EECon - 2021

Permanent URI for this collectionhttp://192.248.9.226/handle/123/17340

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Browsing EECon - 2021 by Author "Abeygunawardane, SK"

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    Application of machine learning algorithms for predicting vegetation related outages in power distribution systems
    (Institute of Electrical and Electronics Engineers, Inc., 2021-09) Melagoda, AU; Karunarathna, TDLP; Nisaharan, G; Amarasinghe, PAGM; Abeygunawardane, SK; Abeykoon, AMHS; Velmanickam, L
    A large number of faults in power distribution systems is caused due to vegetation growing near power lines. Therefore, to maintain high system reliability, outages should be prevented as much as possible before they occur. This paper proposes a data-driven approach to predict vegetation-related outages in power distribution systems. Three Machine Learning (ML) methods i.e., the Neural Network (NN), Decision Tree Classifier (DTC) and Random Forest Classifier (RFC) are used to predict the vegetation-related outages. Historical outage data and weather data are used as the inputs to the ML methods. Then, the ML models are trained and used to predict the probability of occurrence of an outage in the next fourteen days. A risk map is generated by incorporating the geographical location of distribution feeders based on the predicted outage probabilities. Moreover, a real-time outage prediction platform is developed to provide the utilities a better insight into vegetation-related outages. The accuracy of predicting failures is found to be 72.57%, 84.06% and 93.79% for NN, DTC and RFC, respectively.
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    Short-term wind power forecasting using a Markov model
    (Institute of Electrical and Electronics Engineers, Inc., 2021-09) Jeyakumar, P; Kolambage, N; Geeganage, N; Amarasinghe, G; Abeygunawardane, SK; Abeykoon, AMHS; Velmanickam, L
    Large-scale wind power integration to power systems has been significantly increasing since the last decade. However, the reliability of power systems tends to degrade due to the intermittency and uncontrollability of wind power. Future wind power generation forecasts can be used to reduce the impacts of intermittency and uncontrollability of wind power on the reliability of power systems. This paper proposes a Markov chain-based model for the short-term forecasting of wind power. The first-order and second-order Markov chain principles are used as they require lesser memory and have lower complexities. Seasonal variation is also incorporated into the proposed model to further improve the accuracy. Results obtained from both Markov models are validated with real wind power output data and evaluated using evaluation metrics such as Mean Square Error and Root Mean Square Error. The results show that the accuracy of the first-order and second-order Markov models for a high wind regime is 81.33% and 82.61%, respectively and for a low wind regime is 83.50% and 89.27% respectively.