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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]

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real‐time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience‐based decisions for risk mitigation. The IEEE 24‐bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method. [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]

System integrity protection schemes (SIPS) are a widely used solution to the challenges in operating electrical power transmission systems during the last few decades. Since these protection schemes have become an integral part of the system, it must be ensured that their performance satisfies the reliability requirements of electrical utilities, when expressed in terms of dependability and security. This study proposes a method based on Markov modelling and fault tree analysis for assessing the reliability of a generic SIPS, but it is illustrated using the Dinorwig intertrip scheme, located in North Wales and operated by National Grid (Great Britain system operator). In addition, two reliability indices, widely used in the process control industry, are suggested for quantifying the reliability level of SIPS: (i) safety integrity level and (ii) spurious trip level. Many operators tend to have SIPS permanently in service; this reduces the probability of a 'failure to operate' because of a problem in the arming software or an error by a human operator that prevented the scheme being armed when required. Therefore, the impact of having SIPS always armed on SIPS reliability is compared with the impact of switching IN the scheme only when the arming conditions are fulfilled. [ABSTRACT FROM AUTHOR]