Maintenance Management of a Transmission Substation with Optimization

Kitak, Peter; Belak, Lovro; Pihler, J.; Ribič, Janez

doi:10.3390/app112411806

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Kitak et al [ 6 ] demonstrate that the transmission substation's reliability-centered maintenance (RCM) is the subject of this study. An optimization algorithm was used to design and carry out the maintenance procedure.…”

Section: Related Workmentioning

confidence: 99%

“…The autocorrelation arrays are all homogeneous matrices. Equation ( 6 ) is used to calculate the covariance matrix.…”

Section: Proposed Workmentioning

confidence: 99%

“…To replace RNN neurons, LSTM employs memory blocks with three types of gates (input, forget, and output gates). Equations could be used to express the data calculated inside LSTM memory blocks [3][4][5][6][7][8].…”

Section: Tweaked Long Short-term Memory (Tlstm)mentioning

confidence: 99%

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Application Based on Artificial Intelligence in Substation Operation and Maintenance Management

Xin

Zhang

Shi

2022

Computational Intelligence and Neuroscience

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To fulfill state grid Industry’s demands for smart and digitized business growth, traditional technological approaches have fallen short. Artificial intelligence (AI) technology enables coming up with solutions because electricity business types and volumes are constantly expanding and developing. Intelligent automation was a part of China’s smart grid development from the outset, and it continues to grow in the country’s electricity system. Smart substation operations and maintenance could benefit from the use of this system. There are new technological tools and theoretical concepts for the repair and control of power equipment owing to AI’s advancements in performance, accuracy, and self-learning capacity in the detection, forecasting, improvement, and judgment jobs. Substation operations and maintenance management are examined in this research using a new hybridized convolutional neural network and tweaked long short-term memory (HCNN-TLSTM) technique. Normalization is used to gather and preprocess the data immediately. Kernel-based linear discriminant analysis (K-LDA) is used to extract the features. A substation’s functioning and maintenance can then be investigated using the new approach. The genetic algorithm (GA) is used to improve the effectiveness of the proposed method. Finally, the presented technique’s performance is analyzed and compared with specific current models to achieve the largest performance in the proposed method for the management of substation operation and upkeep.

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Section: Related Workmentioning

confidence: 99%

“…The autocorrelation arrays are all homogeneous matrices. Equation ( 6 ) is used to calculate the covariance matrix.…”

Section: Proposed Workmentioning

confidence: 99%

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Application Based on Artificial Intelligence in Substation Operation and Maintenance Management

Xin

Zhang

Shi

2022

Computational Intelligence and Neuroscience

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“…At the tactical level, models that are based on BSC, capable of discovering structures and behavior patterns that are relatively hidden in work orders, and use machine learning have been analyzed [3]. Regarding the operational level, a computer application has been designed, implemented, and validated through the incorporation of reliability-centered Electronics 2024, 13, 639 2 of 18 maintenance (RCM) cases that were successfully carried out on equipment [4] or on a productive process [5].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Industrial Maintenance Plans through Assistance-Driven Reliability-Centered Maintenance and Case-Based Reasoning Design

Rodríguez-Padial,

Marín,

Domingo

2024

Electronics

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The present work builds on studies where the industrial market is currently characterized by a highly variable demand in terms of the quantities and flexibility of manufacturing or mass customization, which translates into a more demanding production context in terms of the continuous changes that are required in the production systems, the effect of which results in an increase in the fatigue of the machines that make up the production systems. However, current production systems tend to use highly communicative and sensorized cyber–physical systems; these characteristics can be used to integrate them into decision-assisted systems to improve the availability of the industrial plant. The developed assisted system focuses on collecting and taking advantage of historical knowledge of industrial plant failures and breakdowns. By ideally integrating the reliability-centered maintenance (RCM) methodology and case-based reasoning (CBR) algorithms implemented in a Java application, it is possible to design maintenance plans that are adjusted to the real and changing operational context of any industrial plant. As a result, faster and more accurate decisions are made, because they are based on data. This article focuses on improving certain aspects of the developed assisted system by adding more value by incorporating fuzzy logic (FL) techniques. The aim is to improve the way of entering information about risk factors and their relative importance by incorporating natural language instead of a numerical score, resulting in increased precision in the calculation of the risk priority number (RPN) of the new cases that are incorporated into the assisted system. On the other hand, an attempt has been made to correct two of the main inherent and recognized weaknesses in the classic RPN calculation method by implementing an appropriate mix of fuzzy logic techniques.

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“…Recently, many companies have gradually been introducing condition-based maintenance and its upgraded version, called reliabilitycentered maintenance (RCM), which considers the reliability of the entire system and not only individual elements. An RCM model is developed and tested for a transmission substation in [6], where the planning and actual performance of maintenance are carried out using an optimization algorithm. The novelty of this paper consists in its integrated treatment of all maintenance processes that are included in the pre-processing phase and used in the optimization process for reliability-centered maintenance.…”

mentioning

confidence: 99%