Outdoor operations of multiple quadrotors in windy environment

Coordinated multiple small unmanned aerial vehicles (sUAVs) offer several advantages over a single sUAV platform. These advantages include improved task efficiency, reduced task completion time, improved fault tolerance, and higher task flexibility. However, their deployment in an outdoor environment is challenging due to the presence of wind gusts. The coordinated motion of a multi-sUAV system in the presence of wind disturbances is a challenging problem when considering collision avoidance (safety), scalability, and communication connectivity. Performing wind-agnostic motion planning for sUAVs may produce a sizeable cross-track error if the wind on the planned route leads to actuator saturation. In a multi-sUAV system, each sUAV has to locally counter the wind disturbance while maintaining the safety of the system. Such continuous manipulation of the control effort for multiple sUAVs under uncertain environmental conditions is computationally taxing and can lead to reduced efficiency and safety concerns. Additionally, modern day sUAV systems are susceptible to cyberattacks due to their use of commercial wireless communication infrastructure. This dissertation aims to address these multi-faceted challenges related to the operation of outdoor rotor-based multi-sUAV systems. A comprehensive review of four representative techniques to measure and estimate wind speed and direction using rotor-based sUAVs is discussed. After developing a clear understanding of the role wind gusts play in quadrotor motion, two decentralized motion planners for a multi-quadrotor system are implemented and experimentally evaluated in the presence of wind disturbances. The first planner is rooted in the reinforcement learning (RL) technique of state-action-reward-state-action (SARSA) to provide generalized path plans in the presence of wind disturbances. While this planner provides feasible trajectories for the quadrotors, it does not provide guarantees of collision avoidance. The second planner implements a receding horizon (RH) mixed-integer nonlinear programming (MINLP) model that is integrated with control barrier functions (CBFs) to guarantee collision-free transit of the multiple quadrotors in the presence of wind disturbances. Finally, a novel communication protocol using Ethereum blockchain-based smart contracts is presented to address the challenge of secure wireless communication. The U.S. sUAV market is expected to be worth $92 Billion by 2030. The Association for Unmanned Vehicle Systems International (AUVSI) noted in its seminal economic report that UAVs would be responsible for creating 100,000 jobs by 2025 in the U.S. The rapid proliferation of drone technology in various applications has led to an increasing need for professionals skilled in sUAV piloting, designing, fabricating, repairing, and programming. Engineering educators have recognized this demand for certified sUAV professionals. This dissertation aims to address this growing sUAV-market need by evaluating two active learning-based instructional approaches designed for undergraduate sUAV education. The two approaches leverages the interactive-constructive-active-passive (ICAP) framework of engagement and explores the use of Competition based Learning (CBL) and Project based Learning (PBL). The CBL approach is implemented through a drone building and piloting competition that featured 97 students from undergraduate and graduate programs at NJIT. The competition focused on 1) drone assembly, testing, and validation using commercial off-the-shelf (COTS) parts, 2) simulation of drone flight missions, and 3) manual and semi-autonomous drone piloting were implemented. The effective student learning experience from this competition served as the basis of a new undergraduate course on drone science fundamentals at NJIT. This undergraduate course focused on the three foundational pillars of drone careers: 1) drone programming using Python, 2) designing and fabricating drones using Computer-Aided Design (CAD) and rapid prototyping, and 3) the US Federal Aviation Administration (FAA) Part 107 Commercial small Unmanned Aerial Vehicles (sUAVs) pilot test. Multiple assessment methods are applied to examine the students’ gains in sUAV skills and knowledge and student attitudes towards an active learning-based approach for sUAV education. The use of active learning techniques to address these challenges lead to meaningful student engagement and positive gains in the learning outcomes as indicated by quantitative and qualitative assessments

New Tendencies in Wind Energy Operation and Maintenance

[Abstract] Both the reduction in operating and maintenance (O&M) costs and improved reliability have become top priorities in wind turbine maintenance strategies. O&M costs typically account for 20% to 25% of the total levelized cost of electricity (LCOE) of current wind power systems. This paper provides a general review of the state of the art of research conducted on wind farm maintenance in recent years. It shows the new methods and techniques, focusing on trends and future challenges. In addition to this, this work includes a review of the following items: (i) operation and maintenance, (ii) failure rate, (iii) reliability, (iv) condition monitoring, (v) maintenance strategies, (vi) maintenance and life cycle and (vii) maintenance optimization As for offshore wind turbines, it is crucial to limit the maximum faults, since the maintenance of these wind farms is more complex both technically and logistically. Research into wind farm maintenance increased by 87% between 2007 and 2019, with more than 38,000 papers (Scopus) including “wind energy” as the main topic and some keywords related to O&M costs. The LCOE in onshore wind projects has decreased by 45%, while in offshore projects it has decreased by 28%. The O&M costs of onshore wind projects fell 52%, while in the case of offshore projects, they have declined 45%. Thus, the results obtained in this paper suggest that there is a change in research on wind farm operation and maintenance, as in recent years, scientific interest in failure has been increasing, while interest in the various techniques of wind farm maintenance and operation has been decreasing.This research was funded by the University of A Coruña (Spain) (Grant No. 64900)

An optimisation model for scheduling the decommissioning of an offshore wind farm

An optimisation model is proposed for scheduling the decommissioning of an offshore wind farm in order to minimise the total cost which is comprised of jack-up vessel, barge (transfer) vessel, inventory, processing and on-land transportation costs. This paper also presents a comprehensive review of the strategic issues relating to the decommissioning process and of scheduling models that have been applied to offshore wind farms. A mathematical model using integer linear programming is developed to determine the optimal schedule considering several constraints such as the availability of vessels and planning delays. As the decommissioning problem is challenging to solve, a matheuristic approach based on the hybridisation of a heuristic approach and an exact method is also proposed to find near optimal solutions for a test set of problems. A set of computational experiments has been carried out to assess the proposed approach

Intelligent power system operation in an uncertain environment

This dissertation presents some challenging problems in power system operations. The efficacy of a heuristic method, namely, modified discrete particle swarm optimization (MDPSO) algorithm is illustrated and compared with other methods by solving the reliability based generator maintenance scheduling (GMS) optimization problem of a practical hydrothermal power system. The concept of multiple swarms is incorporated into the MDPSO algorithm to form a robust multiple swarms-modified particle swarm optimization (MS-MDPSO) algorithm and applied to solving the GMS problem on two power systems. Heuristic methods are proposed to circumvent the problems of imposed non-smooth assumptions common with the classical approaches in solving the challenging dynamic economic dispatch problem. The multi-objective combined economic and emission dispatch (MO-CEED) optimization problem for a wind-hydrothermal power system is formulated and solved in this dissertation. This MO-CEED problem formulation becomes a challenging problem because of the presence of uncertainty in wind power. A family of distributed optimal Pareto fronts for the MO-CEED problem has been generated for different scenarios of capacity credit of wind power. A real-time (RT) network stability index is formulated for determining a power system\u27s ability to continue to provide service (electric energy) in a RT manner in case of an unforeseen catastrophic contingency. Cascading stages of fuzzy inference system is applied to combine non real-time (NRT) and RT power system assessments. NRT analysis involves eigenvalue and transient energy analysis. RT analysis involves angle, voltage and frequency stability indices. RT Network status index is implemented in real-time on a practical power system --Abstract, page iv

Stochastic Scheduling of Wind-Integrated Power Systems

The cost of balancing supply and demand will increase as power systems are decarbonised, because the requirement for operating reserve will increase with the wind penetration, while the flexible fossil-fuel generators, which have been the traditional providers of reserve, will be displaced. While these costs can be mitigated through increased interconnection, energy storage, and demand-side market participation, a fundamental review of system operational policy is also needed to ensure that the available reserves are scheduled optimally. Stochastic Unit Commitment can find the commitment and dispatch decisions that minimise the expected system costs, including the potential costs of unserved energy, given the short-term uncertainties of wind and other variables. It therefore has the potential to provide the most efficient possible paradigm for the operation of wind-integrated systems. Because the system’s ability to respond to wind fluctuations is constrained by intertemporal limitations of the other components, time domain simulations are needed to assess the performance of different operational strategies or generator fleet characteristics. However, Stochastic Unit Commitment has demanding computational requirements that can render it impractical for long-term simulations of a large power system. This thesis develops a new tool for simulating the operation of large, wind-integrated power systems using stochastic scheduling, with the emphasis on computational efficiency. Embedded within it are new models for characterising time series of aggregated wind output and wind forecast errors; these models are integrated with a Stochastic Unit Commitment algorithm within a Monte Carlo framework. We explore simplifications that can mitigate the computational burden without unduly compromising the quality of the analysis. Simulations with the tool show that fully stochastic scheduling can reduce operating costs by around 4% relative to traditional deterministic approaches, in a system with a 50% wind penetration

Modelling the agronomic and environmental impacts of irrigation management on turfgrass for golf greens in northern europe.

Irrigation is an essential component of turfgrass management for golf. During dry periods, it helps maintain turf health, stimulates nitrogen uptake, promotes germination, reduces canopy temperature, as well as assures high standards of quality for playability. In recent years, rising competition for water coupled with new environmental regulations has exerted pressure on water allocations for golf. Improving water efficiency and water management in golf have become major industry priorities. The aim of this thesis was to understand and asses the relationships between irrigation management and turfgrass water use, soil water availability, dry matter production, drainage and nitrate leaching in golf greens under Northern European climate conditions. The research combined published science and industry evidence with field and experimental data, in order to calibrate and validate an irrigation ballistics-based model and a biophysical crop model (STICS). From this, an integrated model (BalliSTICS) was developed and used to simulate the impacts of irrigation uniformity on turfgrass growth and development and leaching risks, under contrasting management and climate scenario. The modelling showed that system design plays a crucial role in achieving high irrigation uniformity, particularly sprinkler position and spacing. A larger spacing between sprinklers resulted in a decrease in irrigation rates and a significant decrease in uniformity, particularly when wind speeds exceeded 2 m s-1. Surprisingly, the range of pressure and nozzle sizes investigated did not significantly impact on irrigation uniformity. Non-uniform irrigation was found to have a considerable impact on the spatial variability in turf growth, soil moisture content, drainage and leaching. Under northern European climate conditions, irrigation strategy had a more significant impact on turfgrass response than irrigation uniformity. A moderate deficit strategy (replacement of 60% potential evapotranspiration) was sufficient to achieve the highest growth values (233 ± 10.6 g m-² season¯¹). This strategy resulted in not only a reduction of irrigation water use but also minimised the amount of nitrate leached in drainage. However, an inadequate irrigation schedule combined with poor irrigation uniformity (CU < 60%) led to a threefold increase in water use, and an average 114% and 50% increase in drainage and nitrate leaching, respectively. Inadequate irrigation practices had little impact on turfgrass growth, which could be misleading as excessive irrigation might not affect plant growth and visual quality but would mask poor irrigation uniformities, lead to excessive water use and an increase in risks of groundwater contamination from leaching. The research provides valuable and novel insights into better understanding the combined impacts of irrigation performance and management on turfgrass. The findings will support greenkeepers and the turfgrass industry and increase awareness of the importance of irrigation