Dynamic Optimization Method on Electromechanical Coupling System by Exponential Inertia Weight Particle Swarm Algorithm

Unlike steady-state optimization and linear model predictive control, dynamic optimization and nonlinear model predictive control is underway to industry application. This paper discusses the implementation issues of two-level control strategy integrated dynamic real-time optimization and nonlinear model predictive control of a heat-integrated distillation column system. To realize this scheme, four nonlinear programming problems: economic operation optimization, data reconciliation and state estimation, parameter estimation and nonlinear model predictive control, are considered based on system rigorous dynamic model. The economic operation optimization is to minimize the cost of the plant. The unmeasured states are estimated by measurements of temperatures, pressures and flow rates. Time-variant parameters such as tray efficiencies and feed composition are updated on-line. The sequential quadratic programming and orthogonal collocation are used to solve the dynamic nonlinear optimization problems.

The power systems of the future - smart grid - will see an increase in both renewable energy sources and load demand increasing the need for fast dynamic reconfiguration of system parameters. Advanced simulation capabilities are needed to support future planning and to implement real-time dynamic optimization. To achieve this, intelligent algorithms are needed for prediction and system monitoring along with advanced hardware to provide speed. FASTSIM capabilities allows for fast prediction of how a proposed reconfiguration will affect the system permitting implementation of radical reconfigurations without fear of disturbing vital system settings since the proposed solution will be checked before being implemented. Power systems will need to be continuously monitored for power losses and system contingencies. A combination of system states prediction, dynamic load flow, system optimization, and solution stability checking will allow for a more reliable, affordable and clean power grid. This paper presents a dynamic optimization method based on adaptive critic designs for the smart grid environment.

Compared with traditional metal-materials, fiber-reinforced composite materials have better performance in many aspects such as strength, stiffness, and fracture resistance. At present, fiber-reinforced composite materials have been widely used in automotive, aerospace, and other industrial fields. This paper proposes a topology optimization method for solving the fundamental frequency maximization of undamped free vibration of continuous fiber-reinforced composite structures. To achieve the simultaneous optimization of the structural topological configuration and the fiber angle. A dynamic topological optimization model is established with the permitted material usage as the constraint and the structure's first-order eigenvalue as the objective function. The model includes density design variables that characterizes the topological configuration of the structure and angular design variables that characterizes the fiber orientation. The analytical sensitivity formulas of the objective function of eigenvalue with respect to density design variables and angle design variables are derived in detail, and the method of moving asymptotes (MMA) is used to solve the optimization problem. Finally, three numerical examples are performed to verify the effectiveness of the proposed method, which includes a static optimization example with the stiffness maximization as the goal and two dynamic optimization examples with the first-order eigenvalue maximization as the goal. The results show that the proposed method can achieve a stable iterative history and fast convergence, and can effectively improve the structural frequency while achieving the integrated optimization of the structural topological configuration and the fiber angle.

Electromechanical equipment is indispensable in the modernization. The motions of electromechanical systems are becoming increasingly complex, which inevitably involves nonlinearity. The recent advances in design, analysis and control are to improve dynamic performance, steady-state stability, and anti-interference ability to meet modern industry upgrading requirements. In recent years, with the increasing complexity of application scenarios, one has higher anticipation for the nonlinear dynamic performance, intelligence, and automation of electromechanical systems, which also brings new challenges to the research, design, and control of electromechanical systems. Therefore, how to combine nonlinear dynamics, artificial intelligence, and automation with the research, design, and control of the electromechanical system has become a hot issue. The advanced analysis, design and control methods contribute to develop and improve nonlinear electromechanical systems with high stability and dynamic performance. We gather the recent advances on nonlinear dynamics and control of electromechanical systems to provide new perspectives to the research and design of the future products.

This paper presents a dynamic reliability-based optimization technique for the seismic design of structures with tuned mass damper (TMD). Firstly, the governing equation of multi-degree-of-freedom structure with TMD is established. Then, the main structure is unfolded by the first mode. Considering that the damping is non-classical and the total main structure and TMD system is un-symmetric, the complex modal analysis is adopted to uncouple the governing equation and the analytical solutions of stochastic seismic response under the Kanai-Tajimi spectrum loading are obtained. Taking the ratio between the first-order modal displacement standard deviation of the structure with TMD and the one without TMD as the objective function, the dynamic reliability of the TMD system displacement as the constraint, the optimal design parameters of the TMD system are obtained through the penalty function method. A 10-story building with TMD system illustrates the proposed dynamic reliability-based optimization method. It is believed that such an optimization technique provides an effective tool for the seismic design of structures with TMD.

Simulation-based optimization of thermal systems

The steering system has an important impact on the energy consumption, maneuverability, and driving stability of vehicles. In order to improve the system’s comprehensive performance, this research takes electro-hydraulic active steering (EHAS) system as the design object and utilizes the dynamic constraints collaborative optimization (DCCO) method for the parametric optimization of the EHAS system. Steering modality is combined with dynamic performance evaluation indices such as steering energy consumption, steering road feel, steering sensibility, and steering stability, to evaluate the comprehensive performance of the EHAS system. Then, a hierarchical multidisciplinary optimization model of EHAS system is established based on DCCO method, which takes the comprehensive performance of steering system as the overall system and steering energy consumption, steering road feel, steering sensibility, and steering modality as subsystems respectively. The multidisciplinary performance evaluation indices of EHAS system are derived for the first time, among which the evaluation index of steering modality is obtained by combining of design of experiments (DOE) design method and response surface method. The optimization results show that the EHAS system optimized by DCCO method can effectively improve steering energy consumption, steering road feel, and steering sensibility.

