DC distribution network faults seriously affect the reliability of system power supply. Therefore, this paper proposes a fault recovery reconfiguration strategy for DC distribution networks, based on hybrid particle swarm optimization. The original particle swarm algorithm is improved by simplifying the distribution network structure, introducing Lévy Flight, and designing an adaptive coding strategy. First, the distribution network structure is equivalently simplified to reduce the problem dimensionality. Further, the generated branch groups are ensured to satisfy the radial constraints based on the adaptive solution strategy. Subsequently, Lévy flight is introduced to achieve intra-group optimality search for each branch group. The method is simulated in several distribution systems and analyzed in comparison with the particle swarm algorithm, genetic algorithm, and cuckoo algorithm. Finally, the results validate the accuracy and efficiency of the proposed method.

Multiple demand responses and electric vehicles are considered, and a micro-grid day-ahead dispatch optimization model with photovoltaic is constructed based on stochastic optimization theory. Firstly, an interruptible load model based on incentive-based demand response is introduced, and a demand response mechanism for air conditioning load is constructed to implement an optimal energy consumption curve control strategy for air conditioning units. Secondly, considering the travel demand and charging/discharging rules of electric vehicles, the electric vehicle optimization model is built. Further, a stochastic optimization model of micro-grid with demand response and electric vehicles is developed because of the uncertainty of photovoltaic power output. Finally, the simulation example verifies the effectiveness of the proposed model. The simulation results show that the proposed model can effectively tackle the uncertainty of photovoltaic, as well as reduce the operating cost of micro-grid. Therefore, the effective interaction between users and electric vehicles can be realized.

Different locations and types of faults affect the safe and reliable operation of DC microgrids. Therefore, this paper proposes a secondary multiple fault-tolerant control scheme for a DC microgrid based on a sliding mode observer to ensure the voltage is restored to the rated value and realize the proportional current sharing of all sources. Firstly, the secondary control model of the DC microgrid is established, considering the multiple faults of actuators and sensors simultaneously. Secondly, the system model is transformed into two subsystems by bilinear coordinate transformation, and multiple faults decoupling between the sensor and actuator is realized. Then, two sliding mode observers are designed for the two transformed subsystems. The sliding mode variable structure equivalent principle is used to reconstruct the faults at different positions without knowing the fault models in advance, which is convenient for subsequent processing. Then, the fault-tolerant controller based on the sliding mode observer is designed, which uses the reconstructed value to offset the influence of sensor and actuator faults on the DC microgrid and realizes the fault-tolerant control of the DC microgrid. Finally, the effectiveness of the proposed control strategy is verified by experiments.

The mutual inductance between coils directly affects many aspects of performance in wireless power transmission systems. Therefore, a reliable calculation method for the mutual inductance between coils is of great significance to the optimal design of transmission coil structures. In this paper, a mutual inductance calculation for circular coils sandwiched between 3-layer magnetic mediums in a wireless power transmission system is proposed. First, the structure of circular coils sandwiched between 3-layer magnetic mediums is presented, and then a mutual inductance model of the circular coils is established. Accordingly, a corresponding magnetic vector potential analysis method is proposed based on Maxwell equations and the Bessel transform. Finally, the mutual inductance calculation method for circular coils between 3-layer magnetic mediums is obtained. The correctness of the proposed mutual inductance calculation method is verified by comparing the calculated, simulated, and measured mutual inductance data.

This paper focuses on the high-performance bidirectional DC-DC converter required in distributed electric propulsion (DEP) systems, with the dual active bridge (DAB) converter chosen as the subject of study. To achieve the goal of stabilizing the output voltage while improving the converter’s anti-interference ability and dynamic performance, this paper proposes a novel strategy. In particular, it combines the Linear Extended State Observer (LESO) with a sliding mode control (SMC), proposing a sliding mode control strategy based on the Linear Extended State Observer (LESO-SMC). Notably, this control strategy not only retains the fast dynamic performance of Linear Active Disturbance Rejection Control (LADRC) and the robustness of SMC but also addresses the significant chattering issue inherent in traditional SMC. Comparing the traditional PI, LADRC, and SMC strategies, the results show that when the load changes, the voltage fluctuation of the LESO-SMC strategy proposed in this paper is 0.165 V (0.25 V) in the Matlab/Simulink and RT-Lab platforms, and the average adjustment time is 4 ms (3.5 ms). In contrast, the average voltage fluctuations of PI and LADRC strategies were 3.7 V (4.9 V) and 0.55 V (1.35 V), and the average adjustment times were 99.5 ms (201 ms) and 71.5 ms (77.5 ms), respectively. When the input voltage changes, the proposed LESO-SMC strategy adjusts faster and has almost no voltage fluctuations, while the average voltage fluctuations of the PI and LADRC strategies in the simulation are 0.5 V and 0.1 V, and the average adjustment times are 89.5 ms and 35 ms, and the change in the input voltage in the RT-Lab platform has very little effect on the output voltage. Compared with SMC, the LESO-SMC strategy has no chattering problem. In summary, compared to the other three control strategies, the LESO-SMC strategy proposed in this paper exhibits superior performance in terms of voltage fluctuation and adjustment time during load changes and input voltage changes. It shows a robust anti-interference ability and a rapid dynamic response performance.

Traditional reactive power optimization mainly considers the constraints of active management elements and ignores the randomness and volatility of distributed energy sources, which cannot meet the actual demand. Therefore, this paper establishes a reactive power optimization model for active distribution networks, which is solved by a second-order cone relaxation method and interval optimization theory. On the one hand, the second-order cone relaxation technique transforms the non-convex optimal dynamic problem into a convex optimization model to improve the solving efficiency. On the other hand, the interval optimization strategy can solve the source–load uncertainty problem in the distribution network and obtain the interval solution of the optimization problem. Specially, we use confidence interval estimation to shorten the interval range, thereby improving the accuracy of the interval solution. The model takes the minimum economy as the objective function and considers a variety of active management elements. Finally, the modified IEEE 33 node arithmetic example verifies the feasibility and superiority of the interval optimization algorithm.