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A Hybrid Search Algorithm for Midterm Optimal Scheduling of Thermal Power Plants

MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-13页

作者： Liao, Shengli Cheng, Chuntian Wang, Jing Feng, Zhongkai Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Huadian Elect Power Res Inst Hangzhou 310030 Zhejiang Peoples R China

A hybrid search algorithm consisting of three stages is presented to solve the midterm schedule for thermal power plants (MTSFTPP) problem, where the primary objective is to achieve equal accumulated operating hours of installed capacity (EAOHIC) for all thermal power plants during the selected period. First, feasible spaces are produced and narrowed based on constraints on the number of units and power load factors. Second, an initial feasible solution is obtained by a heuristic method that considers operating times and boundary conditions. Finally, the progressive optimality algorithm (POA), which we refer to as the vertical search algorithm (VSA), is used to solve the MTSFTPP problem. A method for avoiding convergence to a local minimum, called the lateral search algorithm (LSA), is presented. The LSA provides an updated solution that is used as a new feasible starting point for the next search in the VSA. The combination of the LSA and the VSA is referred to as the hybrid search algorithm(HSA), which is simple and converges quickly to the global minimum. The results of two case studies show that the algorithm is very effective in solving the MTSFTPP problem accurately and in real time.

关键词： SEARCH algorithms computer scheduling PROBLEM solving BOUNDARY value problems computer algorithms

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Power-efficient real-time solution for adaptive vision algorithms

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IET computerS AND DIGITAL TECHNIQUES 2015年第1期9卷 16-26页

作者： Tabkhi, Hamed Sabbagh, Majid Schirner, Gunar Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

This study focuses on embedded realisation of adaptive vision algorithms, and illustrates the challenges using mixture of Gaussian (MoG) background subtraction. MoG is a frequently used adaptive vision kernel, for example, for surveillance applications. It involves massive computation and communication demands, which renders a software approach infeasible considering a 1 W power budget. To address these challenges, the authors employ a systematic system-level design approach and first analyse the demands at high-level, explore opportunities for bandwidth reduction, and derive a customised system-level specification. Based on the system-level exploration, this study then proposes a communication-centric architecture template that simplifies implementing embedded adaptive vision algorithms. To achieve high efficiency, they propose to separate steaming and algorithm-intrinsic traffic. This allows customising the traffic handling based on role of the data, as well as simplifying interconnecting multiple heterogeneous nodes. The authors demonstrate the benefits of traffic separation and the communication-centric architecture template based on MoG. They realise MoG on the Zynq-7000 SoC processing 1080p 30 Hz stream in real-time. The MoG processing kernel consists of 77 pipeline stages operating at 148.5 MHz. The authors' solution is more than 600 x faster than an ARM Cortex-A9 with 666 MHz. It only consumes 151 mW of on-chip power operating in real-time.

关键词： computer algorithms REAL-time computing GAUSSIAN mixture models (Statistics) ADAPTIVE computing systems HETEROGENEOUS computing KERNEL (Mathematics) BANDWIDTHS

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Computation planning in multiprocessor real time automated control systems with an additional resource

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AUTOMATION AND REMOTE CONTROL 2015年第3期76卷 487-492页

作者： Furugyan, M. G. Russian Acad Sci Dorodnicyn Comp Ctr Moscow Russia

We consider the problem of composing an admissible schedule with interruptions for a multiprocessor real time ACS in case when directive intervals are given, processors may have arbitrary performances, and the durations of jobs depend linearly on the amount of additional resource assigned to them. We develop algorithms based on a reduction of the original problem to a minimal cost flow problem and to a linear programming problem.

