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Data-driven mixed-Integer linear programming-based optimisation for efficient failure detection in large-scale distributed systems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第1期303卷 337-353页

作者： Er-Rahmadi, Btissam Ma, Tiejun Univ Southampton Southampton Business Sch Dept Decis Analyt & Risk Ctr Risk Res Bldg 2Univ Rd Southampton SO17 1BJ Hants England Univ Edinburgh Artificial Intelligence Applicat Inst Sch Informat Informat Forum Crichton St Edinburgh EH8 9AB Midlothian Scotland Edinburgh Res Ctr Huawei Technol R&D 2 Semple St Edinburgh EH3 8BL Midlothian Scotland

Failure detectors (FDs) are fundamental building blocks for distributed systems. An FD detects whether a process has crashed or not based on the reception of heartbeats' messages sent by this process over a communication channel. A key challenge of FDs is to tune their parameters to achieve optimal performance which satisfies the desired system requirements. This is challenging due to the complexities of large-scale networks. Existing FDs ignore such optimisation and adopt ad-hoc parameters. In this paper, we propose a new Mixed Integer Linear programming (MILP) optimisation-based FD algorithm. We obtain the MILP formulation via piecewise linearisation relaxations. The MILP involves obtaining optimal FD parameters that meet the optimal trade-off between its performance metrics requirements, network conditions and system parameters. The MILP maximises our FD's accuracy under bounded failure detection time while considering network and system conditions as constraints. The MILP's solution represents optimised FD parameters that maximise FD's expected performance. To adapt to real-time network changes, our proposed MILP-based FD fits the probability distribution of heartbeats' inter-arrivals. To address our FD scalability challenge in large-scale systems where the MILP model needs to compute approximate optimal solutions quickly, we also propose a heuristic algorithm. To test our proposed approach, we adopt Amazon Cloud as a realistic testing environment and develop a simulator for robustness tests. Our results show consistent improvement of overall FD performance and scalability. To the best of our knowledge, this is the first attempt to combine the MILP-based optimisation modelling with FD to achieve performance guarantees.

关键词： nonlinear programming Mixed integer linear programming Distributed systems Failure detection Heartbeats

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Optimization of doubly-fed wind generator based on beetle swarm optimization algorithm 3

Optimization of doubly-fed wind generator based on beetle sw...

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3rd International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Yi, Puting Xie, Wei Shanghai Maritime University Shanghai201306 China

ISBN: (纸本)9781510680449

Based on the requirements of the low-carbon energy system, it is necessary to optimize the 1.2MW doubly-fed wind generator. Based on the operating characteristics of the generator, selected the cost of generator materials and the efficiency at rated load as the objective function, transforming the engineering problems into an optimization problems, and establish a relevant mathematical model for generator optimization. Due to the fact that generator design is a complex problem with multiple constraints and nonlinear, a BSO algorithm improved by BAS is proposed, which can effectively handle nonlinear optimization problems and achieve optimal design of 1.2MW doubly fed wind generator. The results show that the algorithm has good convergence, stability, and speed, while also reducing the probability of falling into local optimal solutions. The final optimized design scheme is also relatively ideal. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： nonlinear programming

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Adaptive resource management for cognitive power line communication system

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IET COMMUNICATIONS 2022年第14期16卷 1674-1685页

作者： Fu, Meiming Wang, Kang Xu, Bo Xu, Shuming Wu, Weihua Shenzhen Smartchip Microelect Technol Co Ltd Shenzhen Peoples R China State Grid Lab Power Line Commun Applicat Technol Shenzhen Peoples R China Shenzhen Guodian Technol Commun Co Ltd Shenzhen Peoples R China Xidian Univ Sch Telecommun Engn State Key Lab ISN 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou Peoples R China

The authors investigate the adaptive resource allocation problem for cognitive power line communication system. The dynamic optimisation model is employed to maximise the energy-aware network utility, which is defined as the difference of the weighted network throughput and the power consumption. Then, the authors decompose the stochastic optimisation into a traffic management subproblem and a joint subchannel and power allocation subproblem. Since the joint subchannel and power allocation subproblem is a mixed integer non-linear optimisation, a continuity relaxation and Lagrange dual method is proposed to obtain the optimal solution of the mixed integer non-linear optimisation subproblem. After that, the authors develop an adaptive power line communication resource allocation algorithm to cope with the power line communication network dynamics only according to the current network state information. Finally, we derive a trade-off mechanism between network utility and network delay, in which the network delay is proportional to the control parameter V and the network utility is proportional to the parameter 1/V$1/V$. Simulation results not only show the effectiveness but also verify the theoretical analysis of our proposed algorithm.

