Simultaneous deployment of the electric vehicle charging stations (EVCSs) and distributed photovoltaic stations (DPVSs) in the distribution systems is an effective way to reduce greenhouse gas emissions, promote renewable power adoption, and achieve sustainable development in energy utilization. In this context, how to deploy the EVCSs and DPVSs in the distribution systems with a reasonable scheme is of great importance. In this article, a joint planning model is developed to optimize locations and capacities of the EVCSs and DPVSs simultaneously to reduce energy losses in the distribution systems. In the joint planning model, constraints on bus voltage deviations and line currents are both formulated as chance constraints to ensure that the distribution systems are in reasonable operating statues. To quantify these two chance constraints, a scenario-based method is developed to calculate the probabilistic power flow of the distribution systems during a typical planning day, in which random characters of the DPVS generations and the EVCS charging loads are both considered. The joint planning model of the EVCSs and DPVSs developed in this article is difficult to be solved by mathematical optimization methods. Therefore, genetic algorithm (GA) is customized and utilized to solve the joint planning model of the EVCSs and DPVSs. Finally, a case study based on IEEE 33-bus distribution systems validates the joint planning model and its solving algorithm.

A power electronic transformer (PET) is applied to the high-speed train for lightweight demand. A 300 kW/5 kHz high-power medium-frequency transformer (HPMFT) using hollow conductors in a power unit of the PET is optimally designed in this paper. The target of the design is to balance the loss, leakage inductance, and weight of the HPMFT. For this purpose, the design parameters of the HPMFT are firstly confirmed according to the system structure and parameters of the PET. Secondly, the design process of HPMFT is developed. Finally, the results of 48 design schemes of core-type and shell-type structures are compared by the comprehensive evaluation standard, which equilibrates the three above objective parameters of the HPMFT. According to the optimal scheme, a prototype is manufactured, whose test results verify the correctness of the optimal design method.

In this letter, we investigate a backbone-assisted wireless powered communication network (WPCN) under the protocol of “harvest-then-transmit" protocol, where user nodes (UNs) harvest energy from the sinks in the downlink (DL), then they use the energy to transmit signals to the sinks by a non-orthogonal multiple access in the uplink (UL). We consider joint successive interference cancellation and backbone-assisted sink cooperation to reduce the impact of co-channel interference on the throughput performance. We maximize the system throughput by jointly optimizing user scheduling and the time allocation for DL energy harvesting and UL transmission. For solving the maximum throughput problem efficiently, we propose a column generation method based on a greedy algorithm. Numerical results show that our proposed energy and information transfer design is superior to other reference methods in WPCNs.

This paper investigates one kind of high performance control methods for Micro-Electro-Mechanical-System (MEMS) gyroscopes using adaptive sliding mode control (ASMC) scheme with prescribed performance. Prescribed performance control (PPC) method is combined with conventional ASMC method to provide quantitative analysis of gyroscope tracking error performances in terms of specified tracking error bound and specified error convergence rate. The new derived adaptive prescribed performance sliding mode control (APPSMC) can maintain a satisfactory control performance which guarantees system tracking error, at any time, to be within a predefined error bound and the error convergences faster than the error bound. Besides, adaptive control (AC) technique is integrated with PPC to online tune controller parameters, which will converge to their true values at last. The stability of the control system is proved in the Lyapunov stability framework and simulation results on a Z-axis MEMS gyroscope is conducted to validate the effectiveness of the proposed control approach.

To remove parameter dependence in existing sensorless control strategies, a parameter-free model predictive current control is proposed for permanent magnet synchronous motor without any position sensor. First, the current variation during one sampling period is analyzed and divided into two elements: natural attenuation and forced response. Second, recursive least squares algorithm is utilized to estimate the future current variation so that the model predictive current control can be successfully executed paying no attention to motor parameters. Meanwhile, the position information is obtained by the arc tangent function according to the estimated forced response of current variation. At last, experimental results verify that the estimation errors of rotor position are reduced to around 0.1 rad with smaller current prediction error even at low speed where no motor parameters are required.

In this paper, a neural fractional order prescribed performance control is proposed for micro-electromechanical system (MEMS) gyros. Gyros tend to become smaller in size and more precise in structure with the development of micro-manufacturing technology. The operational security for MEMS gyros in cases of disturbances and parameter uncertainties becomes a challenging problem that has attracted much attention. The proposed method incorporates a prescribed performance technique to accomplish a bounded (within 10% of the vibration amplitude) gyro trajectory tracking error dynamic to secure the gyro’s operation. Meanwhile, fractional calculus is integrated into the controller’s design to provide precise adjustments to the gyro’s motion and thus further improve gyro control performance (shortening error convergence time by 20%). Furthermore, to enlarge the application scope and to improve gyro system robustness, a modified neural network estimator with a constrained input mapping mechanism is proposed to approximate unknown time-varying angular-velocity-related gyro dynamics. Notably, the constrained input mapping mechanism can help guide neural parameter initialization to avoid a time-consuming parameter adjustment process. The stability of the closed-loop gyro control system is proved in the framework of Lyapunov stability theory, and comparisons of simulation results are presented to demonstrate the effectiveness of the proposed method.