Abstract
This paper originally presents a photovoltaic (PV) evaluation and fault detection (PVEFD) system for PV applications based on the Internet of Things (IoT) technology. The PVEFD system consists of an STM32F103C8T6 chip with a 32-bit Arm Cortex-M3 reduced instruction set computer (RISC) and 12-bit resolution analog-to-digital converter (ADC) to measure important parameters of PV applications, such as solar irradiance as well as the back-surface cell temperature, operating voltage, and output current of PV devices. The measured data of irradiance as well as back-surface cell temperature and operating voltage of PV devices are then fed into a built-in PV model in the on-chip Arm Cortex-M3 RISC for hardware-in-the-loop (HIL) simulation to obtain the simulated output current and power of PV devices. The resulting data are transmitted to a cloud server for remote monitoring and automatic warning function through a Raspberry PI 3 module and WiFi network. The simulation results are compared with in-field measurement data from PV modules and displayed on a human-machine interface (HMI) and an Android app. The results of the study illustrated that the proposed system features high accuracy and sufficient confidence. Furthermore, the fault detection function through the built-in HIL simulation function in PV systems was validated. Therefore, the proposed system is a small, compact, and cost-effective HIL-on-chip machine for remote surveillance of PV power systems.