A Hybrid Randomized Learning System for Temporal-Adaptive Voltage Stability Assessment of Power Systems

With the deployment of phasor measurement units (PMUs), machine learning based data-driven methods have been applied to online power system stability assessment. This article proposes a novel temporal-adaptive intelligent system (IS) for post-fault short-term voltage stability (STVS) assessment. Unlike existing methods using a single learning algorithm, the proposed IS incorporates multiple randomized learning algorithms in an ensemble form, including random vector functional link networks and extreme learning machine, to obtain a more diversified machine learning outcome. Moreover, under a multi-objective optimization programming framework, the STVS is assessed in an optimized temporal-adaptive way to balance STVS accuracy and speed. The simulation results on New England 39-bus system and Nordic test system verify its superiority over a single learning algorithm and its excellent accuracy and speed without increased computational efficiency. In particular, its real-time assessment speed is 27.5-37.3% faster than the single algorithm based methods. Given such faster assessment speed, the proposed method can enable earlier and more timely stability control (load shedding) for less load shedding amount and stronger effectiveness.