面向节能的电梯群控系统调度策略研究

发布时间：2018-03-09 06:06

本文选题：电梯群控　切入点：不确定交通流　出处：《湖北工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着工业现代化不断扩大和人们生活水平不断提高,电梯节能及电梯群控系统技术逐渐成为建筑节能降耗的重要部分。保证乘客的基本乘梯指标的同时降低整体能耗已经成为电梯群控技术重要方向。本文的主要内容是解决在装有电能回馈设备的电梯机房中及不确定交通流条件下,实现面向节能的电梯群控调度问题。论文以电梯能耗代价和时间代价为分析基础,以不确定交通流自动识别和转换为核心。具体在如下几方面开展研究:一、针对不确定交通流条件下乘客到达率未知的问题,本文从电梯功率、电梯能量回馈功率与轿厢内人数关系入手,利用统计学规律分析和确定交通流。同时分析单台电梯实际运行状态,从中获取电梯群能耗代价的组成部分,并推导出在装有电能回馈设备的条件下电梯群的能耗代价函数。最后在考虑电梯能耗代价的同时,给出电梯时间代价问题和做相关的分析。二、在确定交通流模式下论文分析并提出一种基于蚁群算法的电梯群调度策略。首先给出蚁群优化算法的基本概念和根据概率统计方法得到的电梯群调度模型,然后结合两者给出相应的调度算法并给出模型约束条件,在保证有序、优先级调度和稳定的前提下,给出相应的仿真模型并对算法进行仿真验证。三、针对不确定交通流模式下的面向电梯群控调度问题,利用电梯能量回馈器参数、电梯参数和楼层参数等信息,构建了一个新的电梯群控调度模型。介绍了基本粒子群算法和大雁-粒子群算法的基本概念,之后给出在不确定交通流模式下的电梯群控调度模型,并且针对电梯群控系统能耗代价和时间代价耦合的问题,提出新的解耦方案,最终通过仿真测试。四、根据本文中的理论和模型数据,搭建了一个面向节能的电梯群控系统仿真测试平台,并对文章提出的算法的性能进行了相关的验证和测试。同时利用实际的电梯能力回馈器、电梯传感器产生的数据,设计不同群控调度算法程序,并通过了相关的测试,对未来设计实际可行的群控策略提供有效的支持。
[Abstract]:With the continuous expansion of industrial modernization and the continuous improvement of people's living standards, The technology of elevator energy saving and elevator group control system has gradually become an important part of building energy saving and consumption reduction. It has become an important direction of elevator group control technology to ensure the passenger's basic ladder index and reduce the overall energy consumption. Capacity is solved in the elevator room equipped with electric energy feedback equipment and under the condition of uncertain traffic flow, Based on the analysis of elevator energy cost and time cost, this paper focuses on the automatic identification and conversion of uncertain traffic flow. The following aspects of the research are carried out: 1. In order to solve the problem of unknown passenger arrival rate under uncertain traffic flow, this paper starts with the relationship between elevator power, elevator energy feedback power and the number of people in the car. The traffic flow is analyzed and determined by statistical law, and the actual running state of a single elevator is analyzed, from which the components of the energy consumption cost of the elevator group are obtained. The energy cost function of elevator group under the condition of electric energy feedback equipment is deduced. Finally, the time cost of elevator is given and the related analysis is made. This paper analyzes and proposes an elevator colony scheduling strategy based on ant colony algorithm under determined traffic flow mode. Firstly, the basic concept of ant colony optimization algorithm and the elevator colony scheduling model based on probability and statistics method are given. Then, the corresponding scheduling algorithms are given and the model constraints are given. On the premise of ensuring order, priority scheduling and stability, the corresponding simulation model is given and the algorithm is verified by simulation. In view of the elevator group control scheduling problem in uncertain traffic flow mode, the elevator energy feedback parameters, elevator parameters and floor parameters are used. In this paper, a new elevator group control scheduling model is constructed, and the basic concepts of the basic particle swarm optimization algorithm and the goose particle swarm optimization algorithm are introduced, and then the elevator group control scheduling model under the uncertain traffic flow mode is given. And aiming at the coupling of energy cost and time cost of elevator group control system, a new decoupling scheme is proposed, which finally passes the simulation test. Fourthly, according to the theory and model data in this paper, A simulation test platform for energy saving elevator group control system is built, and the performance of the algorithm proposed in this paper is verified and tested. At the same time, using the actual elevator capability feedback device, the data generated by elevator sensor are used. Different group control scheduling algorithms are designed, and relevant tests are carried out to provide effective support for the design of practical and feasible group control strategies in the future.
【学位授予单位】：湖北工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TU857

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