Accurately estimating the state of health（SOH）of lithium-ion batteries is a crucial prerequisite for ensuring the safe and stable operation of energy storage systems. The key to improving the accuracy of SOH estimation lies in the rational selection of health characteristics that can effectively reflect the state of health of lithium-ion batteries. By analyzing the current characteristics of lithium-ion batteries during the constant voltage charging stage，a healthy combination of features containing the slope of the first and last points of the current curve，the standard deviation，and the mean value were extracted from the current curve data during the constant voltage charging stage. To validate the effectiveness of the proposed feature combination，SOH estimation model based on kernel ridge regression（KRR）and support vector regression（SVR）was designed，and model validation was successfully completed. The experimental results demonstrate that the proposed feature combination can achieve high-precision SOH estimation across different models，exhibiting excellent model adaptability.

New energy has obvious volatility and anti-peak-shaving characteristics，and the integration of large-scale new energy into the power grid poses challenges to the peak-shaving. Hydrogen energy has a good potential for application as a potential peak-shaving resource. A multi-time scale peak-shaving scheduling model based on electrolytic hydrogen production was proposed with the aim of solving this problem. Firstly，the evaluation index model of peak-shaving capacity of electrolytic hydrogen was introduced and different types of electrolytic hydrogen peaking capacity were caculated. Meanwhile，an equivalent load disaggregation model based on coordinate distance minimization and wavelet transform techniques was constructed for matching different equivalent load components with different electrolytic hydrogen production systems. Subsequently，a multi-time scale peak-shaving scheduling scheme for different load components was proposed with the objective of improving the economics and effectiveness of peak-shaving. Finally，the validity of the proposed model was confirmed through simulations on the IEEE-30 node system.

In order to alleviate the intensive computational burden in the model predictive control（MPC）of the matrix converter，the MPC of the matrix converter was divided into the predictive control of the virtual rectifier and virtual inverter based on the equivalent indirect modulation of the matrix converter. Compared with the traditional direct MPC，the computational burden and execution time of the proposed strategy were reduced. Considering the issue of the high dependence of MPC on model parameters，the extended Kalman filter（EKF）was used to identify system model parameters online，thereby improving the robustness and anti-interference ability of MPC. The experimental results show that the proposed indirect MPC based on the extended Kalman filter parameter identification algorithm offers a good control performance on the load current and the grid side power factor control，and the dependence on the model parameters is reduced.

Over the years，machine learning has made some breakthroughs in the insulation defects of gas insulated switchgear（GIS），but the traditional methods have the disadvantages of incomplete information，excessive reliance on artificial feature extraction and low diagnosis rate. In order to solve these problems，a diagnosis method based on deep graph convolutional neural network （DGCN）was proposed. Firstly，a partial discharge （PD） experimental platform was built on a 220 kV real GIS and the partial discharge signals collected by ultra high frequency sensor were converted into frequency domain spectrogram samples by Fourier transform. Then，the spectrogram samples were input into the DGCN，which undergoes graph convolution，coarsening and pooling operations to make the spectrogram structure was clearer and enrich the input information. Finally，the test samples were used to test the DGCN with set parameters. The experimental results show that the proposed method can achieve a recognition rate of 98.77% for GIS fault defects，which is significantly higher than other methods and has good robustness.

The micro-grid solves the problem that the intermittent and fluctuating power generation has adverse effects on the stable operation of the distribution network when the distributed generation is connected to the distribution network. In order to meet the economic operation of micro-grid under grid connected mode and improve power supply reliability，a micro-grid energy dispatching strategy based on peak-valley price and energy storage state of charge （SOC） was proposed. The strategy divided the whole day into three periods：peak，average and valley. During the real-time scheduling cycle，different scheduling strategies were applied based on different time interval and the SOC of energy storage. Reasonable energy storage charging and discharging penalty functions were designed in different time interval，and the maximum energy storage charging and discharging constraint factor was introduced to further improve the charging and discharging of the energy storage device. The minimum operating cost of micro-grid was took as the objective function and solved it through particle swarm optimization algorithm. The effectiveness of the strategy was verified by an example analysis.

