인공 신경망 예측기를 이용한 새로운 데이터 모델 기반 PV 어레이 고장 진단용 기준 신호 발생 방법

Abstract

The power supply part of a PV system is called a PV array which is composed of many PV strings connected in parallel. A PV-string which is a set of PV modules is made by serial connection of many PV modules. PV modules presents irregularity of ouput power which is generated due to various factors such as average performance degradation of about 0.923% to 1.54% according to various researches and born power variations over production stages. Power degradation and born power variation are due to various factors such as material interactions(connection state of connectors between PV cells, corrosion, browning of encapsulation materials ...etc) and environment factors such as shading and soiling which refers to the accumulation of snow, dirt, leaves and bird droppings on PV modules. Such various minor factors can make side effects in safety side and economic one. Therefore several methodology for diagnosis of PV-array have been developed, which are classified into three types – image-based diagnosis approach, model-based approach and data-driven approach. In this paper, a new data-based approach(called data model) with good failure diagnosis reliability and economy is proposed, and verified by simulation using Python, published data and reasonable data generation. Based on the bathtub failure rate function, the fault diagnosis model requires a well-functioning predictor to generate a reference signal which evaluates the output characteristics of PV array under instantaneously varying environmental condition such as solar irradiance, temperature, etc. To implement a new data-based approach, an Artificial Neural Network (ANN) based predictor is applied as a reference generator for PV array’s fault diagnosis. r² value and the RMSE(Root Mean Square Error) are used to evaluate how far away individual learned predictions are from the actual measured values. Based on the reliability obtained from the learning result for a specific PV string, it is confirmed through pair comparison analysis(called t-test) that the learning result(called clustering possibility) between PV strings under different installation conditions is also reliable.