Automatic classification of power quality disturbances using optimal feature selection based algorithm

The development of renewable energy sources and power electronic converters in conventional power systems leads to Power Quality (PQ) disturbances. This research aims at automatic detection and classification of single and multiple PQ disturbances using a novel optimal feature selection based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). DWT is used for the extraction of useful features, which are used to distinguish among different PQ disturbances by an ANN classifier. The performance of the classifier solely depends on the feature vector used for the training. Therefore, this research is required for the constructive feature selection based classification system. In this study, an Artificial Bee Colony based Probabilistic Neural Network (ABCPNN) algorithm has been proposed for optimal feature selection. The most common types of single PQ disturbances include sag, swell, interruption, harmonics, oscillatory and impulsive transients, flicker, notch and spikes. Moreover, multiple disturbances consisting of combination of two disturbances are also considered. The DWT with multi-resolution analysis has been applied to decompose the PQ disturbance waveforms into detail and approximation coefficients at level eight using Daubechies wavelet family. Various types of statistical parameters of all the detail and approximation coefficients have been analysed for feature extraction, out of which the optimal features have been selected using ABC algorithm. The performance of the proposed algorithm has been analysed with different architectures of ANN such as multilayer perceptron and radial basis function neural network. The PNN has been found to be the most suitable classifier. The proposed algorithm is tested for both PQ disturbances obtained from the parametric equations and typical power distribution system models using MATLAB/Simulink and PSCAD/EMTDC. The PQ disturbances with uniformly distributed noise ranging from 20 to 50 dB have also been analysed. The experimental results show that the proposed ABC-PNN based approach is capable of efficiently eliminating unnecessary features to improve the accuracy and performance of the classifier

Multivariate detection of power system disturbances based on fourth order moment and singular value decomposition

This paper presents a new method to detect power system disturbances in a multivariate context, which i s based on Fourth Order Moment (FOM) and multivar iate analysis implemented as S ingular Value Decomposition (SVD). The motivati on for this development is that power systems are increasingly affected by various disturbances and t here is a requirement for the analysis of measurement s to detect these disturbances. The ap plication results on the measurements of an actual power system in Europe illustrate that the proposed multivariate detection method achieves enhanced detection reliability and sensitivity

Recognition and classification of power quality disturbances by DWT-MRA and SVM classifier

Electrical power system is a large and complex network, where power quality disturbances (PQDs) must be monitored, analyzed and mitigated continuously in order to preserve and to re-establish the normal power supply without even slight interruption. Practically huge disturbance data is difficult to manage and requires the higher level of accuracy and time for the analysis and monitoring. Thus automatic and intelligent algorithm based methodologies are in practice for the detection, recognition and classification of power quality events. This approach may help to take preventive measures against abnormal operations and moreover, sudden fluctuations in supply can be handled accordingly. Disturbance types, causes, proper and appropriate extraction of features in single and multiple disturbances, classification model type and classifier performance, are still the main concerns and challenges. In this paper, an attempt has been made to present a different approach for recognition of PQDs with the synthetic model based generated disturbances, which are frequent in power system operations, and the proposed unique feature vector. Disturbances are generated in Matlab workspace environment whereas distinctive features of events are extracted through discrete wavelet transform (DWT) technique. Machine learning based Support vector machine classifier tool is implemented for the classification and recognition of disturbances. In relation to the results, the proposed methodology recognizes the PQDs with high accuracy, sensitivity and specificity. This study illustrates that the proposed approach is valid, efficient and applicable

Generalized Wavelet Fisher’s Information of 1

This paper defines the generalized wavelet Fisher information of parameter q. This information measure is obtained by generalizing the time-domain definition of Fisher’s information of Furuichi to the wavelet domain and allows to quantify smoothness and correlation, among other signals characteristics. Closed-form expressions of generalized wavelet Fisher information for 1/fα signals are determined and a detailed discussion of their properties, characteristics and their relationship with wavelet q-Fisher information are given. Information planes of 1/f signals Fisher information are obtained and, based on these, potential applications are highlighted. Finally, generalized wavelet Fisher information is applied to the problem of detecting and locating weak structural breaks in stationary 1/f signals, particularly for fractional Gaussian noise series. It is shown that by using a joint Fisher/F-Statistic procedure, significant improvements in time and accuracy are achieved in comparison with the sole application of the F-statistic

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

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

Improvement of Cepstrum Analysis for the Purpose to Detect Leak, Feature and Its Location in Water Distribution System based on Pressure Transient Analysis / Hanafi.M.Yusop ...[et al.]

Nowadays, pipeline system is one of the powerful technologies to be implemented in the real world. It is very essential for transporting fluid especially water from one point to the next point. But the pipeline system will also defect as leaks due to many reasons. Pressure Transient signal is a newly developed method to detect and localize leak phenomena since the signal has information about that phenomenon .The basic principal is the fact of water spouting out of a leak in pressurized pipe that generates a signal, and the signal may contain information to whether a leak exists and where it is located. To extract this signal, many signal analysis methods were implemented by researchers such as cross-correlation, genetic algorithm, and wavelets transform. Cepstrum analysis is proposed as a method to extract leak and pipe feature information from pressure transient signal by considering this method to analyse non-stationary data. Since in the real test, the originality and pure data are hard to be captured due to noise generated from environment and the noise level ratio is very low, pre-processing method as a filtering technique is implemented to analyse the real signal before the signal goes through cepstrum analysis as post-processing method. This research focused on the improvement of cepstrum analysis in order to extract information about the leak, pipe feature, and its location. In this research, cepstrum analysis was proposed as Post-Processing method. Discrete Wavelets Transform (DWT) and Principal Component Analysis (PCA) were proposed as Pre-Processing Methods. The pressure Transient signal was analysed using Matlab software. The results satisfactorily predicted the leak location as the comparison analysis using theoretical calculation and experimental results were just 0.4% to 3.8%. Therefore, PCA and DWT were recommended as data pre-processing methods to improve cepstrum analysis result

Protection of Future Electricity Systems

The electrical energy industry is undergoing dramatic changes: massive deployment of renewables, increasing share of DC networks at transmission and distribution levels, and at the same time, a continuing reduction in conventional synchronous generation, all contribute to a situation where a variety of technical and economic challenges emerge. As the society’s reliance on electrical power continues to increase as a result of international decarbonisation commitments, the need for secure and uninterrupted delivery of electrical energy to all customers has never been greater. Power system protection plays an important enabling role in future decarbonized energy systems. This book includes ten papers covering a wide range of topics related to protection system problems and solutions, such as adaptive protection, protection of HVDC and LVDC systems, unconventional or enhanced protection methods, protection of superconducting transmission cables, and high voltage lightning protection. This volume has been edited by Adam Dyśko, Senior Lecturer at the University of Strathclyde, UK, and Dimitrios Tzelepis, Research Fellow at the University of Strathclyde

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...