Detection Of Power Quality Disturbances Research Articles

An advanced signal processing based multiclass power quality disturbance detection and classification technique for grid connected solar PV farm

This paper presents an efficient event detection and classification technique for multiple power quality (PQ) disturbances. Initially synthetic power quality disturbances are simulated and then are directly processed to proposed algorithms to generate the target feature sets which comprises of energy, entropy, root mean square (RMS), mean, standard deviation, kurtosis, variance and maximum peak respectively. After the overall data analysis, it was found that thirteen power quality events out of the overall generated PQ disturbances were distinctively classified. Eventually these target features are passed through simple decision tree based event classifier for PQ events classification. The proposed algorithms are change detection filter (CDFT) with noise, without noise and synchrosqueeze wavelet transform (SST) has been scrutinized for number of disturbances presented in the PQ events. The proposed technique SST is applied for PV based microgrid to enhance the real time performance of the proposed technique where it has been verified as a superior technique as compared with the some of the existing event classification techniques such as wavelet transform (WT), stock well transform (SR),etc. The entire process has been verified in the in the MATLAB /Editor. The proposed technique evades the need of further signal processing techniques for detection and classification PQ events, thus ensconced less computational complexity and faster execution. Hence it is an efficient algorithm for real time applications.

Detection of Power Quality Disturbance using a Multidimensional Approach in an Embedded System

Power Quality (PQ) has emerged as an important research field in recent years. The development and increasing use of high power converters and the increase of non linear loads with high power cause unwanted changes in the electrical signals (current and voltage). These changes are called electrical disturbances. To understand such disturbances and investigate their causes, they must be firstly detected. This work proposes a real time multidimensional approach for detecting PQ disturbances. The innovation of this work is the use of a deviation measure easily extracted from a multidimensional data space to quantify the PQ disturbances. The method was implemented in a FPGA (Field-programmable gate array) device using a LabVIEW interface. It performed well with high detection rates and low computational complexity for both simulated and real signals. The proposed deviation measure may be useful as a general power quality index.

Detection of Power Quality Disturbances Based on Residual Analysis Using Kalman Filter Based on Maximum Likelihood

This paper presents the estimated residuals for the detection of power quality (PQ) disturbances using Kalman filter (KF) based on the maximum likelihood (KF-ML), which uses the ML method to adaptively optimize the error covariance matrices and the initial conditions as the parameters. Aiming at the sensitiveness to noise, residuals between the observed values and the estimated values by the KF-ML are proposed to detect the disturbances, and the estimated residuals exhibit mutation at the starting point and the ending point of disturbances. Thus, the singularities of residuals can be used for exactly detecting disturbances. Simulations on a variety of disturbances, such as voltage sag, impulse interruption, and harmonics with sag, are performed in the presence of noise. Simulation results verify that the detection method based on residuals can exactly determine the starting and ending time of the disturbances. Also, this method is highly sensitive to a variety of disturbances and less sensitive to random noise. Therefore, the method is a better choice for disturbance detection and is especially appropriate for estimation of unknown measurement noise in real applications.

Detection of inconspicuous power quality disturbances through step changes in rms voltage profile

Power quality disturbances commonly observed in power systems have been studied for decades, resulting in numerous algorithms for detecting the events that affect the voltage and/or current waveforms. However, a considerable amount of disturbances is not visually observable in the raw waveforms, especially switching operations. These events must be detected through an alternative feature, such as abrupt variations in the root-mean-square (rms) voltage profile. This study examines the methods commonly used for detecting power quality disturbances in the waveform or rms voltage profile domains and identifies their limitations. Afterwards, a novel step change detector is proposed based on a modified median filter and rms voltage gradient values to overcome the deficiencies of the existing methods. The effectiveness of the proposed method is assessed by applying it to both simulated and field data. This assessment shows that the method detects all switching events with no false positives for the datasets under analysis.

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.

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Robust detection of real-time power quality disturbances under noisy condition using FTDD features

ABSTRACTTo improve power quality (PQ), detecting the particular type of disturbance is the foremost thing before mitigation. So monitoring is needed to detect the PQ disturbance that occurs in a short duration of time. Classification of real-time PQ disturbances under noisy environment is investigated in this method by selecting an appropriate signal processing tool called fusion of time domain descriptors (FTDD) at the feature extraction stage. It’s a method of extracting power spectrum characteristics of various PQ disturbances. Few advantages like algorithmic simplicity and local time-based unique features makes the FTDD algorithm ahead of other techniques. PQ events like voltage sag, voltage swell, interruption, healthy, transient and harmonics mixed with different noise conditions are analysed. multiclass support vector machine and Naïves Bayes (NB) classifiers are applied to analyse the performance of the proposed method. As a result, NB classifier performs better in noiseless signal with 99.66%, wherein noise added signals both NB and SVM are showing better accuracy at different signal to noise ratios. Finally, Arduino controller-based hardware tool involved in the acquisition of real-time signals shows how our proposed system is applicable for industries that make detection simple.

