Aimed at the problems of unbalanced capacitor neutral-point voltage and high common-mode voltage in a permanent magnet synchronous motor drive system powered by a T-type three-level inverter, a model predictive instantaneous torque control (MPITC) strategy based on finite voltage vector set optimization is proposed. First, in view of the influence of voltage vectors on the neutral-point voltage, only zero, small and large vectors are selected to participate in MPITC. Second, according to the relationship between the switching states and common-mode voltage, it is confirmed that 13 low common-mode voltage vectors participate in the control. To improve the operation performance of the motor, the long vector synthetic virtual voltage vector is used to replace the medium vector to participate in the model predictive control. Finally, according to the neutral-point potential and the motor current direction, the voltage vector which is favorable for maintaining the neutral-point voltage balance is selected from 19 voltage vectors as the preselected vector set. Experimental results show that the proposed control strategy can effectively reduce the electromagnetic torque, flux linkage pulsation and common-mode voltage amplitude, and the neutral-point voltage achieves balanced control.

To improve the accuracy and efficiency of diagnosis when a converter fails, an active current limiting method is applied to realize the converter fault diagnosis in a power system. Active current limiting control is used to limit the output DC current from the converter to 1.2 times of rated current and limit the fault current, thus improving the stability of fault diagnosis. Based on a prediction model, the fault current distribution characteristics of the converter in the power system are diagnosed, and the bridge arm current is taken as the diagnosis parameter. The difference between the measured bridge arm current and predicted value is compared. If the difference is greater than the threshold value, it is judged that the converter has a fault. Experimental results show that the proposed method had a high accuracy and a high diagnosis efficiency in diagnosing the converter faults in an experimental power system, and the rates of false diagnosis and missed diagnosis were low, indicating that the converter diagnosis effect was satisfying.

The accurate estimation of the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries is always a key scientific prob-lem that needs to be solved urgently. In this paper, based on a second-order fractional-order equivalent circuit model, the state space equation of a lithium-ion battery is established, and the discretization expressions of fractional-order differential and integral equations of battery parameters and SOC are derived. Then, a dual fractional-order extended Kalman filter method is studied to estimate the equivalent circuit parameters, SOC and battery capacity simultaneously. In addition, a time weighting sequence method based on estimated SOC and battery capacity is proposed, different discharge currents and cumulative time are monitored, and the available capacity of the battery is calculated online, thus achieving real-time estimation of the SOH of the battery at any discharge depth and any discharge rate. Finally, under the conditions of dynamic stress test, three lithium iron phosphate batteries of the same manufacturer, the same model and different aging degrees were used for experimental verification.

A fast and accurate estimation of the state-of-charge (SOC) of lithium batteries is critical for the battery management system. Aimed at the problem that the Kalman filter algorithm lacks reasonable constraints on the resistance-capacitance (RC) parameters when estimating the SOC of lithium batteries, an optimization method of RC parameters filtering is proposed, and it is combined with unscented Kalman filter (UKF) to achieve the fast and accurate convergence of lithium battery SOC estimation. First, an equivalent circuit model of lithium battery is established by combing the polynomial equation. Then, forgetting factor recursive least squares is used to obtain the time-varying and time-invariant model RC parameters. The expression of RC parameters filtering relationship is established by setting the Kalman gain threshold, and an RC optimization UKF algorithm is proposed for lithium battery SOC estimation. Finally, hybrid pulse-power characteristic experiment, intermittent constant-current discharge experiment and dynamic stress test experiment were designed to verify the convergence and robustness of the proposed algorithm. The maximum estimation error of SOC was less than 1.0%, and the reference range of gain threshold was also given.

The application of multi-phase interleaved parallel coupled inductors technology can effectively reduce the phase current ripple and improve the dynamic response speed. Aimed at different design objectives, the influencing factors for the steady-state and dynamic performances of direct- and indirect-coupled inductors are analyzed. Subsequently, based on the invariant equivalent dynamic inductance before and after coupling, the direct- and indirect-coupled inductors are designed to enhance the steady-state performance. Similarly, based on the invariant equivalent steady-state inductance before and after coupling, direct- and indirect-coupled inductors are designed to improve the dynamic performance. Finally, the cor-rectness and effectiveness of the theoretical analysis were verified by experimental results, demonstrating that the two different coupling methods can significantly enhance the steady-state and dynamic performances of the converter, respectively.

