Reliability analysis of photovoltaic grid connected power generation system based on grey prediction
To improve the accuracy of fault prediction and operational reliability of photovoltaic grid connected power generation systems,a reliability analysis method for photovoltaic grid connected power generation systems based on grey prediction is proposed.According to the fuzzy C clustering algorithm,the output values of photovoltaic grid connected power generation systems under different weather types are clustered.Based on the clustering results,the probability of system state transition is statistically analyzed,and the time series algorithm(ARMA)method is combined to calculate the transition states of different systems.The construction of a time series model for photovoltaic grid connected power generation systems is completed,and the gray prediction method is applied to construct a state prediction model for photovoltaic grid connected power generation systems,Complete reliability prediction for photovoltaic grid connected power generation systems.The experimental results show that the system generated output power of the proposed method is basically consistent with the actual system generated output power.The reliability analysis time of the proposed method is shorter,and the fault rate,actual availability rate,and equipment fault derating operation rate of the power generation system are closer to the actual values.This indicates that the proposed method can accurately obtain the future operational status of the system and has a good application effect.
Grey predictionPower generation systemReliabilityData clusteringFuzzy C clustering algorithm
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