Analysis Of Power Quality Disturbances Research Articles

Generation of Power Spectrum of Power Quality Disturbances using MATLAB

The aim of this paper is to present power spectrum of electrical power quality disturbances obtained by mathematical modeling. Signals considered for analysis are transients, harmonics, interruption, voltage sag, voltage swell, voltage fluctuations and voltage flicker. These power quality disturbances affect the continuity of supply and results in reduction of stability of the power system. So there is a necessity for an analysis of power quality disturbances in different perspectives. Power spectrum representation of the different disturbance signals is obtained. The values of power in Decibels and frequency in Hertz are obtained which gives more information about the signals in time domain. Power spectrum gives useful information about the variations in signals. Waveforms for transients, interruption, sag, swell, harmonics, voltage fluctuations, and voltage flicker are generated using mathematical equations and their corresponding power spectrum representations are obtained in MATLAB environment.

Analysis, monitoring, and mitigation of power quality disturbances in a distributed generation system

Despite the numerous benefits of distributed generation (DG), such as reliable energy supply and environmental friendliness, there are a number of challenges associated with integrating DG with the grid. In the field of DG, the complication of power quality (PQ) is a major technical challenge. Custom power devices (CPDs) are used in the distribution system to solve complications. This study presents different PQ disturbances (PQDs), monitoring techniques, and fundamental standards. Furthermore, with the widespread literature, the study critically reviews different PQ mitigation techniques such as distribution static compensator (DSTATCOM), dynamic voltage restorer (DVR), unified power quality conditioner (UPQC), and uninterruptable power supply (UPS). The present research work is not only limited to surveying the existing techniques but also analyzing the performance of UPQC using the ANFIS controller. Overall, this study is intended to provide researchers working on improving PQ in the distribution system with a valuable resource that will aid them in enriching their research.

Research on power quality disturbance analysis and identification based on LSTM

In view of the cumbersome and inaccurate process caused by manual feature extraction in power quality disturbance classification, according to the characteristics of power quality classification and time sequence. This article presents a method of power quality disturbance analysis and identification based on LSTM. Firstly, the random single electric energy signal is spliced into a large signal to form a continuous electric energy signal sequence. Secondly, based on the existing neural network, an LSTM model suitable for PQD classification is constructed, and then the spliced large signals are used as input to train and optimize the model. The LSTM model will classify different power quality disturbances. Finally, six common power quality disturbances such as voltage sag, voltage swell, interruption, impact, oscillation and harmonic are simulated and verified respectively. The analysis results show that the high accuracy of the method is reflected, which proves the correctness and effectiveness of the proposed method, and is suitable for the power quality disturbance identification system.

S-transform application in phase extraction of spectrally resolved interferometry measuring step height.

Spectrally resolved interferometry is widely used in the measurement of distance, displacement, film thickness, and surface morphology in micro/nano-scale geometric measurement. The core of spectrally resolved white-light interferometry is phase extraction. Temporal phase shifting, Fourier transform, wavelet transform, and other methods are commonly used in phase extraction of spectrally resolved interferometry. The S-transform, providing frequency-dependent resolution and having good time-frequency characteristics, is widely used in power quality disturbance analysis, seismic wave analysis, and phase recovery in profilometry. S-transform is used to extract the phase of the spectrally resolved white-light interferometry signal measuring step height. Compared with Fourier transform and wavelet transform, it is proved that S-transform is a feasible method in phase extraction of spectrally resolved interferometry measuring step height.

Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm

In this paper, an adaptive multiscale enhanced average combination morphological filter is proposed to analyze voltage signals captured at the point of common coupling of the photovoltaic integrated direct current microgrid for diagnosing several power quality disturbances. This method performs multiscale operation on the power quality disturbance signals from which the optimized structuring element scales are chosen by using maximum value of sparse envelope kurtosis index. Further, statistical features are extracted by using optimized scales. The extracted features are used as inputs to a deep stacked extreme leaning machine autoencoder to retrieve the most distinguishable unsupervised features and these features are processed through kernel random vector functional link network based supervised classifier to classify the power quality disturbances. Further, to improve the classification performance of the proposed technique, a meta heuristic chaotic particle swarm optimization integrated with sine cosine optimization algorithm is used. This optimization algorithm optimizes the kernel parameters by minimizing the mean absolute error. The efficacy of the proposed technique is tested in terms of classification accuracy and confusion matrix and is verified by multifunction high-speed national instrument device for monitoring of power quality disturbances in the MATLAB/Simulink platform. The simulations and hardware results demonstrate that the proposed technique exhibits remarkable accuracy, faster training time and low computational complexity as compared with other existing methods.