关键词： carrier transmission on power lines power allocation subproblem adaptive power line communication resource allocation algorithm mixed integer nonlinear optimisation subproblem network delay cognitive power line communication system power consumption stochastic optimisation dynamic optimisation model resource allocation adaptive resource management optimisation weighted network throughput adaptive resource allocation problem energy-aware network utility traffic management subproblem nonlinear programming integer programming joint subchannel current network state information power line communication network dynamics

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Formulation and Solution Methodology for Reducing Energy Consumption in Two-Machine Bernoulli Serial Lines

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第1期19卷 522-530页

作者： Yan, Chao-Bo Cheng, Xingrui Gao, Feng Guan, Xiaohong Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Automat Sci & Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ MOEKLINNS Lab Xian 710049 Peoples R China

Machines consume intensive energy in some production systems. Although substantial efforts have been devoted to performance analysis, continuous improvement, and design of production systems, the research on reducing the energy consumption in these systems is limited. In this article, the problem of minimizing the energy consumed by machines in the two-machine Bernoulli serial line, which has been formulated as nonlinear programming with production rate constraint, is investigated. Specifically, structural characteristics and optimality conditions of the problem are analyzed, and two nonlinear algebraic optimality equations are established. To solve the optimality equations, their properties are explored, and an effective algorithm based on the binary search method is developed. Furthermore, the sensitivity of the optimal solution with respect to system parameters is quantitatively analyzed. Based on the sensitivity analysis, some useful insights on the optimal objective value are extracted. Note to Practitioners-In energy-intensive manufacturing enterprises, reducing the energy consumption in their production systems is urgent and challenging. In this article, an energy consumption optimization problem in a simple model of production systems (i.e., in the two-machine Bernoulli line) is investigated. Machine efficiencies are optimized so that the total energy consumption of machines is minimized, while a required production rate is ensured. The reported results enable a novel managerial paradigm for production systems, especially for energy-intensive systems. Although the model of the two-machine Bernoulli line is simple, as a cornerstone, this research will be extended, in the future, to long Bernoulli lines and more practical production systems, e.g., lines with geometric, exponential, and non-exponential machine reliability models.

关键词： Energy consumption Production Production systems Optimization Mathematical model Sensitivity analysis Search methods Monotonicity nonlinear programming optimality equations production rate sensitivity analysis

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Parameter inversion of a polydisperse system in small-angle scattering

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2022年第4期55卷 966-977页

作者： Leng, Kuangdai King, Stephen Snow, Tim Rogers, Sarah Markvardsen, Anders Maheswaran, Satheesh Thiyagalingam, Jeyan Rutherford Appleton Lab Sci Comp Dept STFC Didcot OX11 0QX Oxon England Rutherford Appleton Lab STFC ISIS Neutron & Muon Source Didcot OX11 0QX Oxon England Rutherford Appleton Lab Diamond Light Source Didcot OX11 0QX Oxon England

A general method to invert parameter distributions of a polydisperse system using data acquired from a small-angle scattering (SAS) experiment is presented. The forward problem, i.e. calculating the scattering intensity given the distributions of any causal parameters of a theoretical model, is generalized as a multi-linear map, characterized by a high-dimensional Green tensor that represents the complete scattering physics. The inverse problem, i.e. finding the maximum-likelihood estimation of the parameter distributions (in free form) given the scattering intensity (either a curve or an image) acquired from an experiment, is formulated as a constrained nonlinear programming (NLP) problem. This NLP problem is solved with high accuracy and efficiency via several theoretical and computational enhancements, such as an automatic data scaling for accuracy preservation and GPU acceleration for large-scale multi-parameter systems. Six numerical examples are presented, including both synthetic tests and solutions to real neutron and X-ray data sets, where the method is compared with several existing methods in terms of their generality, accuracy and computational cost. These examples show that SAS inversion is subject to a high degree of non-uniqueness of solution or structural ambiguity. With an ultra-high accuracy, the method can yield a series of near-optimal solutions that fit data to different acceptable levels.

