Types Of Power Disturbances Research Articles

Identification of Power Disturbances Using Generalized Morphological Open-closing and Close-opening Undecimated Wavelet

This paper proposes a new technique, generalized morphological open-closing and close-opening undecimated wavelet (GMOCUW), based on which a power disturbance identification scheme is developed. In order to extract features of power disturbances, the proposed scheme employs undecimated wavelet transform for its advantage in retaining information and reducing waveform distortion, multiscale morphological analysis for its ability in frequency analysis, and generalized morphological open-closing (GMOC) and generalized morphological close-opening (GMCO) operations for their advantages in information preserving. Power system computer aided design/electro-magnetic transient in dc system (PSCAD/EMTDC) was employed to construct a test power system to simulate eight types of power disturbances. Additionally, a laboratory platform was established to generate power quality (PQ) signals under real operating conditions. The performance of GMOCUW has been compared with that of morphological gradient wavelet (MGW), new dual neural-network-based methodology (NDNM), S-transform (ST), and Daubechies 4 wavelet (DB4W). Comparison results have proved that, in power disturbance detection, GMOCUW is more accurate and faster than these methods.

Applications of wavelets and neural networks for classification of power system dynamics events

This paper investigates the possibility of classifying power system dynamics events using discrete wavelet transform (DWT) and a neural network (NN) by analyzing one variable at a single network bus. Following a disturbance in the power system, it will propagate through the system in the form of low-frequency electromechanical oscillations (LFEOs) in a frequency range of up to 5 Hz. DWT allows the identification of components of the LFEO, their frequencies, and magnitudes. After determining the energy components' share of the analyzed signal using DWT and Parseval's theorem, the input data for the classification process using a NN are obtained. A total of 5 classes of disturbances, 3 different wavelet functions, and 2 different variables are tested. Simulation results show that the proposed approach can classify different power disturbance types efficiently, regardless of the choice of variable or wavelet function.

Hybrid soft computing techniques for feature selection and parameter optimization in power quality data mining

Recognition of the presence of any power disturbance and classifying any existing disturbance into a particular type is the first step in combating the power quality problem. In spite of the extensive number of power disturbances classification methods, a research on the selection of useful features from the existing feature set and the parameter optimization for specific classifiers was omitted. The kernel parameters setting for support vector machine (SVM) classifier in training process along with feature selection will significantly impact the classification accuracy. Two novel wrapper based hybrid soft computing techniques are proposed in this paper for feature selection and parameters optimization to classify nine types of power disturbances without degrading the SVM classification accuracy. The feature items were selected from discrete wavelet transform across several decomposition levels of the disturbance signals and from the duration of disturbance occurrence. This analysis selects the more useful feature set and optimized parameters for two types of kernels namely the polynomial kernel and radial basis function kernel for SVM. Compared with the traditional grid algorithm the proposed genetic algorithm and simulated annealing based approach significantly improves the classification accuracy rate by eliminating relatively useless feature items and proper parameter selection for the classifier.

On Power Quality Indices and Real Time Measurement

Power quality (PQ) indices are used to quantify the quality of the power supply and serve as the basis for comparing the negative impacts of different disturbances on power networks. To overcome the limitations and deficiencies of the practical applications of some power quality indices in common use, a set of three new indices, namely the fundamental frequency deviation ratio (FDR), waveform distortion ratio (WDR), and symmetrical components deviation ratio (SDR) are proposed in this paper to summarize different types of power disturbances in a comprehensive manner. As instantaneous quantities, these novel indices can reveal the time varying characteristics of power disturbances in real time. Hence, the new PQ indices can well accommodate practical waveform distortions in power networks, which may be caused by multiple types of time varying power disturbances. They can therefore be further used to evaluate both the effectiveness and dynamic responses of PQ mitigation equipment in practical applications. To fully realize the advantages of the new PQ indices, a novel Atom (transform kernel) based time frequency transform and its recursive algorithm are also proposed as the supporting measurement technique. The new Atom approach can continuously measure the instantaneous frequencies and amplitudes of signal components in a nonstationary disturbance waveform with high accuracy, and then update the new PQ indices at each sample. The effectiveness of the new PQ indices and the supporting measurement technique were ascertained using various PQ events, both simulated events and those recorded at an industrial site.

Wavelet-Based Neural Network for Power Disturbance Recognition and Classification

In this paper, a prototype wavelet-based neural-network classifier for recognizing power-quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural-network (PNN) model to construct the classifier. First, the multiresolution-analysis technique of DWT and the Parseval's theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Then, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the distorted signal features without losing its original property, less memory space and computing time are required. Various transient events tested, such as momentary interruption, capacitor switching, voltage sag/swell, harmonic distortion, and flicker show that the classifier can detect and classify different power disturbance types efficiently.

Power quality: a review

The various types of power disturbances are briefly defined, and their causes are examined. Techniques used to mitigate power quality disturbances are reviewed. The coordination of protection methods is discussed. The importance of taking power quality considerations into account at the design stage is stressed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>