For a hybrid converter composed of full bridge sub-modules and half bridge sub-modules, the possible problem of half bridge sub-modules being unable to equalize voltage during voltage reduction operation is analyzed, and a control strategy for voltage reduction of the hybrid converter is proposed. Firstly, a mathematical model of the bridge arm current of the hybrid converter is established and its characteristics are analyzed. In response to the possibility that the bridge arm current of a hybrid converter may only be positive or negative during voltage reduction operation, a control strategy of reactive power compensation is proposed to achieve positive and negative bridge arm current by utilizing the flexible reactive power control characteristics of the modular multilevel converter （MMC). Finally, a hybrid converter model of voltage source converter-high voltage direct current （VSC-HVDC) is established in PSCAD/EMTDC. The simulation results show that the proposed control strategy can meet the voltage equalization requirements of the sub-modules under the condition of significant voltage reduction operation in the DC system, avoiding the situation where the sub-modules cannot be equalized during the DC voltage reduction operation.

The increase of converters in the multi-terminal flexible HVDC transmission system makes the topological structure of the network increasingly complex, and the problem of resonance instability becomes more and more prominent. The traditional DC admittance analysis method cannot effectively analyze the coupling effect of the AC and DC sides, and cannot distinguish the system instability problems caused by different reasons such as the AC side, the DC side, or the AC and DC coupling effect. Based on AC/DC hybrid admittance, a stability evaluation model of multi-terminal flexible HVDC electric power transmission is established. On this basis, the generalized Nyquist criterion based on Determinant is introduced to judge the stability of the system and classify the different causes of system instability, including the interaction of AC side, DC side and AC/DC side coupling. A method has been proposed for effectively identifying the causes of unstable systems.Different impedance reshaping strategies are proposed to stabilize the system instability caused by different reasons. The results showed that the proposed method can effectively identify the causes of system instability and maintain system stability.

Flexible HVDC transmission, as a new generation of DC transmission technology, is an important way to build a smart grid. During the operation of the core equipment in the valve hall of the converter station, due to the safety distance, maintenance personnel are unable to approach for detection. Its detection relies on indoor monitoring systems and inspection robots, etc. The risk of equipment failure generated cannot be dealt with in a timely manner. Therefore, risk prediction of the detection data generated in the flexible HVDC valve hall is particularly important. A risk prediction model for multi-modal data generated by flexible HVDC valve halls is proposed. This model is based on the generative adversarial network, uses non abnormal data for training, learns the potential distribution of data, reconstructs the data, and judges the degree of abnormality of data according to the error generated by reconstruction. Through the actual collected experimental data, it has been proven that the constructed model can effectively learn the potential distribution of the data, and can effectively identify abnormal data. Compared to traditional methods, this model is more accurate.

High-voltage cable joint short-circuit arcs can easily cause explosions and fires, resulting in secondary damage, so protective devices need to be installed. The irregular structure of the protection device leads to the deformation of the stress distribution, which becomes the weak link of explosion protection. A method to improve the safety margin of cable joint protection device based on equilibrium stress distribution is proposed. Using the explosion source equivalent method and the finite element calculation method of coupling temperature field, magnetic flux field and displacement field, the simulation calculation of the short-circuit arc impact process in the protection device is carried out. Taking the 220 kV spring contraction energy release method cable joint protection device as a calculation example，the area with the most concentrated stress inside the protective device is located at the end contraction, with a stress distortion rate of 24%, and the stress at the end contraction results in a safety margin value of only 4.09% for the device. It is considered that the key to improve the safety margin of the cable joint protection device is to reduce the stress distortion rate. A method of covering the inner stress concentration area of the cable joint protection device with ceramic silicon rubber polymer composite refractory material is proposed to improve the safety margin of the protection device. After calculation, a material with a thickness of 6 mm is selected to cover the inner stress concentration area, which can reduce the stress of the device. The distortion rate at the stress distortion point is reduced to 7%, and the safety margin value of the protection device is increased to 31.81%, increasing the overall safety margin of the protection device to 18.18%. Compared with the traditional improvement method, it is more targeted, promotes the development of lightweight equipment, and provides new solutions and methods for the safety design of 220kV high-voltage cable joints.

In view of the difficulty of accurately extracting voltage flicker envelope and real-time estimation of flicker parameters in new power system, the improved the square detection method is used to accurately extract voltage flicker envelope, and construct adaptive TLS-ESPRIT algorithm based on adjacent singular value ratio to extract frequency information of envelope signal. The flicker frequency formula of adaptive TLS-ESPRIT algorithm is derived, and the flicker amplitude information is calculated. Based on this, a flicker parameter detection method based on improved square detection and adaptive TLS-ESPRIT is proposed, and an experimental platform for voltage flicker parameter detection based on virtual instrument is developed. Simulation experiments show that the proposed algorithm can effectively estimate voltage flicker parameters under noise and harmonic interference, and can effectively overcome the influence of single-frequency and multi-frequency amplitude modulated wave modulation and power grid fundamental frequency offset. Compared with the traditional voltage flicker parameter detection algorithm, the proposed algorithm has simple calculation and small flicker parameter detection error.

