This article proposes a rotor attitude estimation (RAE) method for spherical motors using the multiobject Kalman kernel correlation filter (MKKCF) algorithm in monocular vision. A permanent magnet spherical motor (PMSpM) is adopted as the research object, and a monocular area scan camera is equipped in the RAE system with a visual feature component mounted on top of the rotor output shaft. In the proposed MKKCF algorithm, the Kalman filter is used to enhance the robustness and accuracy of KCF tracker for each object. To simplify the proposed algorithm verification, a one-object tracking comparison is conducted among the KCF algorithm, the fast discriminative scale space tracking (FDSST) algorithm, and the Kalman KCF algorithm, and the results show that Kalman KCF tracker is more applicable for RAE. In addition, an RAE test bench is developed, and the MKKCF-based RAE method and the micro-electro-mechanical system (MEMS) (MPU9250) method are compared when estimating the rotor attitude. To set a benchmark, the contact rotor position measurement with encoders is used. The comparison results indicate that the MKKCF-based RAE method works with higher accuracy than the MEMS method. Finally, a closed-loop PMSpM control experiment is conducted by using the proposed MKKCF-based RAE method, and the practicability of the proposed method is proved.

Eccentricity in a permanent magnet synchronous motor (PMSM) can cause additional electromagnetic forces and resolver error, which in turn can lead to an aggravation of vibration and noise in the electric drive system. In this paper, the additional electromagnetic forces and the resulting vibration and noise under the conditions of the three-dimensional (3-D) static and dynamic motor eccentricities, the resolve error, and the coupling between these faults, are analytically derived and analyzed, with special emphasis on the coupling effect of the motor eccentricity and resolver error. The results of the present theoretical analyses agree well with those from the finite element simulations and the experimental measurements. It can be concluded from the results that the coupling between the 3-D motor eccentricity and the resolver error will induce additional electromagnetic forces with much more diverse spatial orders and frequencies.

To realize the multiple-degree-of-freedom motion, in this article, we propose a new spherical reluctance motor (SPRM). For the nonlinear characteristics of flux linkage and excessively complex torque calculation, this article proposes a new adaptive analytical model for SPRM based on hybrid trigonometric function–power function. A sixth-degree polynomial is used to represent the five coefficients of this model, and these coefficients are calculated from flux-linkage–rotor angle–phase current data using numerical curve fitting with nonlinear least square. Based on the criterion of minimizing the sum of squared error between the flux linkage obtained by finite-element analysis and the flux linkage calculated by analytical model, the optimal trigonometric function angular frequency coefficient and the optimal power function exponent are adaptively obtained by iterative algorithm. The feasibility and accuracy of this model are validated by comparing with the existing models. Furthermore, the experimental results show that the calculated torque has good agreement with the measured torque, which further verifies the validity of the proposed analytical model. The versatility of the proposed model for SPRMs with different structure parameters is discussed as well.

Train bearing is one of the key parts of a train wheel, and its fault diagnosis is crucial for train safety. This paper proposes an Improved Linear Discriminant Analysis (ILDA) algorithm to diagnose train bearing faults. A new distance function is employed to adjust the between-class divergence matrix of LDA. The adjusted between-class divergence matrix is adopted for selecting the projection direction with the best clustering performance and reducing the ambiguity or overlap among different classes. The sample clustering evaluation index( SI ) is adopted to evaluate the clustering performance of fusion features obtained by ILDA. First, twenty-four original features are extracted from wayside acoustic signals after reducing the Doppler effect. Then, ILDA is adopted to extract fusion features. Finally, the train bearing faults are identified by extreme learning machine(ELM). The experimental results show that the fusion features extracted by the proposed method can be effectively identified, which have good clustering performance and high identification rate. Furthermore, the identification results of two datasets provided by different universities show that the fusion features obtained by ILDA also have good clustering performance and high identification rate, which further verify the validity of the proposed method.

This article proposes a position detection method for a multi-to-one wireless power transmission (WPT) system for the first time and applies it to the position detection of a spherical motor. The traditional contact detection system has high friction resistance and poor rotor positioning accuracy. Although some noncontact measurement systems are not directly connected to the rotor, the structure or calculation of the measurement system is more complicated. To solve the abovementioned problems, this article proposes two structural topologies, which are evenly arranged on the upper part of the rotor and the receiving coil is placed on the rotor shaft. When the motor rotor rotates along with the three motion states of yaw, spin, and tilt, the receiving and multitransmitting coils will have an angular offset. The rotor position is obtained by a method of accurately estimating the mutual inductance. This method avoids complicated finite element calculations and saves a lot of time. Finally, the experiment verifies the above analysis and compares the data with the microelectromechanical system three-degree-of-freedom attitude detection method. This article provides insights into the position detection of spherical motors. This method simplifies the previous measurement system and obtains higher positioning accuracy.

This paper proposes a hierarchical optimization method for the energy scheduling of multiple microgrids (MMGs) in the distribution network of power grids. An energy market operator (EMO) is constructed to regulate energy storage systems (ESSs) and load demands in MMGs. The optimization process is divided into two stages. In the first stage, each MG optimizes the scheduling of its own ESS within a rolling horizon control framework based on a long-term forecast of the local photovoltaic (PV) output, the local load demand and the price sent by the EMO. In the second stage, the EMO establishes an internal price incentive mechanism to maximize its own profits based on the load demand of each MG. The optimization problems in these two stages are solved using mixed integer programming (MIP) and Stackelberg game theory, respectively. Simulation results verified the effectiveness of the proposed method in terms of the promotion of energy trading and improvement of economic benefits of MMGs.