Monitoring and Diagnostics of Photovoltaic Power Plants

Abstract

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

Diagnostic method for photovoltaic systems based on light I–V measurements

“…In comparison with yield measurements, I-V curves can provide significantly more information regarding the condition and electrical properties of the PV generator, such as: short-circuit current, open-circuit voltage, fill factor, series and shunt resistance, ideality factor (Meyer and van Dyk, 2004;Sera et al, 2008;Tina et al, 2014), as well as indicate the presence of shading and soiling (Miwa et al, 2006;Sera et al, 2011), or other conditions. In the past, field measurement of the I-V curve of PV modules, strings, and arrays, required dedicated hardware, such as I-V tracers.…”

A comprehensive study of diagnosis faults techniques occurring in photovoltaic generators

“…Other methods are presented in [58], [59], and [60]. All of the developed electrical FDD based techniques can be done through offline adaptation (test of the technique's effectiveness) [36] or online adaptation at a real-time [60][61][62][63].…”

Photovoltaic Fault Detection and Classification Using Common Vector Approach Based on I-V Curve

“…Here,X c = 1 n n i=1 X ci is the mean vector of class c. Then the eigenvalue-eigenvector decomposition is applied to the class covariance matrix obtained in (7). From this decomposition, the m-dimensional feature space spanned by all the eigenvectors can be divided into two subspaces, such as (n − 1)-dimensional difference subspace and (m − n + 1)-dimensional orthogonal indifference subspace.…”

“…These studies generally differ in terms of main features such as rapid fault detection, required input data, and the separability of different faults [3]. Faults can be mainly grouped into two categories as to be visual and thermal methods, and electrical methods [7]. Electrical methods use electrical signals data as the input data to the fault diagnosis systems.…”