Operational Reliability Evaluation Method Based on Big Data Technology

Hu, Lijuan; Liu, Keyan; Diao, Yinglong; Meng, Xiao‐Li; Sheng, Wanxing

doi:10.1109/cyberc.2016.71

Cited by 13 publications

(7 citation statements)

References 2 publications

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“…One must be able to comprehensively describe the overall reliability of the system and reveal the reliability of key elements, key nodes, key areas and key links to reflect shortterm reliability and evaluate long-term reliability [76].…”

Section: A Reliability Index Of Independent Subsystems 1) Reliability Index Of Urban Distribution Network Systemsmentioning

confidence: 99%

“…System reliability can be measured from three angles [77]: the reliability of structural elements, the connection reliability between node pairs, and system performance reliability, which means ensuring that the infrastructure equipment is not loaded or keeping a minimum water head (pressure) to meet maintenance requirements. For this reason, a power system must establish a relatively perfect four-dimensional index system, including a state dimension, degree dimension, hierarchical dimension and time dimension [76].…”

Section: A Reliability Index Of Independent Subsystems 1) Reliability Index Of Urban Distribution Network Systemsmentioning

confidence: 99%

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Reliability Modeling and Evaluation of Urban Multi-Energy Systems: A Review of the State of the Art and Future Challenges

Yuan

Yang

et al. 2020

IEEE Access

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Urban multi-energy systems (UMESs) are integrated energy systems (IESs) which can alleviate the current energy crisis, improve energy utilization and realize multi-energy complementarity of modern. Various subsystems involved in the coupling of UMESs, including power grids, gas pipeline networks, cold/heat networks, transportation networks, and energy cyber-physical system, exhibit coupling characteristics such as multi-energy source modeling, complex uncertainty factor modeling, mutual influence of information and physical coupling. At present, the modeling and reliability assessments of UMESs are the most urgent tasks. In this paper, the latest research results on the reliability modeling and evaluation of UMESs are analyzed by considering their coupling components. In addition, the reliability modeling methods and the evaluation indexes of UMESs, including power-gas systems, power-thermal systems, powertraffic systems, and energy cyber-physical systems, are presented in this paper, with specific UMES modeling methods divided into model-driven modeling and data-driven modeling. Finally, future challenges for the reliability modeling and evaluation of UMESs are proposed, such as the dispatch strategy of the coupling components needs to be developed and continuously optimized to improve the reliability of UMESs, and the resilience of UMESs under the extreme scenarios needs to be further studied.INDEX TERMS Urban multi-energy system, reliability indexes, reliability modeling, coupling components, resilience.

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Section: A Reliability Index Of Independent Subsystems 1) Reliability Index Of Urban Distribution Network Systemsmentioning

confidence: 99%

Section: A Reliability Index Of Independent Subsystems 1) Reliability Index Of Urban Distribution Network Systemsmentioning

confidence: 99%

Reliability Modeling and Evaluation of Urban Multi-Energy Systems: A Review of the State of the Art and Future Challenges

Yuan

Yang

et al. 2020

IEEE Access

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“…To reflect the users' true perceptions of power supply reliability and provide personalized user services, reference [21] establishes a comprehensive evaluation index system of power supply reliability in distribution networks, which accounts for the user's experience of power consumption. In reference [22], the reliability indicators of the distribution network operation are expanded on the basis of conventional reliability indicators, the load shedding probability, the expected energy not supplied (EENS), and the overvoltage expectation, which are taken as the main indicators to reflect the multi-time scale reliability levels of the grid-side dispatching operation at different layers.…”

Section: ) Reliability Indicatorsmentioning

confidence: 99%

“…In (22), , 0 t i ω > is a parameter that varies with the user and time; α is a parameter given in advance; and , t i…”

Section: Figure 1 Power Consumer Satisfaction Modelmentioning

confidence: 99%

Review and Prospects for Evaluating Power Grid Dispatching Service Quality

Peng

Tang

2020

IEEE Access

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To reasonably evaluate the dispatching service quality of a power grid and comprehensively reflect the diversified demand satisfaction extent of the relevant stakeholders in the power system, it is imperative to construct an evaluation index system for power grid dispatching service quality, which should thoroughly consider the demand satisfaction of generation-side stakeholders, grid-side stakeholders, and load-side stakeholders. In this paper, the research status of the source-grid-load stakeholders' satisfaction indexes is overviewed from the perspective of their dispatching demand. The dispatching demand satisfaction indexes of the source-grid-load stakeholders and major evaluation methods in the literature are also summarized. According to current research progress, proposals for improving the rationality and efficiency of power grid dispatching service quality evaluation are also described. Finally, by combining the development demands of power grid dispatching service quality, we outline future research directions that involve dispatching demand satisfaction indexes evaluation for source-grid-load stakeholders in the power system.

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“…The ANNs and ARs mining approaches are promising methodologies for prediction and classification, but more research is needed in terms of combination of the two. A work integrating the ARs mining and ANNs approaches operational reliability issues is already existing in literature: specifically, the ARs are extracted to define the factors influencing power distribution reliability; then, such factors are given in input to an ANN for the normal behavior modelling of the distribution net [57]. The approach proposed in the current paper is the opposite: indeed, the normal behavior is firstly estimated through an ANN and, when a substantial deviation is noticed, the ARs mining is applied to diagnose whether such a deviation impacts on the efficiency and performance of the asset.…”

Section: State Of the Art: Ar Mining Applications In Ammentioning

confidence: 99%

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

2019

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In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.

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Operational Reliability Evaluation Method Based on Big Data Technology

Cited by 13 publications

References 2 publications

Reliability Modeling and Evaluation of Urban Multi-Energy Systems: A Review of the State of the Art and Future Challenges

Reliability Modeling and Evaluation of Urban Multi-Energy Systems: A Review of the State of the Art and Future Challenges

Review and Prospects for Evaluating Power Grid Dispatching Service Quality

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

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