A Fault Diagnosis and Operation Advising Cooperative Expert System Based on Multi-Agent Technology

Zhao, Wei; Bai, Xiaoqing; Ding, Jian; Fang, Zhu; Li, Zaihua

doi:10.3182/20060625-4-ca-2906.00039

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…DL infrared fault detection methods realize the location and segmentation of thermal defects through model design and training, which learn fault characteristics in an autonomous manner. Zhao et al 19 took multiagent based model as framework, refined with expert system characteristics, realizing collaborative diagnosis of complicated faults, but the priori knowledge of expert system is inaccessible. Both Zou et al 18 and Leksir et al 20 utilized SVM for fault detection, the former first extracts statistical features via K-means algorithm, while the latter first classifies and identifies ROI regions.…”

Section: Infrared Fault Detection Methodsmentioning

confidence: 99%

Thermal defect segmentation of electrical equipment based on saliency constraint

Luo

Zhu

et al. 2022

International Symposium on Artificial Intelligence and Robotics 2022

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Thermal defect detection aims to identify overheated areas of electric accessory with the help of infrared imaging technology. In this paper, we propose a thermal defect segmentation method based on saliency constraint. Specifically, we first design a convolutional neural network for infrared image classification, the thermal ones of which are then denoised and enhanced by image preprocessing; Next, the modified K-means clustering algorithm is utilized for region segmentation, which splinters infrared images as environment area, normal area and thermal area; Finally, we perform saliency detection on infrared images to obtain approximate region of temperature anomaly, and the overheated area is likewise segmented based on the modified K-means clustering algorithm, which is subsequently used to revise the thermal area segmented based on enhanced images to satisfy saliency constraint. Experimental results suggest that our method can improve the diagnostic efficiency of infrared images and realize the precise positioning of thermal defects, which outperforms the state-of-the-arts.

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Section: Infrared Fault Detection Methodsmentioning

confidence: 99%

Thermal defect segmentation of electrical equipment based on saliency constraint

Luo

Zhu

et al. 2022

International Symposium on Artificial Intelligence and Robotics 2022

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“…With the continuous development of power grids, accurate and fast fault diagnosis of the power system plays a very important role in ensuring the safe operation of power grids. Up to now, many scholars have used different intelligent methods, such as the expert system [1], analytical model [2,3], and Bayesian network [4][5][6], to diagnose power system faults. It has been proved by practice that these methods can correctly judge the fault situation when the alarm information is complete and accurate.…”

Section: Introductionmentioning

confidence: 99%

“…It can be calculated that a total of 24 buses (1,2,4,6,7,8,9,10,12,13,15,17,18,19,20,21,22,23,24,25,27,28,29,39) should be configured with PMU.…”

mentioning

confidence: 99%

A Power Grid Fault Diagnosis Method in the Case of Alarm Information Loss Based on the Top-k Skyline Query Algorithm

Jin

Chen

et al. 2021

Energies

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After the failure of the power system, a large amount of alarm information will flood into the dispatching terminal instantly. At the same time, there are inevitable problems, such as the abnormal operation of the protection and the circuit breaker, the lack of alarm information, and so on. This kind of uncertainty problem brings great trouble to the fault diagnosis algorithm. As a data processing algorithm for an uncertain information set, Top-k Skyline query algorithm can eliminate the data points that do not meet the requirements in the information set, and then output the final K results in order. Based on this background, this paper proposes a power grid fault diagnosis method based on the Top-k Skyline query algorithm considering alarm information loss. Firstly, the fault area is determined by using the information of the electrical quantity and switching value. Then, backward reasoning Petri nets are established for the nodes in the fault area to form the data set of fault hypotheses. Then, the Top-k Skyline query algorithm is used to sort the hypotheses and choose the hypothesis with higher reliability. Finally, an IEEE 39-bus system example is given to verify the reliability of the proposed method.

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“…Internet of Things and Artificial Intelligence technologies have made great progress in the past decade, and meanwhile, multi-agent systems [1] begin to be widely employed in realworld applications, such as unmanned systems [2], intelligent distributed traffic signal control systems [3], UAV formation combat systems [4], social networks [5], smart manufacturing [6], collaborative fault diagnosis systems [7], and robot rescue systems [8]. In a specific scenario, agents usually need to finish specific tasks, such as firefighting, excavation, obstacle clearing, crowd evacuation, rescue, and transportation of materials to designated locations.…”

Section: Introductionmentioning

confidence: 99%

Task Allocation for Multi-Agent Systems Based on Distributed Many-Objective Evolutionary Algorithm and Greedy Algorithm

et al. 2020

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Task allocation is a key issue in multi-agent systems, and finding the optimal strategy for task allocation has been proved to be an NP-hard problem. Existing task allocation methods for multi-agent systems mainly adopt distributed full search strategies or local search strategies. The former requires a lot of computation and communication costs, while the latter cannot ensure the diversity and quality of solutions. Therefore, in this paper, we combine a distributed many-objective evolutionary algorithm called D-NSGA3 with a greedy algorithm to search the task allocation solutions, and we comprehensively consider the constraints related to space, time, energy consumption and agent function in multi-agent systems. Specifically, D-NSGA3 is used to optimize multiple objectives simultaneously so as to ensure the search capability and the diversity of solutions. Alternate search between D-NSGA3 and the greedy algorithm is conducted to enhance the local optimizing ability. Experimental results show that the proposed method can effectively solve large-scale task allocation problems (e.g., the number of agents is not less than 250, and that of tasks is not less than 1000). Compared with the existing work called MSEA, the proposed method could achieve better and more diverse solutions.

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A Fault Diagnosis and Operation Advising Cooperative Expert System Based on Multi-Agent Technology

Cited by 6 publications

References 3 publications

Thermal defect segmentation of electrical equipment based on saliency constraint

Thermal defect segmentation of electrical equipment based on saliency constraint

A Power Grid Fault Diagnosis Method in the Case of Alarm Information Loss Based on the Top-k Skyline Query Algorithm

Task Allocation for Multi-Agent Systems Based on Distributed Many-Objective Evolutionary Algorithm and Greedy Algorithm

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