The detailed model of a grid-connected doubly-fed induction generator (DFIG) system has characterristics such as a high order and nonlinearity, which brings great difficulties to system stability analysis and fault ride-through control. Although there have been many studies on the dimensionality reduction method for grid-connected DFIG system model, the number of model dimension is still high. A reduced-order modeling method based on the singular perturbation theory is proposed. First, the 12th-order detailed model of grid-connected DFIG system under weak grid is established, and its state space model is obtained by linearization. Then, based on the singular perturbation reduced-order theory and participation factor analysis, the state variables of the state space model are divided into multiple time scales, so as to separate the speed variables and deduce the 4th-order model of the system. A comparison of eigenvalues before and after the order reduction shows that the 4th-order model can well reflect the dominant dynamic characteristics of the full-order model while accurately reflecting the stability of grid-connected wind turbines under weak grid. Simulation and experimental results verify the accuracy of the reduced-order method as well as the effectiveness of the reduced-order model in stability analysis.

The current-voltage (I-V) characteristics of solar cells are an important means to accurately evaluate their performance and diagnose their faults. In this context, five kinds of I-V measurement methods for solar cells (e.g., variable resistance load method, electronic load method, DC-DC converter load method, four-quadrant power supply method and capacitive load method) proposed in recent years are systematically summarized, and their advantages and disadvantages are pointed out. Then, the applications of these I-V measurement methods in extracting the cell parameters and diagnosing the aging of cells and components, partial shading, dust accumulation and short-circuit faults are analyzed. Finally, the development trend of I-V measurement methods for solar cells in the future is prospected, and it is predicted that the DC-DC converter load method and capacitive load method will be the mainstream methods for measuring the current-voltage characteristics.

With the steady advancement of the Dual-carbon goal in China, more and more user-side microgrids which mainly include renewable energy sources are being connected to the distribution system, which makes the operating modes and methods of distribution system change greatly. In particular, when a distribution system failure occurs, how to efficiently use these microgrids to provide active power support for the distribution system will be one of the key factors that promote the regional autonomy capability and the safe and reliable operation level of a novel distribution system. However, the existing distribution automation level cannot meet the demand of massive user-side grid-connected microgrids which participate in the active power support in real time. In addition, the uncertainties in renewable energy sources and loads in microgrids pose a challenge to the power supply restoration in the distribution system. Based on the emergence of cloud-edge collaboration architecture and taking into account the advantage of edge computing, a limited risk power supply restoration method considering uncertainties in renewable energy sources and loads in microgrid group is proposed in this paper, in which the risk is modeled and characterized by conditional value-at-risk. First, the power supply restoration architecture of distribution system with microgrid group based on the cloud-edge collaboration technology is put forward, and a support power model of microgrid is established. Second, with the goal of minimizing the cost of central cloud power supply restoration and maximizing the profit of edge cloud microgrid operator, an optimization model of power supply restoration is established, in which each edge cloud microgrid operator can make power supply restoration decisions independently, and the central cloud makes overall arrangements to ensure that the risk constraints on the whole system are satisfied. Finally, through simulation analysis, results show that the proposed method can effectively restore power at a smaller risk index while maximally reducing the power supply restoration cost, and it can well improve the profit of microgrid operators. Moreover, it is also proved that the cloud-edge collaboration technology can improve the flexibility of power supply restoration and effectively reduce the computational burden.

With more and more microgrids (MGs) connected to power system, their operation control technology has become one of the key technologies to improve the utilization of renewable energy sources (RESs). However, when an MG switches between grid-connected and islanding operation modes, problems such as large voltage and frequency fluctuations will occur due to uncertainties in RESs output and the inconsistency of voltage, frequency and phase angle in the two operation modes. On this basis, a smooth switching control method of MG modes based on linear quadratic regulator (LQR) is proposed to reduce the drastic fluctuations of MG-related parameters during the mode switching. The proposed method uses the traditional LQR method and the state feedback theory to establish a smooth regulation model. Then, a smooth LQR based on a 2-DOF structure and its parameter selection principle are designed to minimize the transient changes in MG-related parameters during the mode switching. Finally, the simulation analysis and experimental results validated the effectiveness and superiority of the proposed method. Compared with other methods, the novel method can effectively reduce the overshoot percentage of MG-related parameters by about 22% and shorten the time of MG voltage and frequency oscillations by about 13%. In addition, it is easy to implement, and it has good compatibility with the common double-loop PI control.

To realize a high-precision online identification of multiple electrical parameters of a permanent magnet synchronous linear motor (PMSLM), a dual-model-based recursive least squares online identification algorithm for electrical parameters is proposed in this paper. First, according to the dq-axis voltage equation of the motor, the first model for identifying the stator resistance and permanent magnet flux linkage and the second model for identifying the q-axis inductance and d-axis inductance are established, respectively, and the two identification models are combined cyclically. Second, based on the dual-model structure, the recursive least squares algorithm is used to realize the online identification of electrical parameters, and a piecewise variable forgetting factor with saturation characteristics is proposed for the characteristics of a large number of dynamic processes during the operation of PMSLM. Meanwhile, the error voltage caused by the power switch non-ideal factor is compensated, which further improves the identification accuracy. Finally, simulation and experimental results prove the effectiveness of the identification algorithm, indicating that it has advantages such as a fast convergence speed, a high accuracy of identification results, and applicability under multiple working conditions.

