Metodologia para identificação de condições não ideais de operação de plantas fotovoltaicas

Abstract

The use of photovoltaic solar power generation is rising as worldwide energy demand increases. Therefore, reliability, safety, life cycle, and improved efficiency of photovoltaic plants have all become a major concern in research nowadays. In this context, monitoring systems are necessary to guarantee the required operating productivity and to avoid overpriced maintenance costs. This paper studies the non-ideal operating conditions for grid-connected photovoltaic plants and proposes an anomaly detection methodology that combines the advantages of the 2-sigma, short-window simple-moving average control charts with shading strength and irradiance transition parameters to detect early deviation in photovoltaic plant operation data. The key aspect of proposed methodology is that it requires neither historical data for model training procedure nor parameters from previous simulation. Only instantaneous meteorological and electrical parameters are required. The efficiency of the condition monitoring methodology has been validated through experimental results conducted in real operating conditions. Results demonstrated that the proposed methodology is effective to identify non-ideal operating conditions for grid-connected photovoltaic plants, i.e., (i) normal operating condition, (ii) natural dynamic shading, (iii) artificial dynamic shading, and (iv) artificial static shading. Moreover, a low-cost and non-invasive internet-of-things-based embedded architecture is proposed to monitor photovoltaic plant operation in real-time. As part of the research, an embedded monitoring architecture based on Internet of Things (SAD-IoT) concepts, low cost and non-invasive, is proposed to monitor the operation of the PV plant in real time. The performance requirements of the developed SAD-IoT (parameters and resolution) are compared according to the IEC61724 standard. Maximum errors of 1.20 % and 1.45 % are obtained for the parameters of the ambient temperature and the operating temperature of the PV modules, respectively. Regarding solar irradiance, a maximum error of 0.68 % is obtained, remaining within the maximum uncertainty range recommended by the standard. The main advantage of the developed SAD-IoT is its scalability, reliability and ease of changing sensors, in addition to providing real-time information collected using the Internet of Things MQTT protocol. Through permanent generation monitoring, it is possible to ensure that PV plants operate within ideal conditions and reach the expected generation levels throughout their useful life.