Optimization Of Control Strategy Research Articles

Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System

Intelligent Transportation Systems (ITS) have the potential to improve traffic conditions and reduce travel delays. As a decision support software system for ITS, DynasTIM is based on the principle of dynamic traffic assignment and developed for real-time online simulation, prediction and optimization of dynamic traffic flows in urban or expressway networks. This paper introduces the models, algorithms and some typical applications of DynasTIM. The main contents include: the functional architecture; the application architecture of the system; dynamic OD (Origin-Destination) flows estimation method with novel formula for assignment matrix computation; mesoscopic traffic model using variable-length speed influence region and calibrating speed online based on connected vehicles data; and parallel SPSA algorithm based urban area signal optimization method. The functions of DynasTIM are implemented basically through three main modules: state estimation (ES), state prediction and control strategy optimization (PS&CSO), and guidance strategy optimization (GSO). The case study is aimed at the populated Futian Central Business District (CBD) road network in Shenzhen, China, which has an area of about 7 square kilometers. Based on the archived turning counts collected from 359 video traffic detection locations, DynasTIM was calibrated offline for this network, in order to validate the capability of simulating actual traffic conditions, and to set up basic conditions for testing signal optimization methods. The results show that the simulation output flows of DynasTIM have fairly good matching accuracy with the real surveillance flows in the field. Furthermore, for the CBD network with 38 signalized intersections, the signal optimization method is evaluated and better signal timing plans are found which can reduce about 13% average travel delay, compared with the signal plans currently implemented in the field.

Robust collaborative passenger flow control on a congested metro line: A joint optimization with train timetabling

With the rapid increase in residents in megacities, the passenger demand of metro systems is rising sharply and steadily, bringing immense pressure to train operations. To improve the service quality, this paper discusses systematically investigating a joint optimization of the robust passenger flow control strategy and train timetable on a congested metro line. A deterministic model for train timetabling and passenger flow control at each station is first developed to make a trade-off between operation efficiency and service fairness. Then, the uncertain passenger demand is further considered at each station, and three integer linear programming models are formulated to derive the robust passenger flow control strategies. The first two models are related to the technique of Light Robustness, in which the uncertainty is handled by inserting expected protection levels at stations or on trains. In addition, with a stochastic scenario set that characterizes the uncertain passenger information, the last model aims to find a solution that is feasible for all involved scenarios, and thus, reduces the impact of the uncertainty in metro systems. To improve the computational efficiency of large-scale instances, a customized decomposition-based algorithm is developed. Finally, some real-world case studies based on the operation data of the Beijing metro Batong line are conducted to verify the performance and effectiveness of the proposed approaches.

Optimisation of Model Predictive Torque Control Strategy with Standard and Multi-Objective Genetic Algorithms

Abstract In this paper, the flux error-related weighting factor (WF) of the predictive torque control (PTC) strategy for induction motor (IM) control is optimised by a standard genetic algorithm (SGA) through speed errors only and multi-objective genetic algorithm (MOGA) through torque and flux errors. This paper compares the performances of both optimisation methods. Compared to MOGA, SGA offers a straightforward way to select WF and does not need a decision-making method to choose a final solution. But MOGA considers the given problem in a multi-objective way and directly optimises the control objectives of the PTC strate-gy. Comparisons are made over the flux and torque ripples, total harmonic distortion of stator phase current, and average switching frequency for different operating conditions. Simulation results show that both methods choose a close WF value. Consequently, SGA stands out in the optimisation of the PTC strategy with its simple structure.