关键词： small-angle scattering polydispersity inversion neutron scattering X-ray scattering nonlinear programming

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Comprehensive performance optimisation method of the hybrid simulation platform of an MMC-HVDC system

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INTERNATIONAL JOURNAL OF ELECTRONICS 2022年第11期109卷 1915-1934页

作者： Le, Jian Zhao, Liangang Zhou, Qian Liao, Xiaobing Wuhan Univ Sch Elect Engn & Automat Wuhan Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Wuhan Peoples R China

Digital-physical hybrid simulation (DPHS) technology provides an efficient means to investigate the complex interaction mechanism and dynamic behaviour of a modern power grid integrated with numerous power electronic devices. However, its further development and wide application are obstructed by many factors, especially the large investment of the physical subsystem. This paper presents a comprehensive parameter optimisation method for the development of the DPHS platform based on tools from similarity theory and nonlinear programming theory. The parameter optimisation model is formulated to minimise the one-time investment of the primary equipment of the physical subsystem and the constraints to guarantee the desired performance of the hybrid simulation platform derived according to similarity criteria. An MMC-HVDC project is taken as an example to specifically explain the implementation of the proposed method with the focus on the solution of similarity criteria. Results obtained from steady and dynamic simulation show that the proposed approach is effective for improving the technical and economic performance of a hybrid simulation platform.

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Delay Robustness of PID Control of Second-Order Systems: Pseudoconcavity, Exact Delay Margin, and Performance Tradeoff

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第3期67卷 1194-1209页

作者： Chen, Jianqi Ma, Dan Xu, Yong Chen, Jie City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Coll Informat Sci & Engn Shenyang 110819 Peoples R China Guangdong Univ Technol Guangdong Prov Key Lab Intelligent Decis & Cooper Sch Automat Guangzhou 510006 Peoples R China

In this article, we study delay robustness of PID controllers in stabilizing systems containing uncertain delays. We consider second-order systems and seek analytical characterization and exact computation of the PID delay margin, where by PID delay margin we mean the maximal range of delay values within which the system can be robustly stabilized by a PID controller. Our primary contribution is threefold. First, we show that the delay margin achieved by PID control coincides with that by PD controllers. Second, we show that the PID delay margin can be computed efficiently by solving a pseudoconcave unimodal problem, i.e., a univariate optimization problem that admits a unique maximum and, hence, is a convex optimization problem in one variable. Finally, we demonstrate analytically the tradeoff between achieving delay margin and tracking performance, showing that for several canonical performance criteria, integral control reduces the delay margin. These results lend useful insights into the PID control of delay systems, and useful guidelines in the tuning and analytical design of PID controllers.

关键词： Delays PD control PI control Linear systems Robustness Optimization Delay systems Delay margin nonlinear programming PID controller pseudoconcavity robust stabilization

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Distributionally robust co-optimization of the demand-side resources and soft open points allocation for the high penetration of renewable energy

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IET RENEWABLE POWER GENERATION 2022年第4期16卷 713-725页

作者： Liu, Hong Li, Junkai Zhang, Shida Ge, Shaoyun Yang, Baijie Wang, Chengshan Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China

The new technologies, such as soft open points (SOPs) and demand response (DR), offer original approaches to stabilize the system operation and accommodate the high penetration of distributed generators (DGs). However, the applied effects of these technologies are closely affected by the uncertainties of the consumer's willingness and the outputs of DGs. To solve the problems caused by the uncertainty of the resources in active distribution system, this paper proposes a distributionally robust co-optimization model for the demand side resources and SOPs, which realizes the combination of investment economy and operation robustness. Next, using equivalent substitution and polyhedron linearization technique, this paper proposes the mathematical method which transforms the original non-linear programming model into a mixed-integer linear programming model, and obtains the solution to the distributionally robust model with column and constraint generation (CCG) algorithm. Finally, the effectiveness of the proposed model is verified with the sample from a power distribution system in northern China. The result demonstrates that the co-optimization model can realize complementary advantages of the demand side resources and SOPs, which can not only guarantee the economy of scheme, but also improve the accommodation of DGs.

