Hierarchical Optimization Method Research Articles

A Hierarchical Optimization Method for Parameter Estimation of Diesel Generators

Diesel generators (DGs) are used to provide electrical power to consumers because their power outputs can be scheduled, and they offer stable operating characteristics in a standalone or microgrid system. The parameters for DGs are set to ensure reliable and accurate simulation for distributed energy resources (DERs), which increases system reliability, maintains power supply quality and reduces operational costs. There are many parameters that must be estimated for a DG. These parameter settings such as system gains and time constants may vary, as facilities are run for a few days. Parameters for a DG must then be re-estimated to ensure accurate simulation in a microgrid system. The proposed method uses a sensitivity analysis to classify parameters into three different categories. A hierarchical optimization method combined with an enhanced whale optimization algorithm (EWOA) is then used to estimate the parameter settings for a DG using actual measurement data. The proposed method is applied to a practical microgrid system, and the results show that the proposed method determines the optimal parameter settings for a DG that enables accurate simulation and robust implementation for a microgrid system.

Reliability optimization of smart grid based on optimal allocation of protective devices, distributed energy resources, and electric vehicle/plug-in hybrid electric vehicle charging stations

There is a knowledge gap to introduce a comprehensive simultaneous optimal allocation of protective devices, distributed generation units, and electric vehicle/plug-in hybrid electric vehicle charging stations. This paper tries to fill such a knowledge gap by proposing a method that simultaneously considers all discussed optimization variables. In the proposed hierarchical optimization method, three stages and seven scenarios are introduced. Because of the complexity increment of optimization problem as well as the number of the optimization variables, it is useful to have the criteria for the results cross-checking. In the proposed hierarchical optimization method, the third stage optimization results which consist of all three types of optimization variables are examined with those of first and second stages.To illustrate the advantages of the proposed method, it applies to the IEEE 33-bus and IEEE 69-bus test systems in the widespread presence of non-renewable and renewable distributed generation units, e.g. wind turbine and photovoltaic power plants, and electric vehicles. The hierarchical optimization method comparison results of different stages determine the importance of any variable type on the system reliability. By getting insight into how any variable affects the system reliability, it is possible to investigate the most effective corrective and improving solutions.

Hierarchical Multiobjective H-Infinity Robust Control Design for Wireless Power Transfer System Using Genetic Algorithm

In inductive wireless power transfer (IPT) system, parameter perturbation and disturbances not only cause power fluctuation but also worsen system performances. Thus, robust control methods are introduced for improving the performances of uncertain IPT system. In robust control design, robust performance is often considered first. However, the enhancement of robust performance may lead to inferior time-domain performance. To deal with this contradiction, the weighting functions in the robust control design structure shall be chosen wisely. Considering the complex mutual dependence of weighting parameters, the selection of weighting parameters will become a complicated and practically time-consuming problem. Therefore, this brief presents a hierarchical multiobjective optimization method to select weighting parameters of H-infinity controller automatically. The inner level designs the H-infinity controller and guarantees robust performance. The outer level improves the system’s time-domain performance and robust performance at the same time with a specially designed objective function using genetic algorithm. An experimental IPT prototype is established for the verification of the proposed optimization method. The optimization results show both good robust performance and fast time response about 5–8ms in different experiment conditions.

Hierarchical Optimization of Landing Performance for Lander with Adaptive Landing Gear

A parameterized dynamics analysis model of legged lander with adaptive landing gear was established. Based on the analysis model, the landing performances under various landing conditions were analyzed by the optimized Latin hypercube experimental design method. In order to improve the landing performances, a hierarchical optimization method was proposed considering the uncertainty of landing conditions. The optimization problem was divided into a higher level (hereafter the “leader”) and several lower levels (hereafter the “follower”). The followers took conditioning factors as design variables to find out the worst landing conditions, while the leader took buffer parameters as design variables to better the landing performance under worst conditions. First of all, sensitivity analysis of landing conditioning factors was carried out according to the results of experimental design. After the sensitive factors were screened out, the response surface models were established to reflect the complicated relationships between sensitive conditioning factors, buffer parameters and landing performance indexes. Finally, the response surface model was used for hierarchical optimization iteration to improve the computational efficiency. After selecting the optimum buffer parameters from the solution set, the dynamic model with the optimum parameters was simulated again under the same landing conditions as the simulation before. After optimization, nozzle performance against damage is improved by 5.24%, the acceleration overload is reduced by 5.74%, and the primary strut improves its performance by 21.10%.

