High-performance IoT Module for real-time control and self-diagnose PV panels under working daylight and dark electroluminescence conditions

Abstract

This article examines the needs of future solar photovoltaic modules in relation to monitoring and optimizing their performance, and it presents the design of a new IoT module that aims to meet these needs. The proposed IoT Module provides a hardware and software platform applied to individual PV panels within PV strings. It introduces innovative capabilities such as real-time and precise monitoring at high rate for individual PV panels, local processing of collected information within the module, and active control actions at PV panel level. These actions include connection/disconnection and active bypass within the PV string, both during daylight generation and dark EL testing conditions. The IoT module allows each panel to acquire electrical and environmental data, trace I–V curves, reconfigure its connections within a PV string, and conduct individual EL testing. It also incorporates Wi-Fi and BLE communication protocols, enabling communication with other panels and IoT devices. Thus, it can coordinate certain reconfiguration actions and locally apply distributed algorithms for edge computing. As an example, a distributed algorithm for marginal partial-shading anomaly detection is included to demonstrate the abilities of the IoT Modules to self-diagnose and perform edge-computing processing. The functionality and performance of the IoT Module are verified through simulations and experimental tests. The results confirm that the designed IoT module provides a novel monitoring and management solution for PV panels, enabling them to enhance their performance and progress towards PV digitization.