Review of Sequential Steps to Realize Power System Resilience

Omogoye, Okeolu Samuel; Folly, Komla A.; Awodele, Kehinde

doi:10.1109/saupec/robmech/prasa48453.2020.9040967

Cited by 11 publications

(7 citation statements)

References 60 publications

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“…Operational measures, on the other hand, are employed to manage the grid systems before, during, and after natural events to minimize the load loss [17]. The summary of power systems resilience assessment techniques based on the short and long-term planning was presented in Omogoye et al [9] and Lin et al [11], respectively.…”

Section: Power System Resilience Evaluation Methodologiesmentioning

confidence: 99%

“…The summary of power systems resilience assessment techniques based on the short and long‐term planning was presented in Omogoye et al. [9] and Lin et al. [11], respectively.…”

Section: Overview Of Past Current Practices and Limitations Of Proact...mentioning

confidence: 99%

“…The detailed literature review on sequential steps to achieve power system resilience improvement against natural events has been published in Omogoye et al. [9]. The system resilience improvement strategies were reviewed from the perspective of system hardening and operational measures for quick load restoration after natural events.…”

Section: Overview Of Past Current Practices and Limitations Of Proact...mentioning

confidence: 99%

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A review of power system predictive failure model for resilience enhancement against hurricane events

Omogoye

Folly

Awodele

2021

The Journal of Engineering

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Natural events such as hurricanes usually cause unimaginable destruction to the electric power system infrastructures across the globe leading to large‐scale power outages. While the transmission network offers relatively high resilience to the hurricane extreme wind speed intensity (HEWSI), the distribution power system network (DPSN) is always the worst hit. To enhance the DPSN against hurricane events, both the pre‐ and post‐event power system resilience enhancement techniques can be reviewed, and their limitations improved. Handling hurricane risks proactively for effective recovery plans requires rigorous techniques for locating and estimating the number of system component's damage causing outages on a DPSN. A review of the resilience evaluation methodologies utilized for a proactive statistical system component's failure predictive model is presented in this paper. As a contribution, this article presents the current practices, the problems, and points out the future research directions for a statistical system components’ line outage predictive model that can greatly enhance the DPSN against hurricane events for short‐term operational planning.

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Section: Power System Resilience Evaluation Methodologiesmentioning

confidence: 99%

“…The summary of power systems resilience assessment techniques based on the short and long‐term planning was presented in Omogoye et al. [9] and Lin et al. [11], respectively.…”

Section: Overview Of Past Current Practices and Limitations Of Proact...mentioning

confidence: 99%

Section: Overview Of Past Current Practices and Limitations Of Proact...mentioning

confidence: 99%

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A review of power system predictive failure model for resilience enhancement against hurricane events

Omogoye

Folly

Awodele

2021

The Journal of Engineering

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“…In this view, this article is motivated to focus on improving the operational resilience of the DSN against hurricane events. The goal is to lessen the expected load loss, and the impact of the protracted widespread power outages proactively, leveraging the proposed DSN predictive algorithms in this paper for the DSN proactive operational planning decision-making A comprehensive literature review of the steps and strategies utilized to achieve power system resilience improvement against HILL events such as hurricanes was presented in [12], [13]. The papers suggested the need to change the reactive operational resilience improvement techniques proposed by many researchers in assessing the resilience improvement of the DSN against hurricane events, to a more defensive proactive operational resilience improvement approach [14].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study Between Bayesian Network and Hybrid Statistical Predictive Models for Proactive Power System Network Resilience Enhancement Operational Planning

2023

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Enhancing the operational resilience of the distribution system network (DSN) proactively in a hurricane-prone region requires a pre-hurricane event DSN optimization model, built on accurate hurricaneinduced DSN line fault prediction scenarios. In the past, the resilience evaluation methods such as statistical sequential and non-sequential Monte Carlo simulation (MCS) contingency-based technique, and Machine learning-based Bayesian Networks (BN) technique, have been proposed to strengthen the operational resilience of the DSN proactively against the forecasted oncoming hurricane events. However, a comparative study is largely unexplored to evaluate which of these two methods is best for proactive operational planning decision-making against the forecasted oncoming hurricane event. In this paper, the Bayesian network (BN) and combined statistical DSN's Fragility-curve(FC)-Monte Carlo simulation (MCS)-Scenario reduction (SCENRED) predictive algorithms were developed. The DSN line fault prediction scenarios simulated leveraging the predicted oncoming hurricane Ewiniar data were utilized to perform pre-hurricane DSN optimization to proactively decrease the DSN expected load loss. The pre-event system optimization problems were formulated in a mixed integer linear programming (MILP) approach and solved using a CPLEX solver in general algebraic modelling system (GAMS) on a redesigned 48-bus DSN. The simulated initial expected load loss of 39% of 35 MWh was decreased to 35.34%, and then to 30.71% with the use of combined statistical DSN's FC-MCS-SCENRED, and the BN-DSN predictive models. These results were validated using the Electrical transient analyzer program (ETAP).This study confirmed that the BN-DSN predictive model is a better operational planning tool compared to combined statistical DSN's line FC-MCS-SCENRED predictive model. RCS "t"Remote-controlled switching time. ω x F Weight factor at load bus "x". 𝐿 𝐷𝑆The binary parameter will be 0, at a damaged scenario (s) of the line′𝑙′. V max, V min Maximum and minimum operational constrained voltage(s). S maxGrid maximum apparent power ,max ,min , DG DG xx PPThe maximum and minimum real power output of the DGs at bus "x".

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“…Therefore, Omogoye et al. [7], published a literature review on the sequential steps to realize power system resilience. It was noted in the paper that there is a need to develop a proactive resilience enhancement evaluation model that can anticipate system line failure caused by the approaching predicted natural events like a hurricane.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model

Omogoye

Folly

Awodele

2021

The Journal of Engineering

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Enhancing the grid resilience against hurricane events proactively requires a pre‐disaster system optimization built on accurate system network line outage prediction. In the past, the statistical system level failure predictive model such as generalized linear model (GLM), generalized additive model (GAM), system tree‐based mining model (classification regression tree (CART), Bayesian additive regression model (BART), and the topology‐based system components’ fragility curve (FC)‐Monte Carlo simulation (MCS) model have been used to estimate the hurricane‐induced damage on the grid system. Although these models are suitable for a long term infrastructural planning, increased prediction approximation error with high computational time limit their applications as a components level predictive model that can be used for a short‐term proactive operational planning. To solve these problems, a dynamic bayesian network (BN) model is proposed. The investigation is performed on a standard IEEE 15‐bus system using hurricane events data. The proposed BN's system line outage prediction accuracy and efficiency are validated using the statistical system grid components’ FC‐MCS‐scenario reduction (SCENRED) predictive model. Therefore, creating a platform to develop a cost‐effective pre‐disaster system optimization for system network resilience enhancement against the predicted approaching hurricane events.

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Review of Sequential Steps to Realize Power System Resilience

Cited by 11 publications

References 60 publications

A review of power system predictive failure model for resilience enhancement against hurricane events

A review of power system predictive failure model for resilience enhancement against hurricane events

A Comparative Study Between Bayesian Network and Hybrid Statistical Predictive Models for Proactive Power System Network Resilience Enhancement Operational Planning

Enhancing the distribution power system resilience against hurricane events using a bayesian network line outage prediction model

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