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O retorno de investimento sobre uma planta fotovoltaica (FV) de geração distribuída ao longo de cinco ou mais anos de vida útil exige o acompanhamento e garantia de eficiência de sua operação. Embora não exista uma métrica única que forneça a eficiência global da planta fotovoltaica, pode-se acompanhar a qualidade de operação da planta por meio de um conjunto variado de métricas referenciadas nas normas e materiais técnicos e científicos da área. Este artigo realiza um estudo sobre métricas de performance e seleciona um conjunto de oito métricas para a realização da análise de eficiência de um sistema fotovoltaico de 5 kWp, conectado à rede elétrica. São apresentados os resultados obtidos em um período de amostragem de trinta dias permitindo observar o comportamento preliminar e as inter-relações entre as métricas consideradas, e tornando esta análise mais abrangente do que comumente encontrado na literatura.

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

Photovoltaic (PV) energy use is growing fast all over the world, due to new policies to reduce the application of fossil fuels. However, for achieving higher efficiency in the Renewable Power Plants (RPP), some Monitoring and Management System (MMS) are required, aiming to find the optimal operating conditions for the system. The main goal of a monitoring system is to evaluate the RPP’s performance, throughout its operating time. In the same way, a management system aims to administrate and act over the system, in order to maximize its performance. In this scenario, the main objective of this paper is the proposition of one MMS, which focuses specifically on micro-generation (medium-scale), creating the possibility for final users and for Electric Utility Companies monitoring and operating plant parameters. In order to achieve that, electrical and environmental variables are monitored and computationally analyzed, producing instantaneous and historical data, allowing to estimate the plant efficiency and some actions over the plant’s parameters, such as the power factor through the power inverter. We present the initial results of this MMS including a detailed description of each stage of the system, calibration procedures and a short-period monitoring, showing the feasibility of the proposal in a reduced PV plant of 5kW.