An On-Line Low-Cost Irradiance Monitoring Network with Sub-Second Sampling Adapted to Small-Scale PV Systems

Abstract

Very short-term solar forecasts are gaining interest for their application on real-time control of photovoltaic systems. These forecasts are intimately related to the cloud motion that produce variations of the irradiance field on scales of seconds and meters, thus particularly impacting in small photovoltaic systems. Very short-term forecast models must be supported by updated information of the local irradiance field, and solar sensor networks are positioning as the more direct way to obtain these data. The development of solar sensor networks adapted to small-scale systems as microgrids is subject to specific requirements: high updating frequency, high density of measurement points and low investment. This paper proposes a wireless sensor network able to provide snapshots of the irradiance field with an updating frequency of 2 Hz. The network comprised 16 motes regularly distributed over an area of 15 m × 15 m (4 motes × 4 motes, minimum intersensor distance of 5 m). The irradiance values were estimated from illuminance measurements acquired by lux-meters in the network motes. The estimated irradiances were validated with measurements of a secondary standard pyranometer obtaining a mean absolute error of 24.4 W/m2 and a standard deviation of 36.1 W/m2. The network was able to capture the cloud motion and the main features of the irradiance field even with the reduced dimensions of the monitoring area. These results and the low-cost of the measurement devices indicate that this concept of solar sensor networks would be appropriate not only for photovoltaic plants in the range of MW, but also for smaller systems such as the ones installed in microgrids.

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

“…The mechanisms of LSTM cells can be explained through the mathematical formulations presented in (30) to (35). For the first layer, the forget gate of the input 𝑥 𝑡 at time 𝑡 can be determined as:…”

“…In [25] Other protection techniques were also proposed to solve the fault detection and classification issues of grid-connected PV systems based on Thevenin Equivalent Resistance [28], radial basis function networks [29], wavelet transform [24], statistical monitoring [30], fuzzy inference [31] and etc. Furthermore, several studies were proposed to diagnose the PV faults used in different applications by first identifying the root causes of these faulty situations and then proposing the corresponding solutions to prevent their occurrence in the future, including the fault diagnosis [32,33], prediction [34], cost irradiance monitoring [35], maintenance [10], and islanding [36][37][38]. Fault monitoring and diagnosing are the effective maintenance mechanisms used by most existing PV systems to prevent the occurrence of faulty issues and corresponding consequences.…”

Design and Implementation of Fuzzy Compensation Scheme for Temperature and Solar Irradiance Wireless Sensor Network (WSN) on Solar Photovoltaic (PV) System

“…This motivates the development of an IoT-based wireless network PV modules system to build a monitoring network for PV modules suitable for large-scale solar farm applications [ 23 ]. Fault detection is also another justification for the monitoring of individual PV modules.…”

Equivalent Circuit Model for Cu(In,Ga)Se2 Solar Cells Operating at Different Temperatures and Irradiance

“…In Equation (1), V and I stand for cell output voltage and current, respectively. Although this circuital model might seem simple, it is able to describe accurately the current-voltage behavior of different PV cell technologies [19] and can be used for several interesting PV-related applications, such as irradiance measurement [26,27], device degradation assessment [28] and maximum power point tracking [29][30][31][32][33]. As stated before, the main problem in the use of this circuital model is the extraction of its parameters from measured current-voltage curves.…”