Application of wavelet transform to steady-state approximation of power electronics waveforms

Author name used in this publication: Chi K. TseRefereed conference paper2002-2003 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

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

Measurements, Models, Systems and Design

531 s.

Insight on Effectiveness of Frequently Exercised PQ Classification Techniques

The growing demands of global consumer market in green energy system have opened the doors for many technologies as well as various sophisticated electrical devices for both commercial and domestic usage. However, with the increasing demands of energy and better quality of services, there is a significant increase in non-linearity in load distribution causing potential effect on the Power Quality (PQ). The harmful effects on PQ are various events e.g. sag, swell, harmonics etc that causes significant amount of system degradation. Therefore, this paper discusses various significant research techniques pertaining to the PQ disturbance classification system introduced by the authors in the past and analyzes its effectiveness scale in terms of research gap. The paper discusses some of the frequently exercised PQ classification techniques from the most relevant literatures in order to have more insights of the techniques

Minimization of power loss in newfangled cascaded H-bridge multilevel inverter using in-phase disposition PWM and wavelet transform based fault diagnosis

AbstractNowadays multilevel inverters (MLIs) have been preferred over conventional two-level inverters due to reduced harmonic distortions, lower electromagnetic interference, and higher DC link voltages. However, the increased number of components, complex PWM control, voltage-balancing problem, and component failure in the circuit are some of the disadvantages. The topology suggested in this paper provides a DC voltage in the shape of a staircase that approximates the rectified shape of a commanded sinusoidal wave to the bridge inverter, which in turn alternates the polarity to produce an AC voltage with low total harmonic distortion and power loss. This topology requires fewer components and hence it leads to the reduction of overall cost and complexity particularly for higher output voltage levels. The component fault diagnostic algorithm is developed using wavelets transform tool. Finally an experimental prototype is developed and validated with the simulation results

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

Detection of faults in a scaled down doubly-fed induction generator using advanced signal processing techniques.

The study ventures into the development of a micro-based doubly fed induction generator (DFIG) test rig for fault studies. The 5kW wound rotor induction machine (WRIM) that was used in the test rig was based on a scaled-down version of a 2.5MW doubly fed induction generator (DFIG). The micromachine has been customized to make provision for implementing stator inter-turn short-circuit faults (ITSCF), rotor ITSCF and static eccentricity (SE) faults in the laboratory environment. The micromachine has been assessed under the healthy and faulty states, both before and after incorporating a converter into the rotor circuit of the machine. In each scenario, the fault signatures have been characterised by analyzing the stator current, rotor current, and the DFIG controller signals using the motor current signature analysis (MCSA) and discrete wavelet transform (DWT) analysis techniques to detect the dominant frequency components which are indicative of these faults. The purpose of the study is to evaluate and identify the most suitable combination of signals and techniques for the detection of each fault under steady-state and transient operating conditions. The analyses of the results presented in this study have indicated that characterizing the fault indicators independent of the converter system ensured clarity in the fault diagnosis process and enabled the development of a systematic fault diagnosis approach that can be applied to a controlled DFIG. It has been demonstrated that the occurrence of the ITSCFs and the SE fault in the micro-WRIM intensifies specific frequency components in the spectral plots of the stator current, rotor current, and the DFIG controller signals, which may then serve as the dominant fault indicators. These dominant components may be used as fault markers for classification and have been used for pattern recognition under the transient condition. In this case, the DWT and spectrogram plots effectively illustrated characteristic patterns of the dominant fault indicators, which were observed to evolve uniquely and more distinguishable in the rotor current signal compared to the stator current signal, before incorporating the converter in the rotor circuit. Therefore, by observing the trends portrayed in the decomposition bands and the spectrogram plots, it is deemed a reliable method of diagnosing and possibly quantifying the intensity of the faults in the machine. Once the power electronic converter was incorporated into the rotor circuit, the DFIG controller signals have been observed to be best suited for diagnosing faults in the micro-DFIG under the steady-state operating condition, as opposed to using the terminal stator or rotor current signals. The study also assessed the impact of undervoltage conditions at the point of common coupling (PCC) on the behaviour of the micro-DFIG. In this investigation, a significant rise in the faulted currents was observed for the undervoltage condition in comparison to the faulty cases under the rated grid voltage conditions. In this regard, it could be detrimental to the operation of the micro-DFIG, particularly the faulted phase windings, and the power electronic converter, should the currents exceed the rated values for extended periods