Optimization of Engine Control Strategies for Low Fuel Consumption in Heavy-Duty Commercial Vehicles

Optimization of Engine Control Strategies for Low Fuel Consumption in Heavy-Duty Commercial Vehicles

Experimental Study on Flow Characteristics of Large-scale Liquid Oxygen Delivery System

To deeply explore the flow law of the large-scale liquid oxygen delivery system, several liquid oxygen delivery tests have been conducted and measured. On this basis, the model of the pipeline characteristic of the liquid oxygen delivery system is constructed according to the hydrodynamic theory to obtain the relationship equation between the gas cushion pressure and liquid oxygen flow, and the frequency conversion model of the liquid oxygen pump is constructed according to the pump affinity laws to obtain the relationship equation between the liquid oxygen pump frequency, flow, and head. Under the working pressure limitation of the storage tank, liquid oxygen pump, and pipeline, the ultimate capacity of the liquid oxygen delivery system is calculated using the above models. This provides a database and theoretical basis for further optimization of liquid oxygen delivery process parameters and control strategies, as well as evaluation and exploitation of system performance potentials.

Analysis of Additional Damping Control Strategy and Parameter Optimization for Improving Small Signal Stability of VSC-HVDC System

The voltage source converter based high voltage direct current (VSC-HVDC) system is based on voltage source converter, and its control system is more complex. Also affected by the fast control of power electronics, oscillation phenomenon in wide frequency domain may occur. To address the problem of small signal stability of the VSC-HVDC system, a converter control strategy is designed to improve its small signal stability, and the risk of system oscillation is reduced by attaching a damping controller and optimizing the control parameters. Based on the modeling of the VSC-HVDC system, the general architecture of the inner and outer loop control of the VSC-HVDC converter is established; and the damping controllers for DC control and AC control are designed in the phase-locked loop and the inner and outer loop control parts respectively; the state-space state model of the control system is established to analyze its performance. And the electromagnetic transient simulation model is built on the PSCAD/EMTDC simulation platform to verify the accuracy of the small signal model. The influence of the parameters of each control part on the stability of the system is summarized. The main control parts affecting stability are optimized for the phenomenon of oscillation due to changes in operation mode occurring on the AC side due to faults and other reasons, which effectively eliminates system oscillation and improves system small signal stability, providing a certain reference for engineering design.

Optimizing Solar Power Using Array Topology Reconfiguration With Regularized Deep Neural Networks

Reconfiguring photovoltaic (PV) array connections among different topologies such as series-parallel, bridge-link, honeycomb, or total-cross-tied is a popular strategy to mitigate impediments in power production caused by partial shading. Conventional approaches rely on either by-passing or replacing shaded modules with auxiliary panels through complex control mechanisms, optimization strategies, or simulator driven approaches to obtain the best topology. However, these solutions are not scalable and incur significant installation costs and computational overhead, motivating the need to develop ‘smart’ methods for topology reconfiguration. To this end, we propose a regularized neural network based algorithm that leverages panel-level sensor data to reconfigure the array to the topology that maximizes power output under arbitrary shading conditions. Based on our simulations that include wiring losses in different configurations, we observe power improvement of up to 11% through reconfiguration. The proposed algorithm can be easily integrated in any cyber-physical PV system with reconfiguration capabilities and is scalable.

A review of optimization of control strategies for non-linear input solar PV system

In recent years, solar photovoltaic (PV) units have rapidly grown to become a major contributor to the installed generating capacity worldwide. However, the intermittent and fluctuating nature of renewable generation can negatively impact power quality and generating units. The backbone of these problems is often related to the control system of the entire network. In a solar-based generation, extracting maximum power known as Maximum Power Point Tracking (MPPT), is a major challenge due to its dependence on climatic conditions and location. This research aims to review the steps taken to enhance output by using different control systems for maximum power point extraction and to achieve optimization in the control system technique, resulting in the best output of any location's current solar radiation scenario.

