Electric power quality: a tutorial introduction

Heydt, G.T.

doi:10.1109/67.648490

Cited by 62 publications

(27 citation statements)

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“…The total encoded message length (two parts) for different models is then calculated and the best model (shortest total message length) is selected. The MML expression is given as (1) where K mixture of clusters in the model; L (K) message length of model K; message length of the data given the model K; total message length.…”

Section: A Unsupervised Clustering Using MMLmentioning

confidence: 99%

“…ITH the increased use of power electronics in residential, commercial, and industrial distribution systems, combined with the proliferations of highly sensitive microprocessor-controlled equipment, more distribution customers are sensitive to excessive harmonics in the supply system [1], some even leading to the failure of equipment. An increasing number of electric distribution network service providers are installing harmonic monitoring equipment to measure the three-phase harmonic voltage and current waveforms in their power system to detect and mitigate the harmonic distortion problems [2]- [7].…”

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confidence: 99%

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Analyzing Harmonic Monitoring Data Using Supervised and Unsupervised Learning

Asheibi

Stirling

Sutanto

2009

IEEE Trans. Power Delivery

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Abstract-Harmonic monitoring has become an important tool for harmonic management in distribution system. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical medium-voltage distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial, and industrial load sectors. Data over a three year period have been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of the measurement data are of importance. Based on this information, a closer inspection of smaller data sets can then be carried out to determine the reasons for its detection. In this paper, we classify the measurement data using unsupervised learning based on clustering techniques using the minimum message length technique, which can provide the engineers with a rapid, visually oriented method of evaluating the underlying operational information contained within the clusters. Supervised learning is then used to describe the generated clusters and to predict the occurrences of unusual clusters in future measurement data.

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Section: A Unsupervised Clustering Using MMLmentioning

confidence: 99%

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confidence: 99%

Analyzing Harmonic Monitoring Data Using Supervised and Unsupervised Learning

Asheibi

Stirling

Sutanto

2009

IEEE Trans. Power Delivery

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“…It contains information about how particular groups of feature vectors are scattered around its centre value. The deviation vector D is calculated using equation (2).…”

Section: The Self Adapting Artificial Neural Network (Saann)mentioning

confidence: 99%

“…In a typical power quality monitoring system, the voltage and/or current waveforms at certain buses are recorded and then analysed to identify known power quality disturbance signatures [1,2]. If the process of power quality monitoring is fully automated, it will enable the utilities to respond more effectively to customer complaints.…”

Section: Introductionmentioning

confidence: 99%

Electric power quality disturbance classification using self-adapting artificial neural networks

Wijayakulasooriya

Putrus

Minns

2002

IEE Proc., Gener. Transm. Distrib.

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The issue of power quality is now recognised as an essential feature of a successful electric power system. This is mainly due to the rapid increase of loads, which generate noise and, at the same time, are sensitive to the noise present in the supply system. As a result, power quality monitoring has become an important issue in modern power systems.This paper presents a technique for classifying electrical power quality disturbance events. The technique is based on a novel Self-Adapting Artificial Neural Network (SAANN), which has the unique capability of adapting to new disturbance features.In the proposed technique, distinctive feature vectors from disturbance events captured are extracted using Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT). The feature vectors are then fed to two SAANN based classifiers, which classify the captured events into different categories of power quality disturbances. The proposed technique is tested using a number of disturbance events and results are presented.

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“…HE proliferation of highly sensitive computerized equipment places increasingly more stringent demands on the quality of electric power supplied to the customer [2]. Today, power quality (PQ) has become a very interesting cross-disciplinary topic, coupling power engineering and power electronics with digital signal processing, software engineering, networking, and VLSI.…”

Section: Introductionmentioning

confidence: 99%

Classification of Power Quality Events Using Optimal Time-Frequency Representations—Part 2: Application

Wang

Rowe

Mamishev

2004

IEEE Trans. Power Delivery

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Abstract-Better software and hardware for automatic classification of power quality (PQ) disturbances are desired for both utilities and commercial customers. Existing automatic recognition methods need improvement in terms of their capability, reliability, and accuracy. This paper presents the theoretical foundation of a new method for classifying voltage and current waveform events that are related to a variety of PQ problems. The method is composed of two sequential processes: feature extraction and classification. The proposed feature extraction tool, time-frequency ambiguity plane with kernel techniques, is new to the power engineering field. The essence of the feature exaction is to project a PQ signal onto a lowdimension time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes. The technique of designing an optimized TFR from timefrequency ambiguity plane is for the first time applied to the PQ classification problem. A distinct TFR is designed for each class. I. INTRODUCTIONHE proliferation of highly sensitive computerized equipment places increasingly more stringent demands on the quality of electric power supplied to the customer [2]. Today, power quality (PQ) has become a very interesting cross-disciplinary topic, coupling power engineering and power electronics with digital signal processing, software engineering, networking, and VLSI.Voltage disturbances are the most frequent cause of a broad This work is supported by the Advanced Power Technologies (APT) Center at the University of Washington. The APT Center is supported by ALSTOM ESCA, LG Industrial Systems, RTE France, and Mitsubishi Electric Corp. This work is also partially supported by the National Science Foundation Career Award Grant #0093716 and the American Public Power Association DEED program.Both authors are with the SEAL (Sensors, Energy, and Automation Laboratory), Department of Electrical Engineering, Box 352500, University of Washington, Seattle, WA98195. (E-mails: mwang@ee.washington.edu; mamishev@ee.washington.edu) range of disruption in industrial and commercial power supply systems. These disturbances, often referred to as power quality problems, significantly affect many industries. Major causes of PQ-related revenue losses are interrupted manufacturing processes and computer network downtime. The examples abound in semiconductor industry, chemical industry, automobile industry, paper manufacturing, and ecommerce. A report by CEIDS (Consortium for Electric Infrastructure to Support a Digital Society) shows that the U.S. economy is losing between $104 billion and $164 billion a year due to outages and another $15 billion to $24 billion due to PQ phenomena [3].The conventional methods currently used by utilities for power quality monitoring are primarily based on visual inspection of voltage and current waveforms. Highly automated monitoring software and hardware is needed in order to provide adequate coverage of the entire system, understand the causes of these disturba...

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Electric power quality: a tutorial introduction

Cited by 62 publications

References 1 publication

Analyzing Harmonic Monitoring Data Using Supervised and Unsupervised Learning

Analyzing Harmonic Monitoring Data Using Supervised and Unsupervised Learning

Electric power quality disturbance classification using self-adapting artificial neural networks

Classification of Power Quality Events Using Optimal Time-Frequency Representations—Part 2: Application

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