Detection Of Power Quality Disturbances Research Articles

Identification of Power Quality Disturbances Based on Sample Entropy and Weighted Optimization Random Forest

With the increasing number of non-linear loads and impact loads in the actual power grid, power quality problems are becoming more and more serious. Accurate and rapid identification of power quality disturbance signals is of great significance for improving the power quality and ensuring stable operation of the power grid. For this reason, this paper proposes a multi-resolution analysis of wavelet transform and a combination of sample entropy to extract the characteristics of power quality disturbance signals. The classification effect is better than the initial samples. The weighted optimization random forest classifier is used to classify the features, which reduces the error caused by the mode voting method of the random forest. The simulation and actual test results show that the power quality disturbances detection algorithm based on sample entropy and weighted optimization random forest has better classification accuracy than the decision tree, extreme random tree and traditional random forest.

Deep learning methods and applications for electrical power systems: A comprehensive review

Over the past decades, electric power systems (EPSs) have undergone an evolution from an ordinary bulk structure to intelligent flexible systems by way of advanced electronics and control technologies. Moreover, EPS has become a more complex, unstable and nonlinear structure with the integration of distributed energy resources in comparison with traditional power grids. Unlike classical approaches, physical methods, statistical approaches and computer calculation techniques are commonly used to solve EPS problems. Artificial intelligent (AI) techniques have especially been used recently in many fields. Deep neural networks have become increasingly attractive as an AI approach due to their robustness and flexibility in handling nonlinear complex relationships on large scale data sets. Major deep learning concepts addressing some problems in EPS have been reviewed in the present study by a comprehensive literature survey. The practices of deep learning and its combinations are well organized with up-to-date references in various fields such as load forecasting, wind and solar power forecasting, power quality disturbances detection and classifications, fault detection power system equipment, energy security, energy management and energy optimization. Furthermore, the difficulties encountered in implementation and the future trends of this method in EPS are discussed subject to the findings of current studies. It concludes that deep learning has a huge application potential on EPS, due to smart technologies integration that will increase considerably in the future.

Research on Power Quality Disturbance Detection Method Based on Improved Ensemble Empirical Mode Decomposition

With the increasing proportion of various unbalanced loads in the power grid, power quality is seriously challenged. It is of great significance to effectively detect, analyze, and evaluate the power quality problems. First, this paper introduces the current situation of power quality (PQ) disturbance detection methods. It summarizes that the current PQ disturbance detection methods include Wavelet Transform (WT), Hilbert–Huang Transform (HHT), and Ensemble Empirical Mode Decomposition (EEMD). EEMD has a better detection accuracy, but its running time is longer. Therefore, to reduce the running time of the EEMD algorithm, this paper proposed two improvements: increasing the screening threshold and selecting piecewise cubic Hermite interpolation polynomial fitting. At the same time, the mathematical models of transient power quality disturbance and harmonic were established for comparative verification. The experimental results showed that the improved Ensemble Empirical Mode Decomposition (IEEMD) algorithm greatly reduced the running time of the algorithm on the premise of ensuring the detection accuracy. Hence, the improvement of this paper is of great significance for the industrial application of the EEMD algorithm.

A Novel Feature Selection Method for the Detection and Classification of Power Quality Disturbances

A Novel Feature Selection Method for the Detection and Classification of Power Quality Disturbances

Adaptive Detection of Islanding and Power Quality Disturbances in a Grid-Integrated Photovoltaic System

Global photovoltaic (PV) generation is increasing steadily at about $$30\%$$ growth rate over the last decade. Depleting environment owing to extensive use of fossil fuels is expected to further continue with this growth rate. With such large PV penetration in the utility grid, perturbation-based active islanding detection methods are becoming detrimental, marred with issues like degradation of power quality and deteriorating system stability. This paper uses morphological filters combined with empirical mode decomposition (EMD) to implement an efficient adaptive signal processing-based detection for islanding as well as PQ disturbances. Two-stage morphological median filter (MMF-2) is used to overcome the noise vulnerability associated with EMD. Field programmable gate array implementation is developed for real-time detection of PQ events. Classification of power quality disturbances is obtained using a support vector machine classifier. The results demonstrate fast and accurate real-time detection under various noisy scenarios without applying any parameter perturbations.

Near-Perfect Time–Frequency Analysis of Power Quality Disturbances

Time-frequency analysis is a key requirement for the detection of power quality disturbances (PQDs). Due to non-stationary nature of the disturbances and resolution restriction posed by the uncertainty principle, most signal processing techniques fail to single-handedly detect different types of disturbances with desired precision. As a solution to this problem, a near-perfect time-frequency analysis technique is presented for PQDs. It utilizes the concept of instantaneous frequency (IF)-based time–frequency representations. A carefully chosen Gaussian window in the windowed Fourier transform followed by the slight post-processing gives a high spectral concentration on the IF trajectory by eliminating unwanted frequencies. Several comparative case studies are shown, which support the effective detection and classification capabilities offered by the proposed technique.

