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38th Chinese Control conference (CCC)

作者： Pan, Wei Xu, Rui Zhou, Miaolei Jilin Univ Dept Control Sci & Engn Changchun 130022 Peoples R China

ISBN: (纸本)9789881563972

To describe the rate-dependent hysteresis of piezo-driven stages, a modified rate-dependent Krasnosel'skii-Pokrovskii (RDKP) model is proposed in this paper. In this method, both the differential of the output displacement and rate-dependent parameters are introduced into the classical Krasnosel'skii-Pokrovskii (CKP) model. Then the interior point algorithm and bat search optimization algorithm are used to obtain the density function and rate-dependent parameters of the RDKP model respectively. Moreover, the relationship between the obtained rate-dependent parameters and input voltage frequency is produced to an exponential function. simulation results show that the RDKP model with the rate-dependent exponential function can describe the rate-dependent hysteresis of piezo-driven stages with a higher modeling precision compared with the CKP model.

关键词： Krasnosel'skii-Pokrovskii model Rate-dependent hysteresis Piezo-driven stages Interior point algorithm Bat search optimization algorithm

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Expedited Design optimization of Antenna Input Characteristics Using Trust-Region Search with Adaptive Jacobian Updates

Expedited Design Optimization of Antenna Input Characteristi...

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IEEE MTT-S International conference on Numerical Electromagnetic and Multiphysics modeling and optimization (NEMO)

作者： Pietrenko-Dabrowska, Anna Koziel, Slawomir Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland Reykjavik Univ Engn Optimizat & Modeling Ctr IS-101 Reykjavik Iceland

ISBN: (纸本)9781538695166

Reducing the number of electromagnetic (EM) simulations in the course of design closure procedures is a key to make them more practical for antenna designers. This applies to both direct and surrogate -assisted optimization algorithms. In either case, local methods such as gradient search are ultimately applied to find an improved design at either (low- or high-fidelity) level of antenna evaluation. In this paper, a simple yet robust technique to reduce the computational cost of gradient-based optimization process is proposed. Our methodology involves selective application of Jacobian updating formulas controlled by the alignment between the recent design relocation and coordinate system basis vectors, with the acceptance threshold made dependent on the algorithm convergence status. The algorithm is demonstrated to provide considerable speedup of up to 63 percent as compared to the reference trust-region-based procedure. The results are consistent across a three-antenna benchmark set with the robustness confirmed by means of statistics over multiple runs with random initial designs.

关键词： Antenna design simulation-driven design design optimization trust-region framework sensitivity updating formulas

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optimization modeling for Offshore Wind Farms 55

Optimization Modeling for Offshore Wind Farms

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55th International Universities Power Engineering conference (UPEC)

作者： Hamzah, Siti Khadijah Lacey, Gill Pillai, Gobind Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England

ISBN: (纸本)9781728110783

Offshore wind farms have emerged as the biggest contributor of renewable energy to the national grid over the last decade, driven by advanced technology, higher investment, and lowering operational and maintenance costs. This demonstrates the value of improving the efficiency in wind farms, with designs to be selected for the most effective transmission system implementation. This paper describes the simulation models developed with network simulation software (IPSA and PowerWorld) for evaluating and analyzing the load flow by observing the losses, voltage magnitude and transmitted power, both active and reactive. There were three different models, one using standard inter-array cable (33 kV) and one with upgrade inter-array cable to 52 kV. The third model proposed replacing high voltage side transformers with a mechanically switched capacitor (MSC) rather than the usual static Var compensator. Results from 52 kV models revealed that high voltage cable in offshore wind farms is capable of transmitting more active power than medium voltage. Indeed, the losses in this design are in the range of theoretical value in between 0.3% and 11%. The MSC losses agreed using a value of 1.7% the results of the third model showed that static Var produced more active power than mechanically switched capacitor. At the same time, the static Var produced the highest reactive power at export cable. Although the static Var models have performed better than mechanically switched capacitors, in terms of monetary value mechanically switched capacitors are better than static Var compensators. The parameters of the design were given by Siemens, where their supplier confirmed that initial costs including operation and maintenance cost for mechanically switched capacitor are lower compared to the transformer.

关键词： Offshore wind farm 52 kV mechanically switched capacitor static Var compensator

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An Efficient Trust-Region Algorithm for Wideband Antenna optimization 13

An Efficient Trust-Region Algorithm for Wideband Antenna Opt...

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13th European conference on Antennas and Propagation (EuCAP)

作者： Koziel, Slawomir Pietrenko-Dabrowska, Anna Reykjavik Univ Engn Optimizat & Modeling Ctr IS-101 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

ISBN: (纸本)9788890701887

simulation-driven design closure is a necessary step in the design of modern antenna structures. It is typically realized using parameter sweeping. Application of numerical optimization routines brings better results but it may be computationally expensive due to numerous electromagnetic (EM) analyses involved in the process. Reduction of the optimization cost can be achieved using various methods, e.g., surrogate-assisted techniques, reduction of the problem dimensionality, etc. Yet, either the main task or its sub-problems (e.g., surrogate model optimization) have to be solved directly. Here, an improved trust-region algorithm working with numerical derivatives is proposed that permits considerable reduction of the number of EM simulations in the optimization process. Our approach utilizes a smart management of antenna response Jacobian updates derived from relative changes of geometry parameters during subsequent iterations. Our methodology is demonstrated using a benchmark set of wideband antennas. Comparison with a conventional trust-region routine is provided along with a comprehensive analysis of algorithm performance involving multiple starting points. The impact of control parameters is also investigated. The average computational savings across the benchmark set are around 50 percent with acceptable degradation of the solution quality.

关键词： Antenna design simulation-driven design design optimization trust-region framework gradient search

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Quantifying the Resilience-Informed Scenario Cost Sum: A Value-driven Design Approach for Functional Hazard Assessment

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JOURNAL OF MECHANICAL DESIGN 2019年第2期141卷 021403页

作者： Hulse, Daniel Hoyle, Christopher Goebel, Kai Tumer, Irem Y. Oregon State Univ Sch Mech Ind & Mfg Engn Corvallis OR 97330 USA NASA Ames Res Ctr Intelligent Syst Div Discovery & Syst Hlth Moffett Field CA 94035 USA Lulea Tech Univ Div Operat & Maintenance Engn S-97187 Lulea Sweden

Complex engineered systems can carry risk of high failure consequences, and as a result, resilience-the ability to avoid or quickly recover from faults-is desirable. Ideally, resilience should be designed-in as early in the design process as possible so that designers can best leverage the ability to explore the design space. Toward this end, previous work has developed functional modeling languages which represent the functions which must be performed by a system and function-based fault modeling frameworks have been developed to predict the resulting fault propagation behavior of a given functional model. However, little has been done to formally optimize or compare designs based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. The work described herein closes this gap by introducing the resilience-informed scenario cost sum (RISCS), a scoring function which integrates with a fault scenario-based simulation, to enable the optimization and evaluation of functional model resilience. The scoring function accomplishes this by quantifying the expected cost of a design's fault response using probability information, and combining this cost with design and operational costs such that it may be parameterized in terms of designer-specified resilient features. The usefulness and limitations of using this approach in a general optimization and concept selection framework are discussed in general, and demonstrated on a monopropellant system design problem. Using RISCS as an objective for optimization, the algorithm selects the set of resilient features which provides the optimal trade-off between design cost and risk. For concept selection, RISCS is used to judge whether resilient concept variants justify their design costs and make direct comparisons between different model structures.

关键词： optimization Resilience simulation Design Flow (Dynamics) Failure

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Automated data-driven model generation of energy systems using piecewise linear regression 32

Automated data-driven model generation of energy systems usi...

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32nd International conference on Efficiency, Cost, optimization, simulation and Environmental Impact of Energy Systems, ECOS 2019

作者： Hüttermann, Andreas Leenders, Ludger Bahl, Björn Bardow, André Institute for Technical Thermodynamics RWTH Aachen University Aachen Germany Forschungszentrum Jülich GmbH Jülich Germany

ISBN: (纸本)9788361506515

Operation cost of energy systems is often minimized using mathematical optimization. For this purpose, a mathematical model of the energy system is required that is accurate and computationally efficient. If measured data is available, data-driven model generation is possible. Commonly, data is then used to model each component of the energy system independently. However, independent modeling of each component may lead to models that are unnecessarily complicated and, thus, inefficient in application. In this work, we propose a method for the automated data-driven model generation of energy systems using piecewise linear regression. The method balances the errors caused by each component model in the overall energy system and iteratively identifies the minimal number of linear sections for an accurate model of the energy system. Model accuracy of the energy system is measured in terms of cost. The method uses the Corrected Akaike Information Criterion AICc to discourage overfitting. The method provides a Mixed-Integer Linear Programming (MILP) model of the energy system. The proposed method is applied to a case study from literature. The method is shown to provide an energy system model that needs less linear sections than single-component regression while retaining high accuracy. Thereby, the proposed method enables efficient data-driven modeling as the basis for energy system optimization. © ECOS 2019 - Proceedings of the 32nd International conference on Efficiency, Cost, optimization, simulation and Environmental Impact of Energy Systems. All rights reserved.

关键词： Integer programming

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Robust data-driven optimization using machine learning and monte-carlo simulation 19

Robust data-driven optimization using machine learning and m...

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Proceedings of the Winter simulation conference

作者： Vincent Béchard Difference S.E.N.C.Montreal Canada

ISBN: (纸本)9781728132839

This paper presents a framework that was developed to achieve data-driven robust optimization of processes. The main idea is to automatically build a single and global predictive model using a machine learning technique (random forests), and then to use a derivative-free black-box optimization technique (MADS) to maximize a performance criterion. Monte-Carlo simulation is used to estimate pointwise prediction variance. This automated framework is designed to find optimal variance-stabilizing solutions while preserving the noisiness, non-smoothness, non-linearity, non-convexity and disjoint nature of real-life data. The time it takes to prepare the dataset depends on the volume of data, but this step occurs only once in the procedure. The time it takes to iterate during optimization depends on three user-specified quantities; therefore, the iterative portion is insensitive to data volume. Implementation was done using the R programming language which offers a wide variety of data processing, modelling and optimization capabilities.

