Determination of alarm templates for decision support in nuclear power plants alarm floods using evolutionary computation

Schirru, Roberto; Schirru, Mikael; Boas, Manoel Villas; Nicolau, Andressa dos Santos; Lima, Alan Miranda Monteiro de; Pinheiro, Victor Henrique Cabral

doi:10.1016/j.pnucene.2020.103308

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Content may change prior to final publication. [160] Colored Petri Nets [193], [197], [198] Tree Methods [50], [200] Unsupervised Learning [161], [181] Fuzzy Methods [159], [182]- [189], [192], [195], [201] Other Models/Algorithms Physical Plant-centered [39], [53], [86]- [88], [123], [127]- [129], [150], [175], [193] Cyber System Layer & Physical System Layer ANN [105], [160] Fuzzy Colored Petri Nets (FCPN) [106] Reinforcement Learning (RL) [107], [108], [134]- [137] Recurrent Neural Network (RNN) [95], [97], [138], [138], [139], [146] Convolutional Neural Network (CNN) [140]- [142] Deep Belief Network [90], [121] SVM [118], [119], [122] Tree Methods [124]-…”

Section: Discussionmentioning

confidence: 99%

“…The decision making would become more reliable and efficient with such risk estimation and a better maintenance strategy could be taken. Roberto et al [201], however, apply Quantum Evolutionary Algorithm (QEA) in the decision support system in order to handle alarm floods. More recent researches attempt to design a deeper integration system of AI and traditional NPPs' decision-making systems.…”

Section: ) Decision-making Assistancementioning

confidence: 99%

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Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Lyu

Zhang

et al. 2020

IEEE Access

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Nuclear energy can make an important contribution to low-carbon energy supply for Industry 4.0, while Industry 4.0 can reform this industry in return. As a typical and complex man-machine-network integration system, various faults, insufficient automation and stressed human operators limit the further popularization of nuclear power plants (NPPs) while these issues can be addressed by the aid of artificial intelligence (AI) technologies. In this work, we try to present a systemic review of how AI can benefit NPPs in a top-to-down fashion. We discuss limitations in current NPPs and introduce the concept of Nuclear Power Plant Human-Cyber-Physical System (NPPHCPS) as the top-level design. Then, we category AI-related nuclear power applications into Physical-Plant-Centered and Human-Operator-Centered technologies and review research works from 7 typical NPP functional scenarios in the recent two decades. In each NPP functional scenario, how researchers integrate AI into NPPs is presented following timeline. We hope this review can be used as the guideline for NPPs' Design in the future and contribute to green Industry 4.0.

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Section: Discussionmentioning

confidence: 99%

Section: ) Decision-making Assistancementioning

confidence: 99%

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Lyu

Zhang

et al. 2020

IEEE Access

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“…However, due to the complexity and large scale of the power grid, there may be various abnormal situations in the power system, such as equipment failures, voltage fluctuations, current overload, etc. If these abnormal data cannot be detected and processed in a timely manner, it will have a negative impact on the normal operation and power supply reliability of the power grid [1][2][3]. However, traditional manual analysis methods have problems such as low efficiency and low reliability.…”

Section: Introductionmentioning

confidence: 99%

Automated alarm method for abnormal data in power grid reports based on Paxos algorithm

Xing,

Wu,

Wang

2024

Third International Conference on Computer Technology, Information Engineering, and Electron Materials (CTIEEM 2023)

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With the increasing scale and complexity of the power system, timely monitoring and accurate identification of abnormal data in power grid reports are crucial for ensuring the safe operation of the power grid. This paper proposes an automated alarm method for abnormal data in power grid reports based on the Paxos algorithm. Automatically collect power grid report data using RPA technology, and then use the isolated forest algorithm of sub forest progressive update incremental learning to detect anomalies in the collected data. Based on the anomaly detection results, the PAXOS algorithm is used to trigger the automated alarm system and promptly notify the operation and maintenance personnel. The experimental results show that this method can accurately detect abnormal situations in the power grid and improve the comprehensiveness of alarms, which is beneficial for improving the security of the power grid.

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“…They effectively identified corresponding alarms and their evolution path . Schirru et al adopted quantum evolutionary algorithms to determine a minimum alarm set to isolate one fault from others and provided critical information enabling operators to effectively implement measures to mitigate current failures. In addition, there are a large number of data-driven causal algorithms, e.g., the gray theory, which is based on discrete data sequence modeling, the Granger causality analysis algorithm, which calculates the scale of the relationship between sequences, and the intuitive Bayesian network technique …”

Section: Introductionmentioning

confidence: 99%

Pattern Mining of Alarm Flood Sequences Using an Improved PrefixSpan Algorithm with Tolerance to Short-Term Order Ambiguity

Zhu

Jin

et al. 2021

Ind. Eng. Chem. Res.

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The alarm system monitors industrial plants in real-time to ensure safe operation. The scale of modern plants is expanding rapidly, processes are becoming increasingly complicated, and the cost of alarm configuration in modern control systems is decreasing. However, alarm systems suffer from low performance. A large number of alarms are often indicated to operators within a short period, known as alarm floods. Analysis and mining of similar patterns among different alarm floods is an efficient approach. Alarm flood analysis facilitates cause analysis of historical flood data, thus locating poorly configured alarms and predicting incoming alarm floods. The strongly correlated alarms almost appear simultaneously, and the order is uncertain. A pattern containing strongly correlated alarms cannot be directly extracted according to the order of occurrence. In this paper, an improved PrefixSpan algorithm is proposed to identify similar patterns with a tolerance to short-term order ambiguity, which treats alarm floods as time-stamped sequences. The effectiveness of the proposed algorithm is verified with Tennessee Eastman process simulations.

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Determination of alarm templates for decision support in nuclear power plants alarm floods using evolutionary computation

Cited by 8 publications

References 6 publications

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Automated alarm method for abnormal data in power grid reports based on Paxos algorithm

Pattern Mining of Alarm Flood Sequences Using an Improved PrefixSpan Algorithm with Tolerance to Short-Term Order Ambiguity

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