A methodology for detection and classification of power quality disturbances using a real‐time operating system in the context of home energy management systems

A methodology for detection and classification of power quality disturbances using a real‐time operating system in the context of home energy management systems

A hybrid Hilbert Huang transform and improved fuzzy decision tree classifier for assessment of power quality disturbances in a grid connected distributed generation system

This paper focuses on discrete Hilbert Huang transform (HHT) and improved fuzzy decision tree (IFDT)-based detection and classification of power quality (PQ) disturbances as a new contribution to the literature. A distributed generation (DG)-based microgrid has been modelled with wind and solar. Different PQ disturbances have been simulated with various wind speed and PV penetration. The PQ signals are passed through empirical mode decomposition (EMD) to obtain the intrinsic mode functions (IMFs). These IMFs are enforced to the Hilbert transform (HT) to extract the instantaneous attributes. These attributes of Hilbert transform (HT) are used for features extraction. Based on these extracted features improved fuzzy rules are formed for classification of the PQ disturbances. Synthetically PQ disturbances are simulated to check the performance of the proposed method. All these signal samples are processed through the proposed algorithm. The proposed method has been found to be capable of accurate detection and classification of PQ disturbances than many other techniques in the literature.

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

A novel multiple impact factors based accuracy analysis approach for power quality disturbance detection

Nowadays, power quality problems are affecting people's daily life and production activities. With the aim to improve disturbance detection accuracy, a novel analysis approach based on multiple impact factors is proposed in this paper. First, a multiple impact factors analysis is implemented in which two perspectives, i.e., the wavelet analysis and disturbance features are considered simultaneously. Five key factors, including wavelet function, wavelet decomposition level, redundant algorithm, event type and disturbance intensity, start and end moment of disturbance, have been considered. Next, an impact factors based accuracy analysis algorithm is proposed, through which each factor's potential impact on disturbance location accuracy is investigated. Three transforms, i.e., the classic wavelet, lifting wavelet and redundant lifting wavelet are employed, and their superiority on disturbance location accuracy is investigated. Finally, simulations are conducted for verification. Through the proposed method, the wavelet based parameters can be validly selected in order to detect power quality disturbance accurately.

Power Quality Detection, Classification and Monitoring Using LABVIEW

In recent years, Power Quality becomes increasingly a major concern for both electric utilities and end users. Accordingly, the electrical engineering community has to deal with the analysis, diagnosis and solution of PQ issues using system approach rather than handling these issues as individual problems. This paper describes the analysis of PQ using advanced signal processing tools represented in Hilbert & Wavelet Transforms (HT-WT) and artificial intelligence tools represented in Artificial Neural Network & Support Vector Machine (ANN-SVM) for detection and classification of power quality disturbances respectively. These techniques were successfully simulated using LABVIEW software capabilities. The results of simulation indicate that the signal processing techniques are effective mechanisms to detect and classify power quality disturbances. At the end, the combination of WT as a tool of detection and features extraction with SVM as a classifier tool resulted as the best combination for PQ monitoring system.

Universal Waveshape-Based Disturbance Detection in Power Quality Data Using Similarity Metrics

The increasing deployment of triggerless power quality monitors provides valuable data about power systems and their components. However, efficient and accurate data mining techniques are necessary for parsing through large datasets and obtaining useful insights about the power system operation. While multiple studies can be found in the literature for detection of power quality events, they either detect only severe disturbances or are applicable to a specific subset of disturbances. This study proposes a universal approach for detecting power quality disturbances within large datasets regardless of the event root causes. It is based on the comparison between the shapes of multiple cycles of waveform data through similarity scores. Pre-processing techniques to enhance the detector accuracy are presented. They include correction of time misalignment between successive cycles caused by variations in the power system operating frequency, and reduction of load variation effects. The threshold values for detecting abnormal waveforms is time-adaptive, so that detection is based on local outliers. The proposed framework performance is assessed through multiple types of field datasets, and its superiority over other waveform abnormality detectors is empirically demonstrated. Moreover, current waveform is shown to be more suitable than that of voltage for novelty detection in power quality data.

Maximal overlap discrete wavelet transform and deep learning for robust denoising and detection of power quality disturbance

This study presents a new technique for power quality (PQ) disturbance detection. The technique focuses on voltage sags and interruptions that are related to various faults, i.e. transmission line, feeder, and transformer faults. A maximal overlap discrete wavelet transform-based PQ detection algorithm is proposed to provide accurate points of disturbance initiation and recovery. The proposed PQ detection algorithm is robust even without a detection threshold and independent of the sampling frequency of PQ recording. In consideration of the presence of noise conditions, the preprocessed PQ waveforms are converted into 2D binary vectors using space vector transformation. Then, an improved stacked sparse denoising autoencoder combined with supervised backpropagation training is proposed as a robust classifier. Results show that the proposed method is suitable for detecting various types of PQ disturbances and possesses high recognition accuracy despite insufficient training samples.

