Sensor data-driven applications for IBRs in Transmission and Distribution Network

The proliferation of inverter-based resources (IBRs) in modern power systems necessitates advanced monitoring, control, and optimization techniques to ensure efficient and reliable operation. Sensor data-driven applications have emerged as a critical enabler in this context, offering real-time insights into IBR performance, grid conditions, and operational parameters. This abstract provides a comprehensive review of Sensor Data-Driven Applications for Inverter-Based Resources (IBRs), highlighting their diverse applications, challenges, and transformative potential. Sensor data plays a pivotal role in enabling real-time visibility and management of distributed energy resources (DERs), including solar photovoltaics (PV), wind turbines, battery storage systems, and electric vehicles (EVs). By capturing granular information on voltage, current, temperature, and other relevant parameters, sensors facilitate accurate monitoring and control of IBR assets, optimizing their integration into the grid and enhancing overall system resilience. The applications of sensor data in IBRs are multifaceted, spanning various operational domains. Fault detection and diagnostics leverage sensor data to detect anomalies and preemptively address potential issues, minimizing downtime and maintenance costs. Predictive maintenance strategies utilize sensor measurements to assess equipment health and schedule maintenance activities proactively, improving asset reliability and longevity. Dynamic voltage regulation and frequency support are critical functions enabled by sensor data, allowing IBRs to actively participate in grid stability and power quality enhancement. By leveraging real-time sensor measurements, IBRs can adjust their output to mitigate voltage fluctuations and frequency deviations, thereby enhancing grid stability and resilience in the face of dynamic operating conditions. Furthermore, sensor data-driven applications facilitate optimized grid-tied operation of IBRs, enabling seamless integration of renewable energy into the grid while ensuring compliance with regulatory requirements and grid codes. Advanced analytics techniques, such as machine learning and optimization algorithms, are employed to extract actionable insights from sensor data, enabling intelligent decision-making and control strategies that maximize the value of IBRs to grid operators and end-users alike. In conclusion, sensor data-driven applications hold immense promise for optimizing the management and operation of inverter-based resources, driving the transition towards a more flexible, resilient, and sustainable energy infrastructure. Continued research, innovation, and collaboration will be essential in unlocking the full potential of sensor data in shaping the future of energy systems.