Application Based on Artificial Intelligence in Substation Operation and Maintenance Management

Abstract

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.

Abnormal data detection method of smart substation based on K-means-SVM

“…After the SVM training is completed, use the trained plane to test the two types of data obtained by K-means clustering, and use the data predicted by the SVM to retrain the SVM segmentation plane, and iteratively update the SVM segmentation plane in this way until the SVM is used to predict the data. Until the number of errors no longer changes 4 , after a sequence of computations and optimizations, the ultimate outcome is the attainment of the maximum interval hyperplane. The procedural details of this approach are visually depicted in Figure 2, presenting the flow chart that outlines the step-by-step progression of the method.…”

“…Finally, the dual problem is solved to obtain the solution of the original problem, and the maximum margin hyperplane is obtained. Then input data into the established model to judge whether the data is abnormal data 14 . Figure 1 displays the outcomes attained through the utilization of support vector machines for classification purposes.…”

“…Classification methods commonly used in machine learning include: k-means, Support Vector Machine (SVM), integrated classifier, Bayesian classification, etc. 4 . The K-means clustering algorithm, introduced by J.B. MacQueen in 1967, stands as a quintessential and extensively adopted partition-based clustering technique within the realm of distance-based clustering algorithms.…”

Retracted: Application Based on Artificial Intelligence in Substation Operation and Maintenance Management

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”