New energy vehicles are the main direction for the transformation and development of automobile industry in China, and their safety issues have also attracted the attention from the whole society. To study the safety issues of new energy vehicle power battery, statistics of new energy vehicle accidents which have been publicly disclosed over the past six years are collected in this paper, and the corresponding vehicle models and fire causes are analyzed. The mechanisms of safety accidents such as battery overcharge, overdischarge, internal and external short-circuit faults, extrusion and collision, and thermal runaway are described. The characteristic parameters of battery thermal runaway are summarized. Based on the operating data, the variation in the characteristic parameters of one accident car when it was on fire is analyzed. Finally, some solutions to the existing issues of new energy vehicles are given, and the battery models and intelligent algorithms which are established on the basis of big data are introduced in detail, providing a basis for power battery fault diagnosis in the future.

Aimed at the problem that the state-of-charge (SOC) of batteries estimated by a combination algorithm is not ideal when there exits deviation from the initial value, a BP-multi-timescale unscented Kalman filter (UKF) optimization algorithm is proposed in this paper, which applies the multi-timescale theory to the combination algorithm. First, the multi-timescale UKF is used to preliminarily estimate the SOC value. Then, a BP model is constructed to train the data generated by the filtering algorithm, and the self-learning and adaptive capability of the BP algorithm is used to compensate and further reduce the error. The multi-timescale extended Kalman filter (EKF) modeling can not only reduce the SOC estimation error caused by changes in parameters, but also provide reliable data for error compensation, thus improving the model’s generalization capability and reducing the data dependence by the data-driven part. Simulation results show that the proposed algorithm depends on less data, and it outperforms other algorithms in terms of estimation accuracy, generalization capability and robustness. The root mean square error and mean absolute error are less than 1%, and the maximum absolute error is about 1%.

Limited by the switching frequency, the frequency-controlled LLC resonant converter is difficult to achieve a wide output voltage range. To solve this problem, an expandable variable-mode interleaved parallel LLC resonant converter is studied. The secondary-side of this converter adopts a voltage doubling rectifier circuit, which can work in a parallel or series mode according to different switch combinations of two half-bridges on the primary-side, and it can adapt to the wide output voltage range of 1-3N times. A fixed-frequency PWM control method is proposed. In the middle region between the parallel and series modes, the fixed switching frequency is taken as the resonant frequency, and the duty cycle of one bridge arm is changed to realize voltage control. PSIM simulation results show that the wide output voltage range of 1-3N times can be realized by expanding 2N resonator cavities. The experimental results of a 100 W prototype demonstrate that the wide output voltage range of 1-3 times can be achieved with two half-bridges and two resonant cavities, and the effectiveness of the proposed converter and its control strategy was verified.

In an energy storage system, the current-fed dual-active-bridge converter has a large current stress and the corresponding soft-switching range is limited, which limits the converter’s efficiency and power density. To solve these problems, combined with the coupled inductor technology, a current-fed dual-active-bridge converter with a low current ripple on the energy storage side and a wide soft-switching range is proposed. Two current-fed full bridges are connected in parallel on the energy storage side, thus effectively reducing the current stress of switches therein. By adjusting the phase shift angle between the two parallel full bridges on the energy storage side, the current ripple is reduced. By reasonably designing the coupling filter inductance, the obtained mutual inductance current is large enough to satisfy the soft-switching conditions for switches. The working principle and steady-state analysis of the converter were given in detail, and a 400 W experimental prototype was designed to verify the superiority and feasibility of the proposed converter.

To address the issue that a dual-active-bridge DC-DC converter will produce large current stress when voltages mismatch and result in a large reduction of its efficiency, a combined dual-phase-shifting (CDPS) control strategy is proposed, which combines dual-internal-phase-shifting (DIPS) and interlaced-dual-phase-shifting (IDPS). First, the working principles of the two control strategies are analyzed, and the mathematical models of transmission power and current stress are established. Second, with the minimum current stress as the objective, the optimal phase-shifting ratios are solved by using the Lagrange multiplied method under the Karush-Kuhn-Tucker condition. Third, the optimization methods under the two control strategies are combined according to different voltage ratios and transmission power. The CDPS control is used to obtain the optimal solution of current stress, which is compared with those obtained under the existing single-phase-shifting and dual- phase-shifting control strategies. Results show that the proposed control strategy can further reduce the current stress and reactive power under the condition of high voltage ratios and improve the efficiency. Finally, an experimental prototype was built to verify the feasibility of the proposed control strategy.