Monitoring and Diagnostics of Photovoltaic Power Plants

Photovoltaic (PV) systems should be monitored in order to control their production and detect any possible faults. Different possibilities exist for data analysis. Some perform it yearly, analyzing the performance of the PV system over a significant time period of operation and comparing it with similar systems. This shows that a system is performing poorly, but it has the disadvantage that it cannot be used to explain the causes of this underperformance. Others use high-resolution monitoring (minutely to hourly intervals) to analyze the performance of the system; with this higher resolution, a fault diagnosis procedure can be executed. These systems can detect general faults like constant energy losses, total blackout, and short-time energy losses, and the best can also detect shading; however, they cannot identify the exact cause. With the introduction of distributed maximum power point tracking (DMPPT) systems—power optimizers and micro-inverters—a new level of PV system monitoring is possible. Since these systems require the monitoring of the modules’ operating voltage and current (for the maximum power point tracking (MPPT) algorithm), the use of voltage and current sensors for each module is at no extra cost. It is only necessary to add a communication module since it is not directly incorporated in the DMPPT board.Based on the use of such appliances, a wireless sensor network (WSN) that allows monitoring, at panel level, the efficiency of PV panels has been proposed; nodes of the WSN, which are installed on each PV module, are equipped with voltage, current, irradiance, and temperature. Acquired data are then transferred to a management center which is in charge of estimating efficiency losses and correlated causes at the level of the single module.This research has been further developed in this chapter. The authors propose the possibility of using the DC/DC converter inside the system for MPPT. It allows to span the PV module voltage within a certain range, to measure a partial current–voltage (I-V) curve under the assumption that failures can be detected mostly by the parameter variation of RS, RSH, and diode factor η in the I-V curve based on equivalent circuit equation. These parameter variations should be calculated from I-V curve variation. The failure pattern would be presumed from the parameter variations caused by I-V curve variation, if the field data were accumulated. Due to the fact that I-V is partially available, new algorithms, analytical and metaheuristic, for parameter identification have been developed.The calculated parameters are used not only to detect long-term faults (e.g., aging, soling, delamination, and so on), but also to build an I-V curve reference when an impromptu fault happens (e.g., shading, breakdown diode, and so on).An experimental hardware and software (in LabVIEW/MATLAB environment) setup has been realized, so many measurement campaigns have been done. The proposed algorithms for parameter identification have been checked against real outdoor conditions. The chapter contains several graphs and charts which explain the relationship between I-V curve shapes and type of faults. A full literature survey will also be included in this study.