Symmetrical pattern and PCA based framework for fault detection and classification in power systems

Alsafasfeh, Qais; Abdel‐Qader, Ikhlas; Harb, Ahmad

doi:10.1109/eit.2010.5612179

Cited by 26 publications

(18 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The fault detection and classification technique is wavelet based. [5] uses readings of the phase current only during the first one-forth of a cycle in an integrated method that combines symmetrical components technique with the principal component analysis (PCA) to declare, identify, and classify a fault. The fault section determination method is artificial neural network based and uses the local current and voltage signals to estimate the faulted section.…”

Section: State Of the Artmentioning

confidence: 99%

Multiple Fault Diagnosis in Electrical Power Systems with Dynamic Load Changes Using Soft Computing

González

2013

Advances in Computational Intelligence

View full text Add to dashboard Cite

Power systems monitoring is particularly challenging due to the presence of dynamic load changes in normal operation mode of network nodes, as well as the presence of both continuous and discrete variables, noisy information and lack or excess of data. In this domain, the need to develop more powerful approaches has been recognized, and hybrid techniques that combine several reasoning methods start to be used. The present work is a variant of a proposal made by the author. This paper proposes a fault diagnosis framework that is able to locate the set of nodes involved in multiple fault events. The proposal is a methodology based on the system history data. It detects the faulty nodes, the type of fault in those nodes and the time when it is present. The framework is composed of two phases: In the first phase a probabilistic neural network is trained with the eigenvalues of voltage data collected during normal operation, symmetrical and asymmetrical fault disturbances. The second phase uses an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to give the final diagnosis. A set of simulations are carried out over an electrical power system proposed by the IEEE. To show the performance of the approach, a comparison is made against two different diagnostic systems.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Multiple Fault Diagnosis in Electrical Power Systems with Dynamic Load Changes Using Soft Computing

González

2013

Advances in Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Principal Component Analysis (PCA) based Fault Detection, Classification and Localization Method. PCA has proven to achieve excellent results in feature extraction and data reduction in large datasets [9]. Typically PCA is utilized is to reduce the dimensionality of a dataset in which there is a large number of interrelated variables while the current variation in the dataset is maintained as much as possible [9].…”

Section: Fault Ellipse Signaturementioning

confidence: 99%

“…Wavelet transform is adopted to discriminate the faults type from the magnetizing inrush current [5]. Others incorporated wavelet transform with other methods such as Probabilistic Neural Network (PNN), adaptive resonance theory, adaptive neural fuzzy inference system, and support vector machines [6][7][8][9][10]. Fuzzy logic was also combined with discrete Fourier transform, adaptive resonance theory, principles of estimation and independent component analysis to enhance performance [9].…”

Section: Introductionmentioning

confidence: 99%

“…Others incorporated wavelet transform with other methods such as Probabilistic Neural Network (PNN), adaptive resonance theory, adaptive neural fuzzy inference system, and support vector machines [6][7][8][9][10]. Fuzzy logic was also combined with discrete Fourier transform, adaptive resonance theory, principles of estimation and independent component analysis to enhance performance [9]. Unfortunately, most of the available tools for fault detection and classification are not efficient and are not investigated for real time im-plementation there is a need for new algorithms that have high efficiency suitable for real time usage especially for power distribution system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online Diagnosis and Monitoring for Power Distribution System

Almashaqbeh¹,

Arfoa²

2012

EPE

View full text Add to dashboard Cite

Recently, power distribution system is getting larger and more complex. It is very difficult even for the experts to diagnosis and monitoring to made best action. This motivated many researchers to investigate power systems in an effort to improve reliability by focusing on fault detection and classification. There have been many studies on problems but the results are not good enough for applying to real power system. In this paper, a new protective relaying framework to diagnosis and monitoring faults in an electrical power distribution system with. This work will extract fault signatures by using ellipse fit using least squares criterion during fault condition. By utilizing principal component analysis methods, this system will identify, classify and localize any fault instantaneously

show abstract

“…Over the years, knowledge and capabilities of monitoring and analyzing power quality attributes have matured and technologies such as sensors and disturbance analyzers are also made available to both electric utilities and industrial users. In the meantime, using the same electrical "signatures" to monitor individual equipment as means of health monitoring [11], fault detection [12]- [14] and usage tracking [15] are also emerging.…”

Section: Introductionmentioning

confidence: 99%

An alternative use of power quality information - Load signature studies & applications

Cheng¹,

Liang³

2012

2012 IEEE 15th International Conference on Harmonics and Quality of Power

View full text Add to dashboard Cite

Electrical appliances have distinct consumption patterns of which can be interpreted as signatures and used for various applications. Based on traditional power quality monitoring techniques, coupled with optimization algorithms and innovative pattern recognition methods and stochastic simulation tools, we are going to demonstrate in this paper how we defined different load signatures systematically, structured a disaggregation framework to identify and track individual appliances, assembled different Monte-Carlo simulators for testing purposes and proposed some innovative applications such as smart metering and equipment health monitoring using different load signatures. Index Terms-Load signature, smart metering, load disaggregation, power quality, equipment health monitoring, visualization tool.

show abstract

Symmetrical pattern and PCA based framework for fault detection and classification in power systems

Cited by 26 publications

References 13 publications

Multiple Fault Diagnosis in Electrical Power Systems with Dynamic Load Changes Using Soft Computing

Multiple Fault Diagnosis in Electrical Power Systems with Dynamic Load Changes Using Soft Computing

Online Diagnosis and Monitoring for Power Distribution System

An alternative use of power quality information - Load signature studies & applications

Contact Info

Product

Resources

About

Symmetrical pattern and PCA based framework for fault detection and classification in power systems

Cited by 26 publications

References 13 publications

Multiple Fault Diagnosis in Electrical Power Systems with Dynamic Load Changes Using Soft Computing

Multiple Fault Diagnosis in Electrical Power Systems with Dynamic Load Changes Using Soft Computing

Online Diagnosis and Monitoring for Power Distribution System

An alternative use of power quality information - Load signature studies &amp; applications

Contact Info

Product

Resources

About

An alternative use of power quality information - Load signature studies & applications