关键词： investment distributed generators power distribution system linear programming distributed power generation equivalent substitution nonlinear programming model mixed-integer linear programming model distributionally robust co-optimization model consumer nonlinear programming integer programming optimisation active distribution system SOPs demand response renewable energy investment economy soft open points allocation applied effects polyhedron linearization technique system operation distributionally robust model original approaches operation robustness

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Fast Reinforcement Learning Based MPC based on NLP Sensitivities 22

Fast Reinforcement Learning Based MPC based on NLP Sensitivi...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Adhau, Saket Reinhardt, Dirk Skogestad, Sigurd Gros, Sebastien Norwegian Univ Sci & Technol Dept Chem Engn N-7491 Trondheim Norway Norwegian Univ Sci & Technol Dept Engn Cybernet N-7491 Trondheim Norway

ISBN: (纸本)9781713872344

This paper proposes a comprehensive approach to improve the computational efficiency of Reinforcement Learning (RL) based Model Predictive Controller (MPC). Although MPC will ensure controller safety and RL can generate optimal control policies, combining the two requires substantial time and computational effort, particularly for larger data sets. In a typical RL-based MPC and Q-learning workflow, two not-so-different MPC problems must be evaluated at each RL iteration, i.e. one for the action-value and one for the value function, which is time-consuming and prohibitively expensive in terms of computations. We employ nonlinear programming (NLP) sensitivities to approximate the action-value function using the optimal solution from the value function, reducing computational time. The proposed approach can achieve comparable performance to the conventional method but with significantly lower computational time. We demonstrate the proposed approach on two examples: Linear Quadratic Regulator (LQR) problem and Continuously Stirred Tank Reactor (CSTR). Copyright (c) 2023 The Authors.

关键词： Reinforcement learning Economic model predictive control nonlinear programming Sensitivity

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NUMERICAL OPTIMIZATION OF BALLISTIC PANELS 37

NUMERICAL OPTIMIZATION OF BALLISTIC PANELS

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37th Annual European Simulation and Modelling Conference (ESM)

作者： Jackowska-Zduniak, Beata Warsaw Univ Life Sci Inst Informat Technol Str Nowoursynowska 159 Bldg 34 PL-02776 Warsaw Poland

ISBN: (纸本)9789492859280

A methodology for numerical modeling and optimization of the structure of the multi-layer panel for ballistic protection was developed. Usefulness of the proposed approach to solve problem was tested in the experiment involving impact of the selected type of armour piercing projectile 7.62x54R B32 a multi-layers ballistic panel. The scope of paper includes preparation of numerical models formulated using axisymmetric and 3D descriptions. Built models were validated using experimental results. Then, using advanced optimization techniques, the most effective variant of the ballistic panel was selected. The subject of the selection was the type of material as well as the thickness and sequence of individual layers of the panel. The surface density of the panel was assumed as the objective function. The residual kinetic energy of the projectile was used as a limiting condition. The conducted multi-variant numerical analyzes show that the developed numerical model satisfactorily corresponds to the real course of the perforation process for the assumed experimental conditions. An interesting result of the research is the fact that in the group of the best configurations of the ballistic panel there are variants in the system: steel/steel, ceramics/steel, and aluminum alloy/steel, where the ceramic/steel configuration is not the most effective solution, contrary to the observations known from literature. The resulting optimal variant of the ballistic panel meets the requirement to protect light vehicles in the 3rd resistance class according to Stanag 4569. Achieving the intended goal was possible thanks to mastering the technique of combination of the LS-OPT optimization package with the LS-DYNA MES calculation code, and the HyperMorph pre-processor module.

关键词： Mechanical engineering nonlinear programming Optimization Numerical methods Computer Aided Engineering

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