Implementation of Power Disturbance Classifier Using Wavelet-Based Neural Networks

نویسنده

  • ZWE-LEE GAING
چکیده

In this paper, a 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 multi-resolution analysis (MRA) technique of DWT and the Parseval’s theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Second, 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 features of distorted signal without losing its original property, less memory space and computing time are required. Various transient events are tested, the results show that the classifier can detect and classify different power disturbance types efficiently. Keywordspower quality, wavelet transform, Parseval’s theorem, probabilistic neural network

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

School of Computing and Information Technology A Simulated Power Quality Disturbance Recognition System

The paper presents a prototype of power quality disturbance recognition system. The prototype contains two main components: a simulator to generate power quality disturbances and a classifier to identify these disturbances. Based on the results of site measurements, the disturbance generator is designed to simulate different power quality disturbances frequently encountered at power system sub-...

متن کامل

Implementation of Power Quality Disturbance Classifier in FPGA Employing Wavelet Transform, ANN and Fuzzy Logic

Intelligent power quality monitoring systems have become an essential component of high technology and high availability-oriented industries. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. The first step towards any solution for a disturbance is to recognize the presence of a particular type of disturbance...

متن کامل

Wavelet and PCA to Power Quality Disturbance Classification Applying a RBF Network

The quality of electric power became an important issue for the electric utility companies and their customers. It is often synonymous with voltage quality since electrical equipments are designed to operate within a certain range of supply specifications. For instance, current microelectronic devices are very sensitive to subtle changes in power quality, which can be represented as a disturban...

متن کامل

A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks

A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...

متن کامل

Implementation of a programmable neuron in CNTFET technology for low-power neural networks

Circuit-level implementation of a novel neuron has been discussed in this article. A low-power Activation Function (AF) circuit is introduced in this paper, which is then combined with a highly linear synapse circuit to form the neuron architecture. Designed in Carbon Nanotube Field-Effect Transistor (CNTFET) technology, the proposed structure consumes low power, which makes it suitable for the...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2003