Abstract
The thyristor is the key device for the converter of the ultra-high-voltage DC (UHVDC) project to realize AC–DC conversion. The reliability of thyristors is directly related to the safe operation of the UHVDC transmission system. Due to the complex operating environment of the thyristor, there are many interrelated parameters that may affect the aging state of thyristors. To extract useful information from the massive high-dimensional data and further obtain the aging state of thyristors, a supervised tensor domain classification (STDC) method based on the adaptive syn-thetic sampling method, the gradient-boosting decision tree, and tensor domain theory is proposed in this paper. Firstly, the algorithm applies the continuous medium theory to analogize the aging state points of the thyristor to the mass points in the continuous medium. Then, the algorithm applies the concept of the tensor domain to identify the aging state of the thyristor and to transform the original state-identification problem into the state classification surface determination of the tensor domain. Secondly, a temporal fuzzy clustering algorithm is applied to realize automatic positioning of the classification surface of each tensor sub-domain. Furthermore, to solve the problem of unbalanced sample size between aging class data and normal class data in the state-identification domain, the improved adaptive synthetic sampling algorithm is applied to preprocess the data. The gradient-boosting decision tree algorithm is applied to solve the multi-classification problem of the thyristor. Finally, the comparison between the algorithm proposed and the conventional algorithm is performed through the field-test data provided by the CSG EHV Power Transmission Company of China’s Southern Power Grid. It is verified that the evaluation method proposed has higher recognition accuracy and can effectively classify the thyristor states.
Highlights
The operating environment of thyristors in ultra-high-voltage DC (UHVDC) transmission converters is complex, and there are a large number of interrelated parameters that can affect the aging state of the thyristors
A large amount of high-dimensional state monitoring data is sampled for the state evaluation of the thyristors in UHVDC transmission converters [1,2]
The aging state of the thyristor is identified through the concept of the tensor domain, and the original state-identification problem is transformed into the determination of the state of the tensor domain category classification surface
Summary
The operating environment of thyristors in UHVDC transmission converters is complex, and there are a large number of interrelated parameters that can affect the aging state of the thyristors. The improved adaptive synthetic sampling algorithm is proposed in this paper to solve the multi-category data imbalance problem and can be better applied to thyristor state evaluation. The gradient-boosting decision tree algorithm is applied to determine the tensor sub-domain classification surface to realize the state evaluation of thyristors. The data in (2) are preprocessed to balance the data samples of each category, and the gradient-boosting decision tree algorithm is applied to obtain the tensor sub-domain classification surfaces of the multi-classification problem. The physical meaning of gi(ξj, t) is the deformation of a state material point in the tensor domain at time t This covariant basis vector gi(ξj, t) is derived with respect to time to obtain the deformation rate of the continuous medium in the tensor domain corresponding to the aging state as: dgi = dt
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