Voltage Modulated DPC Strategy of DFIG Using Extended Power Theory under Unbalanced Grid Voltage Conditions

This paper develops a voltage modulated direct power control (VM-DPC) strategy of a doubly fed induction generator (DFIG) using extended power theory under unbalanced grid voltage conditions. By introducing the modulated voltage of the active and reactive powers and the non-linear VM controller, the proposed VM-DPC strategy enables the generator dynamics to change from the time-varying differential equations into the time-invariant ones. Then, in order to deal with the voltage unbalance, three available power feedbacks, a combination of real active power and extended reactive power, a combination of extended active power and real reactive power, a combination of extended active power and extended reactive power, are developed. Together with a conventional controller (PI + R and feedforwards) and a non-linear VM controller, the power feedbacks are well controlled to track their references with the achievement of the constant active power, the constant reactive power and the balanced stator current. The main advantage of the VM-DPC strategy is the introduction of the modulated voltage and the non-linear VM controller making the generator dynamics time-invariant, which is easy for applying various control methods. Furthermore, the application of extended power can avoid the sequence extractions and the power compensations under unbalanced grid voltage. Finally, the simulation results demonstrate the effectiveness of the developed VM-DPC strategy

Enhancement of Drought Tolerance in Trifoliate Orange by Mycorrhiza: Changes in Root Sucrose and Proline Metabolisms

Sucrose and proline metabolisms are often associated with drought tolerance of plants. This study was conducted to investigate the effects of two arbuscular mycorrhizal fungi (AMF) species (Funneliformis mosseae and Paraglomus occultum) on root biomass, lateral root number, root sucrose and proline metabolisms in trifoliate orange (Poncirus trifoliata) seedlings under well-watered (WW) or drought stress (DS). All the AMF treatments significantly increased root dry weight, taproot length, and the number of lateral roots in 1st, 2nd, and 3rd class under WW and DS. Mycorrhizal seedlings conferred considerably higher fructose and glucose concentrations but lower sucrose accumulation, regardless of soil water status. Under DS, F. mosseae treatment significantly increased root sucrose synthase (SS, degradative direction) and sucrose phosphate synthase (SPS) activity but deceased root acid invertase (AI) and neutral invertase (NI) activity, and P. occultum inoculation markedly increased root AI, NI, SS, and SPS activities. AMF treatments led to a lower proline accumulation in roots, in company with lower activities of Δ1-pyrroline-5-carboxylate synthetase (P5CS), δ-ornithine aminotransferase (OAT), Δ1-pyrroline-5-carboxylate reductase (P5CR), and proline dehydrogenase (ProDH) in roots. It appears that the AM symbiosis induced greater root development and sucrose and proline metabolisms to adapt DS

Throughput Maximization for Intelligent Refracting Surface Assisted mmWave High-Speed Train Communications

With the increasing demands from passengers for data-intensive services, millimeter-wave (mmWave) communication is considered as an effective technique to release the transmission pressure on high speed train (HST) networks. However, mmWave signals ncounter severe losses when passing through the carriage, which decreases the quality of services on board. In this paper, we investigate an intelligent refracting surface (IRS)-assisted HST communication system. Herein, an IRS is deployed on the train window to dynamically reconfigure the propagation environment, and a hybrid time division multiple access-nonorthogonal multiple access scheme is leveraged for interference mitigation. We aim to maximize the overall throughput while taking into account the constraints imposed by base station beamforming, IRS discrete phase shifts and transmit power. To obtain a practical solution, we employ an alternating optimization method and propose a two-stage algorithm. In the first stage, the successive convex approximation method and branch and bound algorithm are leveraged for IRS phase shift design. In the second stage, the Lagrangian multiplier method is utilized for power allocation. Simulation results demonstrate the benefits of IRS adoption and power allocation for throughput improvement in mmWave HST networks.Comment: 13 pages, 7 figures, IEEE Internet of Things Journa