Aiming at the problem of insufficient frequency modulation capability of direct-driven wind turbine under traditional virtual inertia control，a control strategy of super capacitor assists direct-driven wind turbine participating in frequency modulation was proposed to improve the frequency stability of power grid after wind power is connected. Firstly，the influence of the fan speed and output power on the frequency modulation capability when the direct-driven wind turbine participates in the frequency modulation through virtual inertia control is analyzed，and the frequency modulation coefficient was established through the Sigmoid function to characterize the frequency modulation capability of it under different operating conditions. Then，based on the frequency modulation coefficient，a frequency modulation strategy for wind storage was proposed，that is when the frequency modulation ability of direct-driven wind turbine is strong，the direct-driven wind turbine participates in the frequency modulation independently，while when the frequency modulation capability is weak，the power required for the frequency modulation is jointly provided by the direct-driven wind turbine and the super capacitor. At the same time，the charging and discharging coefficient was introduced into the power control of the super capacitor to avoid its over-charging and over-discharging. At last，the simulation results show that the proposed method is better than the traditional virtual inertia control when frequency modulation capability of the direct-driven wind turbine is insufficient，and it also avoids overcharging and discharging of super capacitor.

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.

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.

With the continuous increase in the scale of distribution network construction and operation，a large number of electricity load control problems have emerged，especially the weakening of sample time series characteristics in electricity load data analysis，resulting in incomplete acquisition of local load characteristics，making it impossible for the distribution network electricity load prediction system to maintain the balance of energy in the power grid. In the past，the impact of "source-load" uncertainty was also rarely considered in the distribution network electricity load processing and distribution，which can easily lead to low probability of electricity load classification recognition，significant errors in electricity load prediction，and long-term problems such as high generation costs and insufficient distribution balance. In response to the above situation，the uncertainty of "source" and "load" was conducted research through probability distribution functions. A multi-objective function composed of two probability distribution functions was set as the constraint conditions for the coordinated output balance. The improved cluster eddy current search algorithm was used to solve the problem，and a coordinated control scheme for electricity load was obtained. The test results show that at 17：00，the power of photovoltaic device 1 is 1 050 kW， at 11：00，the power of photovoltaic device 2 is 980 kW，based on the source-load uncertainty root-mean-square error of less than 0.7%. The cost of the scheme based on "source-load" uncertainty is 453 200 yuan，and the balance degree is 0.94. The collaborative control method based on "source-load" uncertainty has a higher balance than the collaborative control method without "source-load" uncertainty，and the collaborative control technology is more reasonable.

Transmission line tower as the normal operation of the power system of the necessary equipment，its grounding resistance accurate detection for the safe and stable operation of the entire power system is particularly important，especially in the judgment of lightning protection performance and other aspects. Thus，a multi-frequency tower grounding resistance measurement method was proposed based on radial basis function （RBF） fitting. The method was able to use multi-frequency current as an excitation under the premise of not disconnecting the tower grounding lead，measure the grounding resistance value obtained from the input current of different frequencies，and then use radial basis function neural network fitting based on these data to realize the accurate measurement of the tower grounding resistance. At the same time，the accuracy and reliability of the method was proved through the measurement of grounding resistance of multiple towers，and the proposed method is simple to operate，which largely facilitates the measurement of inspectors on the spot，and has strong practical significance.

The power cable plays a very important role in today's social development and urban operation. With the increase of people's electricity consumption，the amount of cable laying is also increasing synchronously.In order to prevent the cable from being damaged，the environmental vibration signals around the cable duct gallery are monitored，and potential threats are found in time to ensure the normal operation of the cable，a vibration monitoring system for underground cable damage prevention based on Matlab wavelet noise reduction algorithm and EMD-AR spectrum analysis was proposed. The system was composed of solar power supply module，six axis gyroscope attitude sensing module，vibration sensor module and internet of things communication module.The main control chip uses STM32F4 microcontroller. The wavelet threshold de-noising algorithm was used to remove the complex high-frequency noise and improve the signal identifiability. Then the de-noised signal was subjected to empirical mode decomposition（EMD）and AR spectrum analysis of the first six intrinsic mode function（IMF）components.Finally，the AR spectrum of the first six IMF components was accumulated to obtain the energy contrast map of the signal. The research shows that the application of this method can accurately analyze the type of vibration，which can provide a more simple and convenient method for the monitoring and analysis of vibration near the cable duct gallery，and effectively protect the safe and stable operation of the cable.

Because of crystal oscillator error，inter-symbol interference and baseline drift in industrial optical fiber communication，the optical fiber receiver has the problem of high error rate of data recovery. In long-distance industrial communication，the electrical level jitter of serial data at the receiver will increase. To solve this problem，a method of clock and data recovery for long-distance industrial optical fiber communication was proposed. Six-times frequency clock was used for sampling. The rising or falling edge of serial data can be determined and collected under the same clock，which can select dynamical sampling clock. According to the electrical level jitter tolerance，the validity of the sampled data under different conditions can be verified. After data processing，the six-times sampled data was restored to serial data under the local clock，which is finally converted to the parallel data. Simulation and test verified the effectiveness of the proposed method.

A sliding mode linear self disturbance rejection control strategy was proposed to address the output voltage fluctuations caused by sudden load changes，changes in DC bus voltage，disturbance addition，and other disturbances in three-phase interleaved parallel DC-DC converters. Firstly，a mathematical model of three-phase interleaved parallel DC-DC converter in the s domain was established and the principle of energy transmission imbalance was analyzed. Then，based on the system order，appropriate observers and linear auto-disturbance rejection controllers were designed. Furthermore，in order to improve the ability to suppress the fluctuation of the output voltage of the system，sliding mode control was introduced to form a sliding mode linear auto disturbance rejection control，which improves the anti-interference and rapidity of the system.Finally，multiple operating conditions were designed to simulate and verify the new control strategy. The results show that compared with traditional control strategies，sliding mode linear active disturbance rejection control not only has excellent voltage fluctuation suppression ability，but also has superior steady-state operation ability and transient traversal ability，ensuring the balance between system capability transmission.

With the proposal of the "dual carbon" goal，the application of EVs is becoming increasingly widespread. In order to further reduce the charging cost of EVs so as to easing the charging cost burden of EV users，a coordinated control strategy for EV charging DC microgrid was proposed，which includes photovoltaic（PV），energy storage system（ESS）and power grid. This strategy can coordinate the input and output states of different ports based on the power relationship between each port，reduce the fluctuation of EV charging load through peak shaving and valley filling thus lowering the charging cost. Firstly，the control strategies of PV and ESS were introduced. Then，Monte Carlo algorithm was used to predict the charging load of EVs. Next，six modes and four voltage bands during microgrid operating was divided，mode switching condition and coordinated control strategies between each port were proposed. Subsequently，the feasibility of coordinated control was verified in Matlab/Simulink and Yuankuan semi-physical simulation platform. Finally，by calculating EV charging costs under five typical weather scenarios and comparing them with other literature，it was found that the proposed strategy can reduce charging costs by up to 28.1%，its significant effect in reducing costs was demonstrated.