关键词： AUTOMATIC control MULTIPROCESSORS -- Performance REAL-time control COST control LINEAR programming computer algorithms

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A Yaw Stability Control Algorithm for Four-Wheel Independently Actuated Electric Ground Vehicles considering Control Boundaries

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-10页

作者： Lin, Cheng Xu, Zhifeng Zhang, Ru Beijing Inst Technol Natl Engn Lab Elect Vehicle Beijing 100081 Peoples R China Pan Asia Tech Automot Ctr Shanghai 201201 Peoples R China

A hierarchical control algorithm of direct yaw moment control for four-wheel independently actuated (FWIA) electric ground vehicles is presented. Sliding mode control is adopted to yield the desired yaw moment in the higher layer of the algorithm due to the possible modeling inaccuracies and parametric uncertainties. The conditional integrator approach is employed to overcome the chattering issue, which enables a smooth transition to a proportional + integral-like controller, with antiwindup, when the system is entering the boundary layer. The lower level of the algorithm is given to allocate the desired yaw moment to four wheels by means of slip ratio distribution and control for a better grasp of control boundaries. Simulation results, obtained with a vehicle dynamics simulator, Carsim, and the Matlab/Simulink, show the effectiveness of the control algorithm.

关键词： computer algorithms ELECTRIC vehicles FOUR-wheel drive vehicles SLIDING mode control UNCERTAINTY PARAMETER estimation

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A Belief Rule-Based Safety Evaluation Approach for Complex Systems

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-10页

作者： Zhang, Jun Jiang, Jiang Chen, Yingwu Yang, Kewei Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

Plenty of hazards underlie complex systems, which have negative effects on the normal functionality of engineering events. To minimize the uncertainty, a comprehensive preevent checkout is necessarily required to judge if the engineering events will be carried out successfully under current circumstance, through which further improvements can be made. A generic belief rule-based safety evaluation approach for large-scale complex systems is proposed. The overall system is firstly decomposed and filtered into the measurable attributes that may potentially contribute to uncertainty. Belief structure is then applied to measure the uncertainty of vagueness and incompleteness and represent heterologous information in a unified scheme. With this scheme, a rule base is established with all antecedents, consequents, and attributes presented in belief degrees, which is used to determine the relationship between attributes, aggregate the influences, and generate the final inference with evidential reasoning algorithm. In the end, an estimation of uncertainty is achieved in the representation of distribution. It describes how the systems perform with given conditions and sources. A numeric case in aerospace program is provided for feasibility illustration.

关键词： RULE-based programming computer algorithms UNCERTAINTY (Information theory) INFORMATION theory SCHEME (computer program language)

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A New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-8页

作者： Li, Jingchao Guo, Jian Shanghai Dianji Univ Elect Informat Coll Shanghai 200240 Peoples R China Western New England Univ Coll Engn Springfield MA USA

Identifying communication signals under low SNR environment has become more difficult due to the increasingly complex communication environment. Most relevant literatures revolve around signal recognition under stable SNR, but not applicable under time-varying SNR environment. To solve this problem, we propose a new feature extraction method based on entropy cloud characteristics of communication modulation signals. The proposed algorithm extracts the Shannon entropy and index entropy characteristics of the signals first and then effectively combines the entropy theory and cloudmodel theory together. Compared with traditional feature extraction methods, instability distribution characteristics of the signals' entropy characteristics can be further extracted from cloud model's digital characteristics under low SNR environment by the proposed algorithm, which improves the signals' recognition effects significantly. The results from the numerical simulations show that entropy cloud feature extraction algorithm can achieve better signal recognition effects, and even when the SNR is -11 dB, the signal recognition rate can still reach 100%.

