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Power systems are prone to cascading outages leading to large-area blackouts with significant social and economic consequences. Intentional controlled islanding (i.e. the separation of the system into sustainable islands) is an effective strategy to mitigate these catastrophic events. To ensure a correct separation, nonetheless, it is crucial to define a suitable time to split the system (i.e. to answer the when to island question). To consider the probability of the event, the reliability of the system components, the reliability of the information and communication technologies, and the potential economic costs of the event, answering the above question within a risk-based framework becomes critical. To date, however, this has not been done. This study proposes a risk-based methodology to define, in an adaptive manner, a suitable time to split the system following an event. This methodology complements the well-studied where to island question, resulting in an integral solution of the islanding problem. To illustrate the approach, the IEEE 118-bus dynamic system is adopted considering realistic security criteria. Simulation results demonstrate the effectiveness and flexibility of the methodology in identifying a suitable time for the creation of islands, which, in turn, results in the prevention of blackouts that would otherwise be obtained. [ABSTRACT FROM AUTHOR]

Power systems are prone to cascading outages leading to large‐area blackouts with significant social and economic consequences. Intentional controlled islanding (i.e. the separation of the system into sustainable islands) is an effective strategy to mitigate these catastrophic events. To ensure a correct separation, nonetheless, it is crucial to define a suitable time to split the system (i.e. to answer the when to island question). To consider the probability of the event, the reliability of the system components, the reliability of the information and communication technologies, and the potential economic costs of the event, answering the above question within a risk‐based framework becomes critical. To date, however, this has not been done. This study proposes a risk‐based methodology to define, in an adaptive manner, a suitable time to split the system following an event. This methodology complements the well‐studied where to island question, resulting in an integral solution of the islanding problem. To illustrate the approach, the IEEE 118‐bus dynamic system is adopted considering realistic security criteria. Simulation results demonstrate the effectiveness and flexibility of the methodology in identifying a suitable time for the creation of islands, which, in turn, results in the prevention of blackouts that would otherwise be obtained. [ABSTRACT FROM AUTHOR]

Parallel power system restoration (PPSR) restores isolated sections (islands) of the network in parallel, thus the overall restoration process is accelerated. These islands are defined during the preparation stage of PPSR as part of a sectionalising strategy (SS). During this process, it is important that the operators only use updated post‐blackout system information. This study proposes a new methodology based on the 'cut‐set' matrix defined in graph theory, which can identify a shortlist of suitable SSs that satisfy the critical PPSR constraints in a few minutes. This short list of SSs, not identified in previous works, can be presented to the operators to help them select a restoration plan that is tailored to the specific changes in topology and asset availability that the blackout has caused. The methodology is illustrated using the IEEE 9‐bus system, and validated using the IEEE 118‐bus and the Polish 3375‐bus system to demonstrate the efficiency of the new approach for large‐scale networks. Multiple case studies are developed to demonstrate the adaptability of the methodology to different system conditions, for example, the unavailability of assets. In every case, the methodology quickly identified a number of SSs that create suitable islands for parallel restoration. [ABSTRACT FROM AUTHOR]

Power outages due to cascading failures which are triggered by extreme weather pose an increasing risk to modern societies and draw attention to an emerging need for power network resilience. Machine learning (ML) is used for a real‐time selection process on preventive actions, such as topology reconfiguration and islanding, aiming to reduce the risk of cascading failures. Training data is obtained from Monte Carlo simulations of cascading failures triggered by extreme events. The trained ML‐based decision‐making process uses only predictors that are readily available prior to an extreme event, such as event location and intensity, network topology and load, and requires no further time‐consuming simulations.The proposed decision‐making process is compared to time‐consuming but ideal decision‐making and fast but trivial decision‐making. Demonstrations on the German transmission network show that the proposed ML‐based selection process efficiently prevents the uncontrolled propagation of cascading failures and performs similarly to an ideal decision‐making process whilst being computationally three orders of magnitude faster. [ABSTRACT FROM AUTHOR]