Research on Distributed Power Quality Disturbance Detection Based on ILMD

The local mean decomposition method is effective in analyzing non-linear and non-stationary data, and it is suitable for the detection of power quality disturbance signals. The endpoint effect caused by the method is studied, and the original method is improved for the problem that the disturbance signal cannot be accurately located. An improved Local Mean Decomposition (ILMD) method is proposed. ILMD uses cubic spline interpolation instead of smoothing to obtain local mean function and envelope estimation function. Radial Basis Function (RBF) neural network is used to extend the information at both ends of the data, which improves the endpoint effect. Combined with Hilbert transform, the instantaneous frequency of power quality disturbance signal can be more accurately calculated. The improved method is also applicable to disturbance signals with weak periodic law, and has less requirement for disturbance signal conditions and universal applicability. The effectiveness of ILMD is validated by simulation examples and the measured data of voltage signal at low voltage side of 35kV bus transformer in a wind farm.

Online power quality disturbance detection by support vector machine in smart meter

Power quality assessment is an important performance measurement in smart grids. Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters. Addressing this issue, in this study, we propose segregation of the power disturbance from regular values using one-class support vector machine (OCSVM). To precisely detect the power disturbances of a voltage wave, some practical wavelet filters are applied. Considering the unlimited types of waveform abnormalities, OCSVM is picked as a semi-supervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data. This model is able to automatically detect the existence of any types of disturbances in real time, even unknown types which are not available in the training time. In the case of existence, the disturbances are further classified into different types such as sag, swell, transients and unbalanced. Being light weighted and fast, the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring. The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.

Homogeneity-PMU-Based Method for Detection and Classification of Power Quality Disturbances

Over the past few years, power quality (PQ) monitoring has become of paramount importance for utilities and users since poor PQ generates negative consequences. In monitoring, fast detection and accurate classification of PQ disturbances (PQDs) are desirable features. In this work, a new method to detect and classify PQDs is proposed. The proposal takes advantage of the low computational resources of both a phasor measurement unit (PMU)-based signal processing scheme and the homogeneity approach. To classify the PQDs, if–then–else rules are used. To validate and test the proposal, synthetic and real signals of sags, swells, interruptions, notching, spikes, harmonics, and oscillatory transients are considered. For the generation of real signals, a PQD generator based on a power inverter is used. In the proposed method, the PMU information is directly used to classify sags, swells, and interruptions, whereas the homogeneity index is used to distinguish among the remaining PQDs. Results show that the proposal is an effective and suitable tool for PQ monitoring.

Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network

This paper proposes a novel, two-stage and hybrid approach based on variational mode decomposition (VMD) and the deep stochastic configuration network (DSCN) for power quality (PQ) disturbances detection and classification in power systems. Firstly, a VMD technique is applied to discriminate between stationary and non-stationary PQ events. Secondly, the key parameters of VMD are determined as per different types of disturbance. Three statistical features (mean, variance, and kurtosis) are extracted from the instantaneous amplitude (IA) of the decomposed modes. The DSCN model is then developed to classify PQ disturbances based on these features. The proposed approach is validated by analytical results and actual measurements. Moreover, it is also compared with existing methods including wavelet network, fuzzy and S-transform (ST), adaptive linear neuron (ADALINE) and feedforward neural network (FFNN). Test results have proved that the proposed method is capable of providing necessary and accurate information for PQ disturbances in order to plan PQ remedy actions accordingly.

Detection of Power Quality Disturbance of the Best ASD Based on Dynamic PSO Search

This paper mainly studies the power quality disturbance feature detection and optimization method based on atomic sparse decomposition algorithm. In order to solve the problem of large amount of matching pursuit algorithm, this paper proposes a Particle Swarm Optimization dynamic search (PSO-DS) algorithm based on the PSO optimization algorithm, using the prior information provided by the fast Fourier transform and wavelet transform to search for parameters. The range and optimization of the search for the best atom are optimized. The simulation of the example shows that the PSO-DS algorithm can effectively extract the signal features with less decomposition times, avoiding the generation of unrelated and erroneous components, improving the detection accuracy of the disturbance signal and the simplicity and accuracy of the signal representation.

Flexible Entropy Based Feature Selection and Multi Class SVM for Detection and Classification of Power Quality Disturbances

Flexible Entropy Based Feature Selection and Multi Class SVM for Detection and Classification of Power Quality Disturbances

Three-Layer Bayesian Network for Classification of Complex Power Quality Disturbances

In this paper, a new classification approach for detection and classification of complex power quality disturbances (PQDs) using a three-level multiply connected Bayesian network is proposed. First, the model consisting of features evidence layer, disturbances state layer, and circumstance evidence layer is established, which represent the features extracted from the sample signal, the state of each single label of PQDs and the circumstance factors that may affect the PQDs, respectively. Second, the parameters of three-level multiply connected Bayesian network (TLBN) are studied from statistical data and Monte Carlo simulations. Finally, the classification is determined by computing the posterior marginal probabilities of each event given observed evidences. The new method not only utilizes the existing features extracting methods, but also takes the historical data, and other surrounding factors into account. Simulation results and real-life PQ signal tests show that the performances of TLBN classification of complex disturbances are better than the other approaches in existing literatures.