Aimed at the time-delay oscillation of a bidirectional H4 bridge converter in a single-phase energy storage inverter, a unified control method for the bidirectional H4 bridge converter is proposed. In this method, a voltage regulator is used to control the power flow of the converter, and a set of bidirectional feasible control parameters are derived based on the power balance theory. At the same time, in order to realize AC current tracking input voltage without static error and increase the stability, the current inner-loop adopts a quasi proportional resonance controller, and a second-order generalized integrator is used to design a phase-locked loop. PSIM simulation and experimental results show that the proposed method can realize seamless switching between the rectification and active inverter modes, and it also has a good effect in the startup and switching between different modes. Therefore, it can realize stable control of the bidirectional AC-DC bridge converter in a single-phase photovoltaic energy storage system and obtain a good dynamic performance.

Electric vehicle fast charging piles are prone to overheating of power devices under high-power operation, causing potential safety hazards. However, the existing cooling strategy adopts a rule-based forced air cooling method, and the cooling fan rotates at a high speed and generates large environmental noise. To protect the thermal safety of core components in the module while optimizing the cooling regulation strategy, an optimal thermal management method for electric vehicle fast charging module based on data-driven model predictive control (MPC) is proposed. This method adopts a data-driven method to construct a prediction model of module temperature distribution based on the long short-term memory neural network, and it combines MPC to control the fan speed, thus optimizing the thermal management strategy for the fast charging module and reducing the fan noise. Through experimental tests, it was verified that this method can effectively reduce the average fan speed by 1 293 rpm and reduce the average noise by 4.99 dB while ensuring that the key components are not overheated, which ensures the thermal safety of core components and the durability of the cooling fan.

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.

Since DC bias is one of the main reasons for increases in the vibration and noise of a large-scale transformer, it is essential to fully understand the vibration and noise characteristics of large-scale transformers under DC bias for the evaluation of the operating state of transformers and the reduction of noise and vibration. A 406 MVA EHV large-scale transformer is taken as the research objective, and its vibration and noise characteristics are studied. First, based on the field-circuit coupling finite element method, the no-load operation characteristics under different DC bias currents are simulated and analyzed, and the law of excitation current under different DC biases is analyzed. Second, a multi-physics coupling model of circuit-magnetic field-solid mechanics-pressure acoustics is established, and the effective value of vibration displacement and the time-frequency characteristics of noise signal at different measuring points of the transformer under DC bias are obtained considering the influence of magnetostriction. Third, the sound level is measured at different measuring points around the transformer, and the simulated value is compared with the actual measured value to verify the effectiveness of the proposed calculation method for transformer vibration and noise. Finally, the Hilbert-Huang transform method is used to extract the vibration and noise characteristic quantities of one large-scale transformer under DC bias, and a transformer vibration characteristic recognition method based on the energy ratio of the noise signal intrinsic mode function is proposed. This method can effectively recognize the severity of DC bias of the transformer and accurately grasp its operating state, providing a theoretical basis for timely taking measures to suppress the DC bias.

The development of industry and economy has caused a huge consumption of energy, which brings serious energy crisis and environmental pollution. Therefore, building a safe and clean energy interconnection network is a way to solve the relationship among social development, environment and energy at present. Nowadays, different countries have proposed their policies for the development of new energy electric vehicles (EVs). As the core component of EVs, lithium-ion batteries are directly related to the driving performance and safety of EVs. The state-of-charge (SOC) estimation is a core parameter of lithium-ion batteries used in various industries, and the estimation accuracy is directly related to the service life and efficiency of batteries. In this paper, the problem of battery SOC estimation accuracy in EV applications is studied, and an SOC estimation method based on the extended Kalman filter (EKF) optimized by the whale optimization algorithm (WOA) is proposed. On the basis of constructing the covariance matrix of system noise and observation noise, the improved and optimized WOA-EKF algorithm is used to optimize the noise covariance matrix under dynamic conditions, thus improving the SOC estimation accuracy. The model parameter identification and comparative simulation verification are carried out in MATLAB/ Simulink. Results show that the SOC estimation of lithium-ion batteries based on the WOA optimized EKF algorithm can control the SOC estimation error to be within 2% under different working conditions, which is of significance to the promotion of develop- ment of batteries in the new energy field.