中央空调并联变频泵组群控优化研究

中央空调循环泵组全年运行时间长,在空调系统中能耗占比较高,但由于设计选型和调控水平的限制,通常运行在低效区间导致大量能源浪费且加减泵时管网流量、压力波动大,易造成系统不稳定。文中提出一种能够准确表征并联泵组变频运行特性的数学模型和群控优化策略,通过模型仿真,实时在线优化管网当前流量、扬程需求下的水泵运行台数和频率,并应用于某机场能源站二级泵组群控优化。结果表明,该模型预测精度高,通过模型在线群控优化,可使二级泵组在中低负荷工况下节能20%~60%,并且在加减泵过程中,能够避免管网压力和流量大幅波动,保证系统安全稳定运行;泵组群控在线优化系统投入使用后,2023年5月、6月泵组输送系统同比提升23.9%、14.8%,节能效果显著。

一种光伏电站群控组合优化方法

针对光伏电站的群控组合问题,提出了基于效率穷举的群控组合优化方法,借之分析了群控系统中逆变器数量与欧洲效率之间的关系。进而提出了逆变器最大可行台数与群控单元的概念,以之作为群控技术实用化的一种手段:根据光伏电站容量的要求,形成群控单元的组合方式,对于不同的群控单元组合方式,采用效率穷举法求出其欧洲效率,然后通过对欧洲效率提高程度的分析得出可选优化组合。最后以3MW光伏电站为例进行了逆变器群控组合的优化设计,采用群控单元的群控技术有望在实际工程中得到应用,以达到有效提高系统发电量并具有较好经济性的目的。

基于变频控制的空调系统能耗优化研究

本研究探讨变频控制技术在空调水系统中的应用,定温差和定压差优化策略,降低冷冻水与冷却水系统的能耗,研究建立了群控优化模型,对冷水机组、水泵和冷却塔的能耗数据进行监测与分析,验证了优化方案的节能效果。结果表明,定温差和定压差控制技术显著提高了系统能效比,有效延长了设备使用寿命,为建筑空调系统的高效节能提供了技术支持和理论依据。

Application and optimization design of non-obstructive particle damping-phononic crystal vibration isolator in viaduct structure-borne noise reduction

The problems associated with vibrations of viaducts and low-frequency structural noise radiation caused by train excitation continue to increase in importance.A new floating-slab track vibration isolator-non-obstructive particle damping-phononic crystal vibration isolator is proposed herein,which uses the particle damping vibration absorption technology and bandgap vibration control theory.The vibration reduction performance of the NOPD-PCVI was analyzed from the perspective of vibration control.The paper explores the structure-borne noise reduction performance of the NOPD-PCVIs installed on different bridge structures under varying service conditions encountered in practical engineering applications.The load transferred to the bridge is obtained from a coupled train-FST-bridge analytical model considering the different structural parameters of bridges.The vibration responses are obtained using the finite element method,while the structural noise radiation is simulated using the frequency-domain boundary element method.Using the particle swarm optimization algorithm,the parameters of the NOPD-PCVI are optimized so that its frequency bandgap matches the dominant bridge structural noise frequency range.The noise reduction performance of the NOPD-PCVIs is compared to the steel-spring isolation under different service conditions.

NSGA-Ⅱ based traffic signal control optimization algorithm for over-saturated intersection group

In order to improve the efficiency of traffic signal control for an over-saturated intersection group, a nondominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ） based traffic signal control optimization algorithm is proposed. The throughput maximum and average queue ratio minimum for the critical route of the intersection group are selected as the optimization objectives of the traffic signal control for the over-saturated condition. The consequences of the efficiency between traffic signal timing plans generated by the proposed algorithm and a commonly utilized signal timing optimization software Synchro are compared in a VISSIM signal control application programming interfaces （SCAPI） simulation environment by using real filed observed traffic data. The simulation results indicate that the signal timing plan generated by the proposed algorithm is more efficient in managing oversaturated flows at intersection groups, and, thus, it has the capability of optimizing signal timing under the over-saturated conditions.

Preference-based multiobjective artificial bee colony algorithm for optimization of superheated steam temperature control

In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel reference point based preference expression method is addressed.The fitness assignment function is defined based on the nondominated rank and the newly defined preference distance.An archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the archive.The experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved simultaneously.The simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.

