Fault warning method based on extreme learning regression and fuzzy reasoning

Li, Gang; Qiu, Chenguang; Cao, Shuai; Ya-ou, Wang

doi:10.1051/e3sconf/202123604008

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Hubei Electric Power Co., Ltd. adopted the icing prediction model based on extreme learning machine and the icing prediction model based on support vector machine (SVM) to predict the icing of transmission lines. The experimental comparison results show that the prediction model based on SVM is better [ 2 , 3 ]. The second is to divide the status level according to the evaluation result of the transmission line and take corresponding measures to carry out early warning according to the status level.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Technology of Abnormal Displacement of BeiDou Power Line Based on Artificial Neural Network

Yang

Chen

et al. 2022

Computational Intelligence and Neuroscience

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In the practice of power line engineering, navigation and positioning technology is often used in the fields of information collection and analysis, optimized line design, and deformation monitoring. Compared with traditional measurement technology, it has the characteristics of high precision and high reliability. In order to realize the measurement of abnormal displacement of power lines, improve the efficiency and quality of monitoring, and reduce the occurrence of faults, firstly, this study introduces the basic theory of artificial neural network (ANN). The core algorithm of the ANN-BP (back propagation) neural network has been improved. The improved algorithm is used to improve the BeiDou Navigation Satellite System (BDS). The improved and the unimproved BDS are used to solve the collected related data. The results show that the geometric dilution of precision (GDOP) values obtained by conventional BDS are small, all within the range of less than 4. After the introduction of the BP neural network into the system, the geometric space distribution of positioning satellites is improved, the GDOP is reduced, the reliability and availability of satellite positioning are enhanced, and the accuracy requirements are met. The accuracy of the measured data positioning results of the two systems has reached the cm level. There is not much difference between the processing results of the two modes. Among them, the Z direction accuracy has the largest difference, which is 2.5 cm. The introduction of the BP neural network has improved the spatial combination structure, and the positioning results in the three directions of X, Y, and Z are all better. From the perspective of root mean square (RMS), the RMS fluctuation of the simulation results obtained by observing the conventional BDS is large. The RMS value of BDS displacement based on the BP neural network is smaller, and the change is gentle. With the increase in the number of epochs and the increase in the number of simulations, its value is also more convergent. These data show that the quality of BDS observations based on the BP neural network is significantly better. These contents will effectively improve the monitoring accuracy and operational reliability of the system and have important practical significance and application value.

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Section: Introductionmentioning

confidence: 99%