptron neural network (MLNN) is proposed to classify islanding and non-islanding (PQ) events. The performance of the proposed (DEMD-MLNN) technique is verified with IEEE-9 bus distributed generation system dominated by solar &wind energy penetration. The simulation work is carried out in MATLAB/Simulink platform. The efficacy of the proposed DEMD-MLNN is verified by large number of numerical experimentations with and without noise and comparing with existing competitive well-known techniques.

Coherence‐based integrated detection and assessment algorithm for voltages and currents unbalance/disturbance in three‐phase synchronous generators: A validation of simulation results

AbstractThe phenomenon of imbalance is a variation of voltage and current waveforms from a perfect sinusoidal waveform, which is considered a crucial challenge in power systems. Uneven loads and disturbances result in three‐phase voltage/current unbalance that leads to a temperature rise and low output curves at synchronous machines. Thus, the unbalance/fault must be recognized and avoided. This article suggests a methodology to detect and evaluate three‐phase voltage and current unbalance/fault using a coherence algorithm. The suggested scheme develops integrated protection functions that can be divided into three modules: voltage unbalance, current unbalance, and power factor unbalance. Therefore, it can be useful in mitigation techniques applied to compensate the voltage unbalance effects. The present protection introduces a new proposal of closed‐tripping curves based on the values range of nine coherence factors. To investigate the protection performance, a segment of power grid with real parameters’ information is simulated utilizing ATP platform. Several fault types, such as shunt and series faults, are conducted on the simulated system to analyze the results and monitor the system status using the coherence statistic processed in MATLAB software. The results show that the approach is smart, swift, precise, reliable and stable. Moreover, the algorithm can be practically implemented, and represents a base for digital unbalance/fault detectors and relays because its performance is superior in detecting Power Quality Disturbances (PQDs).

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.

Multi-level Signal Decomposition for Power Quality Disturbance Classification

The introduction of electric vehicles impose large disturbance to the grid-level power signal due to the charging and discharging mechanism. Power signal monitoring in the electrical grid can provide several insights such as power quality disturbance detection, major power consumption area, peak power usage period, and their potential catastrophic failure conditions. As for preventive maintenance purpose, automatic classification of power quality disturbance using a hybrid method incorporating wavelet transform and deep LSTM network is proposed in this paper. Multi-level signal decomposition is applied to input signal to increase the resolution of input decomposing into multiple frequency bands. Subsequently, these multi-level frequency components are fed into deep LSTM layer to further extract useful higher order latent feature. Classification performance of the proposed wavelet-based LSTM (WTLSTM) network is bench-marked with deep LSTM method. Additive white Gaussian noise (AWGN) with signal-to-noise (SNR) levels between 20-50dB are inserted during the training process to increase the generalization of signal learning with the realistic scenarios. The classification performance of both WT-LSTM and Deep LSTM networks are tested with 20,30,40,50dB SNR AWGN and noiseless conditions. As a result, the WT-LSTM network obtains an overall classification performance of 89.77% on 20dB and 99.21% on noiseless condition as compared to Deep LSTM, with 88.48% and 98.54% respectively.

Power Quality Disturbances Detection and Classification Based on Deep Convolution Auto-Encoder Networks

Power Quality Disturbances Detection and Classification Based on Deep Convolution Auto-Encoder Networks

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 Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Global attention-based LSTM for noisy power quality disturbance classification

An increased dependency of digital control systems in the modern electrical network demand for a better quality of power signal. The occurrence of power quality disturbances (PQDs) in the network reduces the lifespan of power semiconductors and solid states switching devices. Global attention-based long short-term memory (LSTM) network is proposed to perform automatic time-series PQD detection and classification. Attention-based LSTM helps improve the noise immunity to extract salient features from noisy signal for PQD classification. The aim of this article is to analyse the performance of proposed attention-based LSTM under different noise conditions. Addictive white Gaussian noise is added to synthetic PQDs in different signal-to-noise ratio. These random generated noises are used to train and test the performance of proposed method, as well compared towards generic LSTM model. This work also shows the sensitivity of proposed method towards unknown noises that is not seen by the model during training phase.

