Combined Forecasting Method of Dissolved Gases Concentration and Its Application in Condition-Based Maintenance

In order to predict the changing trend of the dissolved gas in the transformer oil accurately and guide the condition-based maintenance of a power transformer, a combined forecasting model based on cross-entropy is proposed and its application is analyzed. First, a combined forecasting model is established on the basis of radical basis function neural network, back propagation neural network, least squares support vector machine with two different kernel functions, and grey model. Then, the weight coefficient of each algorithm is determined by cross-entropy theory. After that, an application method of the combined forecasting model by the pattern of a variable time window is proposed for the purpose of arranging the equipment maintenance in advance. In this way, the increased budget due to emergency maintenance can be alleviated, and potential loss due to lack of timely maintenance can be reduced. Finally, the accuracy of the combination forecasting method and the effectiveness of the application method are verified by a series of examples.

[1]  C. P. Hung,et al.  Novel grey model for the prediction of trend of dissolved gases in oil-filled power apparatus , 2003 .

[2]  Abdolrahman Peimankar,et al.  Multi-objective ensemble forecasting with an application to power transformers , 2018, Appl. Soft Comput..

[3]  Meng-Hui Wang.  Grey-Extension Method for Incipient Fault Forecasting of Oil-Immersed Power Transformer , 2004 .

[4]  Mingjun Wang,et al.  Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil , 2009 .

[5]  Jashandeep Singh,et al.  Condition Monitoring of Power Transformers - Bibliography Survey , 2008, IEEE Electrical Insulation Magazine.

[6]  Zishu He,et al.  Pattern Synthesis for Arbitrary Arrays via Weight Vector Orthogonal Decomposition , 2018, IEEE Transactions on Signal Processing.

[7]  Zhixin Mao,et al.  Detection of dissolved gas in oil–insulated electrical apparatus by photoacoustic spectroscopy , 2015, IEEE Electrical Insulation Magazine.

[8]  Yu Sun,et al.  Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm , 2008 .

[9]  Lijun Yang,et al.  Forecasting dissolved gases content in power transformer oil based on weakening buffer operator and least square support vector machine–Markov , 2012 .

[10]  Gang Peng,et al.  A novel quantitative spectral analysis method based on parallel BP neural network for dissolved gas in transformer oil , 2016, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[11]  Lijun Yang,et al.  Fuzzy information granulated particle swarm optimisation-support vector machine regression for the trend forecasting of dissolved gases in oil-filled transformers , 2011 .

[12]  Zhang Yu,et al.  Forecasting dissolved gases content in power transformer oil based on particle swarm optimization-based RBF neural network , 2009, 2009 ISECS International Colloquium on Computing, Communication, Control, and Management.

[13]  Zeng Xiangjun.  A New Combination Forecasting Model for Concentration Prediction of Dissolved Gases in Transformer Oil , 2008 .

[14]  Kazuhiro Izui,et al.  Multi-Objective Optimization of Magnetic Actuator Design Using Adaptive Weight Determination Scheme , 2017, IEEE Transactions on Magnetics.

[15]  Georgy Sofronov,et al.  Multiple Break-Points Detection in Array CGH Data via the Cross-Entropy Method , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[16]  Lijun Yang,et al.  Particle swarm optimization-least squares support vector regression based forecasting model on dissolved gases in oil-filled power transformers , 2011 .

[17]  Heesung Kwon,et al.  Optimal kernel bandwidth estimation for hyperspectral kernel-based anomaly detection , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.