Power Quality Disturbance Events Research Articles

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.

Analysis through mathematical model of power quality disturbances to improvement

Power quality studies have developed to analyses power quality data as the use of delicate electronic equipment has increased. The wavelet transformation method was developed to be more useful for analyzing different kinds of power quality events. In this research, we analyses the utilization of several wavelet types at various sizes and decomposition level of the system analyzing actual value of the power quality disturbances events in the transmission system. Signal created using MATLAB backdrop. Testing has been done on voltage sag, voltage swell, and transient events. This technique makes use of Wavelet transform and Artificial Neural Network (ANN) to identify and categories power quality disturbances in the power system. Comparing the suggested method to the standard approach, fewer characteristics are needed for identification. Artificial neural networks are taught to classify events using the feature that the wavelet extracts. The weight gained after neural network training is used to categories PQ concerns between the quantity demand and the price.

All-in-one three-phase smart meter and power quality analyzer with extended IoT capabilities

The traditional power grid is evolving into a new smart grid that requires better coordination of supply and demand, making it necessary to establish precise monitoring strategies in order to determine grid status in real-time. With the aim of providing a low-cost device based on open-hardware and open-source software to the technicians, engineers, and scientists around the world, this paper presents the three-phase openZmeter (3Ph-oZm), an all-in-one device that allows measuring and computing electrical data related to energy and power quality features in three-phase power networks. It has been designed to perform advanced computations for voltage, current, frequency, power, and energy. 3Ph-oZm is able to process high order harmonics, and log power quality disturbance events defined according to the recommendations of some international standards organizations. The data and its associated features are processed on-site using custom software specifically designed and programmed for this purpose that relies on advanced signal analysis techniques. This smart meter significantly improves the capabilities of the single-phase version, and overcomes certain shortcomings of other commercial devices, both in terms of versatility and data acquisition and processing capabilities. The system has been calibrated and validated using laboratory testing set-up and real-world applications, such as long-term photovoltaic power plant metering. The capabilities of 3Ph-oZm can also support a variety of other electrical applications, such as three-phase induction motor health monitoring, energy savings, or microgrid state estimation.

On recognition of multiple power quality disturbance events in electricity networks

Effective power quality disturbance (PQD) events recognition is important for electricity network operation. This paper proposes an improved methodology for detecting and classifying multiple PQD events, which may simultaneously occur in the electricity network. It employs an adaptive dual resolution Stockwell-transform for signal processing and a short-term Renyi entropy estimation for feature extraction. Also, a multi-class support vector machine is employed to classify the types of PQD events. A variety of power quality disturbance signals are synthesized to evaluate the effectiveness and efficiency of the proposed method. These signals include both single fault/abnormal event and mixed events (e.g. a mixture of several PQD events occurring simultaneously). Real-life disturbance recordings from a transmission network service provider are also collected and used to verify the proposed method. The results show that the proposed method can achieve high classification accuracy at noisy environment. Especially, it is capable of identifying the disturbance signals comprising both harmonics with low total harmonic distortion levels and transients. Moreover, the algorithms developed in this paper can achieve a high computational efficiency.

Power quality disturbances classification using complex wavelet phasor space reconstruction and fully connected feed forward neural network

Power quality disturbances (PQD) degrades the quality of power. Detection of these PQDs in real time using smart systems connected to the power grid is a challenge due to the integration of energy generation units and electronic devices. Deep learning methods have shown advantages for PQD classification accurately. PQD events are non-stationary and occur at discrete events. Pre-processing of power signal using dual tree complex wavelet transform in localizing the disturbances according to time-frequency-phase information improves classification accuracy.Phase space reconstruction of complex wavelet sub bands to 2D data and use of fully connected feed forward neural network improves classification accuracy. In this work, a combination of DTCWT-PSR and FC-FFNN is used to classify different complex PSDs accurately.The proposed algorithm is evaluated for its performance considering different network configurations and the most optimum structure is developed. The classification accuracy is demonstrated to be 99.71% for complex PQDs and is suitable for real time activity with reduced complexity.