Control Strategy Optimization of Photovoltaic Energy Storage System in an Office Building

Control Strategy Optimization of Photovoltaic Energy Storage System in an Office Building

Regenerative braking control strategy optimization based on hybrid power source and PSO

Regenerative braking control strategy optimization based on hybrid power source and PSO

Intersection control strategy optimization based on exchangeable lanes modeling

Purpose Due to the conflicts between left turn traffic and opposite straight-going traffic in urban traffic network, some of the traffic lanes cannot be used to discharge vehicles during its green phases and the intersection capacity can be greatly reduced. This study/paper aims to reduce the effect of conflicts and increase its capacity through the reasonable pre-signal phase time with the exchangeable lanes. Design/methodology/approach This paper took into consideration various influence factors to intersection capacity and formulated the capacity optimization model based on 0-1 mixed-integer programming model. This model is efficiently solved by standard branch-and-bound algorithms. Findings The authors took an intersection as an example and solved the optimal signal timing and entrance lane capacity via this model. Then, simulations were carried out to verify the effect of the exchangeable lanes strategy of this intersection through the simulation software VISSIM and take the traffic volume and delay as outputs, which indicated that this model has better performance. Originality/value The front-end control strategy can not only exploit the full potential of the intersection but also significantly improve the operational efficiency of the intersection. It plays a positive role in improving urban intersection congestion.

Numerical analysis onto thermal balance behaviors of motorized spindle unit

Generally, the accuracy stability of a precision motorized spindle unit is influenced by its structural thermal balance behaviors (power matching degree of spindle structural heat generation–dissipations). For the accurate analysis of this spindle heat transfer behaviors based on temperature detections, this paper describes a method constructed with the numerical simulation technology and a proposed genetic algorithm-extreme learning machine (GA-ELM) algorithm. Firstly, the heat–fluid–solid coupling FE transient model is established to simulate spindle thermal behaviors, determinations of its thermal loads/boundary conditions are initially based on empirical calculations. Secondly, according to spindle temperature detections, these initial values are applied to thermal simulations and corrected by GA to make simulated spindle temperatures gradually approach detections. Specially, ELM is adopted to estimate the functional relationships from the parent population to the child population generated by genetic operators with increasing fitness values, and the trained ELM model is utilized to ensure the GA faster convergence. Eventually, based on the corrected thermal load/boundary condition values and simulation results, the time-varying power matching conditions of spindle structural heat generation–dissipation are analyzed. This study provides a theoretical basis for the optimization and promotion of spindle structural design and coolant control strategy.

Control Strategy and Parameter Optimization Based on Grid Side Current Dynamic Change Rate for Doubly-Fed Wind Turbine High Voltage Ride Through

High voltage ride through (HVRT) control strategies for doubly-fed induction wind turbines (DFIG) have mostly focused on the rotor side converter, while the reactive power compensation capability of the grid side converter and the impact of grid side converter current transients have often been ignored on the DC bus voltage and reactive power. Therefore, a control strategy based on grid side current dynamic change characteristics is proposed, which resets the reference values of the grid side active and reactive currents for wind turbine HVRT to ensure partial absorption of reactive power on the grid side. Secondly, the key parameter in the proposed control strategy is optimization to get the most suitable DC bus voltage value with the grey wolf algorithm. Finally, the grid-integrated wind turbine simulation model is built on the MATLAB/SIMULINK and RT-Lab platforms. The simulation test results show that the proposed HVRT control strategy and its parameter optimization method are effective, DFIG can achieve HVRT when the wind turbine voltage rises to 1.3 pu.

Optimization of Control Strategy for Orderly Charging of Electric Vehicles in Mountainous Cities

In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging.

Optimization of Adiabatic Control Strategies Along Non-mixing Curves with Singularities

In this paper, we consider a system driven by a controlled Schrödinger equation with two external control inputs. Motivated by applications to the control of quantum systems having conical or semi-conical eigenvalue intersections, we propose to study the singularities and the parametric bifurcations of the associated non-mixing field, along whose integral curves in the space of controls the adiabatic approximation holds with higher precision. Our results can be applied to optimize the adiabatic control strategies of well known quantum systems such as Qubit systems, Stirap Processes and Eberly-Law models.