With the intensification of the global energy crisis, the proportion of renewable energy in the power system is increasing. Demand response can enhance the regulation capacity of new power systems by introducing flexible controllable loads. In this paper, the control problem of flexible thermostatically controlled loads (TCLs) in demand response is studied, and a distributed hierarchical control strategy is proposed based on a multi-agent consensus algorithm. In this strategy, the optimal power distribution problem for the TCL aggregator is considered in the upper optimization layer, and the distributed coordination mechanism for TCLs is considered in the lower coordination layer. The convergence of the upper distributed optimization algorithm and the lower coordinated control algorithm is proved theoretically for the uncertainty problem of users′ electricity consumption behavior. Numerical simulation experiments are carried out in MATLAB environment to verify the effectiveness of the proposed strategy. Compared with the centralized control methods, the proposed distributed hierarchical control strategy can reduce the communication burden of the manage unit of demand response event and protect users' privacy.

Aiming at the problem of low accuracy of topological identification of substation area based on electrical distance clustering, a line-phase-house topology identification method based on the characteristics of wire impedance is proposed to improve the robustness of clustering. Firstly, the voltage time variation characteristics of the head-end electricity meter in the substation area are analyzed in mechanism. Using correlation clustering will have the problem of phase line clustering errors. On this basis, an improved model of topology identification method based on constant analysis of wire impedance is proposed. The method derives the wire impedance between the down-consumer point and the meter by calculating the down-consumer point voltage, checks and corrects the voltage clustering results according to the evaluation index of impedance constancy. Then, a topology identification model for current optimization is established for the corrected clustered meters. Finally, the feasibility and effectiveness of the proposed method are verified by the analysis of the simulation substation area and the actual substation area.

Calculation of charging load is the basis of studying the impact of electric vehicles (EV) on power grid and planning of charging facilities. This paper proposes a calculation method for charging demand considering the state of health (SOH) of the battery. Firstly, the probability distribution of the characteristic quantity of the charging load related to the EV travel chain is used to enable the complete simulation of the travel behavior of a single EV user. Secondly, the actual capacity and charging characteristics of EV are adjusted based on the battery health state, and the user's mileage anxiety coefficient is proposed to adjust the minimum state of charge (SOC) value remaining after next trip and to improve the charging load calculation model. Finally, the simulation based on the basic data of the national household travel survey (NHTS) shows that SOH affects multiple characteristics of EV users' travel. The larger the EV scale, the more SOH should be considered in the calculation of charging load.

' Carbon Peak, Carbon Neutralization ' accelerates the rapid development of new energy-based power systems. With the access large-scale new energy, it will make the control performance worse. In this paper, a novel EBQ(σ,λ) algorithm based on ensemble learning is proposed to obtain global optimal solution from the perspective of automatic generation control, which can improve the poor control performance standard (CPS) of the grid effectively. The proposed algorithm can not only solve the problem of traditional reinforcement learning estimation bias by reducing the mean squared error of Q value in the next state, but also the introduced sampling parameter σ can weigh between improving the efficiency and better training samples. Meanwhile, the application of eligibility trace can solve the problem of time credit. Ultimately, the proposed algorithm is simulated to be effective in the improved IEEE standard two-area LFC power system model and the Guangdong power grid model. The results show that compared with the traditional algorithm, the proposed method is characterized with exceptional CPS and less carbon emission.

Considering that power system inertia may fluctuate with the change of operation scenario due to large-scale access to renewable energy plants, it is necessary to monitor the equivalent power system inertia for the stable operation of the power grid. Therefore, a set of area devision inertia identification method is proposed based on routine disturbance data. Firstly, the algorithm of total least square estimation of signal parameters via rotational invariance techniques is used to extract the oscillation components of different bus frequncy, and the power gird will be divided according to the extracted results. In this way, the signal to noise ratio (SNR) of the monitoring results of the power fluction between different regions is improved. Then, the subspace identification algorithm is used to identify the dynamic relationship between the area center of inertia frequency and unbalanced power. Futher, area division inertia will be calculate based on the above results. Finally, the effectiveness of the proposed inertia identification method based on the area division is verified in the IEEE 39-bus system.

When a single-phase and high-resistance ground fault occurs in the low-resistance ground system, the feeder zero-sequence current is lower than that of the traditional time limit zero-sequence over current protection fixed value, leading to the problem of protection. This paper proposes a high-resistance ground fault protection method based on zero-sequence admittance in low-resistance ground system. Firstly, the electrical quantitative characteristics are analyzed when the single-phase ground fault of the low-resistance ground system occurs. Through the theoretical derivation, it is found that the zero-sequence admittances of the fault line and non fault line are not related to the transition resistance and fault position, but only to the system zero-sequence resistance and operation mode. And for the fault line and non fault line, there is a significant difference in theirs zero-preparation admittance angles. Furthermore, through quantitative calculations, the fault area and non-fault area are divided in the admittance compound plane. The results show that the tolerance transition resistance capacity of proposed method is above 1.4 kΩ.

