Continuous Real-Time Estimation of Power System Inertia Using Energy Variations and Q-Learning

Abstract

With the growing emphasis on mitigating climate change, the power industry is moving toward renewable energy sources as an alternative to fossil fuel-based power plants. The transition to renewable energy has created numerous challenges, one of which is the low levels of inertia that impact the stability of power systems. Therefore, inertia monitoring has become an integral part of power system operation to dispatch renewable energy sources while maintaining frequency stability. This article presents an online method to continuously estimate the inertia of a power system. The inertia is computed from data provided by Phasor Measurement Units (PMUs) using small variations in frequency and power under ambient conditions. The method uses electrical and kinetic energy variations to compute inertia. In addition, a $Q$ -learning-based method is presented to identify mechanical power changes to discard invalid inertia estimates. The method is demonstrated using the IEEE-39 bus system to monitor the regional inertia of the test system.

Keywords

• Ambient data
• coefficient of variation (CV)
• grid inertia
• inertia estimation
• phasor measurement unit (PMU)
• Q-learning