In off-grid photovoltaic storage microgrids, the DC-side bus voltage amplitude is easily affected by the fluctuation of photovoltaic (PV) output power and load power, the system may experience DC bus voltage oscillation and long voltage recovery time under the traditional control strategies. In order to improve the dynamic stability of bus voltage, a fuzzy-proportional integral (PI) control method based on nonlinear disturbance observer (NDO) power feedforward is proposed. Firstly, the system state equation in storage discharge mode is established, and the disturbance observation link is designed to observe the disturbance power of the PV and load in real time and add it to the current inner loop as a feedforward quantity after correction. Then, a fuzzy-PI controller based on bus voltage deviation is designed to improve the structure of voltage outer loop, and the design process of disturbance observer and controller is given. Finally, the simulation and experimental results verify that the strategy can have good dynamic stability of DC bus voltage under load impact.

Compressed air energy storage (CAES), as an energy storage technology with great development potential, has the advantages of large scale, long life and environmental protection. However, due to its large capacity, if the voltage phase amplitude and frequency are not the same as those of the power grid when it is connected to the power grid, there will be a problem that the grid-connection impulse current is too large and threatens the safe and stable operation of the power system. Therefore, based on linear active disturbance rejection control (LADRC), which is more accurate than traditional PI control, a linear active disturbance rejection control strategy with adaptive adjustment of bandwidth parameters is proposed to achieve smooth grid connection. Finally, the proposed control strategy is verified by simulation experiment and hardware-in-the-loop experiments, and appropriate adjustment parameters are selected. The results show that the proposed adaptive control strategy can effectively reduce the impact of grid-connection and achieve smooth grid-connection, providing a new solution for the safe access of compressed air energy storage to the grid.

The lithium plating is the main reason for the performance degradation, temperature rise and expansion deformation of lithium-ion batteries (LIBs). In order to investigate the lithium plating amount (LPM), temperature variation and the damage of the cathodic particles during the charging of LIBs. Firstly, an electrochemical-thermal-stress coupling model with lithium plating is developed. Then, the influences of charge rate on the cathodic electrical potential and LPM are analyzed. Finally, the effects of the initial temperature, charging mode and cathodic particle radius on the LPM, average temperature and Von Mises stress of cathodic particle are discussed, respectively. Numerical results show that LPM increases with the increase of charge rate. In order to reduce LPM, temperature rise and Von Mises stress during the charge, the charge at lower initial temperature should be avoided, the cathodic particle radius should be reduced or multi-stage constant current and constant voltage charging (4C to 6 A∙h, 3C to the cutoff voltage and constant voltage charging) should be used. This work will provide some guidance for reducing lithium plating.

The initial state of charge (SOC) of the battery is frequently random when users charge the batteries. Therefore, it is difficult to obtain complete charging data of lithium batteries in practical applications. To solve this problem, a state of health (SOH) estimation method of lithium battery based on partial charging data is proposed. Through the established GRU-BP (gated recurrent unit-back propagation) model, a complete charging curve can be obtained from partial charging data. Then the SOH can be estimated by the features extracted from the complete charging curve, which can effectively characterize the aging of battery. This method can achieve SOH estimation under any fixed voltage interval charging data in constant current charging mode. The experimental results show that the proposed method can effectively predict the SOH of lithium batteries with incomplete data, and the estimated root mean square error (RMSE) is below 2%.

Aiming at the problems that the offline parameter identification method can only identify a single fixed parameter which leads to insufficiently accurate state of charge (SOC) estimation, and that the traditional Kalman filtering algorithm is poorly adapted to different temperatures, a fractional-order multi-innovation cubature Kalman filtering combined with fractional-order unscented Kalman filtering (FOMICKF-FOUKF) algorithm is proposed to estimate the SOC of lithium-ion batteries. The algorithm adopts a multi-timescale approach to carry out the online parameter identification with the FOUKF in the macro time scale, and the SOC estimation with the FOMICKF in the micro time scale. The convergence ability of different algorithms varies, and in order to verify the convergence of the algorithms, the algorithms were subjected to the convergence comparison experiment. In addition, it is more difficult to accurately estimate battery SOC at different temperatures where the internal polarization reaction varies significantly. SOC estimation experiments were conducted at 0°C, 25°C and 45°C to verify the adaptability of the proposed algorithm to different temperatures. The results show that the FOMICKF-FOUKF dual Kalman online joint algorithm estimates the SOC with high accuracy and good convergence, and has good adaptability to different temperatures.

