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

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

doi:10.1049/tje2.12091

Cited by 5 publications

(6 citation statements)

References 18 publications

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“…III. The detail explanation of prediction accuracy analysis can be found in [21], [26]. The predicted DSN line's fault scenarios from both the predictive algorithms under hurricane event are leveraged, by the DSO as power system resilience proactive operational planning tool to decrease the expected load loss.…”

Section: Discussion On the Proposed Bn-dsn And Combined Statistical D...mentioning

confidence: 99%

“…The prediction accuracy analysis was the focus of the paper. The study demonstrated that the proposed BN-DSN line fault simulated are more accurate DSN line fault prediction when compared with the combined statistical DSN line's FC-MCS-SCENRED predictive model [20], [21]. In [26], the proposed BN-DSN was also applied to a 48-bus DSN to forecast the DSN line's fault scenario leveraging the predicted oncoming hurricane Ewiniar data.…”

Section: Figurementioning

confidence: 97%

“…Proposed BN-DSN line fault model flow chart [21] The detailed step by step design procedures for the proposed BN-DSN predictive model can be found in [21] using IEEE 15-bus DSN. The prediction accuracy analysis was the focus of the paper.…”

Section: Figurementioning

confidence: 99%

“…The improved combined statistical DSN line's FC-MCS-SCENRED predictive algorithm that validates the proposed BN-DSN predictive model in [19] was presented in [20]. The study where both the BN-DSN and combined statistical DSN line's FC-MCS-SCENRED predictive algorithms were developed using IEEE 15-bus can be found in [21]. The study presented the full analysis of the two predictive algorithms' accuracy and recommended that the BN-DSN predictive model outperformed the combined statistical DSN's line FC-MCS-SCENRED model, to enhance DSN pre-event operational planning that borders on system pre-event optimal reconfiguration and resource allocation for the reduction in the expected load loss.…”

Section: Introductionmentioning

confidence: 99%

“…The study presented the full analysis of the two predictive algorithms' accuracy and recommended that the BN-DSN predictive model outperformed the combined statistical DSN's line FC-MCS-SCENRED model, to enhance DSN pre-event operational planning that borders on system pre-event optimal reconfiguration and resource allocation for the reduction in the expected load loss. However, the proposed model in [21] was never tested with a proactive system optimization model for impact assessment as regards the reduction in the expected load loss. Other literature such as [16], [19], [22] also supported the proposed BN-DSN predictive model as a better operational planning decisionmaking tool for the distribution system operators (DSO) compared to the combined statistical DSN's line FC-MCS-SCENRED predictive algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Discussion On the Proposed Bn-dsn And Combined Statistical D...mentioning

confidence: 99%

Section: Figurementioning

confidence: 97%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Machine learning for power outage prediction during hurricanes: An extensive review

Fatima,

Shareef,

Costa

et al. 2024

Engineering Applications of Artificial Intelligence

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Review of the opportunities and challenges to accelerate mass‐scale application of smart grids with large‐language models

Shi,

Fang,

Chen

et al. 2024

IET Smart Grid

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Smart grids represent a paradigm shift in the electricity industry, moving from traditional one‐way systems to more dynamic, interconnected networks. These grids are characterised by their intelligent automation, robust structure, and enhanced interaction with customers, backed by comprehensive monitoring and data analytics. The key of this transformation is the integration of data‐driven methods into smart grids. Compared to previous big data solutions, large language models (LLMs), with their advanced generalisation abilities and multi‐modal competencies, are crucial in effectively managing and integrating diverse data sources. They address challenges such as data inconsistency, inadequate quality, and heterogeneity, thereby enhancing the operational efficiency and reliability of smart grids. Furthermore, at the system level, LLMs improve human–system interactions, making smart grids more user‐friendly and intuitive. Last but not the least, the structure of LLMs performs inherent advantages in bolstering system security and privacy, alongside in resolving issues related to system compatibility and integration. The paper reviews the data‐empowered smart grids and for the first time finds and proposes opportunities and future directions for adopting LLMs to accelerate the mass‐scale application of Smart Grids.

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

Cited by 5 publications

References 18 publications

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

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

Machine learning for power outage prediction during hurricanes: An extensive review

Review of the opportunities and challenges to accelerate mass‐scale application of smart grids with large‐language models

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