Enhanced Protection against Large Disturbances in Low Inertia Power Systems

  • Gaoyuan Liu

Student thesis: Phd


Power systems are experiencing unprecedented changes due to legally binding decarbonisation targets and emerging technologies. Conventional fossil fuel generation is displaced by renewable generation, leading to a significant reduction of system inertia and a volatile system inertia level during day-to-day operation. In this context, large disturbances can pose more severe threats to stable power system operation. In addition, the reliability of existing power system protection schemes may be affected by the new inertia behaviours. Therefore, it is deemed necessary to develop protection schemes that can provide enhanced performance when confronting large disturbances in low inertia systems. This thesis focuses on large disturbances including short-circuit faults on transmission lines, unstable power swings and generator outages as well as protection schemes against these disturbances including transmission line backup protection, out-of-step tripping protection and under frequency load shedding (UFLS). The thesis provides a thorough investigation into the impact of new inertia behaviours on the conventional approach of these protection schemes. Enhanced protection schemes built upon innovative algorithms and wide area monitoring systems (WAMS) are proposed to address the limitations of existing schemes. Among them an effective and low-demanding wide area scheme is developed to provide backup protection for transmission lines. A wide area generator outage location method is proposed to accurately estimate the location and size of generator outage disturbances. In addition, the method is integrated into an adaptive wide area UFLS scheme. Finally, a communication-free adaptive UFLS scheme is devised as an alternative to the wide area approach before mature WAMS application in UFLS becomes a common practice. The proposed schemes are validated through extensive simulations and proved to have advantages beyond the capabilities of existing solutions.
Date of Award6 Jan 2021
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorVladimir Terzija (Supervisor) & Peter Crossley (Supervisor)

Cite this