The massive access of 5G base stations has injected new vitality into the low-carbon development of integrated energy system （IES）. By stimulating 5G base stations to participate in demand response and incorporating them into the scheduling framework of IES，energy saving and emission reduction of IES can be effectively promoted and the overall economic benefits of IES can be improved. Accordingly，a campus IES day-ahead scheduling model that considers low-carbon empowerment of 5G base stations was proposed. Firstly，the basic structure and main energy consumption inside the 5G base station were analyzed，and a flexible interaction model between the 5G base station and the IES system was constructed；secondly，a price-based demand response model based on the price elasticity matrix and a substitution-based demand response model based on the electric energy-thermal energy interconversion were constructed，and the IES day-ahead scheduling model was built with the lowest scheduling cost as the objective function；meanwhile，the IES risk-averse robust model was further constructed by using the information gap decision theory to deal with the uncertainty of renewable energy output. Finally，the effectiveness of the proposed model and the proposed algorithm were verified by various typical operation scenarios in an IES 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.

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.

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.

The traditional control of electromechanical actuator in aviation electromechanical servo system mostly adopts two-level inverter and PID control，which can not achieve higher precision control of permanent magnet synchronous motor. In view of the above problems and the difficulty of space vector modulation algorithm to solve the problem of neutral point voltage balance，a model predictive control method based on improved virtual space vector method was proposed，and the corresponding control strategy was designed. The model predictive control was used to improve the control response speed and control accuracy of the permanent magnet synchronous motor. The neutral point clamped（NPC） three-level inverter with more sinusoidal output was used. The improved virtual space vector pulse width modulation（VSVPWM） was used to construct a dodecagon virtual space vector diagram closer to the vector circle. The simulation results show that the improved method can realize the stable operation of permanent magnet synchronous motor under low torque ripple，and has a good control effect on the midpoint voltage balance problem.

In order to improve the accuracy and stability of short-term wind direction prediction，a wind direction prediction method based on lidar wind data and an improved nonlinear echo state network （NESN） model was proposed.First of all，wind direction data 100 meters ahead of the wind turbine was obtained by laser wind detection radar. Secondly，the multivariate polynomial function was used to construct the nonlinear relation of the internal state of the reserve pool，the order of the weight matrix and the complexity of model calculation were reduced.Finally，the prediction model was established and the simulation prediction was carried out on different lidar data sets.The results show that compared with the nonlinear echo state network and adaptive neuro fuzzy inference system （ANFIS），the mean absolute error （MAE），root mean square error （RMSE），normalized mean absolute error （NMAE）and normalized root mean square error （NRMSE）of the improved NESN model are significantly reduced，and the prediction accuracy and stability are improved.The accuracy of the wind turbine alignment the wind direction is improved and the mechanical loss of yaw is reduced.

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.

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.

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 potential frequency secondary drop problem of direct-drive wind power systems in the scenario of continuous frequency drop faults in the power grid，the principle of primary frequency regulation of direct-drive wind power systems was analyzed. A primary frequency regulation control strategy for direct-drive wind power systems was proposed based on the product and difference combination of frequency change rates at pre- and post-sampling times. According to the established criteria，the droop coefficient of the direct-drive wind power system in different frequency response stages was adjusted by comprehensively considering the fan speed，frequency deviation，and frequency rate of convergence，to realize the active frequency support and flexible frequency modulation exit of the wind power system. A simulation model of a direct-drive wind power system was established in Matlab/Simulink to verify the effectiveness of the proposed control strategy.