Power quality disturbance analysis using data-driven EMD-SVM hybrid approach

In the modern electrical power system network (EPSN), the power quality disturbances (PSDs) are the serious issue for the power engineer to maintain the uninterrupted and reliable power supply. Generally, PQDs are generated due to non-linear loading conditions, perturb loading and other occurrences such as transient, harmonics, sag, swell and interruptions. These problems of PQDs effect the power demand mapping problem, which effect the reliability and stability of the EPSN operating condition. In this study, a novel approach for PQDs diagnosis (PQDD) is proposed, which includes real-time data generation, data pre-processing, feature extraction, feature selection, intelligent model development for PQDD. Data decomposition approach of EMD is utilized to generate the feature vector of IMFs. These features are utilized as an input variables to the intelligent classifiers. In this study, PQDD is analyzed based on SVM method and obtained results are compared with conventional AI method of LVQ-NN. The results represent the higher acceptability of the proposed approach with diagnosis accuracy of 99.98% (training phase), 93.11% (testing phase) for SVM and 92.56% (training phase) and 91.0% (testing phase) for LVQ-NN based PQDD method.

Analysis of Electrical Power Quality Disturbances Based on Empirical Mode Decomposition and Statistical Parameters

Power quality becomes a severe problem due to the increasing use of nonlinear electrical loads, complex electric power systems on smart grids, inverters in renewable power plants, and electronic control equipment. Power quality problems include variations in voltage or current such as sag, swell, flicker, spike, overvoltage, undervoltage, interruption, transient, harmonics, and frequency fluctuations. Research on power quality disturbances mostly applies signal processing and transformation methods such as Fast Fourier transform, S-transform, and Wavelet. In this paper, we use empirical mode decomposition methods and statistical parameters to analyze power quality disturbance signals. It gives more detailed characteristics of power quality disturbances. We conducted a six-step analysis to get a percentage of each power quality disturbance signal. The developed method provides a preliminary description of the power quality characteristics from the percentage values of RMS, range, and energy levels at the first IMF. The positive percentage indicates the existence of power quality disturbances contains flicker and swell, while negative indicates contain harmonics, sag, and transient.

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.

Real‐time recognition of power quality disturbance‐based deep belief network using embedded parallel computing platform

This paper proposes a novel integrated solution for real‐time power quality (PQ) disturbance analysis. Traditionally, the recognitions are based on the feature extraction and are implemented offline or on the advanced reduced instruction set computer machine/digital signal processor platforms. In this paper, the optimized deep belief network (DBN) analyzes the PQ signals by learning knowledge from raw data of signals directly, which maximizes the features of original signals, and runs on an embedded parallel computing platform (EPCP). In simulation studies, eight types of common PQ disturbances are divided into 17 classes of data frame models according to the PQ disturbances that may occur during a fixed period of time. These samples of data frame models are utilized to train and optimize the DBNs on a central server. Compared with the existing classifiers, the simulation results demonstrate that the proposed approach has higher accuracy and stronger robustness. Then, the optimized DBN is sent to EPCP, and the previous DBN model is updated. The EPCPs are used to recognize the signals of PQ disturbances from a real time digital system (RTDS). The proposed integrated solution has excellent performance regarding accuracy but is time consuming. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Time‐frequency analysis of power quality disturbances using synchroextracting transform

Time‐frequency analysis of power quality disturbances using synchroextracting transform

Real-Time Implementation of Signal Processing Techniques for Disturbances Detection

The real-time analysis of power quality (PQ) disturbances and islanding detection for distributed generation (DG) system using signal processing algorithms are still at a premature stage. Quick and reliable detection of islanding and PQ disturbances needs renewed focus. This paper is concentrated on the implementation of advanced signal processing techniques in field-programmable gate array (FPGA) to identify PQ and islanding events in real time. The proposed scheme is based on orthogonal empirical mode decomposition (OEMD) implemented in FPGA. This technique does not suffer from the loss of signal characteristics with successive decomposition levels. The performance of said technique is demonstrated for different PQ and islanding events simulated on IEEE 13 bus DG system and experiments conducted on a laboratory setup. The detection ability has been compared with discrete wavelet transform (DWT) and basic empirical mode decomposition (EMD). It is found that DWT completely fails for the signal with noise content, while the performance of basic EMD deteriorates with an increased level of noise content. On the other hand, OEMD reliably and accurately detects the disturbances conducted in the study, both simulated and experimental.

Wavelet Based Simulation and Analysis of Single and Multiple Power Quality Disturbances

Wavelet Based Simulation and Analysis of Single and Multiple Power Quality Disturbances

Wavelet Based Simulation and Analysis of Single and Multiple Power Quality Disturbances

Improving power quality disturbance (PQD) detection and automatic classification has been a major concern ever since the emergence of sensitive non-linear devices. The role of distributed generation in a power system is the main source of PQDs. Short-term and long-term duration single and multiple complex PQDs are difficult to monitor and need higher accuracy and time. This paper presents the analysis of different and distinctive combinations of PQDs. Variety of single and multiple PQD samples are generated using Matlab environment conferring to IEEE STD 1159-2009. Such disturbance samples are accurately detected and analyzed from waveform patterns using multi resolution analysis based discrete wavelet transform. The generation of samples and detection lies in fact that it can allow the feature extraction process for the training/testing sample features for machine learning based automatic recognition of disturbance types.

