• Energy Research

Causal inference provides an analytical framework to identify and quantify cause-and-effect relationships among a network of interacting agents. This paper offers a novel framework for analyzing cascading failures in power transmission networks. This framework generates a directed latent graph in which the nodes represent the transmission lines and the directed edges encode the cause-effect relationships. This graph has a structure distinct from the system's topology, signifying the intricate fact that both local and non-local interdependencies exist among transmission lines, which are more general than only the local interdependencies that topological graphs can present. This paper formalizes a causal inference framework for predicting how an emerging anomaly propagates throughout the system. Using this framework, two algorithms are designed, providing an analytical framework to identify the most likely and most costly cascading scenarios. The framework's effectiveness is evaluated compared to the pertinent literature on the IEEE 14-bus, 39-bus, and 118-bus systems.

Causal inference provides an analytical framework to identify and quantify cause-and-effect relationships among a network of interacting agents. This paper offers a novel framework for analyzing cascading failures in power transmission networks. This framework generates a directed latent graph in which the nodes represent the transmission lines and the directed edges encode the cause-effect relationships. This graph has a structure distinct from the system's topology, signifying the intricate fact that both local and non-local interdependencies exist among transmission lines, which are more general than only the local interdependencies that topological graphs can present. This paper formalizes a causal inference framework for predicting how an emerging anomaly propagates throughout the system. Using this framework, two algorithms are designed, providing an analytical framework to identify the most likely and most costly cascading scenarios. The framework's effectiveness is evaluated compared to the pertinent literature on the IEEE 14-bus, 39-bus, and 118-bus systems.

Abstract This paper proposes Phasor Measurement Unit (PMU) based adaptive zone settings of distance relays (PAZSD) methodology for protection of multi-terminal transmission lines (MTL). The PAZSD methodology employs current coefficients to adjust the zone settings of the relays during infeed situation. These coefficients are calculated in phasor data concentrator (PDC) at system protection center (SPC) using the current phasors obtained from PMUs. The functioning of the distance relays during infeed condition with and without the proposed methodology has been illustrated through a four-bus model implemented in PSCAD/EMTDC environment. Further, the performance of the proposed methodology has been validated in real-time, on a laboratory prototype of Extra High Voltage multi-terminal transmission lines (EHV MTL). The phasors are estimated in PMUs using NI cRIO-9063 chassis embedded with data acquisition sensors in conjunction with LabVIEW software. The simulation and hardware results prove the efficacy of the proposed methodology in enhancing the performance and reliability of conventional distance protection system in real-time EHV MTLs.

Abstract This paper proposes Phasor Measurement Unit (PMU) based adaptive zone settings of distance relays (PAZSD) methodology for protection of multi-terminal transmission lines (MTL). The PAZSD methodology employs current coefficients to adjust the zone settings of the relays during infeed situation. These coefficients are calculated in phasor data concentrator (PDC) at system protection center (SPC) using the current phasors obtained from PMUs. The functioning of the distance relays during infeed condition with and without the proposed methodology has been illustrated through a four-bus model implemented in PSCAD/EMTDC environment. Further, the performance of the proposed methodology has been validated in real-time, on a laboratory prototype of Extra High Voltage multi-terminal transmission lines (EHV MTL). The phasors are estimated in PMUs using NI cRIO-9063 chassis embedded with data acquisition sensors in conjunction with LabVIEW software. The simulation and hardware results prove the efficacy of the proposed methodology in enhancing the performance and reliability of conventional distance protection system in real-time EHV MTLs.

This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system's stability. The paper considers a space of control actions that involve both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black box counterparts. One important observation is that strategically~\emph{removing} transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts.

This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system's stability. The paper considers a space of control actions that involve both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black box counterparts. One important observation is that strategically~\emph{removing} transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts.