The amplitudes of strong pull-in current and hold-on current passing through the high speed solenoid valve were regulated accurately by using hysteresis control to realize pulse width modulation of the voltage of fast solenoid valve， the duty cycle of the current pulse passing through the high speed solenoid valve was controlled by variable frequency control with variable duty cycle， and the protection functions of input overvoltage， input undervoltage and output short circuit were designed. A high speed solenoid valve control instrument was designed based on hysteresis control method. By using the proposed high speed solenoid valve control instrument during the maintenance of aeroengine high speed solenoid valve and main fuel control accessories， the fault of main fuel system of aeroengine caused by unstable input signal of controller was eliminated. Benefited from strong anti-interference ability of the proposed instrument， instrument damage caused by misoperation in the maintenance process was prevent， maintenance quality of high speed solenoid valve has been improved.

The circulating current of metal sheath in high voltage cable is the superposition of induced current component and leakage current component. At present， the calculation of induced current component is relatively mature， but the calculation of leakage current component is insufficient， which affects the final circulating current calculation result and the related fault diagnosis effect. For this reason， a calculation method for leakage current component of sheath circulating current in high voltage cable was proposed. Based on the π-type equivalent circuit of "core—sheath"， an equivalent circuit for solving leakage current component of sheath circulating current was established， which take into account the shunting effect of leakage current and circulating current generated by leakage current of cable core. The final leakage current component was obtained based on the iterative method. A cable model with a linear arrangement was built using ATP-EMTP software. The results show that the relative deviation between calculation result and simulation value of this method is within 2% under different length combinations and load currents， compared with existing method， the accuracy is greatly improved. This method can better explain the reason of excessive circulating current at light load， and has a certain reference for the generation of sample data for fault diagnosis of cross-connected systems.

In order to solve the problem of voltage and frequency fluctuations at the grid-connected point of the permanent magnet wind turbine during high and low voltage ride-through，a virtual synchronous generator （VSG） was proposed to control the grid-side converter to improve the inertia and damping of the permanent magnet wind power generation system，so that the voltage and frequency recovery of the grid-connected point could be supported. Through the active power-frequency adjustment function of the VSG，the active power output capacity of the grid-side converter was adjusted according to the system frequency variation，and the frequency fluctuation was suppressed under the action of the moment of inertia and damping coefficient. The virtual electromotive force was adjusted by adjusting the reactive power command of the VSG，so that the dynamic reactive power support could be provided for the grid during the voltage ride-through period，and the voltage of the grid-connected point could be adjusted. The continuous fluctuation of system power and frequency was effectively suppressed during the continuous high and low voltage ride-through period，and the grid-connected stability of the system was improved. Finally，the effectiveness of the proposed control strategy was verified by system simulation.

In order to solve the problem of overvoltage and overcurrent in the process of load sudden change for ion thruster screen grid power supply， the phase shift pulse frequency modulation （PS-PFM） was used to stabilize the output voltage of the LLC resonant converter. When the sampling voltage is lower than the lower limit of the set value， it enters into the frequency control mode， and when the sampling voltage is higher than the upper limit of the set value， it enters into the phase shift control mode. The experiment proves that， compared with the common frequency control， the addition of phase shift control can solve the problem of high voltage drift in light load or no-load mode， and through the adjustment of PID parameters， it can achieve stable and fast adjustment when the load jumps， so that the output voltage remains at the set value. Through the PCB layout and magnetic components optimization design， the developed principle prototype efficiency reaches 98.9% under rated conditions， and the power density reaches 6.47 \times {10}^6 \mathrm{ } \mathrm{W} / {\mathrm{m}}^3， which is advantageous in the same level of power supply products.

Three port converter （TPC） has the characteristics of high integration， high power density and single-stage conversion between ports， but at the same time， the energy flow is affected by the interaction of multiple variables. Model predictive control （MPC） was introduced to optimize the dynamic response of output voltage in order to improve the problem of slow response and large fluctuation of output value when traditional PI controller is used in the output voltage control loop of three port converter. The relationship between the prediction model and the control variable was obtained by modeling the average value of the state space of the three port converter， and the objective function was designed. The optimal value was obtained by traversal optimization， so as to improve the dynamic characteristics of the output voltage. The effect of output dynamic response of output voltage loop under traditional PI control and model predictive control was compared through experiments. The results show that model predictive control has obvious effect on improving output voltage stability.

In order to improve the inertia support ability of grid-connected converter of new energy based on DC capacitor energy virtual inertia control to the power system，an adaptive virtual inertia control method based on linear quadratic regulator （LQR） was proposed. On the basis of analyzing the dynamic characteristics of the virtual synchronous generator （VSG），the mathematical relationship between the inertia parameters and the system frequency response characteristics was obtained，and an adaptive inertia control model based on LQR was established. The optimal feedback matrix was found by LQR cost function to make the state variables approach zero quickly and meet the control requirements with the least input energy. The simulation results show that the method can make the new energy grid-connected converter optimize the allocation of limited DC capacitance energy according to the system frequency fluctuation，quickly damp the frequency change，improve the system frequency response characteristics，enhance the inertia support ability of the new energy grid-connected converter to the power system，and ensure the stability of the power system.

Aiming at the problem of low recognition accuracy of electricity stealing behavior， an electricity stealing behavior recognition model based on joint neural network was proposed. Firstly， the acquired user electricity consumption data was processed， and the user electricity consumption data was two-dimensionally processed by using the Gramian angular field method. Then， for the electricity consumption data of different dimensions， a user electricity stealing behavior recognition model based on the joint neural network was proposed， and the features of the one-dimensional electricity consumption data and the two-dimensional electricity consumption data were extracted by using the convolutional neural network（CNN） and the bidirectional long short-term memory（BiLSTM） neural network. The case analysis shows that the proposed joint neural network model has an accuracy rate of more than 90% for the recognition of electricity stealing behavior， which proves that the established evaluation model provides a practical solution to the electricity stealing problem.

In order to intervale evaluate saddle node bifurcation points（SNB） in a power system during load growth quickly and accurately， a quadratic approximation method to directly calculate the power system voltage collapse point of was proposed. Based on the approximate quadratic PV-curve of output PQ nodes in the system， load parameters were introduced into the node power balance equation， by using the complex function derivative rules to differentiate power equation twice， the first and second derivatives of node voltage to load parameters were derived theoretically， and the expression of PV curve function was further determined， then vertex coordinates were used to determine the initial position of saddle junction bifurcation point in the power system and the voltage collapse point was approximated by multiple iterations of convergence. The proposed method avoids multiple power flow calculations using the continuous power flow method，significantly reducing computational complexity. Simulation results on IEEE 14 and IEEE 118 bus systems demonstrate the effectiveness of this method. Compared with the supplemented P'Q node method and Thevenin's equivalent method， the quadratic form iterative approximation method has higher computational efficiency and robustness.

The accurate recognition of power quality disturbance（PQD） is one of the main problems to be solved after PQD occurrence， which is of great importance for responsibility dividing and power market reform process accelerating. Massive quantities of power quality monitoring data prepare the ground for the recognition of PQD. Since the electrical characteristic is different for different PQD， the waveform difference between different power quality disturbances can be employed for the recognition of PQD. Combing the deep learning， the method for the recognition of complex PQD via bidirectional independently recurrent neural network（Bi-IndRNN）was proposed. In this way， the intrinsic characteristic of PQD was extracted， the internal correspondence between the input sequence and the output sequence was established， the dependence of the analysis result on the physical characteristic quantity was overcome， and the recognition accuracy of PQD was improved. The results illustrate that the diversity of complex PQD can be effectively responded， where the intrinsic characteristic hidden in complex PQD signal can be extracted directly， resulting in high accuracy.

Due to the extensive addition of new energy systems， the number and types of power quality disturbances in the system are also increased accordingly. However， the traditional power quality disturbance （PQD） classification method has the problem of low accuracy and efficiency， and it is difficult to adapt to the existing power quality management of power systems with high new energy penetration. Therefore， a PQD classification method based on graph convolutional neural networks （GCNNs） and Gramian angular field （GAF） was proposed. First， the original PQD signal was normalized and polar coordinate transformation was processed， then GAF was used to graphically transform different kinds of PQD one-dimensional signals to generate two-dimensional images containing different PQD features， and finally， GCNNs were used to train and classify the different kinds of PQD images to achieve the classification of different PQDs. In the experiment part， the IEEE-39 node system was used to simulate and simulate different types of PQD curves， and the method proposed was used for verification. The experiment results show that the proposed method can automatically extract and optimize the features， and meet the high efficiency and accuracy of PQD identification and classification.