Current optimization-based control of dual three-phase PMSM for low-frequency temperature swing reduction

In this paper,a control scheme based on current optimization is proposed for dual three-phase permanent-magnet synchronous motor(DTP-PMSM)drive to reduce the low-frequency temperature swing.The reduction of temperature swing can be equivalent to reducing maximum instantaneous phase copper loss in this paper.First,a two-level optimization aiming at minimizing maximum instantaneous phase copper loss at each electrical angle is proposed.Then,the optimization is transformed to a singlelevel optimization by introducing the auxiliary variable for easy solving.Considering that singleobjective optimization trades a great total copper loss for a small reduction of maximum phase copper loss,the optimization considering both instantaneous total copper loss and maximum phase copper loss is proposed,which has the same performance of temperature swing reduction but with lower total loss.In this way,the proposed control scheme can reduce maximum junction temperature by 11%.Both simulation and experimental results are presented to prove the effectiveness and superiority of the proposed control scheme for low-frequency temperature swing reduction.

A Backward-Looking Optimal Current Lattice Model

An optimai current lattice model with backward-looking effect is proposed to describe the motion of traffic flow on a single lane highway. The behavior of the new model is investigated anaiytically and numerically. The stability, neutrai stability, and instability conditions of the uniform flow are obtained by the use of linear stability theory. The stability of the uniform flow is strengthened effectively by the introduction of the backward-looking effect. The numerical simulations are carried out to verify the validity of the new model. The outcomes of the simulation are corresponding to the linearly analyticai results. The analytical and numerical results show that the performance of the new model is better than that of the previous models.

Modeling and characterization of novel magnetorheological(MR) cell with individual currents

Magnetorheological(MR) cell with multi-coil was designed to enlarge the range of controllable transmission torque by increasing the effective length. Individual input current was proposed to maximize its potential for reducing power consumption and generating large yield stress. Finite element analysis was performed to analyze magnetic field distribution, based on which a prototype MR cell was fabricated and tested to investigate the performance of various combinations of individual input currents. A good correlation was identified between experimental results and FEA predications. The results show that the power consumption can be reduced to 42.4%, maintaining large transmission torque, by distributing the total current(2 A) to three individual magnetic coils. In addition, optimal results of four input currents considering a multi-objective function are obtained by changing the weighting factor λ. The advantage of this design, such as lower power consumption and more control flexibility, makes it more competitive in engineering applications that require large energy consumption.

A Data-driven Method for Fast AC Optimal Power Flow Solutions via Deep Reinforcement Learning

With the increasing penetration of renewable energy,power grid operators are observing both fast and large fluctuations in power and voltage profiles on a daily basis.Fast and accurate control actions derived in real time are vital to ensure system security and economics.To this end,solving alternating current(AC)optimal power flow(OPF)with operational constraints remains an important yet challenging optimization problem for secure and economic operation of the power grid.This paper adopts a novel method to derive fast OPF solutions using state-of-the-art deep reinforcement learning(DRL)algorithm,which can greatly assist power grid operators in making rapid and effective decisions.The presented method adopts imitation learning to generate initial weights for the neural network(NN),and a proximal policy optimization algorithm to train and test stable and robust artificial intelligence(AI)agents.Training and testing procedures are conducted on the IEEE 14-bus and the Illinois 200-bus systems.The results show the effectiveness of the method with significant potential for assisting power grid operators in real-time operations.

High-voltage output triboelectric nanogenerator with DC/AC optimal combination method

The high-voltage power source is one of the important research directions of triboelectric nanogenerator(TENG).In this paper,a high-voltage output TENG(HVO-TENG)is proposed with direct current/alternating current(DC/AC)optimal combination method for wind energy harvesting.Through the optimal design of a direct current generation unit(DCGU)and an alternating current generation unit(ACGU),the HVO-TENG can produce DC voltage of 21.5 kV and AC voltage of 200 V,simultaneously.The HVOTENG can continuously illuminate more than 6,000 light emitting diodes(LEDs),which is enough to drive more possible applications of TENG.Besides,this paper explored application experiments on HVO-TENG.Demonstrative experiments indicate that the high-voltage DC output is used for producing ozone,while the AC output can light up ultraviolet(UV)LEDs.The HVOTENG can increase the ozone concentration(C)in an airtight container to 3 parts per million(ppm)after 7 h and continuously light up UV LEDs.All these demonstrations verify that the HVO-TENG has important guiding significance for designing high performance TENG.

Deep Reinforcement Learning Based Real-time AC Optimal Power Flow Considering Uncertainties

Modern power systems are experiencing larger fluctuations and more uncertainties caused by increased penetration of renewable energy sources(RESs) and power electronics equipment. Therefore, fast and accurate corrective control actions in real time are needed to ensure the system security and economics. This paper presents a novel method to derive realtime alternating current(AC) optimal power flow(OPF) solutions considering the uncertainties including varying renewable energy and topology changes by using state-of-the-art deep reinforcement learning(DRL) algorithm, which can effectively assist grid operators in making rapid and effective real-time decisions. The presented DRL-based approach first adopts a supervised-learning method from deep learning to generate good initial weights for neural networks, and then the proximal policy optimization(PPO) algorithm is applied to train and test the artificial intelligence(AI) agents for stable and robust performance. An ancillary classifier is designed to identify the feasibility of the AC OPF problem. Case studies conducted on the Illinois 200-bus system with wind generation variation and N-1 topology changes validate the effectiveness of the proposed method and demonstrate its great potential in promoting sustainable energy integration into the power system.