The intermittency of renewable energy will increase the uncertainty of the power system, so it is necessary to predict the short-term wind power, after which the electrical power system can operate reliably and safely. Unlike the traditional point forecasting, the purpose of this study is to quantify the potential uncertainties of wind power and to construct prediction intervals (PIs) and prediction models using wavelet neural network (WNN). Lower upper bound estimation (LUBE) of the PIs is achieved by minimizing a multi-objective function covering both interval width and coverage probabilities. Considering the influence of the points out of the PIs to shorten the width of PIs without compromising coverage probability, a new, improved, multi-objective artificial bee colony (MOABC) algorithm combining multi-objective evolutionary knowledge, called EKMOABC, is proposed for the optimization of the forecasting model. In this paper, some comparative simulations are carried out and the results show that the proposed model and algorithm can achieve higher quality PIs for wind power forecasting. Taking into account the intermittency of renewable energy, such a type of wind power forecast can actually provide a more reliable reference for dispatching of the power system.

The magnetic circuit feature of a radial–axial brushless hybrid excitation machine (RA-BHEM) is studied in this article. An axial stator is placed on one side of the rotor, which is wound with ac excitation winding to adjust the air-gap flux flexibly to make it have good flux regulation capability. The flux distribution of the RA-BHEM is a radial–axial 3-D mixed magnetic circuit, which is difficult to be analyzed by the 2-D finite-element analysis (FEA). Therefore, a radial–axial equivalent magnetic circuit (RAEMC) model is established to obtain the air-gap flux density with different excitation magnetomotive forces (MMFs), which simplifies the design process and reduces the amount of calculation. The flux density distribution in different MMFs is analyzed by 3-D finite FEA and the results are consistent with the RAEMC model results. The torque characteristics in the low-speed region and the flux-weakening capability in the high-speed region are analyzed to verify the possibility of the proposed machine in the field of electric vehicles (EVs). Finally, a 2-kW prototype is manufactured to verify the correctness of the above analysis.

This article proposes a multiobjective optimization design framework for a double stator brushless hybrid excitation motor (DSBHEM) to provide high torque, wide flux regulation ability and low torque ripple. The design variables are divided into sensitive level and insensitive level by sensitivity analysis. The response surface model (RSM) and nondominated sorting genetic algorithm 2 (NSGA2) are organically combined to generate a set of Pareto solutions for the sensitive level variables, from which the optimal values of the sensitive level variables are obtained. In addition, the optimal values of insensitive variables are obtained through single parameter scanning optimization, and a set of final optimal design variables are obtained. The electromagnetic performance of the initial design and the optimal design are compared with finite element analysis. Finally, a prototype is manufactured to verify the proposed concepts.

This paper proposes a multi-objective optimization framework for a radial-axial hybrid excitation machine (RAHEM) to provide high average torque, better flux regulation ability and smaller torque ripple, which are applied to electric vehicles (EVs). The design variables related to multiple-objective are analyzed by sensitivity stratification. Non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) based on response surface model (RSM) is adopted for the high sensitivity layer variable. The advantages are selected with the pareto optimal solutions (POS), while the low sensitivity layer variables are optimized by sensitivity ranking for single parameter scanning. The optimization function compares the two sensitive layers results to obtain the optimal design. Three-dimensional (3-D) finite element analysis (FEA) is used to compare the electromagnetic performance of initial and optimal designs. Finally, a prototype is manufactured to verify the effectiveness of the proposed framework.

A radial-axial consequent pole hybrid excitation machine (RACP-HEM) with the advantages of interior permanent magnet synchronous machine (IPMSM) and electric excitation machine is studied, which has high torque output and wide speed range for electric vehicles (EVs). The radial-axial d-q axis model is established to solve the problem that the inherent asymmetry of the airgap density of consequent pole leads to difficult mathematical modeling. The current region control strategy with torque and speed as the criteria is proposed to efficiently improve operation performance of the RACP-HEM, which divides the operation region into different modes to realize high output torque and wide speed region. The maximum torque per ampere (MTPA) control of armature and excitation currents is integrated with the advantages of AC excitation and compared with i_d =0 control for low-speed region to obtain higher output torque. In high-speed region, the hybrid advanced angle field weakening control with three-dimensional (3-D) current vector is adopted to utilize the excitation current to solve the problem of the field weakening out of control caused by the traditional voltage feedback field weakening control to achieve higher speed. Finally, the simulation results and prototype experiments verify the effectiveness of the proposed control strategy for RACP-HEM.

A robust sliding mode observer (SMO) is proposed to achieve multiple fault reconstruction for a wind energy conversion system (WECS) with simultaneous actuator and sensor faults. Firstly, the state equation of the WECS is established. The orthogonal transformation matrix and a post filter are introduced, and a new augmented system is constructed; then, the sensor fault is converted into an actuator fault to diagnose. The fault information is collected by the equivalent output control, and the simultaneous reconstruction algorithm of the sensor fault and the actuator fault is given. Through compensation control, the reliable control input of the WECS is guaranteed, and the function of active fault tolerant control for multiple faults is achieved. Simulation experiments show that the proposed method can accurately reconstruct the actuator and sensor faults, and maximum wind energy capture can be achieved by active fault-tolerant control.