Power-Quality Event Analysis Using Higher Order Cumulants and Quadratic Classifiers
| Autor: | Dogan Gokhan Ece, Ömer Nezih Gerek |
|---|---|
| Přispěvatelé: | Anadolu Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Gerek, Ömer Nezih |
| Rok vydání: | 2006 |
| Předmět: |
Mahalanobis distance
business.industry Power-Quality (Pq) Analysis Feature vector Statistical parameter Energy Engineering and Power Technology Higher Order Statistics Higher-order statistics Pattern recognition Machine learning computer.software_genre Quadratic Classifiers Maxima and minima Metric (mathematics) Artificial intelligence Electrical and Electronic Engineering business computer Cumulant Event (probability theory) Mathematics |
| Zdroj: | IEEE Transactions on Power Delivery. 21:883-889 |
| ISSN: | 0885-8977 |
| DOI: | 10.1109/tpwrd.2006.870989 |
| Popis: | WOS: 000236519200042 In this paper, we present a novel power-quality (PQ) event detection and classification method using higher order cumulants as the feature parameter, and quadratic classifiers as the classification method. We have observed that local higher order statistical parameters that are estimated from short segments of 50-Hz notch-filtered voltage waveform data carry discriminative features for PQ events analyzed herein. A vector with six parameters consisting of local minimas and maximas of higher order central cumulants starting from the second (variance) up to the fourth cumulant is used as the feature vector. Local vector magnitudes and simple thresholding provide an immediate event detection criterion. After the detection of a PQ event, local maxima and minima of the cumulants around the event instant are used for the event-type classification. We have observed that the minima and maxima for each statistical order produces clusters in the feature space. These clusters were observed to exhibit noncircular topology; hence, quadratic-type classifiers that require the Mahalanobis distance metric art! proposed. The events investigated and presented are line-to-ground arcing faults and voltage sags due to the induction motor starting. Detection and classification results obtained from an experimentally staged PQ event data set are presented. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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