A Comprehensive Review of Power Quality Monitoring and Analysis Methods in Grid-Connected Wind Farms Using Smart Measurement Tools

The rapid global shift towards renewable energy, particularly wind power, has introduced new complexities in maintaining power quality (PQ) across grid-connected systems. This paper presents a comprehensive review of the state-of-the-art in PQ monitoring and analysis in wind farms, emphasizing the need for smart, modular, and predictive frameworks. As wind turbines are inherently nonlinear and their output highly variable, they contribute to PQ disturbances such as harmonics, voltage sags/swells, flicker, and frequency deviations. Traditional monitoring approaches fall short in both resolution and responsiveness, necessitating a shift towards intelligent systems powered by advanced sensing and artificial intelligence (AI). We propose a multilayered modular architecture that integrates high-resolution sensors, edge AI processing, and predictive analytics using Long Short-Term Memory (LSTM) models. This framework enables real-time data acquisition, localized disturbance classification, and cloud-assisted decision-making, forming a scalable solution suitable for both centralized grids and distributed microgrids. The review also highlights the role of enabling technologies such as Phasor Measurement Units (PMUs), IoT sensors, digital twins, and ۶G communication networks in enhancing PQ observability and control. A comparative evaluation of ۲۰ recent high-impact studies was conducted, identifying leading methods based on accuracy, response time, cost, and implementation complexity. Furthermore, the paper explores key future challenges including cybersecurity, data scalability, hardware innovation, and integration across diverse renewable sources. The proposed framework demonstrates strong potential for cross-domain applicability and sets the foundation for proactive, intelligent PQ management in future energy systems.

کلیدواژه ها:

Power Quality Monitoring ، Wind Farms ، Smart Sensors ، Edge AI ، LSTM Forecasting