RETRACTED ARTICLE: Detection and classification of power quality disturbances or events by adaptive NFS classifier

In power distributed generation system, analysis of power quality is a major issue and hence it is essential to implement an efficient power quality system. To address and end this issue, the proposed work is mainly focused on the detection and classification of power quality disturbances or events by applying Hilbert–Huang transform (HHT) technique. HHT technique is a novel signal processing algorithm that comprises two processes. First is the empirical mode decomposition (EMD), which is an iterative process where N number of power quality signals are decomposed into intrinsic mode functions (IMFs). The second process, Hilbert transform, is applied to each IMF component. The advantage of using these two processes makes it an attractive power tool for quality event analysis. Once the IMF is obtained, it is easy to construct a time–frequency representation model. Then, the significant features are extracted from amplitude, phase and frequency instantly, which are the contour of IMFs of each power quality disturbance. For classification, adaptive neuro-fuzzy system (NFS) classifier is used. It is an intelligent system used for the combination of neural network and fuzzy logic. Power quality features are given as input to the NFS system. The experimental simulation is conducted in a MATLAB environment, and proposed HHT has higher efficiency compared to existing methods.

Real-Time Disturbance Detection Using STFT Method in Microgrids

Power quality disturbances are the main concerns to be eliminated in microgrids and decrease the power quality and reliability of the grid. Numerous methods based on signal processing have been proposed in the literature for the detection of power quality disturbances. In this study, the proposed STFT-based method is applied to the voltage signal in real-time at the point of PCC in microgrids. By using the proposed method, it is tried to detection the sudden frequency changes and the over/under voltage events in case of fault conditions. As a result, the proposed method can detect faults in microgrids a very short time and with high accuracy within the limit values specified in international standards.

Assessment of Wavelet Types in DWT-based Power Quality Disturbance Detection Method

Power quality parameters are the limit values that allow an electrical device to function as intended without significant loss of performance and life expectancy. It is important to detect power quality disturbances quickly and reliably to ensure reliability and continuity of the power system. Today, unlike conventional methods, signal processing-based disturbance and fault detection methods have gained great importance. Wavelet transform (WT)-based disturbance detection methods are the most prominent among these methods. In this study, the performance of the discrete wavelet transforms (DWT)-based disturbance detection method used in the detection of voltage sag and voltage swell events were evaluated for different wavelet types. Process calculation load, response to event signal and performance values were compared for different wavelet types. The results will be an important source for the selection of appropriate wavelets in the methods using WTs.

Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.

A real-time UWT-based intelligent fault detection method for PV-based microgrids

In this paper, a new un-decimated wavelet transform (UWT)-based fault detection method is proposed to overcome the limitations of WT-based methods in real-time applications. For the first time, UWT method is used to detect power quality disturbances (PQD) in microgrids in real time. UWT reduces the oscillations and noise in the signal and also is useful for selecting threshold values. This method is implemented by using only the voltage signal to detect the PQDs automatically for a PV-based microgrid. The voltage sag/swell, and interruption faults are evaluated in the experimental study by considering the duration and event amplitude of the PQD signal. High accuracy is also provided for detecting PQDs without using any noise filtering process. The results indicate that the proposed technique has a wide application area, fast fault detection time, and it is reliable for microgrids.

Detection and classification of power quality disturbances in distribution network based on VMD and DFA

In this paper, a novel algorithm is proposed to detect and classify the power quality (PQ) disturbances for distribution networks with distributed generation. First, a distribution system with photovoltaic and wind power generation is built as a test platform. Then, nine types of power quality disturbances in the distribution network are decomposed by variational mode decomposition (VMD) and the noise is filtered. Meanwhile, the mode functions containing characteristic information are extracted as input signals of detrended fluctuation analysis (DFA). Power quality disturbances are classified from the view of their distributed energy operational status, and three types of windows are set up to deal with different frequency disturbances. The two-dimensional and three-dimensional scatter plots of each type under three windows are depicted, and the criteria are determined to distinguish the disturbances under the different operating conditions. The simulations show that the algorithm is simpler, more accurate and feasible. It provides an approach for online real-time detection of embedded systems.

Levenberg-Marquardt based MLP for Detection and Classification of Power Quality Disturbances

In recent years, power quality (PQ) has become an important issue for utilities and users. In order to improve PQ, a method for detecting and classifying power quality disturbances (PQDs) is proposed. Hence in addition to identifying the disturbance signals, the proposed method is able to determine its type when occurring. This approach is based on Multilayer perceptron and Levenberg-Marquardt training rule. It is inspired by the desire to take advantage of the parallelism inherent to neural networks in view of hardware implementation using reconfigurable chips. The inputs of these networks are the samples obtained on the power grid in various conditions. The proposed method is tested for sags and swells. To classify the disturbances, the neural architectures have been generalized and configured according to the number and type of disturbances to be treated. To validate and test the proposal, a grid model was built with a three-phase fault generator under Matlab / Simulink R2017a. After comparing the results with those obtained by certain methods in the literature, the proposal proves to be an efficient and reliable tool for monitoring PQ. In fact it has the smallest mean square error and a highperformance with precision of 96%.