Power Quality Management and Classification for Smart Grid Application using Machine Learning

The Efficient Wavelet-based Convolutional Transformer network (EWT-ConvT) is proposed to detect power quality disturbances in time-frequency domain using attention mechanism. The support of machine learning further improves the network accuracy with synthetic signal generation and less system complexity under practical environment. The proposed EWT-ConvT can achieve 94.42% accuracy which is superior than other deep learning models. The detection of disturbances using EWT-ConvT can also be implemented into smart grid applications for real-time embedded system development

Wavelet Transform Analysis to Applications in Electric Power Systems

The wavelet transform has received great importance in the last years on the power system analysis because the multi-resolution analysis presents proprieties good for the transient signal analysis. This chapter presents a review on main application of wavelet transform in electric power systems. The study areas have been classified as power system protection, power quality disturbances, power system transient, partial discharge, load forecasting, faults detection, and power system measurement. The areas in which more works have been developed are the power quality and protections field, where both cover 51% of the articles analyzed

Fault Classification in a DG Connected Power System using Artificial Neural Network

Distributed generation is playing an important role in power system to meet the increased load demand. Integration of Distributed Generator (DG) to grid leads to various issues of   protection and control of power system structure.  The effect of the distributed generators to the grid is changes the fault current level, which makes the fault analysis more complex. From the different fault issues occurs in a distributed generator integrated power system, classification of fault remains as one of the most vital issue even after years of in-depth research. This paper emphasis on the classification of faults in DG penetrated power system using Artificial Neural Network (ANN). Because researchers are attempting to detect and diagnose these faults as soon as possible in order to avoid financial losses, this work aims to investigate the sort of fault that happened in the hybrid system. This paper proposed artificial neural network based approaches for fault disturbances in a microgrid made up of wind turbine generators, fuel cells, and diesel generator. The voltage signal is retrieved at the point of common coupling (PCC). The extracted data are used for training and testing purpose.  Artificial neural network technique is utilized for the classification of fault in the simulated model. Furthermore, performance indices (PIs) such as standard deviation and skewness are calculated for reduction of data size and better accuracy. Both the fault and parameters are varied to check the usefulness of the proposed method. Finally, the results are discussed and compared with different DG penetration

Fault Detection Approaches to Power System: State-of-the-Art Article Reviews for Searching a New Approach in the Future

This paper proposes the state-of-the-art of fault detection approach a power system. Severalarticles presented it in each implementation and method from the last to present (2013). Theadvantage of the approach would be developed to the new detection in the future. Manyinterested topics used for detection of fault in the power system. In this research can beclassified into two types interesting in fault detection. This review of many paper will beused to develop the research or find the new method for an appropriate fault detection in thepower system.DOI:http://dx.doi.org/10.11591/ijece.v3i4.319

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

Power quality disturbance detection and classification using signal processing and soft computing techniques

The quality of electric power and disturbances occurred in power signal has become a major issue among the electric power suppliers and customers. For improving the power quality continuous monitoring of power is needed which is being delivered at customer’s sites. Therefore, detection of PQ disturbances, and proper classification of PQD is highly desirable. The detection and classification of the PQD in distribution systems are important tasks for protection of power distributed network. Most of the disturbances are non-stationary and transitory in nature hence it requires advanced tools and techniques for the analysis of PQ disturbances. In this work a hybrid technique is used for characterizing PQ disturbances using wavelet transform and fuzzy logic. A no of PQ events are generated and decomposed using wavelet decomposition algorithm of wavelet transform for accurate detection of disturbances. It is also observed that when the PQ disturbances are contaminated with noise the detection becomes difficult and the feature vectors to be extracted will contain a high percentage of noise which may degrade the classification accuracy. Hence a Wavelet based de-noising technique is proposed in this work before feature extraction process. Two very distinct features common to all PQ disturbances like Energy and Total Harmonic Distortion (THD) are extracted using discrete wavelet transform and are fed as inputs to the fuzzy expert system for accurate detection and classification of various PQ disturbances. The fuzzy expert system not only classifies the PQ disturbances but also indicates whether the disturbance is pure or contains harmonics. A neural network based Power Quality Disturbance (PQD) detection system is also modeled implementing Multilayer Feed forward Neural Network (MFNN)

Detection of emerging faults in power transformers using self-organising maps

Power transformers are a crucial part of the power system, one of the largest infrastructures in industrialised countries. In particular, wind turbine transformers are subjected to frequent thermal cycling as a function of varying turbine loads. Thus transformers are prone to developing faults and defects that can involve high repair costs for instance due to the repeated thermal stress on the winding. Faults develop mainly when the insulation produces small leakage currents between turns, which if not detected early, might become short circuits that can result in interruptions in electricity supply, and difficult and costly repairs. An optimum overhaul of damaged transformers is not accomplished often because of lack of appropriate inspection tools. Detailed assessment and preventive maintenance work, which will allow the detection and repair of failures at early stages, is believed to be the only suitable way to cope with power transformer degradation at low cost. This paper presents a methodology based on the analysis of current signals converted by the S transform for the detection of incipient faults in transformers. The procedure is based on calculating the energy of the zones of the time-frequency spectrum. Its main advantage is its possible real time implementation that can be applied while the transformer is in use. Experimental results with PSCAD are presented.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Advances in power quality analysis techniques for electrical machines and drives: a review

The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.Peer ReviewedPostprint (published version