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6th International conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

6th International Conference of Pioneering Computer Scientis...

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6th International conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

ISBN: (纸本)9789811579837

The proceedings contain 98 papers. The special focus in this conference is on Pioneering Computer Scientists, Engineers and Educators. The topics include: Research on Online Leakage Assessment;differential Privacy Trajectory Protection Method Based on Spatiotemporal Correlation;a New Lightweight Database Encryption and Security Scheme for Internet-of-Things;Research on MLChecker Plagiarism Detection System;research on Global Competition and Acoustic Search Algorithm Based on Random Attraction;Over-Smoothing Algorithm and Its Application to GCN Semi-supervised Classification;improved Random Forest Algorithm Based on Adaptive Step Size Artificial Bee Colony optimization;visual Collaborative Maintenance Method for Urban Rail Vehicle Bogie Based on Operation State Prediction;occlusion Based Discriminative Feature Mining for Vehicle Re-identification;Multi-factor Fusion POI Recommendation Model;starIn: An Approach to Predict the Popularity of GitHub Repository;MCA-TFP Model: A Short-Term Traffic Flow Prediction Model Based on Multi-characteristic Analysis;research on Competition Game Algorithm Between High-Speed Railway and Ordinary Railway System Under the Urban Corridor Background;Stock Price Forecasting and Rule Extraction Based on L1-Orthogonal Regularized GRU Decision Tree Interpretation Model;Short-Term Predictions and LIME-Based Rule Extraction for Standard and Poor’s Index;a Hybrid Data-driven Approach for Predicting Remaining Useful Life of Industrial Equipment;research on Block Storage and Analysis Model for Belt and Road Initiative Ecological Carrying Capacity Evaluation System;Hybrid optimization-Based GRU Neural Network for Software Reliability Prediction;space modeling Method for Ship Pipe Route Design;simulation of Passenger Behavior in Virtual Subway Station Based on Multi-Agent;effective Vietnamese Sentiment Analysis Model Using Sentiment Word Embedding and Transfer Learning.

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Robust dispatch of integrated electric-heat systems considering weather-parameter-driven residential thermal demands 4

Robust dispatch of integrated electric-heat systems consider...

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4th IEEE conference on Energy Internet and Energy System Integration, EI2 2020

作者： Hua, Guanghui Li, Chen Zhang, Yong Li, Dan Liu, Chuang Wang, Cheng China Electric Power Research Institut State Key Laboratory of Operation and Control of Renewable Energy Storage Systems Nanjing China National Power Dispatching and Control Center Bejing China State Grid Hebei Electric Power Co.LTD. Shijiazhuang China State Grid DC Engineering Construction Company Bejing China North China Electric Power University School of Electrical and Electronic Engineering Bejing China

ISBN: (纸本)9781728196060

Conventional day-ahead scheduling strategies of integrated electricity and heating system (IEHS) are oversimplified for the thermal demand modeling and cannot meet the thermal comfort of the users. Furthermore, this unbalanced power may lead to reserve capacity deficiency in the power system, due to the thermal-electric coupling of the combined heat and power (CHP) units in IEHS. This paper derives a tractable and accurate residential thermal demand (RTD) model, which comprehensively considers the impact of weather conditions. Then, a robust scheduling strategy is employed to tackle the uncertainties of the renewable generation outputs and RTDs, suggesting a double-stage optimization model. The first stage is to identify the unit commitment with minimum operational cost. In the second stage, the feasibility of the first stage unit commitment would be checked to minimize the summation of slack variables. Besides, the robust optimization model is converted into a mixed-integer linear program via the big-M method and solved by column and constraint generation (C CG) algorithm. The simulation results show the effectiveness of the proposed RTD model, as well as the robust scheduling strategy for IEHS. © 2020 IEEE.

关键词： Scheduling

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Dynamic congestion pricing for ridesourcing traffic: a simulation optimization approach 19

Dynamic congestion pricing for ridesourcing traffic: a simul...

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Proceedings of the Winter simulation conference

作者： Qi Luo University of Michigan

ISBN: (纸本)9781728132839

Despite the documented benefits of ridesourcing services, recent studies show that they can slow down traffic in the densest cities significantly. To implement congestion pricing policies upon those vehicles, regulators need to estimate the degree of congestion effect. This paper studies simulation-based approaches to address the two technical challenges arising from the representation of system dynamics and the optimization for congestion price mechanisms. To estimate the traffic state, we use a metamodel representation for traffic flow and a numerical method for data interpolation. To reduce the burden of replicating evaluation in stochastic optimization, we use a simulation optimization approach to compute the optimal congestion price. This data-driven approach can potentially be extended to solve large-scale congestion pricing problems with unobservable states.

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