In response to the poor disturbance rejection performance and issues of parameter perturbation and control precision in traditional control strategies for interior permanent magnet synchronous motors （IPMSM），a fuzzy sliding mode control strategy based on the super-twisting disturbance observer was proposed. This strategy enables wide-speed-range operation of IPMSM，and further improves rotational speed tracking precision and the system's disturbance rejection capabilities. Firstly，the speed control range was broadened by combining maximum torque per ampere ratio with the gradient descent method for weak magnetic control. Secondly，the super-twisting algorithm（STA）was utilized to design a disturbance observer，thus enhancing the system's ability to resist disturbances. In addition，to address the derivative explosion problem in Backstepping control，a second-order sliding mode differentiator was used to approximate the virtual control rate. The fuzzy logic system was adopted to approximate the nonlinear part in the model of IPMSM，reducing the impact of motor parameter perturbation on control performance. Finally，the stability of the proposed control strategy was confirmed by applying Lyapunov theory. Through simulation on the Matlab/Simulink platform，it was verified that under the control strategy proposed，IPMSM demonstrate superior wide-speed-range dynamic performance and robustness.

When the Vienna rectifier is connected to unbalanced loads operating under light load conditions，the slight power difference causes unbalanced voltage of bipolar DC bus，which increases the voltage stress on the switching device and DC-link capacitance. To solve the above problems，a DC offset reduction of neutral point voltage strategy based on reactive current compensation for Vienna rectifier with bipolar DC-link was proposed. By combining the amplitude of zero-sequence component，which is superimposed onto the three-phase reference voltage，the neutral point current for various power conditions can be obtained. The effect of the phase angle difference between the reference voltage and the input current on the neutral point current was analyzed，and the maximum reactive currents of the odd and even sectors was obtained according to the operating region of the Vienna rectifier. In order to increase the neutral point current under light load condition，the reactive current was injected to change the phase angle difference according to the contour of neutral point current，thereby achieving the balance of bipolar DC bus under light load conditions. Finally，the effectiveness of the proposed strategy was verified on the Vienna rectifier experimental platform.

Switching power supply is now developing towards high-frequency soft-switching and high power density，the volume of magnetic components is greatly reduced，and the excitation inductance of transformer no longer meets the assumption of large excitation inductance in traditional analysis. Under this background，the influence of the weak excitation inductance on the full-bridge circuit was analyzed，and proposed that the full-bridge circuit can achieve autonomous soft-switching through the charging and discharging of the capacitor under the condition of the weak excitation inductance. The autonomous soft-switching of the full-bridge converter can be realized when the duty ratio is large. Through principle analysis and calculation，relationship between the charge and discharge of the switch body capacitance and the inductance current and the time constant was obtained，and the above principle through simulation were verified. Finally，an experimental platform with a working frequency of 200 kHz was built to verify the above analysis and simulation results.

The development status of electric drive systems in the industrial sector and their significance in promoting green and low-carbon transformation were studied. As a crucial control and energy-saving equipment in the industrial sector，electric drive systems face numerous challenges posed by unchecked development. It is imperative to enhance the overall quality and energy efficiency levels by optimizing the industry order. Firstly，the current development status of electric drive systems both domestically and internationally were analyzed，and discussed the energy efficiency requirements for products in the global community. Furthermore，problems encountered by electric drive products in the green and low-carbon development of China's industry were discussed. To promote sustainable development of electric drive systems，a green and low-carbon development path was proposed，including strengthening the standardization system for electric drive products，enhancing the implementation of standards，promoting mandatory certifications，and disseminating energy-saving technologies. Simultaneously，the importance of placing emphasis on and regulating the recycling phase of electric drive products was emphasized. Through these measures，electric drive systems hold promise to make a greater contribution towards the green and low-carbon transformation of industry.

In order to solve the problem of large peak-to-peak inductor currents in dual active bridge converters with mismatched input and output voltages，a four-degree-of-freedom modulation（FDFM） strategy was proposed by combining symmetric duty cycle modulation and asymmetric duty cycle modulation. By analyzing the operating modes of the four-degree-of-freedom modulation strategy，the peak-to-peak inductor current and transmission power models were established，and the optimal solution with different transmission powers was solved by using the Karush-Kuhn-Tucker（KKT） condition with the peak-to-peak inductor current as the optimization objective. And the experimental prototype was built to verify the effectiveness of the proposed strategy. The experimental results show that the proposed modulation strategy can effectively reduce the peak-to-peak inductor current of the converter and improve the efficiency of the dual active bridge converter.