Analysis of HIV transmission hotspots and characteristics of cross-regional transmission in Guangxi Zhuang Autonomous Region based on molecular network

Objective: To analyze HIV transmission hotspots and characteristics of cross-regional transmission in Guangxi Zhuang autonomous region (Guangxi) based on the molecular network analysis, and provide evidence for optimization of precise AIDS prevention and control strategies. Methods: A total of 5 996 HIV pol sequences sampled from Guangxi between 1997 and 2020 were analyzed together with 165 534 published HIV pol sequences sampled from other regions. HIV-TRACE was used to construct molecular network in a pairwise genetic distance threshold of 0.5%. Results: The proportion of HIV sequences entering the molecular network of HIV transmission hotspots in Guangxi was 31.5% (1 886/5 996). In the molecular network of HIV cross-regional transmission, the links within Guangxi accounted for 51.6% (2 613/5 062), the links between Guangxi and other provinces in China accounted for 48.0% (2 430/5 062), and the links between Guangxi and other countries accounted for 0.4% (19/5 062). The main regions which had cross-regional linked with Guangxi were Guangdong (49.5%, 1 212/2 449), Beijing (17.5%, 430/2 449), Shanghai (6.9%, 168/2 449), Sichuan (5.7%, 140/2 449), Yunnan (4.2%, 102/2 449), Shaanxi (3.8%, 93/2 449), Zhejiang (2.8%, 69/2 449), Hainan (2.0%, 49/2 449), Anhui (1.5%, 37/2 449), Jiangsu (1.3%, 33/2 449), and other regions (each one <1.0%), respectively. The risk factors of entering the molecular network of HIV transmission hotspots in Guangxi included being aged ≥50 years (compared with being aged 25-49 years, aOR=1.68,95%CI:1.46-1.95), males (compared with females, aOR=1.21,95%CI:1.05-1.40), being single (compared with being married, aOR=1.18,95%CI:1.00-1.39), having education level of high school or above (compared with having education level of junior high school or below, aOR=1.21,95%CI:1.04-1.42), acquired HIV through homosexual intercourse (compared with acquired with HIV through heterosexual intercourse, aOR=1.77, 95%CI:1.48-2.12). The risk factors of cross-regional transmission included males (compared with females, aOR=1.74,95%CI:1.13-2.75), having education level of high school or above (compared with having education level of junior high school or below, aOR=1.96,95%CI:1.43-2.69), being freelancer/unemployed/retired (compared with being farmers, aOR=1.50,95%CI:1.07-2.11), acquired HIV through homosexual intercourse (compared with acquired with HIV through heterosexual intercourse, aOR=3.28,95%CI:2.30-4.72). Conclusion: There are HIV transmission hotspots in Guangxi. Guangxi and other provinces in China form a complex cross-regional transmission network. Future studies should carry out social network surveys in high-risk populations inferred from the molecular network analysis for the timely identification of hidden transmission chains and reduction of the second-generation transmission of HIV.

Digital twin key technology on rare earth process

Digital twin can be defined as a digital equivalent of an object of which it can mirror its behavior and status or virtual replicas of real physical entities in Cyberspace. To an extent, it also can simulate and predict the states of equipment or systems through smart algorithms and massive data. Hence, the digital twin is emerging used in intelligent manufacturing Systems in real-time and predicting system failure and also has introduced into a variety of traditional industries such as construction, Agriculture. Rare earth production is a typical process industry, and its Extraction Process enjoys the top priority in the industry. However, the extraction process is usually characterized by nonlinear behavior, large time delays, and strong coupling of various process variables. In case of failures happened in the process, the whole line would be shut down. Therefore, the digital twin is introduced into the design of process simulation to promote the efficiency and intelligent level of the Extraction Process. This paper proposes the techniques to build the rare earth digital twin such as soft measurement of component content, component content process simulation, control optimization strategy, and virtual workshop, etc. At the end, the validity of the model is verified, and a case study is conducted to verify the feasibility of the whole Digital twin framework.

