Translated Title
Fault diagnosis method for photovoltaic module based on KELM
Translated Abstract
A fault diagnosis method for photovoltaic module based on kernel extreme learning machine (KELM) was presented.The relationship between the fault of various types of photovoltaic modules and photovoltaic module equivalent model parameters were analyzed.The optimal root mean square error (RMSE) for parameter identification was introduced as the characteristic variable of the local intrinsic shadow diagnosis,and the input characteristic vector of KELM fault diagnosis model was formulated and optimized.The simulation model and experimental analysis show that compared to the method directly using the equivalent model parameters as the input to the neural network,the proposed method can be more rapid and precise to identify the conventional short circuit,aging and shadow fault of the photovoltaic module.
Translated Keyword
fault diagnosis
KELM
model parameter
PV module
Access Number
WF:perioarticaldianyjs201804021
