Application of an Ultrasonic Sensor to Monitor Soil Erosion and Deposition

While erosion and deposition are naturally occurring processes, these processes can be accelerated by human influences. The acceleration of erosion causes damage to human assets and costs billions of dollars to mitigate. Monitoring erosion at high resolutions can provide researchers and managers the data necessary to help manage erosion. Current erosion monitoring methods tend to be invasive to the area, record low frequency measurements, have a narrow spatial range of measurement, or are very expensive. There is a need for an affordable monitoring system capable of monitoring erosion and deposition non-invasively at a high resolution. The objectives of this research were to (1) design and construct a non-invasive sediment monitoring system (SMS) using an ultrasonic sensor capable of monitoring erosion and deposition continuously, (2) test the system in the lab and field, (3) and determine the applications and limitations of the system. The ultrasonic sensor measures the time of reflectance of sound waves to calculate the distance to the area non-invasively. The SMS was tested in the lab to determine the extent to which the soil type, slope, surface topography, change in distance and vegetation impact the SMS’s ultrasonic sensor’s measurement. It was found that the soil type, slope and surface topography had little effect on the measurement, but the change in distance of the measurement and the introduction of vegetation impacted the measurement. The error in measurement increased as the sensing distance increased, and vegetation interferes with the measurement. In the field during high flows, as erosion and deposition occur, the changes in distance were determined in near real-time, allowing for the calculation of erosion and deposition quantities. The system was deployed to monitor deposition on sandy streambanks in the Nebraska Sandhills and erosion on a streambank and field plot in Lincoln, Nebraska. The system was proven successful in measuring sediment change during high flow events but yielded some error; ±1.06 mm in controlled lab settings and ±10.79 mm when subjected to environmental factors such as temperature, relative humidity and wind. Advisors: Aaron Mittelstet and Nancy Shan

Small-Signal Modelling and Analysis of Doubly-Fed Induction Generators in Wind Power Applications

The worldwide demand for more diverse and greener energy supply has had a significant impact on the development of wind energy in the last decades. From 2 GW in 1990, the global installed capacity has now reached about 100 GW and is estimated to grow to 1000 GW by 2025. As wind power penetration increases, it is important to investigate its effect on the power system. Among the various technologies available for wind energy conversion, the doubly-fed induction generator (DFIG) is one of the preferred solutions because it offers the advantages of reduced mechanical stress and optimised power capture thanks to variable speed operation. This work presents the small-signal modelling and analysis of the DFIG for power system stability studies. This thesis starts by reviewing the mathematical models of wind turbines with DFIG convenient for power system studies. Different approaches proposed in the literature for the modelling of the turbine, drive-train, generator, rotor converter and external power system are discussed. It is shown that the flexibility of the drive train should be represented by a two-mass model in the presence of a gearbox. In the analysis part, the steady-state behaviour of the DFIG is examined. Comparison is made with the conventional synchronous generators (SG) and squirrel-cage induction generators to highlight the differences between the machines. The initialisation of the DFIG dynamic variables and other operating quantities is then discussed. Various methods are briefly reviewed and a step-by-step procedure is suggested to avoid the iterative computations in initial condition mentioned in the literature. The dynamical behaviour of the DFIG is studied with eigenvalue analysis. Modal analysis is performed for both open-loop and closed-loop situations. The effect of parameters and operating point variations on small signal stability is observed. For the open-loop DFIG, conditions on machine parameters are obtained to ensure stability of the system. For the closed-loop DFIG, it is shown that the generator electrical transients may be neglected once the converter controls are properly tuned. A tuning procedure is proposed and conditions on proportional gains are obtained for stable electrical dynamics. Finally, small-signal analysis of a multi-machine system with both SG and DFIG is performed. It is shown that there is no common mode to the two types of generators. The result confirms that the DFIG does not introduce negative damping to the system, however it is also shown that the overall effect of the DFIG on the power system stability depends on several structural factors and a general statement as to whether it improves or detriorates the oscillatory stability of a system can not be made

A Survey on Problems in Smart Grid with Large Capacity Wind Farm Interconnected

AbstractThe basic concepts and development of smart grid is described. The influence of large capacity wind farm interconnected on the smart grid is analyzed particularly, following contents are emphatically presented: model of wind farm, basic operation performance, output forecasting of wind farm, power flow in smart grid including wind farm, balance of voltage and raeactive power, small disturbance stability, transient stability, faults and reliability, etc., in the hope of offering references for the fast development of smart grids absorbing more capacity of wind power

Power Quality Improvement and Low Voltage Ride through Capability in Hybrid Wind-PV Farms Grid-Connected Using Dynamic Voltage Restorer

© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission.This paper proposes the application of a dynamic voltage restorer (DVR) to enhance the power quality and improve the low voltage ride through (LVRT) capability of a three-phase medium-voltage network connected to a hybrid distribution generation system. In this system, the photovoltaic (PV) plant and the wind turbine generator (WTG) are connected to the same point of common coupling (PCC) with a sensitive load. The WTG consists of a DFIG generator connected to the network via a step-up transformer. The PV system is connected to the PCC via a two-stage energy conversion (dc-dc converter and dc-ac inverter). This topology allows, first, the extraction of maximum power based on the incremental inductance technique. Second, it allows the connection of the PV system to the public grid through a step-up transformer. In addition, the DVR based on fuzzy logic controller is connected to the same PCC. Different fault condition scenarios are tested for improving the efficiency and the quality of the power supply and compliance with the requirements of the LVRT grid code. The results of the LVRT capability, voltage stability, active power, reactive power, injected current, and dc link voltage, speed of turbine, and power factor at the PCC are presented with and without the contribution of the DVR system.Peer reviewe

Power oscillation damping capabilities of doubly fed wind generators

With the increased levels of wind power penetration into power systems, the influence of wind power on stability of power systems requires more investigation. Conventional synchronous generators are increasingly replaced by wind turbines and thus wind turbines have to contribute to power system stability. In this paper, the effects of double fed induction generator (DFIG) based wind farms and their controllers on small signal stability are investigated. Moreover, since wind turbines have to contribute to power system oscillation damping, a power oscillation damping controller within DFIG rotor side converter is developed in this study. The proposed damping control is validated on realistic Western System Coordinating Council (WSCC) power system consisting of DFIG based wind farm and synchronous generators. The simulation results show the effectiveness of the proposed power oscillation damping controller. With the proposed controller, DFIG based wind farm improves the system small signal stability dramatically by damping the system oscillations effectively

The interaction of helical tip and root vortices in a wind turbine wake

Analysis of the helical vortices measured behind a model wind turbine in a water channel are reported. Phase-locked measurements using planar particle image ve- locimetry are taken behind a Glauert rotor to investigate the evolution and breakdown of the helical vortex structures. Existing linear stability theory predicts helical vortex filaments to be susceptible to three unstable modes. The current work presents tip and root vortex evolution in the wake for varying tip speed ratio and shows a breaking of the helical symmetry and merging of the vortices due to mutual inductance between the vortical filaments. The merging of the vortices is shown to be steady with rotor phase, however, small-scale non-periodic meander of the vortex positions is also ob- served. The generation of the helical wake is demonstrated to be closely coupled with the blade aerodynamics, strongly influencing the vortex properties which are shown to agree with theoretical predictions of the circulation shed into the wake by the blades. The mutual inductance of the helices is shown to occur at the same non-dimensional wake distance

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics