Curran Crawford

Probabilistic Load Flow Modeling Comparing Maximum Entropy and Gram-Charlier Probability Density Function Reconstructions

Probabilistic load flow (PLF) modeling is gaining renewed popularity as power grid complexity increases due to growth in intermittent renewable energy generation and unpredictable probabilistic loads such as plug-in hybrid electric vehicles (PEVs). In PLF analysis of grid design, operation and optimization, mathematically correct and accurate predictions of probability tail regions are required. In this paper, probability theory is used to solve electrical grid power load flow. The method applies two Maximum Entropy (ME) methods and a Gram-Charlier (GC) expansion to generate voltage magnitude, voltage angle and power flow probability density functions (PDFs) based on cumulant arithmetic treatment of linearized power flow equations. Systematic ME and GC parameter tuning effects on solution accuracy and performance is reported relative to converged deterministic Monte Carlo (MC) results. Comparing ME and GC results versus MC techniques demonstrates that ME methods are superior to the GC methods used in historical literature, and tens of thousands of MC iterations are required to reconstitute statistically accurate PDF tail regions. Direct probabilistic solution methods with ME PDF reconstructions are therefore proposed as mathematically correct, statistically accurate and computationally efficient methods that could be applied in the load flow analysis of large-scale networks.

Comparison of Surrogate Models in a Multidisciplinary Optimization Framework for Wing Design

The replacement of the analysis portion of an optimization problem by its equivalent metamodel usually results in a lower computational cost. In this paper, a conventional nonapproximative approach is compared against three differentmetamodels: quadratic-interpolation-based response surfaces,Kriging, and artificial neural networks. The results obtained from the solution of four different case studies based on aircraft design problems reinforces the idea that quadratic interpolation is only well-suited to very simple problems. At higher dimensionality, the usage of the more complex Kriging and artificial neural networks models may result in considerable performance benefits.

A Robust and Reliability Based Design Optimization Framework for Wing Design

This paper presents the outline of a framework for simultaneous analysis and robustness and reliability calculations in aircraft design optimization, with the option of employing surrogate models. Robust Design Optimization and Reliability Based Design Optimization are merged into a unied formulation which simplies the setup of optimization problems and aims at preventing foreseeable implementation issues. The code in development expands upon and, in some cases, completely rewrites a previous version of a Multidisciplinary Design Optimization tool that was solely oriented to deterministic problems.

Energy efficient communication networks design for demand response in smart grid

The convergence of electrical power control systems and communication techniques enables the intelligence over current and future power grid system which evolves to the smart grid. Demand response (DR) is considered as a killer application for so-called smart grid. Real-time DR control relies on efficient and reliable communication services. In this paper, the impact of packet losses during communication on DR control has been investigated, using the control strategy in [1]. Then, an analytical model for quantifying the performance of packet loss and energy consumption for transmission in a clustering-based multi-hop wireless communication network has been established. Finally, how to improve the design of wireless communication networks is proposed to satisfy the DR control requirements and to minimize the energy consumption for communications.

A test bed for self-regulating distribution systems: Modeling integrated renewable energy and demand response in the GridLAB-D/MATLAB environment

This paper discusses the development of a simulation test bed permitting the study of integrated renewable energy generators and controlled distributed heat pumps operating within distribution systems. The test bed is demonstrated in this paper by addressing the important issue of the self-regulating effect of consumer-owned air-source heat pumps on the variability induced by wind power integration, particularly when coupled with increased access to demand response realized through a centralized load control strategy.

Overall Efficiency of Ducted tidal current turbines

Computational fluid dynamics (CFD) simulations have been completed to characterize the efficiency of tidal turbines. Efficiency is taken as the ratio of power production (i.e. useful electricity) to total power lost from the upstream incident flow energy. Many tidal turbine developers are considering ducted designs which accelerate the flow through the turbine, improving the power coefficient CP. It has been found that for the designs considered, this increase in CP is associated with decreasing efficiency due to increased overall drag of the entire system. It is also found that for a given turbine, the optimum operating condition for power differs from the optimum condition for efficiency. Thus a compromise between power and efficiency must be made when considering turbines in constricted waters and arrays of turbines.

Embedded feature-selection support vector machine for driving pattern recognition☆

Abstract In this work, a more efficient and robust driving pattern recognition technique, extended Support Vector Machine (SVM) with embedded feature selection ability, has been introduced. Besides statistical significance, this proposed SVM also takes into account the accessibility and reliability of features during feature selection, so as to enable the driving condition discrimination system to achieve higher recognition efficiency and robustness. The recognition results of this extended SVM are compared with results from standard 2-norm SVM and linear 1-norm SVM, using representative driving cycle data to demonstrate the function and superiority of the new technique.

The Behavior of Fixed Point Iteration and Newton-Raphson Methods in Solving the Blade Element Momentum Equations

There is a substantial body of ongoing research improving the Blade Element Momentum (BEM) theory and applying it to the optimization of wind turbine rotors. Both of these developments challenge the suitability of fixed point iteration schemes being applied to advanced BEM models. This article explores the mathematical behavior of the BEM equations, with special attention to the application of numerical methods. Under special conditions, multiple solutions will exist when the airfoil is stalled. This situation gives increased uncertainty, where uncertainty in airfoil behavior is already high. This also demonstrates that there could be circumstances where the wake state has weak dependence on blade state. Fixed point iteration and Newton-Raphson numerical methods are investigated in this paper. Both methods will become unstable under certain conditions. The investigation shows that the Newton-Raphson method has well defined conditions for instability in terms of design variables and airfoil properties. By comparison, the fixed point function used here exhibits instability over a larger range.

Comparison of Potential Flow Wake Models for Horizontal-Axis Wind Turbine Rotors

Potential ow (PF) methods are a promising alternative to mainstream wind turbine aerodynamics tools such as blade element momentum (BEM) methods and mesh-based computational uid dynamics (CFD) approaches. PF is relatively easy to setup and robust with respect to geometry. The advent of the fast multipole method (FMM) brings computational speed to PF methods. These attributes make PF suitable for integration with multidisciplinary design optimization (MDO) tools. A C++ library employing a Weissinger lifting-line model and several PF wake models has been developed. The library utilizes FMM to accelerate the N-body computation of PF element interactions. This paper compares the numerical accuracy and computational speed of PF wake models according to their compositions of vortex particles, laments, and sheets. A standard three-bladed horizontal-axis wind turbine (HAWT) wind rotor under steady tower-free operation and prescribed elliptic circulation is presented. The e ects of numerical artefacts such as wake discretization, truncation, and FMM tuning parameters are explored. Finally, computational speed enhancement is con rmed.

The Canadian Integrated Multi-Trophic Aquaculture Network (CIMTAN)—A Network for a New Era of Ecosystem Responsible Aquaculture

ABSTRACT The Canadian Integrated Multi-Trophic Aquaculture Network (CIMTAN) is a Natural Sciences and Engineering Research Council strategic network that was initiated in 2010. It was triggered by the fact that aquaculture, though the world fastest growing food production sector, is associated with environmental, economic, and societal issues. Integrated multi-trophic aquaculture (IMTA) offers an innovative solution for the environmental sustainability, economic stability, and societal acceptability of aquaculture by taking an ecosystem-based management approach. IMTA is the farming, in proximity, of aquaculture species from different trophic levels, and with complementary ecosystem functions, so that one species’ excess nutrients are recaptured by the other crops and synergistic interactions among species occur. CIMTAN is providing the interdisciplinary research and development and highly qualified personnel training in the following linked areas: (1) ecological design, ecosystem interactions, and biomit...