ANOMALY DATA DETECTION METHOD FOR PHOTOVOLTAIC ARRAYS BASED ON TWO-STEP PAIR-COPULA MODELING
To optimally monitor photovoltaic arrays and forecast their power production,improving the quality of photovoltaic data is an essential and urgent task.To this end,this paper introduces a method for the identification of anomalous data in photovoltaic arrays based on a two-step Pair-Copula approach.This method is divided into two stages:the first stage involves the identification of outliers in the direct current side of the photovoltaic array,while the second stage,building upon the first,involves the identification of outliers in the photovoltaic direct current side voltage.More specifically,the Pair-Copula is utilized to model the dependence structure between photovoltaic current,irradiance,and temperature,with Akaike information criterion employed to optimize the Copula function.Subsequently,a conditional probability model for the photovoltaic current is established,and the formula for calculating the confidence interval of the conditional probability is derived.The confidence interval of the photovoltaic current is then used as the primary criterion for identifying and eliminating current outliers.Finally,building upon the data obtained in the previous step,the aforementioned procedure is repeated to eliminate voltage outliers.The results of simulation experiments demonstrate that,compared with other outlier identification methods,the approach proposed in this paper maintains a low identification error rate while boasting a higher identification accuracy.
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