Detection and Analysis of Sag and Swell Power Quality Disturbances using Fractional Fourier Transform

Detection and Analysis of Sag and Swell Power Quality Disturbances using Fractional Fourier Transform

Power Quality Disturbance Analysis Using S-Transform and Data Mining based Classifier

Power Quality Disturbance Analysis Using S-Transform and Data Mining based Classifier

FPGA-Based Smart Sensor for Detection and Classification of Power Quality Disturbances Using Higher Order Statistics

Power quality (PQ) has received the attention of several research groups due to the impact of PQ disturbances (PQDs) and how they affect the operation of the electrical equipment connected to the grid, especially in industrial and healthcare facilities. The monitoring and analysis of PQD are generally performed with specialized measuring equipment, such as power analyzers, based on the standards. However, this equipment is not suited to perform continuous monitoring and classification of PQD, and it cannot be configured to perform further analysis of the monitored signal. Smart sensors, on the other hand, can provide the functionality that the standard equipment cannot, by integrating several signal processing modules that can be reconfigured using a reconfigurable technology, such as field programmable gate arrays (FPGAs). This paper presents the development of an FPGA-based smart sensor that integrates the processing cores of higher order statistics (HOS) to provide a signal analysis aimed to detect and quantify PQD on electrical installations and an artificial neural network to classify the PQD. Experimentation is performed on the electrical installation in hospital facilities. Results from the HOS processing of electrical signals show that these processing methodologies are suitable for the quantification and classification of PQD on electrical installations.

A novel methodology for modeling waveforms for power quality disturbance analysis

Nowadays, power quality (PQ) analysis is having more relevance in the operation of power systems due to the increase of power quality events and disturbances. Those disturbances are related to the interconnection of renewable energy generators, and to the highly non-linear characteristics of the load, among others. There is a need of having knowledge about the nature of the PQ disturbances and about the mathematical approaches that describe the PQ disturbance. This paper presents the development of a structured methodology in combination with a mathematical model, intended for describing waveforms that contain simultaneous PQ disturbances. The validation process is made by fitting the proposed model to standard reference waveforms along with field recorded waveforms. The proposed mathematical model is capable of being adjusted in order to reproduce waveforms that contain simultaneous PQ disturbances. Five study cases are presented in order to test the proposed methodology.

Parameter optimized method for generalized S-transform algorithm and its application in the analysis of power quality disturbances

Aiming at the difficulties of optimizing parameter r for generalized S-transform (GST), a parameter optimized method for generalized S-transform is proposed and applied to the classification and detection of the power quality disturbances. The value of r for the fundamental frequency is set independently so as to reveal the time disturbance, convenient to optimize the values of r for other frequencies on the concentration of frequency disturbance. So the time-frequency disturbances characteristics can be obtianed simultaneously in desireable resolution. The evaluating indicators for determining r are put forward so as to provide the theoretical basis to determine r adaptively. Based on the time-frequency matrix of this method, four disturbance characteristics are presented for classification by the decision tree classifier. The disturbance parameters such as starting and ending time, disturbance amplitude and harmonic components are further detected in this study. The experiment analysis and the application to real power engineering demonstrate that the method has good noise robustness and satisfactory accuracy.

A Time Frequency Analysis and Classification of Real Time Power Quality Disturbances via Signal Processing Techniques and Back-Propagation Algorithm

This paper develops a real time solution for detecting the Power Quality events. By a voltage Data Acquisition Card, NI DAQ-9225, fourteen Power Quality events are acquired by the Virtual Instrument software package. The acquired data in time domain is transformed into frequency domain by Stockwell Transform and Wavelet Packet Transform. The features extracted from the transformation are fed into the Back Propagation Neural Network for training which aids in automatic classification of Power Quality Events. A combination of the signal processing techniques and Neural Networks are employed to detect and characterize the real time Power Quality Disturbances. The result obtained shows the effectiveness of the Stockwell Transform based Back Propagation algorithm over the Wavelet Transform based Back Propagation Algorithm in classifying the Power Quality Disturbances. The result produced by the proposed methodology is validated with the Power Quality Analyser.

Performance Evaluation of Real Power Quality Disturbances Analysis Using S-Transform

Power quality is main issue because of the impact to electricity suppliers, equipments, manufacturers and user.To solve the power quality problem, an analysis of power quality disturbances is required to identify and rectify any failures on power system. Most of researchers apply fourier transform in power quality analysis, however the ability of fourier transform is limited to spectral information extraction that can be applied on stationary disturbances. Thus, time-frequency analysis is introduced for analyzing the power quality distubances because of the limitation of fourier transform. This paper presents the analysis of real power quality disturbances using S-transform. This time-frequency distribution (TFD) is presented to analyze power quality disturbances in time-frequency representation (TFR). From the TFR, parameters of the disturbances such as instantaneous of root mean square (RMS), fundamental RMS, total harmonic distortion (THD), total nonharmonic distortion (TnHD) and total waveform distortion (TWD) of the disturbances are estimated. The experimental of three phase voltage inverter and starting motor are conducted in laboratory to record the real power quality disturbances. The disturbances are recorded via data logger system which is mplemented using LabVIEW while the analysis is done using Matlab in offline condition. The results show that S-transform gives good performance in identifying, detecting and analyzing the real power quality disturbances, effectively.