A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

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

A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

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

Optimized complex power quality classifier using one vs. rest support vector machine

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a ?One Vs Rest? architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.Fil: de Yong, David Marcelo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Bhowmik, Sudipto. Nexant Inc; Estados UnidosFil: Magnago, Fernando. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin

Application of biosignal-driven intelligent systems for multifunction prosthesis control

University of Technology, Sydney. Faculty of Engineering and Information Technology.Prosthetic devices aim to provide an artificial alternative to missing limbs. The controller for such devices is usually driven by the biosignals generated by the human body, particularly Electromyogram (EMG) or Electroencephalogram (EEG) signals. Such a controller utilizes a pattern recognition approach to classify the EMG signal recorded from the human muscles or the EEG signal from the brain. The aim of this thesis is to improve the EMG and EEG pattern classification accuracy. Due to the fact that the success of pattern recognition based biosignal driven systems highly depends on the quality of extracted features, a number of novel, robust, hybrid and innovative methods are proposed to achieve better performance. These methods are developed to effectively tackle many of the limitations of existing systems, in particular feature representation and dimensionality reduction. A set of knowledge extraction methods that can accurately and rapidly identify the most important attributes for classifying the arm movements are formulated. This is accomplished through the following: 1. Developing a new feature extraction technique that can identify the most important features from the high-dimensional time-frequency representation of the multichannel EMG and EEG signals. For this task, an information content estimation method using fuzzy entropies and fuzzy mutual information is proposed to identify the optimal wravelet packet transform decomposition for classification. 2. Developing a powerful variable (feature or channel) selection paradigm to improve the performance of multi-channel EMG and EEG driven systems. This will eventually lead to the development of a combined channel and feature selection technique as one possible scheme for dimensionality reduction. Two novel feature selection methods are developed under this scheme utilizing the ant colony arid differential evolution optimization techniques. The differential evolution optimization technique is further modified in a novel attempt in employing a float optimizer for the combinatorial task of feature selection, proving powerful performance by both methods. 3. Developing two feature projection techniques that extract a small subset of highly informative discriminant features, thus acting as an alternative scheme for dimensionality reduction. The two methods represent novel variations to fuzzy discriminant analysis based projection techniques. In addition, an extension to the non-linear discriminant analysis is proposed based on a mixture of differential evolution and fuzzy discriminant analysis. The testing and verification process of the proposed methods on different EMG and EEG datasets provides very encouraging results