Feature Extraction of Time Series Data for Wind Speed Power Generation

Khanna, Manju; Srinath, N. K.; Mendiratta, J. K.

doi:10.1109/iacc.2016.40

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…These features include the four first moments, the Root Mean Square (RMS), the non-dimensional parameters in time-domain (shape and crest factors), and the sample histogram. The time-domain features are expressed in equation (4) 17,34,35…”

Section: Data Acquisitionmentioning

confidence: 99%

Application of fuzzy data fusion theory in fault diagnosis of rotating machinery

Jafari

Poshtan

Sadeghi

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, the most common induction motor faults including bearing outer race defect, broken rotor bar, and short-circuit of stator windings are diagnosed with high reliability. The decentralized fuzzy-integral data fusion method is used for information fusion in feature level. In the proposed scheme, the feature vectors are constructed using signatures created by time-domain characteristics obtained from stator three-phase current measurements. Partial matching of each feature is calculated by the fuzzy c-mean classifier algorithm, and features with high diagnosis ability are fused by Choquet fuzzy integral. The technique is validated experimentally on the 4 hp induction motor of an electropump, and the results are presented.

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Section: Data Acquisitionmentioning

confidence: 99%

Application of fuzzy data fusion theory in fault diagnosis of rotating machinery

Jafari

Poshtan

Sadeghi

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Erdem ve Shi [13] rüzgâr hızı ve yön tahmininde ARIMA tabanlı metotlar geliştirmişlerdir. Narayana ve arkadaşları [3] rüzgâr tribünlerinin ileriye yönelik tahmin metotları ile kontrolü üzerine çalışmalar yaparken, Khanna ve arkadaşları [7] rüzgâr gücü üretiminde zaman serisi özelliklerinin belirlenmesi üzerine çalışmalar yapmışlardır. Khandelwal ve arkadaşları [14] ayrık dalgacık dönüşümü ile ARIMA ve yapay sinir ağı modelini birleştiren hibrid bir çalışma ile zaman serisi tahmini yapan bir model ortaya koymuşlardır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

İstatistiksel Metotlar ve Yapay Sinir Ağları Kullanarak Kısa Dönem Çok Adımlı Rüzgâr Hızı Tahmini

Kırbaş

2018

SAÜ Fen Bilimleri Enstitüsü Dergisi

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Bu çalışmada TÜBİTAK T60 ulusal gözlem evi meteoroloji istasyonunun 2016 yılı nisan ayı içerisinde yaptığı gözlem sonuçları PHP programlama dili kullanılarak web sitesi üzerinden derlenmiştir. Elde edilen rüzgâr hızı verileri istatistiksel ve yapay sinir ağı metotları kullanılarak incelenmiş ve meydana getirilen zaman serisi üzerinden ileriye yönelik rüzgâr hızı kestirimlerinde bulunulmuştur. Yapılan hesaplamalar ve gerçek veriler ile kıyaslamalar sonucunda incelenen ARIMA modelleri ve yapay sinir ağları arasında belirgin bir hata oranı farkı görülmüştür. Literatürde yer alan rüzgâr hızı tahmin çalışmaları genellikle sadece tek adım tahmin başarısı üzerinde yoğunlaşırken, önerilen çalışmada sık kullanılan tahmin metotlarının ileriye dönük 12 adım seviyesinde ayrıntılı değerlendirilmeleri gerçekleştirilmiştir.

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“…Khanna et.al. studied the determination of time series properties in wind power generation [9]. For wind turbines in Korea, wind energy was estimated with ANFIS, Sequential Minimal Optimization (SMO), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANN) models using hourly and daily wind speed, wind direction, temperature and time intervals.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of the Effect of Meteorological Parameters on Wind Speed Estimation Using Bagging Algorithm

Emeksiz

Demir²

2018

ijisae

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Wind speed is the most important parameter of the wind energy conversion system. Therefore temperature, humiditiy and pressure data, which has significant effect on the wind speed, have become extremely important. In the literature, various models have been used to realize the wind speed estimation. In this study; Six different data mining algorithms were used to determine the effect of meteorological parameters on wind speed estimation. The data were collected from the measurement station established on the campus of Gaziosmanpaşa University. We focused on the bagging algorithm to determine the appropriate combination of wind speed estimates. The bagging algorithm was used for the first time in estimation of wind speed by taking into account meteorological parameters. To find the most efficiency method on such problem 10-fold cross validation technique was used for comparision. From results, It is concluded that bagging algorithm and temperature-humiditiy-pressure combination showed the best performance. Additionaly, temperature and pressure data are more effective in the wind speed estimation.

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Feature Extraction of Time Series Data for Wind Speed Power Generation

Cited by 4 publications

References 11 publications

Application of fuzzy data fusion theory in fault diagnosis of rotating machinery

Application of fuzzy data fusion theory in fault diagnosis of rotating machinery

İstatistiksel Metotlar ve Yapay Sinir Ağları Kullanarak Kısa Dönem Çok Adımlı Rüzgâr Hızı Tahmini

An Investigation of the Effect of Meteorological Parameters on Wind Speed Estimation Using Bagging Algorithm

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