Power transmission operation regimes are being changed for various technical and economic reasons seeking an improved power system resilience as a goal. However, some of these changes introduce new challenges in maintaining conventional transmission protection system dependability and security when meeting the operating complexities affecting power system resilience. Frequently evolving network topology, as a result of multiple switching actions for corrective, predictive and post event purposes, as well as high penetration of distributed generation into the system are considered as major contradictory changes from the legacy transmission protection standpoint. This research investigates the above-mentioned challenges and proposes effective solutions to improve the transmission protection reliability facing the above-mentioned risks and power system resilience consequently. A fundamental protection scheme based on the Hierarchically Coordinated Protection (HCP) concept is proposed to illustrate various approaches to predictive, adaptive and corrective protection actions aimed at improving power system resilience. Novel computation techniques as well as intelligent machine-learning algorithms are employed in proposing predictive, adaptive, and corrective solutions which fit various layers of the HCP concept and incorporate a fundamental HCP-based approach to supervise the legacy transmission protection function for a dynamic balance between dependability and security. The proposed predictive, adaptive, and corrective protection approaches are tested and verified on various systems, including real-life and IEEE test systems, and their performance effectiveness is compared with the state of the art.
Power transmission operation regimes are being changed for various technical and economic reasons seeking an improved power system resilience as a goal. However, some of these changes introduce new challenges in maintaining conventional transmission protection system dependability and security when meeting the operating complexities affecting power system resilience. Frequently evolving network topology, as a result of multiple switching actions for corrective, predictive and post event purposes, as well as high penetration of distributed generation into the system are considered as major contradictory changes from the legacy transmission protection standpoint.
This research investigates the above-mentioned challenges and proposes effective solutions to improve the transmission protection reliability facing the above-mentioned risks and power system resilience consequently. A fundamental protection scheme based on the Hierarchically Coordinated Protection (HCP) concept is proposed to illustrate various approaches to predictive, adaptive and corrective protection actions aimed at improving power system resilience. Novel computation techniques as well as intelligent machine-learning algorithms are employed in proposing predictive, adaptive, and corrective solutions which fit various layers of the HCP concept and incorporate a fundamental HCP-based approach to supervise the legacy transmission protection function for a dynamic balance between dependability and security. The proposed predictive, adaptive, and corrective protection approaches are tested and verified on various systems, including real-life and IEEE test systems, and their performance effectiveness is compared with the state of the art.
