Maintenance Automation Architecture and Electrical Equipment Fault Prediction Method in Tanzania Secondary Distribution Networks

2021 ◽  
Vol 47 (3) ◽  
pp. 1138-1153
Author(s):  
Hadija Mbembati ◽  
Kwame Ibwe ◽  
Baraka Maiseli
Keyword(s):  
Support Vector Machine ◽  
Power Systems ◽  
Network Architecture ◽  
Prediction Method ◽  
Distribution Networks ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Fault Identification ◽  
Support Vector ◽  
Secondary Distribution

Distribution networks remain the most maintenance-intensive parts of power systems. The implementation of maintenance automation and prediction of equipment fault can enhance system reliability while reducing the overall costs. In Tanzania, however, maintenance automation has not been deployed in secondary distribution networks (SDNs). Instead, traditional methods are used for condition prediction and fault identification of power assets (transformers and power lines). These (manual) methods are costly and time-consuming, and may introduce human-related errors. Motivated by these challenges, this work introduces maintenance automation into the network architecture by implementing effective maintenance and fault identification methods. The proposed method adopts machine learning techniques to develop a novel system architecture for maintenance automation in the SDN. Experimental results showed that different transformer prediction methods, namely support vector machine, kernel support vector machine, and multi-layer artificial neural network, give performance values of  96.72%, 97.50%, and 97.53%, respectively. Furthermore, oil based performance analysis was done to compare the existing methods with the proposed method. Simulation results showed that the proposed method can accurately identify up to ten transformer abnormalities. These results suggest that the proposed system may be integrated into a maintenance scheduling platform to reduce unplanned maintenance outages and human maintenance-related errors. Keywords: Predictive maintenance; fault identification; fault prediction; maintenance automation; secondary electrical distribution network

Trend Prediction of Hydraulic Liquid Leakage Based on ES-LSSVM

2011 ◽  
Vol 137 ◽  
pp. 440-444 ◽  
Author(s):  
Zhi Yong Wu ◽  
Zeng Bing Xu ◽  
Ming Gao
Keyword(s):  
Support Vector Machine ◽  
Hydraulic System ◽  
Prediction Method ◽  
Least Square ◽  
Fault Prediction ◽  
Evolutionary Strategy ◽  
Support Vector ◽  
Short Term ◽  
Trend Prediction ◽  
Internal Parameters

A novel prediction method which combined evolutionary strategy with least-square support vector machine is presented and applied to the trend prediction of hydraulic liquid leakage in this paper. In order to improve the prediction performance, the evolutionary strategy is employed to optimize the internal parameters of least-square support vector machine. Through the experiment study, the result validated the effectiveness of the prediction method, and it is also demonstrated that the method is able to do the short-term fault prediction for the hydraulic system.

scholarly journals Intelligent Detection of Small Faults Using a Support Vector Machine

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6242
Author(s):  
Aiping Zeng ◽  
Lei Yan ◽  
Yaping Huang ◽  
Enming Ren ◽  
Tao Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Production Efficiency ◽  
Random Noise ◽  
Principal Component ◽  
Seismic Attributes ◽  
Forward Model ◽  
Fault Prediction ◽  
Fault Identification ◽  
Support Vector ◽  
Small Fault

The small fault with a vertical displacement (or drop) of 2–5 m has now become an important factor affecting the production efficiency and safety of coal mines. When the 3D seismic data contain noise, it is easy to cause large errors in the prediction results of small faults. This paper proposes an intelligent small fault identification method combining variable mode decomposition (VMD) and a support vector machine (SVM). A fault forward model is established to analyze the response characteristics of different seismic attributes under the condition of random noise. The results show that VMD can effectively realize the attenuation of random noise and the seismic attributes extracted on this basis have a good correlation with the small fault. Through the analysis of the SVM algorithm and the fault forward model, it is proved that it is feasible to realize intelligent predictions of small faults by using seismic attributes as the input of a SVM. The fault prediction method using a SVM that is proposed in this paper has higher accuracy than the principal component analysis method, as the prediction results have important guiding significance and reference value for later coal mining. Therefore, the method presented in this paper can be used as a new intelligent method for small fault identification in coal fields.

scholarly journals Voting Based Classification Method for Diabetes Prediction

2019 ◽  
Vol 8 (2S6) ◽  
pp. 913-918
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Research Work ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Prediction Analysis ◽  
Tree Classifier ◽  
Diabetes Prediction

This research work is based on the diabetes prediction analysis. The prediction analysis technique has the three steps which are dataset input, feature extraction and classification. In this previous system, the Support Vector Machine and naïve bayes are applied for the diabetes prediction. In this research work, voting based method is applied for the diabetes prediction. The voting based method is the ensemble based which is applied for the diabetes prediction method. In the voting method, three classifiers are applied which are Support Vector Machine, naïve bayes and decision tree classifier. The existing and proposed methods are implemented in python and results in terms of accuracy, precision-recall and execution time. It is analyzed that voting based method give high performance as compared to other classifiers.

Research on Fault Prediction Technology of Complicated Equipment

2013 ◽  
Vol 347-350 ◽  
pp. 448-452 ◽  
Author(s):  
Sai Sai Jin ◽  
Kao Li Huang ◽  
Guang Yao Lian ◽  
Bao Chen Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Square ◽  
Fault Prediction ◽  
Support Vector ◽  
Hydraulic Pump ◽  
Fault Information

For the problems of not enough fault information for the complicated equipment and difficult to predict the fault, we apply Support Vector Machine (SVM) to build the fault prediction model. On the basis of analyzing regression algorithm of SVM, we use Least Square Support Vector Machine (LS-SVM) to build the fault prediction model.LS-SVM can effectively debase the complication of the model. Finally, we take the fault data of a hydraulic pump to validate this model. By selecting appropriate parameters, this model can make better prediction for the fault data, and it has higher prediction precision. It is proved that the fault prediction model which based on LS-SVM can make better prediction for fault trend of complicated equipment.

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