面向综合经济效益最大化的全厂负荷分配

针对除尘、脱硫、脱硝改造后全厂负荷优化分配问题,从提高综合经济效益角度出发,提出一种全厂负荷分配模型及其求解算法。基于机组实际运行数据构建供电煤耗和多种污染物排放浓度特性模型,基于除尘、脱硫、脱硝补偿电价和污染物排放费用标准,建立面向综合经济效益最大化的负荷分配模型。根据机组当前负荷构建自适应负荷上下限约束,基于磨煤机出力交替区间构建负荷禁止分配区约束,采用动态罚函数法进行约束条件处理。在标准粒子群优化算法中引入可控随机搜索的速度因子和动态调整的最大速度限制因子,平衡算法的计算开销和寻优能力。某火电厂4台机组的负荷分配仿真实验验证了所提算法的有效性。

Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor

This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.

Advanced Coverage Optimization Techniques for Small Cell Clusters

Coverage challenge for small considered to be a optlmlzation is a main cell clusters which are promising solution to provide seamless cellular coverage for large indoor or outdoor areas. This paper focuses on small cell cluster coverage problems and proposes both centralized and distributed self-optimization methods. Modified Particle swarm optimization （MPSO） is introduced to centralized optimization which employs particle swarm optimization （PSO） and introduces a heuristic power control scheme to accelerate the algorithm to search tbr the global optimum solution. Distributed coverage optimization is modeled as a non-cooperative game, with a utility function considering both throughput and interference. An iterative power control algorithm is then proposed using game theory （DGT） which converges to Nash Equilibrium （NE）. Simulation results show that both MPSO and DGT have excellent performance in coverage optimization and outperform optimization using simulated annealing algorithm （SA）, reaching higher coverage ratio and throughput while with less iterations.

Particle Swarm Optimization based predictive control of Proton Exchange Membrane Fuel Cell (PEMFC)

Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidification system and output performance of PEMFC stack are briefly analyzed. Predictive control of PEMFC based on Support Vector Regression Machine (SVRM) is presented and the SVRM is constructed. The processing plant is modelled on SVRM and the predictive control law is obtained by using Particle Swarm Optimization (PSO). The simulation and the results showed that the SVRM and the PSO re-ceding optimization applied to the PEMFC predictive control yielded good performance.

Intelligent anti-swing control for bridge crane

A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural networks were used to approach the uncertainties of the positioning subsystem,lifting-rope subsystem and anti-swing subsystem.Then,the parameters of the controller were optimized with PSO to enable the system to have good dynamic performances.During the process of high-speed load hoisting and dropping,this method can not only realize the accurate position of the trolley and eliminate the sway of the load in spite of existing uncertainties,and the maximum swing angle is only ±0.1 rad,but also completely eliminate the chattering of conventional sliding mode control and improve the robustness of system.The simulation results show the correctness and validity of this method.

PSO-based Control Algorithm for Polarization Mode Dispersion Self-adaptive Compensation

Polarization mode dispersion(PMD) is considered to be the ultimate limitation in high-speed optical fiber communication systems. Establishing an effective control algorithm for adaptive PMD compensation is a challenging task, because PMD possesses the time-varying and statistical properties. The particle swarm optimization(PSO) algorithm is introduced into self-adaptive PMD compensation as feedback control algorithm. The experiment results show that PSO-based control algorithm has some unique features of rapid convergence to the global optimum without being trapped in local sub-optima and good robustness to noise in the optical fiber transmission line that has never been achieved in PMD compensation before.

Improved S Surface Controller and Semi-physical Simulation for AUV

S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV).However, it is difficult to adjust their control parameters manually.Choosing the optimum parameters for the controller of a particular AUV is a significant challenge.To automate the process, a modified particle swarm optimization (MPSO) algorithm was proposed.It was based on immune theory, and used a nonlinear regression strategy for inertia weight to optimize AUV control parameters.A semi-physical simulation system for the AUV was developed as a platform to verify the proposed control method, and its structure was considered.The simulation results indicated that the semi-physical simulation platform was helpful, the optimization algorithm has good local and global searching abilities, and the method can be reliably used for an AUV.

