Estimation of Number of Voltage Sags in the IEEE 14-Bus Network Using Bayesian and Artificial Neural Network: A Case Study
| Autor: | Sahand Ghaseminejad Liasi, Hamed Jalalat, Mohammad Tavakoli Bina |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Estimation
Artificial neural network Computer science 020209 energy 020208 electrical & electronic engineering Bayesian probability Monte Carlo method 02 engineering and technology Reliability engineering Bus network Voltage sag 0202 electrical engineering electronic engineering information engineering Power quality Voltage |
| Zdroj: | 2020 28th Iranian Conference on Electrical Engineering (ICEE). |
| DOI: | 10.1109/icee50131.2020.9260574 |
| Popis: | Voltage sag is one of the most important power quality issues, which can lead to significant financial losses for the network and consumers. Since monitoring all network buses is not feasible, various approaches are used to estimate the voltage of various buses based on limited number of monitored buses. In this paper, long term data extraction using Monte Carlo method is explained and based on the extracted data, estimation of number of voltage sags has been carried out using Bayesian and artificial neural network approaches. The results have been compared to validate the accuracy and simplicity of the ANN method. |
| Databáze: | OpenAIRE |
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