Accurate estimation of State of Charge (SOC) for lithium-ion batteries is crucial for implementing battery balancing during charging and discharging, thus extending battery lifespan. Given the complex chemical characteristics of lithium-ion batteries and the non-time-varying nature of SOC estimation using the Kalman filter algorithm, a novel method for SOC estimation for lithium-ion batteries is proposed. Firstly, combining the precision and practicality of lithium-ion battery models, a second-order equivalent model for lithium-ion batteries is presented. Subsequently, utilizing the Adaptive Forgetting Factor Recursive Least Squares (AFFRLS) algorithm with adaptive forgetting factors, the impedance-capacitance parameters of the second-order model are identified online, establishing a filtering relationship for impedance-capacitance parameters. The proposed method integrates Particle Filter (PF) and Cubature Kalman Filter (CKF) algorithms, termed Particle Cubature Kalman Filter (PF-CKF), to estimate SOC for lithium-ion batteries. Finally, Hybrid Pulse Power Characterization (HPPC) experiments are designed to validate the accuracy and stability of this method.

The centralized thermoelectric system exhibits multi-peak characteristics under non-uniform temperature distribution (NUTD). Based on the problem that traditional maximum power point tracking (MPPT) methods are prone to falling into local maximum power point (LMPP) and existing metaheuristic algorithms suffer from energy loss due to long search times caused by imbalanced search capabilities during exploration and exploitation phases, an improved hunter-prey optimization (IHPO) algorithm is proposed. This algorithm relies on the clear exploration and exploitation phases search capability mechanism of standard hunter-prey optimization (HPO) algorithm, and improves the original mechanism for multi-peak characteristics, reducing the large amplitude oscillation caused by frequent random jumps of the algorithm itself while quickly tracking the global maximum power point (GMPP). Based on the centralized thermoelectric system model, IHPO algorithm is simulated and compared with perturbation and observation (P&O) method, particle swarm optimization algorithm (PSO), improved PSO (IPSO) and standard HPO under different operating conditions. The results show that the proposed algorithm can quickly and accurately track GMPP under NUTD, significantly reduce the oscillation of the tracking process, and improve energy output by up to 21.8%. The IHPO algorithm can effectively solve the multi-peak problem of centralized thermoelectric systems.

The soft open point (SOP), as a sort of promising flexible power electronic device in future smart distribution network, can take the place of normal open point to achieve flexible interconnection and power regulation. The shared module soft open point (SMSOP), as a new flexible interconnection topology, has the advantages of high efficiency, small size and low cost. However, SMSOP is prone to overmodulation when using conventional pulse width modulation (PWM) and has high losses if the shared module accounts for more. A new sine-wave-like modulation method is proposed. The sine-wave-like modulation adds a square-wave platform on the basis of the sine-wave modulation. Sine-wave modulation is used at the start and end of each half-cycle to prevent over-modulation at zero-crossing time; square-wave modulation is used in the middle to reduce the switching losses of the shared modules. Finally, the correctness and effectiveness of the new modulation strategy are verified by building a three-phase experimental platform.

In a renewable power collecting system, the number of generators is large, the network topology is complex, and the combination of operating conditions is various, which makes the small-signal stability analysis of the system inefficient and the equivalent modelling difficult. Thus, a small-signal equivalent modelling method of the multi-type renewable energy collecting system is proposed based on partitioned similarity transformation of the transfer function matrix of the generators and the network. First, by combining the models of generators and the network equation of the system, a detailed model of the renewable power collecting system is obtained. Second, considering that the system can be divided into several groups in which dynamics of the generators are approximately the same, via the partitioned equivalent decoupling and order reduction, a single-machine equivalent model is established for each group and hence a multi-machine equivalent model for the system is obtained. Finally, the effectiveness of the proposed method is verified via case studies. The method proposed can deal with the differences of dynamics of the generators and the complex network topology and also effectively reduce the computational burden of the small-signal stability analysis.

In the event of a short-circuit in the AC system where the modular multilevel converter-unified power flow controller (MMC-UPFC) is located, both the series and shunt-side MMCs will be impacted by the fault and be blocked due to overcurrent, leading to the complete shutdown of the MMC-UPFC. The fault characteristics of the AC system where the MMC-UPFC is located are analyzed. It clarifies the main reasons for the overcurrent blocking of the shunt-side MMC. Based on this analysis, a fault ride-through strategy for MMC-UPFC is proposed, which is based on a distributed current limiting module. The working principle of this strategy is analyzed in detail, and the guidelines of its parameter tuning are given. The proposed fault ride-through strategy can effectively suppress the DC bus current by inserting a current limiting resistor during the fault period. This ensures that the shunt-side MMC is not blocked during the fault period, allowing it to continue generating reactive power. Finally, simulations in MATLAB/Simulink are used to verify the effectiveness of the proposed fault ride-through strategy and the correctness of the parameter tuning guidelines.