A domain association hierarchical decomposition optimization method for cab vibration control of commercial vehicles

The dynamical vibration responses of a commercial vehicle’s cab can seriously affect ride comfort; Multi-source excitation effects of commercial vehicle will cause a great interference for specific research target. This paper presents a domain association hierarchical decomposition method, researches the possibility of decreasing vibration responses from related vibration transfer path research, enhances the clarity of the analysis and simplifies the number of decomposed subsystems. Firstly, the whole vehicle model is decomposed into subsystem layers and part layers relevant to a cab, and the cab vibration acceleration value is chosen as the optimization objective. Secondly, in terms of different decomposed layer subsystems, the constraint function and the weight factors of the corresponding mathematical models are established, and the elastic element parameters of the hierarchical fault levels are optimized with the appropriate matching improvement scheme. Thirdly, due to the linear parameter matching scheme of the cab mount not being obvious, a series of improved scaling weight factors and combined linear and nonlinear schemes are introduced based on an experimental design. Finally, by proposing an optimization criterion of maximum deviation degree, the optimal matching scheme is found through the interaction of information between the subsystem layers and part layers, obtaining the most suitable matching mode and damping scaling ratio for the part layer. The experimental results show the effect of proposed method and prove its advantages in countering vibration control problems.

Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids

This paper proposes a hierarchical optimization method for the energy scheduling of multiple microgrids (MMGs) in the distribution network of power grids. An energy market operator (EMO) is constructed to regulate energy storage systems (ESSs) and load demands in MMGs. The optimization process is divided into two stages. In the first stage, each MG optimizes the scheduling of its own ESS within a rolling horizon control framework based on a long-term forecast of the local photovoltaic (PV) output, the local load demand and the price sent by the EMO. In the second stage, the EMO establishes an internal price incentive mechanism to maximize its own profits based on the load demand of each MG. The optimization problems in these two stages are solved using mixed integer programming (MIP) and Stackelberg game theory, respectively. Simulation results verified the effectiveness of the proposed method in terms of the promotion of energy trading and improvement of economic benefits of MMGs.

Research Article Analysis of genetic divergence in sweet corn genotypes through hierarchical optimization methods

Research Article Analysis of genetic divergence in sweet corn genotypes through hierarchical optimization methods

Hierarchical Analysis of Thorax Models to Measure Tidal Volume

Abstract Motion tracking of thorax kinematics can be used to determine respiration. However, determining a minimal sensor configuration from 64 candidate sensor locations is associated with high computational costs. Hence, a hierarchical optimization method was proposed to determine the optimal combination of sensors. The hierarchical method was assessed by its ability to quickly determine the sensor combination that will yield optimal modelled tidal volume compared to body plethysmograph measurements. This method was able to find the optimal sensor combinations, in approximately 2% of the estimated time required by an exhaustive search.

