Power quality disturbance detection and classification using wavelets and artificial neural networks

Peruničić, B.; Mallini, M.; Wang, Z.; Liu, Y.

doi:10.1109/ichqp.1998.759843

Cited by 69 publications

(29 citation statements)

References 11 publications

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“…The strength and weakness of different artificial techniques such as expert system (ES), fuzzy system (FS), neural network (NN), genetic algorithm (GA) and support vector machine (SVM) has been presented in Table 3 (Negnevitsky, 2004). Perunicic et al, 1998;Santoso et al, 2000c;Gaouda et al, 2002a;Giang, 2004;He et al, 2006;Mishra et al, 2008. Expert System Based Classifier Santoso et al, 2000b;Styvaktakis et al, 2001;Styvaktakis et al, 2002;Chung et al, 2002;Reaz et al, 2007.…”

Section: Summary Of Pq Classification Methodsmentioning

confidence: 99%

“…To extract the squared wavelet transform coefficients at each scale as inputs to neural networks for classification of the disturbances have been proposed in (Santoso et al, 1996;Santoso et al, 2000c;Gaouda et al, 1999). (Perunicic et al, 1998) used DWT coefficient as inputs to a single layer self organizing map neural network to train and classify the transient disturbance type. An effective wavelet multi-resolution single decomposition method was proposed for analyzing the power quality transient events based on standard derivation (Gaouda et al, 1999) and root mean square value (Gaouda et al, 2002a).…”

Section: Artificial Neural Network Based Classifiersmentioning

confidence: 99%

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Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

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Power quality (PQ) issue has attained considerable attention in the last decade due to large penetration of power electronics based loads and/or microprocessor based controlled loads. On one hand these devices introduce power quality problem and on other hand these mal-operate due to the induced power quality problems. PQ disturbances/events cover a broad frequency range with significantly different magnitude variations and can be non-stationary, thus, accurate techniques are required to identify and classify these events/disturbances. This paper presents a comprehensive overview of different techniques used for PQ events' classifications. Various artificial intelligent techniques which are used in PQ event classification are also discussed. Major Key issues and challenges in classifying PQ events are critically examined and outlined.

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Section: Summary Of Pq Classification Methodsmentioning

confidence: 99%

Section: Artificial Neural Network Based Classifiersmentioning

confidence: 99%

Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

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“…The traditional method is based on RMS measurements and constrained by its accuracy. Approaches for automated detection and classification of PQ disturbances proposed recently are based on wavelet analysis, artificial neural networks, hidden Markov models, and bispectra [4][5][6][7][8] In this paper, the technique of designing an optimized time-frequency representation (TFR) from a time-frequency ambiguity plane is applied to the PQ classification problem for the first time. Because the proposed method is entirely new to the power engineering field and many new concepts are introduced, we present the material with a two-paper series: Part 1 --Theory (i.e., this paper) and Part 2 -Application [1].…”

Section: Introductionmentioning

confidence: 99%

“…The traditional method is based on RMS measurements and constrained by its accuracy. Approaches for automated detection and classification of PQ disturbances proposed recently are based on wavelet analysis, artificial neural networks, hidden Markov models, and bispectra [4][5][6][7][8]. These techniques have been successfully employed in other pattern recognition and signal processing applications, such as speech recognition, audio processing, communications, and radar and sonar applications.…”

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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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“…Recently proposed approaches for automated detection and classification of PQ disturbances are based on wavelet analysis, artificial neural networks, hidden Markov models, and bispectra [2][3][4][5][6]. Real-time PQ monitoring hardware should be capable of acquiring voltage or current waveforms, identifying the event type based on the waveform pattern, understanding the cause of the disturbance, and making system protection and prevention decisions.…”

Section: Introductionmentioning

confidence: 99%