An adaptive deep learning framework to classify unknown composite power quality event using known single power quality events

Distributed generation (DG) sources are preferred to meet today's energy needs effectively. The addition of many different types of renewable energy sources to the grid causes various problems in signal quality. Detection and classification of these problems increase efficiency by both the producer and the consumer. In the literature, incredibly singular and some composite power quality disturbance (PQD) detection is performed effectively. However, the multitude of composite PQD variations degrades the performance of existing algorithms. In this study, the classification of all PQD variations that may occur is performed by using singular PQD and some composite PQD signals. A different number of subcomponents representing the signal are created according to each signal characteristic. Instantaneous energies from these subcomponents are used as deep learning (DL) input. Deep learning cycles are created as much as the instantaneous energy number of each signal. Each cycle has specific features of defining a single event. Therefore, the proposed approach is able to classify composite PQD signals that it has not encountered before. The proposed method's performance is first evaluated with the known PQD events and compared with the current state-of-the-art methods in the literature. Then, a dataset containing the combinations of different events not encountered during the training is created, and the performance is evaluated on this dataset. In the experiments performed, it is revealed that the proposed framework produces higher performance than other state-of-the-art methods

Wavelet Packet Transform Modulus-Based Feature Detection of Stochastic Power Quality Disturbance Signals

Wavelet transform modulus (WTM) has been used to detect or localize transient signal discontinuities. A numerical analysis indicated that these power quality disturbance (PQD) events are extremely sensitive to the random phase offset due to shift-variant wavelet or wavelet packet characteristics, which have not been comprehensively discussed yet. In this paper, we define wavelet packet transform modulus (WPTM) and present a WPTM-based PQD feature detection that is robust to severe power signal channels including random phase offset and low signal-to-noise ratio (≤25 dB). The presented WPTM-based detection that exhibits an exponentially increased degrees of freedom (DoF) and has better correlation properties than existing WTM-based detection of a limited DoF (two or three). We then use a standard median filter to efficiently remove impulsive noise and add a threshold modification step to reduce the false edge detection rate under random phase offset conditions while maintaining a reasonable detection rate. The proposed scheme uses the majority voting-based indirect correlation or root-mean-square metric between wavelet packet coefficients, rather than the conventional wavelet denoising or correlation metric. For a reliable numerical analysis, the proposed scheme uses both double- and single-edge-based detection measures, and the results verify its superiority over the conventional wavelet-based, wavelet-correlation-based, or non-wavelet-based schemes.

Design and Implementation of a Data-Driven Approach to Visualizing Power Quality

Numerous underlying causes of power-quality (PQ) disturbances have enhanced the application of situational awareness to power systems. This application provides an optimal overall response for contingencies. With measurement data acquired by a multi-source PQ monitoring system, we propose an interactive visualization tool for PQ disturbance data based on a geographic information system (GIS). This tool demonstrates the spatio–temporal distribution of the PQ disturbance events and the cross-correlation between PQ records and environmental factors, leveraging Getis statistics and random matrix theory. A methodology based on entity matching is also introduced to analyze the underlying causes of PQ disturbance events. Based on real-world data obtained from an actual power system, offline and online PQ data visualization scenarios are provided to verify the effectiveness and robustness of the proposed framework.

Classification and Visualization of Power Quality Disturbance-Events Using Space Vector Ellipse in Complex Plane

This article proposes a novel algorithm employing space vector ellipse (SVE) in a complex plane to classify and visualize power quality disturbance-events (PQDEs). In the proposed method, at first, the time-domain signal and a reference signal, which are separated by 90°, are mapped in a complex 2D coordinates. Thus, the tip of resultant rotating vector traces an ellipse, from which three parameters, namely, semi-major axis, semi-minor axis and inclination angle, are obtained. Then, the ellipse parameters are exploited to classify and visualize nine types of PQDEs, namely, voltage sag, swell, interruption, harmonic, sag-harmonic, swell-harmonic, notch, flicker and transient. To validate the practicability of the proposed approach, an extensive real-time simulation study is carried out on RTDS platform using a test microgrid network to generate a large number of PQDEs. The test events were successfully classified and visualized in complex plane. Moreover, the noisy and practical signals, recorded by IEEE 1159.2 Working Group, were successfully classified to demonstrate the effectiveness of the proposed method.