Robust Design Method for Adjustable Mechanisms

Most conventional robust design methods assume design solutions are fixed values. Using these methods, designers set each control factor to a fixed value to maximize the robustness of objective characteristics. However, fluctuations in the objective characteristic often exceed the allowable range in a design problem. Consequently, it is difficult to obtain sufficient robustness using conventional methods. This research defines adjustable control factors whose values can be adjusted within a given range to increase robustness and proposes a method to calculate robustness, including factors to adjust the objective characteristic and derive optimum ranges of the factors. The robustness index, which indicates the feasibility that the objective characteristic values are within the tolerance by the adjustment, is calculated by the Monte Carlo method, while the range of adjustable control factors is optimized using the Vector evaluated particle swarm optimization. Finally, an engineering example is presented to demonstrate the applicability of the proposed method.

Elevator Group-Control Policy Based on Neural Network Optimized by Genetic Algorithm

Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algorithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.

Control of Neural Network Feedback Linearization Based on Chaotic Particle Swarm Optimization

A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The new algorithm combines the particle swarm algorithm and the chaotic optimization, using randomness and ergodicity of chaos to overcome the premature convergence of the particle swarm optimization. At the same time, a new neural network feedback linearization control system is built to control the single-machine infinite-bus system. The network parameters are trained by the chaos particle swarm algorithm, which makes the control achieve optimization and the control law of prime mover output torque obtained. Finally, numerical simulation and practical application validate the effectiveness of the method.

Coordination of Synergetic Excitation Controller and SVC Damping Controller Using Particle Swarm Optimization

This paper addresses the enhancement of power system stability by simultaneous tuning of synergetic excitation damping controller and SVC （static var compensator）-based damping controllers. Each machine or generator is considered as a subsystem and its interaction with the remaining part of the system, the SVC inclusive, is modeled as a quadratic function of the active power delivered by the generator. Stable manifold is constructed for each excitation controller and based on that, an effective damping controller is derived. A lead-lag compensator is employed as a supplementary controller for the SVC. PSO （particle swarm optimization） algorithm is effectively utilized to simultaneously tune the parameters for the excitation damping controller（s） and the SVC supplementary controller. The coordination of the controllers effectively dampens the power angle oscillation and regulates the generator terminal voltage when a fault occurs. Simulation results are obtained by using the PAT （power analysis toolbox） for a SMIB （single machine infinite bus） system and a two area power system.

Accelerated Particle Swarm Optimization for Controlling Virtual Power Plant Consisting of Renewable Energy Sources

RES （renewable energy sources）, such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of VPP （virtual power plant） has been developed. The VPP is composed of several RES, from which at least one of them is fully controllable. Because the production of noncontrollable RES can not be forecasted perfectly, therefore an optimal dispatch schedule within VPP is needed. To address this problem, an APSO （accelerated particle swarm optimization） is used to solve the constrained optimal dispatch problem within VPP. The experimental results show that the proposed optimization method provides high quality solutions while meeting constraints.

分布式能源系统发展概述

随着我国能源系统结构体系的进一步完善,能源行业在我国经济发展中的主要任务已从保障国民经济基本能源需求转移到提高供能系统质量和效率上来。基于我国自然资源分布不均衡、能源需求地域分布不均衡的特点.以及国家对环境问题的持续重视情况,分布式能源系统将是能源行业体系优化的一个重要发展方向。

Nonholonomic motion planning for a free-falling cat using spline approximation

An optimal motion planning of a free-falling cat based on the spline approximation is investigated.Nonholonomicity arises in a free-falling cat subjected to nonintegrable velocity constraints or nonintegrable conservation laws.The equation of dynamics of a free-falling cat is obtained by using the model of two symmetric rigid bodies.The control of the system can be converted to the motion planning problem for a driftless system.A cost function is used to incorporate the final errors and control energy.The motion planning is to determine control inputs to minimize the cost function and is formulated as an infinite dimensional optimal control problem.By using the control parameterization,the infinite dimensional optimal control problem can be transformed to a finite dimensional one.The particle swarm optimization(PSO) algorithm with the cubic spline approximation is proposed to solve the finite dimension optimal control problem.The cubic spline approximation is introduced to realize the control parameterization.The resulting controls are smooth and the initial and terminal values of the control inputs are zeros,so they can be easily generated by experiment.Simulations are also performed for the nonholonomic motion planning of a free-falling cat.Simulated experimental results show that the proposed algorithm is more effective than the Newtoian algorithm.