Jiaxuan He

The output voltage of the boost converter is often compromised by unpredictable load and uncertain input voltage disturbance, making the stable control of the output voltage problematic. To address this issue, a dc voltage robust control strategy of the boost converter is proposed with considering system disturbance. First, based on the dynamic model of the boost converter, a Luenberger observer is proposed to estimate the load impedance and input voltage. Subsequently, the nonlinear model of the boost converter is transformed into a linear Brunovsky form by using the feedback linearization method, and a single-loop sliding mode controller is designed based on the system energy function. Since the proposed observer can update the system disturbances for the controller in time, the robustness and dynamic performance of the boost converter are significantly improved. Finally, the effectiveness of the proposed control strategy is verified by simulation and experiment.

The predictive model of aging indicator based on intelligent algorithms has become an auxiliary method for the aging condition of transformer polymer insulation. However, most of the current research on the concentration prediction of aging products focuses on dissolved gases in oil, and the concentration prediction of alcohols in oil is ignored. As new types of aging indicators, alcohols (methanol, ethanol) are becoming prevalent in the aging evaluation of transformer polymer insulation. To address this, this study proposes a prediction model for the concentration of alcohols based on a genetic-algorithm-optimized support vector machine (GA-SVM). Firstly, accelerated thermal aging experiments on oil-paper insulation are conducted, and the concentration of alcohols is measured. Then, the data of the past 4 days of aging are used as the input feature of SVM, and the GA algorithm is utilized to optimize the kernel function parameter and penalty factor of SVM. Moreover, the concentrations of methanol and ethanol are predicted, after which the prediction accuracy of other algorithms and GA-SVM are compared. Finally, an industrial software program for predicting the concentration of methanol and ethanol is established. The results show that the mean square errors (MSE) of methanol and ethanol concentration predictions of the model proposed in this paper are 0.008 and 0.003, respectively. The prediction model proposed in this paper can track changes in methanol and ethanol concentrations well, providing a theoretical basis for the field of alcohol concentration prediction in transformer oil.