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.

Evaluation of wavelet-based feature extraction methods for detection and classification of power quality disturbances

The appearance of Power Quality Disturbances can cause serious damage to the utility grid. Their detection and identification are two of the major problems related to the improvement of Power Quality. This paper presents an evaluation of different combinations of wavelet-based features for the detection and classification of eight types of Single Power Quality Disturbances. A set of disturbances was generated in MATLAB through their mathematical models. The detection stage was performed using Multiresolution Analysis. The extracted features were normalized by Z-score to serve as input to four different classifiers: Multilayer Perceptron, K-Nearest Neighbors, Probabilistic Neural Network, and Decision Tree. The combination of Shannon Entropy and Log-Energy Entropy was found the best with the highest accuracy in all cases. Furthermore, the normalization stage has an impact on classification as it improves accuracy regardless of the classifier used. This fact makes it possible to reduce the computational expense by using only two types of features without compromising the accuracy.

An image-based deep transfer learning approach to classify power quality disturbances

• Pre-trained deep learning models can be effectively used to detect power quality disturbances. • A new approach is proposed and validated using real-time data. • This approach is based on the use of Transfer Learning and adopts images of voltage waveforms. • The proposed approach allows reducing the complexity of the training process and is suitable for real-time deployment. Power quality disturbances (PQDs) consist in deviation of voltage and current waveforms from the ideal sinusoid at fundamental frequency, and need to be monitored to ensure a reliabile electrical supply. While, traditionally, power quality monitoring has been performed using signal processing techniques, coupled with shallow Machine Learning classifiers or wave change detection methods, more recently, new approaches, based on Deep Learning, have been proposed. These methods have the potential to achieve high classification accuracy and to remove the need of extensive data pre-processing, hence being more suitable for real-time deployments. However, high classification performance has been only demonstrated using synthetically generated data. In order to address limitations related to processing time and accuracy, this paper proposes a novel end-to-end framework for automated detection of PQDs based on Deep Transfer Learning. The proposed approach uses a small set of images of voltage waveforms to train the model and classify different types of PQDs. This method leverages on the high performance of existing pre-trained models for image classification and shows consistent high accuracy for data with varying resolution. The proposed methodology provides a pathway towards effective deployment of Deep Learning in power quality monitoring systems and real-time applications.

Combined ST/MST and radial basis function neural networks for power quality disturbance signal classification

In this paper, the classification of power quality disturbances using combined ST/MST (S-Transform/Modified S-Transform) and Radial Basis Function Neural Network (RBFNN) is proposed. The extraction of significant features from the power quality disturbance signals is one of the challenging tasks in recognizing different disturbances. The Stockwell Transform/Modified Stockwell Transform (ST/MST) based features are distinct, understandable and more immune to noise. The important attributes present in the signals are retrieved from the ST/MST contours, MST 3D plots and MST based statistical curves. The relevant features are also extracted from the statistical curves. The extracted features are given as input to the RBFNN for further classification. This method is evaluated under both noisy and noiseless conditions. The performance of the proposed approach is compared with other conventional approaches in the literature. The simulation results demonstrate that the proposed MST based RFNN technique is more effective for the detection and classification of power quality disturbances.

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.

Real-Time Detection and Classification of Power Quality Disturbances.

This paper considers the problem of real-time detection and classification of power quality disturbances in power delivery systems. We propose a sequential and multivariate disturbance detection method (aiming for quick and accurate detection). Our proposed detector follows a non-parametric and supervised approach, i.e., it learns nominal and anomalous patterns from training data involving clean and disturbance signals. The multivariate nature of the method enables joint processing of data from multiple meters, facilitating quicker detection as a result of the cooperative analysis. We further extend our supervised sequential detection method to a multi-hypothesis setting, which aims to classify the disturbance events as quickly and accurately as possible in a real-time manner. The multi-hypothesis method requires a training dataset per hypothesis, i.e., per each disturbance type as well as the ’no disturbance’ case. The proposed classification method is demonstrated to quickly and accurately detect and classify power disturbances.