Power quality monitoring in electric grid integrating offshore wind energy: A review

The rising integration of offshore wind energy into the electric grid provides remarkable opportunities in terms of environmental sustainability and cost efficiency. However, it poses challenges to power quality (PQ) caused by variable operational events and power electronic devices used in wind turbines. This renders early-stage disturbance detection and classification tools critical for managing grid systems with renewable energy sources. While existing reviews specialize in the monitoring measures of partial PQ events, this survey offers a deeper understanding by further exploring root causes and disturbance locations, followed by a critical discussion of emerging algorithmic solutions. In contrast with generalists' stances on PQ, this work delves into the impact of offshore wind energy on PQ events and their early diagnosis. Moreover, the principles and applications of synchronized waveform measurement, a promising measurement technology for detection and classification processes, are highlighted. Then, evaluation metrics for detection and classification algorithms are discussed for the first time. Finally, a novel system-wide monitoring framework is proposed given the need for holistic assessment frames in this field. This review not only illustrates the challenges and future research directions in the level of algorithms, measurements, and frameworks, but can also serve as a guideline for real-time disturbance analysis of offshore wind power grid connection and integration.

Automated summary

The integration of offshore wind energy into the electric grid provides opportunities in terms of environmental sustainability and cost efficiency, but poses challenges to power quality. This survey offers a deeper understanding of disturbance detection and classification tools, exploring root causes, disturbance locations, and algorithmic solutions. It highlights synchronized waveform measurement and discusses evaluation metrics for detection and classification algorithms. Additionally, a novel system-wide monitoring framework is proposed.