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.

Nutrient Analysis in Arctic Waters Using a Portable Sensing Platform

A portable sensing platform for the detection of nutrients (PO43−, NO2−, NO3−) in natural waters has been realized through the use of rapid prototyping techniques, colorimetric chemistries, electronics, and LED-based optical detection. The sensing platform is modular in design incorporating interchangeable optical detection units, with a component cost per unit of ca. €300, and small form factor (20 cm × 6 cm x 3.5 cm). Laboratory testing and validation of the platform was performed prior to deployment at the CNR Dirigibile Italia Arctic Research Station, Ny-Aselund (79°N, 12°E). Results obtained showed excellent linear response, with a limit of detection of 0.05 μM (NO2−, NO3−), and 0.03 μM (PO43−). On the June 22, 2016 a field campaign took place within Kongsfjorden, Ny-Aselund (78.5–79°N, 11.6–12.6°E), during which 55 water samples were acquired using 10 L Niskin bottles on board the MS Teisten research vessel. 23 hydrological casts were also performed using a Seabird 19plus V2 SeaCAT Profiler CTD probe with turbidity and dissolved oxygen sensors. Water samples were subsequently analyzed for PO43−, NO2−, NO3− at the CNR Dirigibile Italia Arctic Research Station Laboratory using the adaptive sensing platform. Nutrient concentrations were compared to hydrological data to assess the processes that influence the nutrient concentrations within the Fjord. This research highlights the potential use of the adaptive sensing platform in remote locations as a stand-alone platform and/or for the validation of deployable environmental sensor networks.

Energy-Efficient Adaptive Sensing Scheduling in Wireless Sensor Networks Using Fibonacci Tree Optimization Algorithm

Wireless sensor networks are appealing, largely because they do not need wired infrastructure, but it is precisely this feature that renders them energy-constrained. The duty cycle scheduling is perceived as a contributor to the energy efficiency of sensing. This paper developed a novel paradigm for modeling wireless sensor networks; in this context, an adaptive sensing scheduling strategy is proposed depending on event occurrence behavior, and the scheduling problem is framed as an optimization problem. The optimization objectives include reducing energy depletion and optimizing detection accuracy. We determine the explicit form of the objective function by numerical fitting and found that the objective function aggregated by the fitting functions is a bivariate multimodal function that favors the Fibonacci tree optimization algorithm. Then, with the optimal parameters optimized by the Fibonacci tree optimization algorithm, the scheduling scheme can be easily deployed, and it behaves consistently in the coming hours. The proposed “Fibonacci Tree Optimization Strategy” (“FTOS”) outperforms lightweight deployment-aware scheduling (LDAS), balanced-energy scheduling (BS), distributed self-spreading algorithm (DSS) and probing environment and collaborating adaptive sleeping (PECAS) in achieving the aforementioned scheduling objectives. The Fibonacci tree optimization algorithm has attained a better optimistic effect than the artificial bee colony (ABC) algorithm, differential evolution (DE) algorithm, genetic algorithm (GA) algorithm, particle swarm optimization (PSO) algorithm, and comprehensive learning particle swarm optimization (CLPSO) algorithm in multiple runs.

Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image Classification

Results of extensive simulations are shown for two state-of-the-art databases: Pavia University and Indian Pines. Furthermore, an experimental setup that performs the adaptive sensing was built to test the performance of the proposed approach on a real data set.

Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image Classification

Results of extensive simulations are shown for two state-of-the-art databases: Pavia University and Indian Pines. Furthermore, an experimental setup that performs the adaptive sensing was built to test the performance of the proposed approach on a real data set.