关键词： FEATURE extraction (Data processing) computer algorithms ENTROPY (Information theory) CLOUD computing computer networks SIGNAL-to-noise ratio

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A Class of Parameter Estimation Methods for Nonlinear Muskingum Model Using Hybrid Invasive Weed Optimization Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-15页

作者： Ouyang, Aijia Liu, Li-Bin Sheng, Zhou Wu, Fan Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Hunan City Univ Sch Informat Sci & Engn Yiyang 413000 Hunan Peoples R China Chizhou Coll Dept Math & Comp Sci Chizhou 247000 Anhui Peoples R China Guangxi Univ Coll Math & Informat Sci Nanning 530004 Guangxi Peoples R China

Nonlinear Muskingum models are important tools in hydrological forecasting. In this paper, we have come up with a class of new discretization schemes including a parameter theta to approximate the nonlinear Muskingum model based on general trapezoid formulas. The accuracy of these schemes is second order, if theta not equal 1/3, but interestingly when theta = 1/3, the accuracy of the presented scheme gets improved to third order. Then, the present schemes are transformed into an unconstrained optimization problem which can be solved by a hybrid invasive weed optimization (HIWO) algorithm. Finally, a numerical example is provided to illustrate the effectiveness of the present methods. The numerical results substantiate the fact that the presented methods have better precision in estimating the parameters of nonlinear Muskingum models.

关键词： PARAMETER estimation NONLINEAR analysis HYBRID systems PROCESS optimization computer algorithms

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Model Predictive Control of Energy Systems with Hybrid Storage

Model Predictive Control of Energy Systems with Hybrid Stora...

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IEEE Power and Energy Society General Meeting

作者： Felix Rafael Segundo Sevilla Chan Park Valerijs Knazkins Petr Korba Power Systems and Smart Grid Lab at the Zurich University of Applied Science ZHAW

ISBN: (纸本)9781509041695

In this work an algorithm to control the power flow of an electric power system with two integrated energy storage systems is investigated. The power system under consideration consists of a conventional distribution feeder that supplies the power to satisfy the customers' demand, a set of photovoltaic (PV) panels that also contribute to the power generation, one unit of Lithium-Ion battery storage for the intra-day use and a combined power-to-gas (PtG) and gas-to-power installation that converts the power excess in the summertime into hydrogen and injects power back to the system in the wintertime. The proposed control algorithm is based on model predictive control tailored for the energy system under investigation. To demonstrate the performance of the proposed control, a set of synthetic PV and demand profiles representing future conditions in Switzerland were created and used as input data to the system model. The synthesized generation and consumption data span a whole year of operation. A number of detailed simulations performed in the framework of the study reported in this paper demonstrate the effectiveness of the proposed control algorithm and provided invaluable insights into the optimum operation of the complex integrated power system.

关键词： Energy storage systems Electric power system Optimal power flow Model predictive control Self-consumption Power-to-gas power systems optimal load flow Model predictive control Predictive control ENERGY SYSTEMS distributed model predictive control nonlinear model predictive control Cache Memory System energy storage Energy storage systems computer algorithms self-consumption optimum operation Power flow Energy sources

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Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment

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NUCLEAR DATA SHEETS 2015年 123卷 57-61页

作者： Khuwaileh, B. A. Abdel-Khalik, H. S. N Carolina State Univ Dept Nucl Engn Raleigh NC 27695 USA

Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.

关键词： NUCLEAR reactions DATA analysis NUMERICAL analysis THERMAL hydraulics computer algorithms

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Stereo matching based on segmented B-spline surface fitting and accelerated region belief propagation

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IET computer VISION 2015年第4期9卷 456-466页

作者： Huang, Jingzhou Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310003 Zhejiang Peoples R China

The authors propose a new stereo matching algorithm based on an iterative optimisation framework including bi-cubic B-spline surface fitting and accelerated region belief propagation (BP). They first compute the initial cost and disparity map by the adaptive support-weight approach and then launch the iterative process in which the disparity space image is refined via the bi-cubic B-spline fitting and optimised via the accelerated region BP. Two innovations are contained in the algorithm: (i) disparity space image refinement based on segmented bi-cubic B-spline surface fitting;and (ii) an accelerated region message passing approach for BP. The algorithm is verified on the Middlebury benchmark and experimental results show the algorithm is effective and achieves the state-of-the-art accuracy.

关键词： computer algorithms MATHEMATICAL optimization SPLINES TECHNOLOGICAL innovations COST analysis

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