Double-Fed Wind Power System Adaptive Sensing Control and Condition Monitoring

This paper presents an in-depth study and analysis of improving the performance of doubly fed wind power systems using adaptive sensing control technology. The maximum wind energy tracking principle is analyzed in this paper with the wind turbine operation characteristics. Considering that the operation state and control strategy of a doubly fed wind power generation system is different before and after grid connection, the no-load simulation model and power generation simulation model are established based on the idea of separate modeling and time-sharing work. Combined with the respective control strategies and enabling modules, the overall simulation system is constituted for the continuous process from no-load operation to power generation operation. To analyze the chaotic mechanism of ferromagnetic resonance of wind farm power system and suppress the problem, based on the ferromagnetic resonance model of wind farm power system, analyze the basic conditions of the system into the chaotic state, consider the resonance phenomenon when external excitation acts, adopt the multiscale method to calculate the approximate solution at the resonance of main parameters and determine the steady-state solution and stability conditions, and explore the influence of external excitation on the dynamic characteristics of ferromagnetic resonance. In this paper, the inverse system approach, applied to the linearized decoupling of doubly fed wind power, a nonlinear, strongly coupled multivariable system, is derived for the no-load inverse system model and the inverse system model for the power control scheme and the speed control scheme to achieve maximum wind energy tracking for grid-connected power generation, respectively. The model further extended to fractional order to study the complex dynamical behavior of the system of different orders and flux chain subsquares. To suppress the system chaotic oscillation phenomenon, a fractional-order finite-time terminal sliding mode controller is proposed based on the frequency distribution model with time-frequency domain conversion, which achieves the suppression of chaotic phenomena in resonant overvoltage infinite time and is compared with the conventional sliding mode to confirm the effectiveness and superiority of the proposed controller. This paper explores and discusses the impact of adaptive sensing control technology on the practice of doubly fed wind power systems, to provide theoretical possibilities for the adaptive sensing control technology to be more effective for the practice of doubly fed wind power systems.

Inverse Limit of an Inverse System of BE-algebras

This paper covers the notion of the inverse limit of an inverse system of BE-algebras and investigates some of its properties. Moreover, this study deals with the completion of a BE-algebra.

Plant gross primary production, plant respiration and carbonyl sulfide emissions over the globe inferred by atmospheric inverse modelling

Carbonyl Sulphide (COS), a trace gas showing striking similarity to CO2 in terms of biochemical diffusion pathway into leaves, has been recognized as a promising indicator of the plant gross primary production (GPP), the amount of carbon dioxide that is absorbed through photosynthesis by terrestrial ecosystems. However, large uncertainties about the other components of its atmospheric budget prevent us from directly relating the atmospheric COS measurements to GPP. The largest uncertainty comes from the closure of its atmospheric budget, with a source component missing. Here, we explore the benefit of assimilating both COS and CO2 measurements into the LMDz atmospheric transport model to obtain consistent information on GPP, plant respiration and COS budget. To this end, we develop an analytical inverse system that optimizes biospheric fluxes for the 15 plant functional types (PFTs) defined in the ORCHIDEE global land surface model. Plant uptake of COS is parameterized as a linear function of GPP of the leaf relative uptake (LRU), which is the ratio of COS to CO2 deposition velocities in plants. A possible scenario for the period 2008–2019 leads to a global biospheric sink of 800 GgS.yr−1, with higher absorption in the high latitudes and higher oceanic emissions between 400 and 600 GgS.yr−1 most of which is located in the tropics. As for the CO2 budget, the inverse system increases GPP in the high latitudes by a few GtC.yr−1 without modifying the respiration compared to the ORCHIDEE fluxes used as a prior. In contrast, in the tropics the system tends to weaken both respiration and GPP. The optimized components of the COS and CO2 have been evaluated against independent measurements over Northern America, the Pacific Ocean, at three sites in Japan and at one site in France. Overall, the posterior COS concentrations are in better agreement with the COS retrievals at 250 hPa from the MIPAS satellite and with airborne measurements made over North America and the Pacific Ocean. The system seems to have rightly corrected the underestimated GPP over the high latitudes. However, the change in seasonality of GPP in the tropics disagrees with Solar Induced Fluorescence (SIF) data. The decline in biospheric sink in the Amazon driven by the inversion also disagrees with MIPAS COS retrievals at 250 hPa, highlighting the lack of observational constraints in this region. Moreover, the comparison with the surface measurements in Japan and France suggests misplaced sources in the prior anthropogenic inventory, emphasizing the need for an improved inventory to better partition oceanic and continental sources in Asia and Europe.