Classification of power quality disturbances by 2D-Riesz Transform, multi-objective grey wolf optimizer and machine learning methods

In this study, a new method combined with two-dimensional Riesz Transform (RT) in feature extraction stage and Multi-Objective Grey Wolf Optimizer (MOGWO) with k-Nearest Neighbor (KNN) algorithm in the feature selection stage is introduced to classify Power Quality (PQ) disturbances. After determining the most suitable feature group, classification models are created by using machine learning approaches. Although one-dimensional (1D) signal processing methods by nature are used in the classification stage of PQ disturbances, it is observed that studies have developed in the literature including two-dimensional (2D) signal processing. 2D signal processing approach is used because it gives good feature diversity and leads to creation a good model. In this study, firstly PQ disturbances events data is collected synthetically and experimentally. 1D signals are converted to 2D signals to apply 2D-RT. In 2D-RT, it is obtained 12 sub bands matrices to find better features for one 2D matrix. 15 statistical and image-based features are calculated for each band. Totally 180 features are obtained for one sub bands matrix. At this point, with the MOGWO-KNN method, it is aimed to create a simple classification model with high performance by selecting the most suitable features obtained by 2D-RT. The models based on KNN, SVM, MLP and ensemble learner methods are created to investigate if there is a better classification accuracy or not. The simulation study is also done for data consists of noisy (40 dB, 30 dB, 20 dB noise levels) and multiple events. The model can classify 9 types of multiple disturbances in 18 PQ disturbances. At the same time, a robust model that classify even noisy situations is created. It is showed that the proposed PQ disturbances classification method gives high performance compared to the methods in the literature for both simulations and real data.

A low rank robust kernel ridge regression classifier for power quality disturbance pattern recognition

This paper presents the development of a new low rank robust kernel ridge regression (KRR) classifier for power quality (PQ) disturbance pattern recognition. It is well known that the kernel methods are used extensively for regression and classification problems using support vector machines (SVM) by mapping the original nonlinear data into a high-dimensional space thereby increasing the generalization performance, and accuracy of classification. On the other hand, the nonlinear kernel ridge regression approach is known for its simple implementation, fast processing speed and accuracy in comparison to the widely used least square support vector machine (LS-SVM). However, for large scale training patterns, the size of kernel matrix becomes large thereby the execution time becomes prohibitive. Besides, the presence of noise and outliers in the data affects the accuracy of the conventional KRR classifier. This paper, therefore, attempts to develop a dimensionally reduced but robust KRR classifier by choosing a random set of support vectors from the training subset to reduce substantially the training time at the cost of a slight loss in accuracy. Further, the KRR algorithm is modified by using a new objective function to minimize the mean and variance of the error to provide robustness and accuracy. For applying this new classifier to power quality disturbance events three relatively new signal processing techniques like the Short-time modified Hilbert Transform (STMHT), Morphological filters, and Fourier kernel S-transform are used to extract the relevant features from the data samples. The simulation results imply that the proposed methods have a higher recognition rate compared with other established techniques such as ELM and Poly SVM while classifying the PQ disturbances. A PC integrated hardware assembly has been used to verify the PQ events classification in real-time.

Diagnosis of Power Quality Events Based on Detrended Fluctuation Analysis

This paper presents a novel technique to diagnose frequently encountered power quality (PQ) disturbance cases both single and mixed event types. Adopting existing approach to emulate signals containing different types of PQ disturbance events, a specially designed hardware setup has been developed in the laboratory. Captured signals from the developed event generator have been analyzed to extract useful information for diagnosis of various PQ disturbances. In the proposed method, a signal tracker capable of continuously tracking normal level of the signal has been employed to extract actual disturbance within it. A suitably designed detrended fluctuation analysis tool has been effectively used to diagnose the PQ disturbances. Implementation of the proposed technique shows the effectiveness in differentiating PQ events distinctly without much involving conventional analytical tools that result in minimum computational burden as compared to the existing methods. To establish the effectiveness and to get acceptability, the proposed method has been rigorously tested on significant number of captured signals containing different types of PQ disturbances and a satisfactory outcome has been observed.

Identification of Power Quality Disturbances Based on FFT and Attribute Weighted Artificial Immune Evolutionary Classifier

Nowadays, the issue of Electromagnetic Compatibility is of great importance and urgency. In this paper, we propose a novel hybrid automatic identification system for power quality disturbances, which lays foundations for further analyzing the electromagnetic compatibility. Specifically, we firstly extract features by using the FFT and envelope detection method. Then we utilize the attribute weighted artificial immune evolutionary Classifier (AWAIEC) for classification of power quality disturbance events. Experimental results have shown that the proposed method performs better than existing approaches.