Hierarchical paradigm for automated optimal design of dual-band wearable antenna with simplified human body models

PurposeThe purpose of this study is to develop a method to reduce the computation time necessary for the automated optimal design of dual-band wearable antennas. In particular, the authors investigated if this can be achieved by the use of a hierarchical optimization paradigm combined with a simplified human body model. The geometry of the antenna under consideration is described via eight geometrical parameters which are automatically adjusted with the use of an evolutionary algorithm to improve the impedance matching of an antenna located in the proximity of a human body. Specifically, the antennas were designed to operate in the ISM band which covers two frequency ranges: 2.4-2.5 GHz and 5.7-5.9 GHz.Design/methodology/approachDuring the studies on the automated design of wearable antennas using evolutionary computing, the authors observed that not all design parameters exhibit equal influence on the objective function. Therefore, it was hypothesized that to reduce the computation effort, the design parameters can be activated sequentially based on their influence. Accordingly, the authors’ computer code has been modified to include this feature.FindingsThe authors’ novel hierarchical multi-parameter optimization method was able to converge to a better solution within a shorter time compared to an equivalent method not exploiting automatic activation of an increasing number of design parameters. Considering a significant computational cost involved in the calculation of the objective function, this exhibits a convincing advantage of their hierarchical approach, at least for the considered class of antennas.Research limitations/implicationsThe described method has been developed for the design of single- or dual-band wearable antennas. Its application to other classes of antennas and antenna environments may require some adjustments of the objective functions or parameter values of the evolutionary algorithm. It follows from the well-recognized fact that all optimization methods are to some extent application-specific.Practical implicationsComputation load involved in the automated design and optimization can be significantly reduced compared to the non-hierarchical approach with a heterogeneous human body model.Originality/valueTo the best of the authors’ knowledge, the described application of hierarchical paradigm to the optimization of wearable antennas is fully original, as well as is its combination with simplified body models.

Multi-objective Hierarchical Optimal Control for Quadruped Rescue Robot

Timely and efficient rescue missions in disasters such as nuclear accidents, fires, and earthquakes require operating robots to work under high payload. This paper treats the problem of optimal planning and control of a walking-trot gait for a hydraulically actuated quadruped robot that has been developed in a collaborative project of the Departments of Mechanical Engineering and Automation at Shanghai Jiao Tong University. To mitigate the challenges of working with high-fidelity models of this highly complex mechanical system, a kinematics and approximate dynamics model is proposed that is shown to provide an accurate description of the walking motions we wish to study while at the same time being simple enough to support analysis. The proposed model is used to design limb movements in a walking trot gait that balance the trade off between energy consumption and the speed of execution of a desired mission. Bezier curves of different orders are used to design trajectories of the robot’s limbs. We verify the accuracy of the proposed model through experiments with the actual robot. We apply hierarchical optimization method to minimize the energy consumption, and we discuss a Pareto optimal solution that trades off mission duration and energy consumption. In the end perform several experiments to verify the effectiveness and superiority of the optimal algorithm we proposed.

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery.

With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited.

Multidisciplinary hybrid hierarchical collaborative optimization of electric wheel vehicle chassis integrated system based on driver’s feel

The optimization design of chassis integrated system mainly involves steering, suspension and brake subsystems, which is essentially a multidisciplinary design optimization. This paper mainly researches the multidisciplinary optimization of the chassis integrated system for the electric wheel vehicle, from the view of ensuring a favorable feel for the driver. The dynamic models of differential steering system, brake system, active suspension system and vehicle are established. Then, taking the coupling relationship of the chassis subsystems into account, this paper proposes an evaluating index of driver’s ride comfort (Drc), which is composed of the steering road feel, brake feel and suspension ride comfort. In order to determine the weight coefficient in the quantization formula of Drc, the technique for order preference by similarity to ideal solution (TOPSIS) method is used to overcome the subjectivity in the selection. Based on these, a multidisciplinary hybrid hierarchical collaborative optimization (HHCO) method is proposed on the basis of the collaborative optimization (CO), which consists of a system level coordinator and a coupling analyzer to solve the problem of poor convergence and the low efficiency of CO method. The optimization results show that the proposed HHCO method has excellent computational efficiency and better convergence compared with the CO method, which can further improve the steering road feel and the drive ride comfort, on the premise of ensuring the brake feel and suspension ride comfort.

