随着绿色能源的快速发展,海上风电场的规模也越来越大,同时柔性直流也是海上风电场对外传输电能的主要方式.海上风电柔直输电系统在遇日常或故障检修后,需要由换流站等独立完成系统的黑启动.本研究从系统黑启动电源的选择、风电场和换流站的启动方式展开,总结出一种适用于海上风电柔性直流输电系统的黑启动策略,并归纳分析了系统启动过程中注意事项及难点.最后,提出系统黑启动的未来发展方向,为今后海上风电柔直输电系统黑启动的研究和应用提供参考.

The accuracy of the lithium battery equivalent circuit model is of great significance to the management of lithium-ion batteries. Second-order Thevenin equivalent circuits is a commonly used for lithium-ion battery equivalent circuits due to the accuracy and computational effort taken into account. The parameters of the equivalent circuit model are influenced by various factors and their values will change with time during operation. Therefore, the recursive least square (RLS) method is used to identify the model parameters to obtain the time-varying parameters. However, the RLS algorithm suffers from "data saturation phenomenon", so this paper combines the RLS algorithm with rectangular window and exponential window, and adds the damping term to the RLS algorithm. It is experimentally verified that the RLS algorithm combining rectangular and exponential windows can improve the identification accuracy of equivalent circuit parameters.

The state of charge (SOC) and the state of health (SOH) are two crucial parameters for monitoring the battery status because SOH determines whether the battery can continue to operate safely and stably, and the SOC determines the battery's endurance. This paper proposes an online synthesis method based on the response characteristics of load surges and an improved fuzzy cerebellar model neural network (IFCMNN) to co-estimate SOH and SOC. Firstly, multiple features are extracted from the voltage response signals. Then, the grey relational analysis is utilized to verify the rationality of the extracted features and to fuse key features for SOH and SOC estimation. Secondly, IFCMNN models aiming at estimating SOH and SOC are proposed to estimate the SOH and SOC simultaneously and quickly. Finally, experimental results on ten batteries with different aging levels show that the proposed method can achieve fast estimation of SOH and SOC at 1.64 % and 2 % resolution accuracy respectively, regardless of the temperature and in rush currents variations. In addition, the proposed model has higher estimation accuracy compared with other traditional methods. Thus, the proposed method has a high generalization ability.

The grid-connected inverter is a key device in the renewable energy power generation system and large-scale energy storage system, which the operational stability and reliability are the basis for the efficient and safe application of electrical energy. A real-time fault diagnosis method of a three-phase for grid-connected application combining a mixed logic dynamic (MLD) model and finite control set model predictive control (FCS-MPC) is proposed. This paper not only realizes the open circuit fault diagnosis and location of the switching devices in the main power circuit, but also discusses the threshold issues and post-fault operations. The advantage of the proposed method is that it directly uses the control data and measurement signals of the controller without extra sensors and calculation, which will shorter the fault diagnosis time and occupy less calculation resource of the main processor. Simulation results illustrate the quickness of the fault identification and accurate position with robustness to the interference of the diagnosis method. Finally, the effectiveness of the diagnosis method was verified by a 1500W experimental prototype in a laboratory.

Batteries are widely used in various vital energy storage occasions, so it is particularly important to monitor the state-of-charge (SOC) and the state-of-health (SOH). To estimate SOC and SOH quickly and accurately, the battery's shock response characteristic is analyzed in this paper while fully considering the degree of aging that could impact their operating status. In this paper, a SOC and SOH simultaneous-estimation scheme is proposed based on shock response characteristics. Firstly, the effective and representative features of the voltage impulse response curve are extracted by combining different feature extraction methods. Then, the Support Vector Machine (SVM) is introduced to estimate the SOH and SOC simultaneously. The feature comes from the voltage impulse response curve, which is very convenient to obtain in practical applications. The simulation results show that this method can accurately estimate SOH and SOC for batteries in any SOH state and SOC state, which has strong robustness and generalization ability.

In recent years, bidirectional DC-AC converters have been widely used in power electronic circuits. However, long-term operation at high frequency and high power will lead to converter aging and component parameters degradation. Therefore, the accurate identification of the component parameters of the converter is of great significance for the stable and efficient operation of the power system. In this paper, a parameter identification method based on model and improved BPNN (Back Propagation Neural Network) is proposed. Firstly, the mathematical model of the converter is established by analyzing the topology, and the state equations of the inductor current and the capacitor voltage are derived as the characteristic equations. The improved BPNN is used to fit the characteristic equations, and the function of the relevant component parameters is used as the adjustable weight value of the improved BPNN. The output of the improved BPNN is compared with the measured reference value. When the fitting error is small enough, the values of inductance, capacitance, ESR and other parameters can be directly extracted from the weights. The simulation results show that the method is feasible and accurate.