Modeling and Optimizing Based on OTCPN in Multi-Tier Shuttle Warehousing System

This paper models the multi-tier shuttle warehousing system and optimizes the system structure and control strategy. This kind of system is a discrete event dynamic system because it only takes place at discrete time points, while previous warehousing system models. We propose the object-oriented timed colored Petri net to model it, to improve the accuracy of the model and avoid the problem of status explosion in the process of Petri net modeling. We propose two control strategy optimization methods: one is to balance the task assignment, and the other is to change the return time point of the buffer status information. The results of numerical experiments show that consideration of both optimization strategies can reduce the total picking time by 40 % ∼ 62 % . Additionally, we analyze the influence of the buffer capacity. The results show that the buffer capacity can reduce the total picking time, and enough amount of buffer capacity has the same effect as control strategy optimization.

Virtual pre-test analysis for optimization of multi-channel control strategies in direct field acoustic testing

Direct field acoustic excitation testing, or DFAX, is gaining the attention of the space industry as a reliable and flexible method to perform acoustic qualification tests of spacecrafts. The test aims at reproducing the sound conditions of the space launch using an array of high-powered loudspeakers placed around the test specimen. To achieve this end, advanced multi-input multi-output (MIMO) control strategies are employed to design the independent signals which drive the different speakers based on the responses acquired at the control locations over the test volume. Consequently, the performance and overall efficiency of the test campaign is then determined by a number of factors, among them stack configuration, test specimen orientation, number of speakers, control matrix and sensor location. To assist engineers in the design of such tests, this work proposes the use of numerical models to simulate the entire test set-up before the on-site campaign takes place. State-of-the-art vibro-acoustic solvers are employed to predict the pressure responses at all points in the test volume for various design choices, allowing test engineers to perform a virtual pre-test analysis based on numerical solutions to optimize their control strategy. The results included in this work illustrate the methodology followed to account for all the transfer functions of the electro-acoustic system as well as the validation of the levels of accuracy which can be achieved in a full-scale DFAX test via direct comparison with experimental measurements.

Research on Wet Clutch Switching Quality in the Shifting Stage of an Agricultural Tractor Transmission System

In order to improve the working quality of wet clutch switching in an agricultural tractor, in this paper, we took a power shift system composed of multiple wet clutches as the research object for full-factorial performance measurement, multi-factor analysis of the degree of influence, establishment of a single evaluation index model, formation of a comprehensive evaluation index, and formulation of adjustable factor control strategies. We studied the simulation test platform of an agricultural tractor power transmission system based on the SimulationX software and obtained 225 sets of sample data under a full-use condition. Partial least squares and range analysis were applied to comprehensively analyze the influence of multiple factors on the working quality of wet clutches. In this paper, we proposed a modeling method for a single evaluation index of the wet clutch (combined with polynomial regression and tentative method, the goal is determined in the form of a model with the maximum coefficient of determination) and two control strategy optimization methods for the wet clutch adjustable factors, i.e., Method 1 (integrated optimization) and Method 2 (step-by-step optimization), both methods were based on an improved genetic algorithm. The results showed that oil pressure, flow rate, and load had significant effects on the dynamic load characteristics (the degrees were 0.38, −0.44, and −0.63, respectively, with a negative sign representing an inverse correlation); rate of flow and load had significant effects on speed drop characteristics (the degrees were −0.56 and 0.73, respectively). A multivariate first-order linear model accurately described the dynamic load characteristics (R2 = 0.9371). The accuracy of the dynamic load characteristic model was improved by 5.5037% after adding the second-order term and interaction term of oil pressure. The polynomial model containing the first-order oil pressure, first-order flow rate, second-order flow rate, and interaction terms could explain the speed drop characteristics, with an R2 of 0.9927. If agricultural tractors operate under medium and large loads, the oil pressure and flow rate in their definitional domains should be small and large values, respectively; if operating under small loads, both oil pressure and flow rate should be high. When the wet clutch dynamic load and speed drop characteristics were improved, the sliding friction energy loss also decreased synchronously (the reduction could reach 70.19%).