Research on Optimization Method of Transit Crude Oil Depot Dispatching Scheme in Oilfield

The minimum total energy consumption cost in a certain scheduling time of oilfield transit crude oil depot is the goal. In the scheduling period, tank running, holding temperature, oil output parameters and equipment combination scheme as a decision variable. A mathematical model for the optimization of the dispatching scheme of the oil depot is established. According to the structural characteristics of the model. Proposed a hierarchical optimization method combined with implicit enumeration method, genetic algorithm and two level hierarchical optimization of the hybrid optimization algorithm. This method is applied to optimize the scheduling scheme of a certain transfer oil storage depot. Results show: Through the optimization of the operation mode of the storage tank, the parameters of the oil depot external transport and the combination of the equipment. Can make the tank holding gas consumption reduced by 77%, the fuel consumption of the heating furnace is reduced by 3.9%, the oil pump power consumption decreased by 0.2%. Total crude oil storage scheduling cost reduced by 5.7%, this scheme has better optimization effect.

On Regularization in Design for Reliability for Nonlinear Planar Beam-Type Resonators

Robustness is a highly desirable quality in microelectromechanical systems (MEMS). Sensors and resonators operating on nonlinear dynamic principles such as internal resonances are no exception to this, and in addition, when nonlinear dynamic phenomena are used to enhance device sensitivity, their requirements for robustness may even be greater. This work discusses two aspects as they relate to the robustness and performance of nonlinear resonators. In the first aspect, different resonator designs are compared to find which among them have a better capacity to deliver reliable and reproducible performance in face of variations from the nominal design due to manufacturing process uncertainties/tolerances. The second aspect attempts to identify the inherent topological features that, if present in a resonator, enhance its robustness. Thus, the first part of this work is concerned with uncertainty analysis of several candidate nonlinear resonators operating under the principle of 1:2 internal resonance and obtained via a hierarchical optimization method introduced by the authors. The second part discusses specific changes to the computational design process that can be made so as to enhance the robustness and reliability of the candidate resonators.

Hierarchical optimization of the composite blade of a stratospheric airship propeller based on genetic algorithm

High-altitude propellers equipped with solar energy systems are widely adopted in stratospheric airships because of their light weight, excellent mechanical performance, and high efficiency. To optimize the composite laminated structure of the blade, a hierarchical optimization method based on genetic algorithm is carried out. Global and local layers are combined according to the structural and loading properties of the blade, and each partitioned region in the local layer is optimized independently. Combined with the finite element method, a subprogram based on the classical lamination theory is developed to simulate the stiffness matrix of the blade and obtain the deflection, weight, etc. as objects. The restricted condition, whether the structure has failed, is determined by the Tsai-Wu criterion. In addition, multiple tasks are delivered and read simultaneously by a specific program for the sake of improving computation efficiency. After verification with a case study, the stacking sequence and thickness of the blade of a stratospheric airship propeller is optimized and an ideal result is obtained.

Handling Geological and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Waterflooding Optimization

Summary Model-based economic optimization of oil production has a significant scope to increase financial life-cycle performance. The net-present-value (NPV) objective in this optimization, because of its nature, focuses on long-term gains, whereas short-term production is not explicitly addressed. At the same time, the achievable NPV is highly uncertain because of strongly varying economic conditions and limited knowledge of the reservoir-model parameters. The prime focus of this work is to develop optimization strategies that balance both long-term and short-term economic objectives and also offer robustness to the long-term NPV. An earlier robust hierarchical optimization method honoring geological uncertainty with robust long-term and short-term NPV objectives serves as a starting base of this work. We address the issue of extending this approach to include economic uncertainty and aim to analyze how the optimal solution reduces the uncertainty in the achieved average NPV. An ensemble of varying oil prices is used to model economic uncertainty with average NPVs as robust objectives in the hierarchical approach. A weighted-sum approach is used with the same objectives to quantify the effect of uncertainty. To reduce uncertainty, a mean-variance-optimization (MVO) objective is then considered to maximize the mean and also minimize the variance. A reduced effect of uncertainty on the long-term NPV is obtained compared with the uncertainty in the mean-optimization (MO) objectives. Last, it is investigated whether, because of the better handling of uncertainty in MVO, a balance between short-term and long-term gains can be naturally obtained by solving a single-objective MVO. Simulation examples show that a faster NPV buildup is naturally achieved by choosing appropriate weighting of the variance term in the MVO objective.