A dual‐tree complex wavelet transform‐based approach for recognition of power system transients

AbstractThe present paper proposes a dual‐tree complex wavelet transform (DTCWT) based approach for recognition of power system transients. Several researchers, all over the world, have so far attempted to solve the problems of recognition of power system transients, hybridizing transform‐based techniques with popular computational intelligence based tools, for example, using wavelet transform and S‐transform for feature extraction, followed by artificial neural networks (ANN) or fuzzy logic‐based classifiers. The proposed method of hybridizing DTCWT‐based feature extraction with ANN‐based classification could efficiently detect several commonly occurring power quality (PQ) disturbance events. The PQ disturbance events considered include four different transient conditions, namely transients due to capacitor switching, transformer inrush currents, transients due to motor switching and transients due to short circuit faults. A detailed performance comparison with several contemporary, competing methods existing in the literatures for similar problems aptly demonstrates the suitability of the proposed method.

An image processing based method for power quality event identification

This paper presents a novel technique to visualize and detect various power quality disturbance events. It is based on the image processing methods known as grayscale images and binary images. Gray image created from recorded disturbance voltage waveform is first represented as a transverse wave having compressions and rarefactions. Then using image enhancement techniques, the unique features of the disturbance waveform are visualized. Furthermore, the patterns obtained for a pure sine signal and the signal with disturbances are compared for identification of the signal with disturbance. The decision regarding the disturbance type is made using binary image analysis techniques. Finally, to exhibit the novelty of the proposed method, a comparison is made with a conventional image processing based power quality event detection method. In addition, evaluation studies for verifying the accuracy of the method are presented.

Detection of Power Quality Disturbances using Phase Corrected Wavelet Transform

Wavelet transform is very widely used for power quality analysis to detect voltage events. Although wavelet transform is an excellent tool to detect and localize the power quality disturbance events, it fails to classify them. This paper shows that phase correction to wavelet transform improves the performance of wavelet transform in identifying the power quality disturbances such as voltage sag, interruption, swell and harmonics. Phase corrected wavelet transform, commonly referred to as S-transform, has excellent time–frequency resolution characteristics and has the ability to detect the disturbance correctly even under noisy condition. This tool is applied on various voltage disturbances, generated as per IEEE Std. 1159 using MATLAB and the analysis can identify the magnitude and duration of the disturbances. The effectiveness of the S-transform is shown for multiple sag/swell condition in the presence of transients or noise. Wavelet transform is also used for analysis and results are compared with S-transform performance. Simulation results are also shown to identify both the harmonic magnitudes and phase angles using S-transform analysis. Experimental verification has been performed with transmission line model connected to linear and non-linear loads and S-transform is used to detect and classify various voltage disturbances.

On Tracking the Source Location of Voltage Sags and Utility Shunt Capacitor Switching Transients

This paper presents a new procedure to track the disturbance source location of voltage sags and shunt capacitor switching transients in a power system based on branch current measurements. In the proposed method, the power-quality metering locations are first determined by the sensitivity of the system fault-current level to the fault voltage. Then, a current deviation index for representing the change of each measured branch current rms magnitudes before and after the disturbance is calculated to track the disturbance source location within an area confined by the directions of the top-priority branches. The IEEE 30-bus benchmark system and an actual transmission system with recorded power-quality disturbance events are used to illustrate the usefulness of the proposed method. Simulation results obtained by the proposed method are also compared with those obtained by a previously developed approach and by actual recorded data. By observing the results, it shows that the proposed method is efficient and accurate to track the source area of voltage sags or shunt capacitor switching transients.

Electric power quality disturbance classification using self-adapting artificial neural networks

Power quality is recognised as an essential feature of a successful electric power system mainly due to the rapid increase of loads which generate noise and, at the same time, are sensitive to the noise present in the supply system. A technique for classifying electrical power quality disturbance events is presented, based on a self-adapting artificial neural network (SAANN), which has the unique capability of adapting to new disturbance features. In the proposed technique, distinctive feature vectors from disturbance events captured are extracted using the fast Fourier and discrete wavelet transforms. The feature vectors are then fed to two SAANN-based classifiers, which classify the captured events into different categories of power quality disturbances. The technique is tested using a number of disturbance events.