Wind Turbine Fire Prevention System Using Fuzzy Rules and WEKA Data Mining Cluster Analysis

Jong-Hyun Kim, Se-Hwan Park, Sang-Jun Park, Byeong-Ju Yun and You-Sik Hong ()
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Jong-Hyun Kim: DXlabz Co., Ltd., Suwon 16679, Republic of Korea
Se-Hwan Park: DXlabz Co., Ltd., Suwon 16679, Republic of Korea
Sang-Jun Park: GaonPlatform Inc., Daejeon 34113, Republic of Korea
Byeong-Ju Yun: R&D Institute Tae Hee Evolution Co., Ltd., Seoul 15845, Republic of Korea
You-Sik Hong: Department of Information and Communication Engineering, Sangji University, Wonju 26339, Republic of Korea

Energies, 2023, vol. 16, issue 13, 1-20

Abstract: With the rapid expansion of the supply of renewable energy in accordance with the global energy transition policy, the wind power generation industry is attracting attention. Subsequently, various wind turbine control technologies have been widely developed and applied. However, there is a lack of research on optimal pitch control, which detects wind direction and changes the rotation angle of the blade in real time. In areas where the wind speed is not strong, such as South Korea, it is necessary to maintain the optimal angle in real time so that the rotating surface of the blade can face the wind direction. In this study, optimal pitch control was performed through real-time analysis of wind speed, direction, and temperature, which is the core of wind turbine maintenance, using fuzzy rules using FIS (Fuzzy Interface System) and WEKA data mining cluster analysis techniques. In order to prevent fires caused by the over-current of wind turbines, over-current control methods such as VCB (Vacuum Circuit Breaker) utilization, prototype utilization such as a modular MCB (Main Circuit Breaker) incorporating VI (Vacuum Interrupter), and vacuum degree change analysis methods using a PD (Partial Discharge) signal were proposed. The optimal control technique for wind turbine parts and facilities was put forth after judging and predicting the annual average wind distribution suitable for wind power generation using HRWPRM (Korea’s High-Resolution Wind Power Resource Maps). Finally, the various wind turbine control methods carried out in this study were confirmed through computer simulation, such as remote diagnosis and early warning issuance, prediction of power generation increase and decrease situation, and automatic analysis of wind turbine efficiency.

Keywords: pitch/yaw/stall control; fuzzy rules; WEKA data mining cluster analysis; wind turbine over-current; wind power big data; fire risk analysis algorithm; wind power resource maps; computer simulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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