Dynamic optimization of ship energy efficiency considering time-varying environmental factors

An enhanced scatter search method for the global dynamic optimization of nonlinear processes using the control vector parametrization (CVP) approach is presented. Sharing some features of the scatter search metaheuristic, this new method presents a simpler but more effective design which helps to overcome typical difficulties of nonlinear dynamic systems optimization such as noise, flat areas, nonsmoothness, and/or discontinuities. This new algorithm provides a good balance between robustness and efficiency in the global phase, and couples a local search procedure to accelerate the convergence to optimal solutions. Its application to four multimodal dynamic optimization problems, compared with other state-of-the-art global optimization algorithms, including an advanced scatter search design, proves its efficiency and robustness, showing a very good scalability.

High-performance computing (HPC) systems consume huge amount of power, while bringing about higher operational costs, lower reliability. Power optimization has become an important target for HPC system design. Network energy optimization is significant for the energy optimization of the whole system. In this paper, the dynamic energy optimization method of network with MPI communication operation is presented, which can find MPI operation implementation with the least network energy by accurate dynamic energy estimation, and reduce the energy consumption of network. Taking barrier as an example, we validate the correctness and usefulness of the dynamic energy optimization method of network.

Dynamic multi-objective optimization problem is one of the most current popular research hotspots and faces great difficulties. In this work, based on the multiple objectives of structural optimization of the management space of forest ecosystems, a dynamic multi-objective optimization model of forest spatial structure is constructed by using the variation of forest growth with time. In order to make the model solve the problem more efficiently, an algorithm which focuses on solving multi-objective optimization problems is applied with a new environment detection mechanism. Compared with two multi-objective optimization algorithms PDMIOA and dDMSMOPSO, four test functions i.e., FDA1, FDA3, dMOP1, and dMOP2, are used. The results show that the proposed algorithm is superior to PDMIOA and dDMSMOPSO in convergence performance, distribution performance, and tracking performance. We have also applied the model to the natural secondary forest stand of the Longhushan Stateowned Forest Farm in South Dongting Lake, and analyzed the model output. The results show that after optimization, the forest structural indicators such as homogeneity index, mixed degree, competition index, health degree, spatial density index and open ratio, are obviously better than those without optimization. In addition, the forest structure is also greatly improved. The proposed dynamic multi-objective optimization model of forest spatial structure can provide both theoretical basis and technical support for intelligent forest management.

Determining the propagation path of a disturbance in multi-rate process and electromechanical systems

Nearest neighbors method for detecting transient disturbances in process and electromechanical systems

Nancy L. Schwartz was educated at Oberlin College (Phi Beta Kappa, AB 1960) and Purdue University (MS 1962, PhD 1964). She taught at Carnegie-Mellon University from 1964 to 1970, and Northwestern University from 1970, where she was appointed Morrison Professor of Managerial Economics and Decision Sciences in 1981. She served as director of Graduate Studies of the Kellogg Graduate School of Management, and as chair of the Department of Managerial Economics and Decision Sciences from 1977 to 1979. She served on the Council of The Institute of Management Sciences (1974–6), on the Board of Editors of the American Economic Review from 1981, and was an Associate Editor of Econometrica from 1981. Her dissertation research dealt with a problem of determining the routing and timing of movements of barges and towboats to carry out given freight movements between pairs of ports at minimal cost. She developed a linear discrete programming problem of this dynamic optimization problem, and was able to solve it for moderate problems. Her later research was mainly in two related areas: methods of dynamic optimization, with particular focus on application, and the effect of industry structure on technological innovation. She also published on other topics; her work includes more than forty published papers, including a fundamental paper on limit pricing under certainty with M.I. Kamien, which appeared in Econometrica in 1971, and two books. Her work in the field of control theory, or dynamic optimization, is contained in the book Dynamic Optimization (1981, with M.I. Kamien). Her contributions to the subject of technological innovation are contained in numerous journal articles culminating in a book, Market Structure and Innovation (1982, with M.I. Kamien). She had a preference for problems related to application, and for hard (as opposed to soft) analysis, for which she had a considerable talent. Her work and personality had a strong effect on her students, many of whom are active contributors to research in Economics and Management Science.

With the development of science and technology, there has been growing the grid size, the growing demand for electricity and the extent of increasing the electricity market.In this paper, a mathematical morphology and genetic algorithm based dynamic reactive power optimization method for distribution network is proposed.By means of constructing mathematical morphology filter, the binary image composed by allelic genes of chromosome is filtered and the problem that deals with the action times of voltage regulating devices for compensation is turned into filtering problem of discrete binary image.With further automate scheduling, distribution automation and unmanned substation reactive power optimization of distribution line running made an urgent request.Reactive power optimization can improve the voltage passing rate, increasing the reliability of the system, the power quality, and the system runs the security and economy of the perfect combination, very broad application prospects.