Wavelet transform-based fault detection method for hydrogen energy-based distributed generators

Integration of hydrogen energy-based distributed generators (HEBDGs) to the main grid causes some power quality (PQ) problems. The conventional fault detection methods fail to detect some PQ disturbances, thus intelligent methods are proposed in the literature recently. This paper focuses on developing a wavelet transform (WT)-based fault detection method for HEBDG systems to detect PQ disturbances in the low-voltage grid connection. The proposed method uses the discrete WT and Daubechies wavelets of order 4 to detect the voltage swell, voltage sag, voltage interruption and transient disturbances in a HEBDG system. The performance of the WT-based method is also compared with the conventional fault detection methods, and using the time-frequency domain analysis by WT increases the stability of the proposed method. The results verify that the conventional methods are not capable of detecting some PQ disturbances properly, but the proposed WT-based method is more reliable according to the conventional methods.

Tunable-Q Wavelet Transform and Dual Multiclass SVM for Online Automatic Detection of Power Quality Disturbances

A new automated recognition approach based on tunable-Q wavelet transform (TQWT) and a dual multiclass support vector machines (MSVM) has been proposed for detection of power quality disturbances. The proposed approach first investigates the presence of low-frequency interharmonics and then tunes the wavelet for decomposition of signal into fundamental and harmonic components. The tuning of Q-factor and redundancy makes the filter design to accurately extract the fundamental frequency component from a distorted input signal. Then, a unique set of features, which clearly reveal the characteristics of disturbances, are extracted. The power quality disturbances are broadly categorized into two groups based on the pre-obtained information of low-frequency interharmonics. Therefore, multiple disturbances are recognized by employing a dual MSVM, one for each group. Results demonstrate the applicability, strength, and accuracy of the proposed approach for classification of single and combined disturbances under different noisy conditions. Moreover, to illustrate the prominence of the features extracted from TQWT, two more classifiers based on decision tree and feedforward neural network have been employed for classification of power quality disturbances.

Variational Mode Decomposition and Decision Tree Based Detection and Classification of Power Quality Disturbances in Grid-Connected Distributed Generation System

This paper presents a variational mode decomposition (VMD) and decision tree based detection and classification method of single and mixed power quality (PQ) disturbances in grid-connected distributed generation system. Applicability of VMD technique is investigated for discrimination of stationary PQ disturbances (such as harmonics, interharmonics, and flicker), non-stationary events (e.g., transients) and noise. Studies indicate usefulness of VMD for accurate estimation of phasor quantities such as amplitude, phase angle, and frequency and other describing features. Features namely, mode central frequencies, relative mode energy ratios, zero crossings, and instantaneous amplitude (IA) are extracted for classification of single and mixed PQ disturbances using a decision tree algorithm. A set of synthetic test signals, disturbance signals obtained from real events as well as signals generated from real time digital simulator platform are used to test effectiveness of proposed method, under various system operating scenarios and noise levels. Proposed method is found to be capable of accurate detection, estimation, localization, and classification of all kinds of PQ disturbances in both noisy and noise-free cases.

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

Real-time system for automatic detection and classification of single and multiple power quality disturbances

It is known that the quality of power has been the subject of several researches aiming to provide relevant information to users of electrical systems that are becoming increasingly smart. This study presents an approach for single and multiple power quality disturbance detection and classification using multidimensional analysis, higher-order statistics and a neuro-tree based classifier. The system was implemented in an FPGA (Field Programmable Gate Array), a real-time processor and a remote computer, with LabVIEW interface. This implementation enables real-time execution and its application to monitor smart grids. It is able to detect deviations in the measured voltage waveform from the nominal one and classify 20 classes of single and multiple disturbances with a global efficiency upper to 97%.

Application of Islanding Detection and Classification of Power Quality Disturbance in Hybrid Energy System

Islanding and power quality (PQ) disturbances in hybrid energy system become more serious with the application of renewable energy sources. In this paper, a novel method based on wavelet transform (WT) and modified feed forward neural network (FNN) is proposed to detect islanding and classify PQ problems. First, the performance indices, i.e., the energy content and SD of the transformed signal are extracted from the negative sequence component of the voltage signal at PCC using WT. Afterward, WT indices are fed to train FNNs midfield by Particle Swarm Optimization (PSO) which is a novel heuristic optimization method. Then, the results of simulation based on WT-PSOFNN are discussed in MATLAB/SIMULINK. Simulations on the hybrid power system show that the accuracy can be significantly improved by the proposed method in detecting and classifying of different disturbances connected to multiple distributed generations.