Fault-induced Voltage Recovery Research Articles

Innovative Load Shedding Scheme to Restore Synchronous Generator-Based Microgrids During FIDVR

Fault induced voltage recovery (FIDVR) phenomenon may occur in microgrids ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> Gs) and distribution systems due to the induction motors (IMs) behavior after system voltage sags especially short circuit faults. For the case that motors reaccelerate, system voltage may recover to its nominal value after few seconds. However, it may remain low during motor stalling condition, which result in unstable (non-recoverable) FIDVR. In this paper, an adaptive load shedding scheme is proposed to mitigate unstable FIDVR in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> Gs containing synchronous dispersed generators (SDGs) to enhance system dynamic behavior. Derivative of the imaginary part of equivalent admittance seen from the distribution feeders with respect to time is utilized to discriminate stable FIDVR from unstable ones. In addition, a new criterion is proposed to determine SDG stability margin during the FIDVR phenomenon. Then, a new load shedding scheme is presented based on the proposed SDG stability margin and IM reacceleration index. Moreover, the load shedding time delay is adaptively calculated based on motor reacceleration time. Performance of the proposed method is evaluated by comprehensive simulation studies on two realistic networks. These investigations fairly reveal security and reliability of the proposed method in various conditions.

A Probabilistic Data-Driven Method for Response-based Load Shedding Against Fault-Induced Delayed Voltage Recovery in Power Systems

Aiming at timely and adaptive remedial control for the fault-induced voltage recovery (FIDVR) events in power systems, this paper develops a probabilistic data-driven method for response-based load shedding (RLS). In the proposed method, a scalable Gaussian process (SGP) model is developed to estimate the required load shedding (LS) amount and the confidence of the corresponding predictions. Based on the probabilistic information, a 2-stage LS process is designed to enhance the effectiveness and efficiency of the scheme, using the mean value for LS at the first stage and the upper-bound value for LS at the second stage. The 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> stage shed the amount of load with the largest likelihood, aiming to alleviate system stress with the least control cost. The 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> stage serves as a safety net to ensure system stability considering the possible prediction error. Compared with the conventional RLS schemes and other state-of-the-art approaches, simulation results verify that the proposed method can effectively mitigate FIDVR with a much less load shedding amount.