AC Cascading Failure Model for Resilience Analysis in Power Networks

Noebels, Matthias; Preece, Robin; Panteli, Mathaios

doi:10.1109/jsyst.2020.3037400

Cited by 38 publications

(24 citation statements)

References 45 publications

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“…Much of the literature on network reconfiguration and island operation schemes have emphasised the importance of multiple or cascading faults [43,139,[145][146][147][148][149]. Panteli et al [43] proposed an adaptive islanding algorithm for cascading failures, which divides the network into stable and selfsufficient islanding schemes, which can effectively isolate the fault.…”

Section: Operation Measuresmentioning

confidence: 99%

Resilience assessment methodologies and enhancement strategies of multi‐energy cyber‐physical systems of the distribution network

Yang

Liu

et al. 2022

IET Energy Syst Integration

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Natural disasters and cyber intrusions threaten the normal operation of the critical Multi‐Energy Systems (MESs) infrastructures. There is still no universally‐accepted definition of MESs resilience under the integration of cyber and physical, and lack of a widely accepted methodology to quantify and assess the resilience in MESs. Hence, this paper introduces an extensive review of the state‐of‐the‐art research of power systems’ resilience. Then, this work proposes the definition of the Multi‐Energy Cyber‐Physical Systems (MECPSs) resilience and its related characteristics. To improve the resilience of MECPSs, this paper investigates extreme natural disaster models and analyses the vulnerability of the system to find the key constraint factors. Furthermore, this work presents the qualitative assessment curve, quantitative indexes, and assessment framework of the MECPSs resilience. Finally, the key improvement measures for the planning and operation of MECPSs resilience and the focus of future research are presented.

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Section: Operation Measuresmentioning

confidence: 99%

Resilience assessment methodologies and enhancement strategies of multi‐energy cyber‐physical systems of the distribution network

Yang

Liu

et al. 2022

IET Energy Syst Integration

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“…Cascading failures are simulated using a quasi‐steady‐state AC cascading failure model that considers overload protection of transmission lines as well as static representations of frequency and voltage protection mechanisms and excitation limiters [31]. This model has previously been shown to produce accurate and validated results following recommendations by the IEEE PES working group on cascading failures in a computationally efficient and robust way [31, 32]. The MC simulation returns a probability distribution of lost load

P_{e}^{LL}

with mean

{\hat{P}}_{e}^{LL}

and standard deviation

σ_{e}^{LL}

in the network after event

e

.…”

Section: Assessing the Risk Of Extreme Eventsmentioning

confidence: 99%

A machine learning approach for real‐time selection of preventive actions improving power network resilience

Noebels

Preece

Panteli

2021

IET Generation Trans & Dist

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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.

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“…CFS is proposed to simulate the cascading failure and corresponding recovery operation strategies of the power systems under attack/disturbance [28]. Basically, they are designed using the dc power flow (DC-PF) model [29]- [32] or the computationally efficient AC model [33]. Some stochastic cascading failure models [34], [35] are further proposed to simulate the cascading failures considering the high uncertainty of load and renewable generations.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [33]- [35] ignored the governor droop coefficients, which reflected the primary frequency regulation. Ref.…”

Section: Introductionmentioning

confidence: 99%

Resilience Assessment for Power Systems Under Sequential Attacks Using Double DQN With Improved Prioritized Experience Replay

Zeng

Yao

Shuai

et al. 2023

IEEE Systems Journal

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The information and communication technology enhances the performance and efficiency of cyber-physical power systems (CPPSs). However, it makes the topology of CPPSs more exposed to malicious cyber attacks in the meantime. This paper proposes a double deep-Q-network (DDQN) based resilience assessment method for power systems under sequential attacks. The DDQN agent is devoted to identifying the least sequential attacks to the ultimate collapse of the power system under different operating conditions. A cascading failure simulator considering the characteristics of generators is developed to avoid a relatively optimistic assessment result. In addition, a novel resilience index is proposed to reflect the capability of the power system to deliver power under sequential attacks. Then, an improved prioritized experience replay technique is developed to accelerate the convergence rate of the training process for DDQN agent. Simulation results on the IEEE 39-bus, 118-bus and 300bus power systems demonstrate the effectiveness of the proposed DDQN-based resilience assessment method.

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AC Cascading Failure Model for Resilience Analysis in Power Networks

Cited by 38 publications

References 45 publications

Resilience assessment methodologies and enhancement strategies of multi‐energy cyber‐physical systems of the distribution network

Resilience assessment methodologies and enhancement strategies of multi‐energy cyber‐physical systems of the distribution network

A machine learning approach for real‐time selection of preventive actions improving power network resilience

Resilience Assessment for Power Systems Under Sequential Attacks Using Double DQN With Improved Prioritized Experience Replay

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