Evaluation of installation quality of a PV System based on Impedance of the PV Inputs.

Abstract

The integration of solar photovoltaic (PV) systems into the power grid has significantly increased, necessitating advancements in solar inverter technology to ensure efficient energy conversion and grid stability. This thesis explores the innovative approach of measuring the impedance of PV strings using the existing hardware within solar inverters, specifically leveraging the Power Line Communication (PLC) and Arc Fault Circuit Interrupter (AFCI) systems. The primary objective is to enhance the functionality of inverters by integrating impedance measurement techniques without incurring additional hardware costs.
The study begins with a comprehensive review of the current state of PV systems, highlighting the importance of proper installation and maintenance to mitigate risks such as electrical faults and fire hazards. It then addresses the challenges posed by the aging of PV system components and the impact of environmental conditions on their performance. The thesis proposes a method for continuous or periodic impedance measurement to adjust the PLC transmitter’s gain, ensuring reliable communication and minimizing electromagnetic interference. Key objectives include the development of a software solution for DC-PLC signal transmission, frequency response analysis, utilization of internal AFCI sensor hardware,
and calibration of PLC signal gain for module-level power electronics. The methodology involves detailed experimental setups and measurements, focusing on the differential impedance of PV strings and the behavior of various system components. The results demonstrate the feasibility of using existing inverter hardware for impedance measurement, with significant implications for improving the reliability and efficiency of PV systems. The study concludes with recommendations for future hardware designs, emphasizing the integration of advanced measurement capabilities and enhanced safety features.

Date of Award	2025
Original language	English (American)
Supervisor	Stephan Selinger (Supervisor)