Hierarchical optimization for topology design of multi-material compliant mechanisms

ABSTRACTMulti-material topology optimization enables potential design possibilities in the multiphysics and structural designing fields. In this article, a bi-level hierarchical optimization method is introduced to address the multi-material design of compliant mechanisms. The hierarchical optimization develops decomposition approaches allowing the original complex multi-material optimization problem to be reduced to set of low-order single-material optimization sub-problems. The solution of the complex multi-material problem is found as a vector of the single-material sub-problems solutions. All the local sub-problems are solved with the solid isotropic material with penalization method independently, and a stiffness spreading technique is worked out to coordinate components of the global solution of the original problem. Several numerical examples are presented to demonstrate the validity of this method.

A Hierarchical Energy Management System Based on Hierarchical Optimization for Microgrid Community Economic Operation

In view of the merits and promising applications of microgrid community (MGC), this paper presents a two-level hierarchical optimization method for MGC’s energy management system in smart grid environment. The lower level focuses on an individual microgrid (MG), and the upper level is responsible for managing the MGs and MG community level devices (MCLDs). It is executed consecutively from lower level to upper level in order to minimize operational costs. First, the lower level optimizes the power output of MGs generators, the charge/discharge power of energy storage system, and the exchanged power with upstream grid. The results of the lower level are transferred to the upper level as its constraints. Second, the upper level makes decisions about MCLDs’ powers and the exchanged power between distribution networks based on results from the lower level while ignoring any internal constituents of MG. The model is built in ways that nonlinearity is removed by linearization approximation and nonconvexity is removed by adding implicit logic constraints. A case study based on a real system in South China’s Guangxi Province is analyzed. The results demonstrate the effectiveness of the method. Of the four scenarios tested, two are proved cost-effective and are recommended for MGC operation.

Integrated design of cellular composites using a level-set topology optimization method

This paper proposes a hierarchical multi-scale topology optimization method for the design of integrated materials and structures by taking advantage of both cellular composites and functionally graded materials. The topology optimization involves two scales: firstly, macrostructural design using SIMP to generate an overall multilayered layout with free material distribution involving intermediate densities; and secondly, microstructural design to produce periodic cellular composite for each layer, by integrating the numerical homogenization into a level set approach. Thus, the cellular composites will be characterized by variation in microstructures and the corresponding changes of properties over layers. The proposed method can generate new artificial composites similar to functionally graded materials but layer-based, to achieve multifunctional properties for energy absorption, anti-impact, thermal isolation, etc. Several numerical examples are used to demonstrate the effectiveness of this method.

Plant‐wide hierarchical optimization based on a minimum consumption model

AbstractPlant‐wide optimization for a complex process consisting of multiple unit processes is an effective method to achieve production with high efficiency and low consumption. However, due to the complexities of the plant‐wide optimization problem such as high dimensionality, numerous constraints, and other factors, plant‐wide optimization for a complex process is always a challenging problem in research. Therefore, this paper presents a plant‐wide hierarchical optimization method based on a minimum consumption model which can solve the feasibility and optimality of the obtained technical indices. The plant‐wide optimization problem is converted into a two‐level optimization problem, including the procedure‐level optimization and the process‐level optimization. The presented method can simplify the original plant‐wide problem and effectively reduce the computational complexity. Moreover, the hierarchical decision‐making is achieved simultaneously and the efficiency of solving the optimization problem and the global convergence is improved. Finally, the presented plant‐wide hierarchical optimization method is applied to the hydrometallurgy process of a gold smelter. The results verify the effectiveness of this method.