Aiming at the problem that the correct rate of line selection decreases due to the weakening of the zero-sequence current similarity of the sound line with multi-cable resonant grounding system and the weak fault information of single-phase high-resistance grounding. A line selection method based on the symbolic aggregation approximation (SAX) and spatial information entropy is proposed. Firstly, low frequency transient zero sequence current is extracted by FIR filter and normalized, and then the low-frequency current sequence and its difference sequence are symbolized on the multi-scale domain, and the three-dimensional fault space is generated after the longest common subsequence check and the spatial information entropy is defined, and finally the entropy difference between lines is compared to complete line selection. The simulation results show that the line selection method has high sensitivity, low hardware requirements, and can overcome the difficulties of sampling asynchronization and three-phase unbalance.

Network structure, load distribution and its growth mode are the main factors affecting the load supply capacity of distribution network. Most traditional evaluation methods working from the perspective of planning have the disadvantages of inconsistent load growth with the reality and ignoring the dynamic adjustment of grid. This paper proposes a load supply capacity evaluation method considering the mode of actual load growth from the perspective of actual load change. Firstly, load forecasting is conducted through the growth of existing load points and the expansion of large consumer, and the load growth mode is determined in combination with sequence operation theory. Secondly, an optimization model of power supply potential that takes into account network reconfiguration and large user access is proposed to prevent the premature appearance of power supply bottlenecks during load growth. Finally the load supply capacity by the improved repeat power flow is obtained. According to the case study on the PG&E 69-bus system with the first scenario of natural load growth and the second scenario of natural load growth with large consumer expansion at the same time, the practicability and validity of the method are demonstrated.

There are a large number of power electronic inverters with weak overload capacity and lack of physical inertia in the microgrid. There is a high risk of instability when the microgrid fails. Therefore, it is urgent to study the transient stability assessment method to ensure the safe and stable operation of the microgrid. In this paper, a time series data-driven stable operation assessment method is proposed. Firstly, the transient interaction mechanism between constant power conversion controller and virtual synchronous machine ( VSG ) parallel grid-connected system is studied. Then a set of feature data sets with strong representation ability is constructed according to the interaction mechanism. Finally, based on long short-term memory neural network, a time series data-driven microgrid stable operation assessment model is established. The simulation results show that the feature selection of the proposed method is reasonable, and the stable operation assessment can be quickly and accurately realized under complex working conditions with good evaluation performance.

In order to solve the problem that the existing transient line selection method is susceptible to fault phase angle, transition resistance, noise, harmonics and criterion threshold, a line selection method based on optimized parameter variational mode decomposition (VMD) and improved K-clustering criterion fusion is proposed. Firstly, the three key parameters of the decomposition process are dynamically optimized, and the number of VMD decomposition layers is determined by using the signal spectrum and component characteristics, and the optimal penalty factor is obtained by the arithmetic optimization algorithm, and the power frequency, noise and harmonic interference are eliminated. The modal center frequency is determined according to the number of decomposition layers and each modal spectrum to improve the decomposition efficiency. Secondly, the optimized VMD is used to obtain cosine similarity, high-frequency component amplitude and DC energy as complementary fault line selection criteria. Finally, the improved K clustering algorithm is used to achieve multi-criteria fusion, which makes up for the limitation of a single criterion. The theoretical analysis, simulation and test results show that the proposed method is suitable for the power grid with distributed power supply, and is not affected by the fault location, fault phase angle and transition resistance, and has excellent anti-harmonic and noise interference performance.

With the development of energy storage converters to large capacity and modularity, silicon carbide (SiC) devices have gradually become a research hotspot due to their low loss and high temperature resistance characteristics. However, the high switching speed of SiC devices makes them more sensitive to stray inductance in the circuit, and the high-temperature operating environment also has an impact on the long-term safe and reliable operation of the devices. Therefore, this paper focuses on the low-inductance design and heat dissipation design methods for SiC MOSFET-based energy storage converter power unit, and proposes an overall design scheme for the power unit. By optimizing the structure of the laminated busbar, the stray inductance of the high-voltage AC module and low-voltage DC module is reduced to 794 μH and 235 μH, respectively, which effectively reduces the turn-off overvoltage of the power unit. Through thermal simulation studies, a heat dissipation scheme is established so that the maximum temperature of the device does not exceed 50 °C during operation. Finally, a power unit prototype is built and dragged for experiments to verify the effectiveness of the optimized design of the laminated busbar structure and the power unit thermal design scheme.