Improved Cluster Intelligent and Complex Optimization Algorithm for Power Equipment CAD-Assisted Intelligent Operation and Maintenance

Song, Yinguan; Su, Yi‐Feng; Wang, Ming; Xie, Zhenjun; Chen, Henglong; Sang, Zhongqing

doi:10.1155/2022/5695453

Cited by 5 publications

(7 citation statements)

References 10 publications

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“…However, no additional measures are calculated, resulting in a reduction in prediction effectiveness. The CAD-IOMPSE approach also generates superior accuracy, recall, and F -measure results (Song et al [ 3 ]). However, the error metric is not given, which leads to inefficient results.…”

Section: Discussionmentioning

confidence: 99%

“…In this stage, the suggested technique's performance is evaluated, and a comparison analysis is computed by comparing it to some existing techniques (Text CNN [Chen et al [ 22 ]], CAD-IOMPSE [Song et al [ 3 ]], Glove-BiLSTM-Attention [Chen et al [ 23 ]], and DA-BiLSTM [Li et al [ 24 ]]) for predicting power equipment breakdowns.…”

Section: Performance Analysismentioning

confidence: 99%

“…Checking and assessing the status of electrical substation equipment is made possible by the acquisition of relevant data. Asset condition and risk evaluation are the two most important components of automated substation maintenance works (Song et al [ 3 ]). Accurate evaluation of power equipment status affects substation risk assessment and maintenance.…”

Section: Introductionmentioning

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: Discussionmentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

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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“…When harmonic issues of the power system are studied, massive data on the operation of equipment are accumulated, which can reflect the historical problems of the equipment and the related solutions [ 1 , 2 ]. Historically, no effective data analysis has been conducted, and the relevant data are often stored in the corresponding system.…”

Section: Introductionmentioning

confidence: 99%

A Power System Harmonic Problem Based on the BP Neural Network Learning Algorithm

Yue

Hu²,

Zhang

2022

Computational Intelligence and Neuroscience

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At present, due to the large-scale use of different kinds of power electronic devices in the power system, the problem of harmonic pollution in the power grid is becoming more and more serious, which will lead to a serious decline in the production, transmission, and utilization rate of electric energy, overheat electrical devices, generate vibration and interference, and then affect the aging and service life of the lines. In order to effectively reduce the harmonic problems caused by different levels of the power system, it is necessary to analyze the harmonic components. In this paper, the BP neural network learning algorithm is introduced into the harmonic problems of the power system. The mapping relationship between input and output signals is obtained by using the BP neural network algorithm, and the harmonic frequency, amplitude, and phase contained in the obtained data are analyzed. According to the type of equipment with problems in the operation of the power system and the rapid diagnosis of existing defects, the problems are quickly located and the causes are analyzed. The practical results show that the BP neural network learning algorithm proposed in this paper has higher detection accuracy and analysis speed for the difficult problems in the power system.

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“…In the power-sensitive early warning data conversion, a lot of data analysis that can re ect the operation of power-sensitive early warning has been accumulated. ese data can show the historical problems of powersensitive early warning and the corresponding solutions [3,4]. For a long time, this data analysis has not been used e ectively and is usually stored in the data system.…”

Section: Introductionmentioning

confidence: 99%

Power-Sensitive Early Warning Data Conversion Method Combined with Multiobjective Differential Evolution Algorithm

Gao

Liang

et al. 2022

Mathematical Problems in Engineering

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Aiming at the problems of economy, security, and reliability of power system operation, the multiobjective gap evolutionary algorithm is introduced into power-sensitive early warning data conversion. A power-sensitive early warning data conversion (PSEWDC) method based on multiobjective gap evolutionary algorithm (MOGEA) is proposed. According to the type of equipment with problems in the operation of the power system and the rapid diagnosis of defects and problems, the method quickly locates the problems and analyzes the causes, replaces and rearranges the original data by using the substitution data method to eliminate the nonlinear autocorrelation, and then, according to the convergence of the long-range correlation index, identifies the abnormal values that have no impact on the overall fluctuation of the original sequence, judges that the current sequence reaches the critical point of extreme events, and provides a reference threshold for real-time risk early warning. Finally, the example analysis shows that this method can finally determine the risk early warning threshold of power peak and valley load by analyzing the convergence of long-range correlation index, so as to avoid the blindness of subjective experience, provide a theoretical basis for power load risk early warning, effectively solve the problems of subjectivity, lack of dynamics, and lack of theoretical basis for setting risk threshold in traditional power risk early warning research, and have an intelligent insight into the security situation of power big data.

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Improved Cluster Intelligent and Complex Optimization Algorithm for Power Equipment CAD-Assisted Intelligent Operation and Maintenance

Cited by 5 publications

References 10 publications

Application Based on Artificial Intelligence in Substation Operation and Maintenance Management

Application Based on Artificial Intelligence in Substation Operation and Maintenance Management

A Power System Harmonic Problem Based on the BP Neural Network Learning Algorithm

Power-Sensitive Early Warning Data Conversion Method Combined with Multiobjective Differential Evolution Algorithm

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