Decoupling motion tracking control for 4WD autonomous vehicles based on the path correction

Since one control loop input disturbs the control of another loop, the dynamic coupling of the longitudinal and lateral directions adversely affects the motion tracking accuracy of autonomous vehicles. With the ability to minimize the interactions between the longitudinal and lateral dynamics, the inverse system learned by the neural network is an effective way to decouple vehicle dynamics. After tracking the vehicle states projected from the desire motion, the dynamic decoupling and the motion tracking are both realized. However, the accumulation of vehicle state tracking errors causes the stable yaw tracking error and the lateral tracking divergence. To solve the accompanying problem, a path correction model is designed to periodically update the desired vehicle states. Moreover, the applicability of the inverse system decoupling method is improved in this paper, because the method usually adopted in distributed drive electric vehicles is applied to four-wheel driving vehicles representing the traditional driving form. Simulation results indicate that the decoupling motion tracking method with the path correction model is suitable for long-distance and complex conditions and has the highest comprehensive tracking accuracy compared with the integrated MPC (model predictive control) and the pure pursuit in the dynamic coupling conditions.

Space of signatures as inverse limits of Carnot groups

We formalize the notion of limit of an inverse system of metric spaces with $1$-Lipschitz projections having unbounded fibers. The construction is applied to the sequence of free Carnot groups of fixed rank $n$ and increasing step. In this case, the limit space is in correspondence with the space of signatures of rectifiable paths in $\mathbb R^n$, as introduced by Chen. Hambly-Lyons's result on the uniqueness of signature implies that this space is a geodesic metric tree. As a particular consequence we deduce that every path in $\mathbb R^n$ can be approximated by projections of some geodesics in some Carnot group of rank $n$, giving an evidence that the complexity of sub-Riemannian geodesics increases with the step.

Continuous LTI Input–Output Stable Systems on $${L^{p}(\mathbb {R})}$$ and $${\mathscr {D'}_{L^{p}}(\mathbb {R})}$$ Associated with Differential Equations: Existence, Invertibility Conditions and Inversion

A usual problem in analog signal processing is to ascertain the existence of a continuous single-input single-output linear time-invariant input–output stable system associated with a linear differential equation, i.e., of a continuous system such that, for every input signal in a given space of signals, yields an output, in the same space, which verifies the equation with known term the input, and to ascertain the existence of its inverse system. In this paper, we consider, as space of signals, the usual Banach space of $${L^{p}}$$ L p functions, or the space of distributions spanned by $${L^{p}}$$ L p functions and by their distributional derivatives, of any order (input spaces which include signals with not necessarily left-bounded support), we give a systematic theoretical analysis of the existence, uniqueness and invertibility of continuous linear time-invariant input–output stable systems (both causal and non-causal ones) associated with the differential equation and, in case of invertibility, we characterize the continuous inverse system. We also give necessary and sufficient conditions for causality. As an application, we consider the problem of finding a suitable almost inverse of a causal continuous linear time-invariant input–output stable non-invertible system, defined on the space of finite-energy functions, associated with a simple differential equation.

Sensorless Control of Bearingless Permanent Magnet Synchronous Motor Based on LS-SVM Inverse System

In order to solve the problems of low integration, low reliability, and high cost caused by mechanical sensors used in bearingless permanent magnet synchronous motor (BPMSM) control systems, a novel speed and displacement sensorless control method using a least-squares support vector machine (LS-SVM) left inverse system is proposed in this paper. Firstly, the suspension force generation principle of the BPMSM is introduced, and the mathematical model of the BPMSM is derived. Secondly, the observation principle of the left inverse system is explained, and the left reversibility of the established speed and displacement subsystem is proved. Thirdly, the left inverse systems of the speed and displacement subsystems are constructed by using the LS-SVM, and the complete speed and displacement sensorless control system is constructed. Finally, the simulations and experiments of the proposed method are performed. The research results demonstrate that the proposed observation method can identify the speed and displacement quickly and accurately, and the sensorless control method can realize the stable operation of the BPMSM without speed and displacement sensors.