The capacitive voltage transformer is an important metering device in power systems, the principal element analysis method has certain limitations in the error evaluation of capacitive voltage transformer (CVT), and this paper proposes an adaptive evaluation method based on moving window-weighted principal component analysis (PCA). This method combines the ideas of moving window PCA and exponential weighted PCA, which can adaptively update the evaluation model and achieve state evaluation of CVT. The moving window-weighted PCA method can effectively identify abrupt and gradual errors, which is more suitable for long-term CVT error monitoring.
Aiming at the problem that it is difficult to obtain the core parameters of the current transformer simulation model, a genetic simulated annealing algorithm is proposed in this paper. This method combines simulated annealing algorithm with genetic algorithm to overcome the premature phenomenon of traditional genetic algorithm. It realizes the fast fitting of core specific parameters of current transformer J-A model, and can quickly build the current transformer simulation model. The effectiveness of the algorithm is verified by example simulation.
A Convolutional Neural Network (CNN) for theoretical station area line loss is proposed in this paper. Considering that CNN has strong nonlinear fitting ability, it is often used to predict the station area line loss. We analyze case, and select appropriate number of input features to verify proposed method’s availability. Meanwhile, the station area line loss is calculated under the most appropriate number of feature inputs. The results show that the station area classification and key factors are identified as the subsequent station area loss calculation model, which optimizes the input variables and improves efficiency and accuracy for station area line loss calculation.
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