Studies of Power Quality: Disturbance Recognition

نویسندگان

  • M. Negnevitsky
  • K. Debnath
  • J. Huang
  • M. Ringrose
چکیده

The issue of Power Quality is very important to both the consumers and the distributors of electric power. There are many facets of power quality disturbances and each has its own source and mitigation techniques. The first step towards any solution for a disturbance is to recognize the presence of a particular type of disturbance and locate its source. Conventional methods for recognition of a power quality disturbance consists of collecting operating data, inspecting the wave forms visually and then identifying any disturbance that may be present in the data. Although the available measuring and recording devices offer substantial help, the process is, in the main, very slow. At the University of Tasmania, a project is under way to automatize this process. The ultimate goal is to develop an Automatic Disturbance Recognition System (ADRS). In its current state, tests with simulated as well as real disturbance data yielded encouraging results, some of which are presented in this paper.

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

Fuzzy Neural Inference System for Pattern Recognition of Power Quality Events Using Rule Generation

This paper presents pattern recognition of time series data and subsequent temporal data mining of power signal disturbance events that occur frequently in power distribution networks using multiresolution S-transform and Fuzzy neural inference system . This system yields relevant features, which are used in a Fuzzy expert system to separate the transient time series data and steady state short...

متن کامل

Power Quality Data Analysis Using Unsupervised Data Mining

The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Data mining is a process that uses a variety of data analysis tools to identify hidden patterns and relationships within large samples of data. This paper presents several d...

متن کامل

Power Quality Disturbance Classification Using Adaptive Linear Neural Network (ADALINE) and Feed Forward Neural Network (FFNN)

Abstract: This paper presents a dual neural network based technique for detecting and classifying the power quality disturbances. In the proposed method, Adaptive Linear Neural Network is used to extract the rms voltage for harmonics and Interharmonics estimations. With the help of these indices, PQ disturbances such as Sag, Swell, Outages are detected and classified, Harmonics and Interharmoni...

متن کامل

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corre...

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 1999