共查询到19条相似文献,搜索用时 343 毫秒
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在对永磁真空断路器合闸远程控制时,都是以传感网络完成控制信号的传输,但是传感网络易受外界干扰,存在分合闸延迟时间较长、合闸误差率较高的问题。为此,深入研究基于以太网通信的远程控制技术,研究永磁真空断路器合闸自动远程控制方法。首先构建永磁真空断路器合闸控制主体电路简化结构,其中包含断路器本体、驱动电路和传感器;然后通过以太网通信技术,滤波处理后传输断路器状态信息数据;最后使用选相分闸控制,从而限制延时,实现精准控制。实验证明,所提出的控制方法下永磁真空断路器分合闸时间明显缩短,分闸时间在11 ms以内、合闸时间在18 ms以内且合闸误差率降低在0.3%以下,优于传统的传感网络方法。 相似文献
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结合PMSM的数学模型和控制理论新发展,引进先进的"复合型控制策略"用以改进控制器性能,把模糊控制和传统PI控制相结合,提出了模糊-PI双模控制策略。其根据事先给定的偏差范围,实现模糊控制和PI控制的自动切换,并基于此设计了模糊-PI控制的永磁同步电机控制系统模型。在Matlab/Simulink平台上,对系统进行建模和仿真,结果表明,该控制器动态响应迅速,基本消除了稳态误差,具有很好鲁棒性、动态特性和稳定性。 相似文献
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永磁操动机构作为新式真空断路器常用的操动机构,其运行状态决定真空断路器的性能,因此有必要对永磁操动机构进行故障诊断研究。本文以ZW45-12型永磁机构真空断路器为研究对象,对真空断路器永磁机构行程曲线进行特征参数分析,选择断路器启动时间、动作时间、刚分合速度、分合闸平均速度等四个参数作为诊断特征参数;基于断路器分合闸实验数据,对比常用的故障诊断算法,结果表明SVM算法性能最优;基于Spark平台搭建使用SVM算法的永磁机构故障诊断模型,并通过不断调整SVM算法的惩罚参数C和核函数kernel,对诊断模型进行优化。优化的SVM故障诊断模型对永磁机构回路电阻增大、机构卡涩及分闸弹簧单根脱落故障诊断精确率均在90%以上,分闸弹簧单根脱落故障诊断准确率可达96%,可以满足永磁机构故障诊断精度需求。研究结果为永磁机构的故障诊断提供参考。 相似文献
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针对高压断路器三相永磁无刷直流电机机构,研究了不同控制策略下电机操动机构的运动特性.考虑高压断路器的分、合闸操作过程,建立了永磁无刷直流电机操动机构运动控制系统的仿真模型,采用数字式双闲环控制,内环为电流环,采用PI控制,外环为速度环,基于传统PID控制器、单神经元PID控制两种不同控制策略控制.通过对伺服控制系统的仿真分析得到了电机操动机构速度跟踪控制特性.结果表明,单神经元PID控制器能够较好的实现触头速度的跟踪控制,使其按理想运动特性曲线运动,是一种较理想、有效的控制方法. 相似文献
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永磁同步电机具有非线性、强耦合的特性,常规的矢量控制方法难以对其进行精确控制。此外,电机系统易受负载扰动影响,从而产生转速和电磁转矩波动。针对转速环参数固定会导致系统响应速度慢、超调量大的问题,文中提出了一种模糊径向基神经网络PID控制策略,用以替代矢量控制系统中转速环PID控制。将神经网络和模糊控制相结合,基于增量式PID控制方式,利用梯度下降优化算法动态调整转速环中的PID参数。系统模型仿真结果表明,模糊神经网络PID控制的电机系统超调量较小,相较于常规PID控制,新模型在低速和高速运行的启动时间分别缩短了66.7%和75.9%,动态响应更快,具有更好的鲁棒性和抗干扰能力。利用DSP搭建了实验平台,实验结果也证明了该控制方法的有效性。 相似文献
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文章针对传统交流接触器分合闸过程中产生的电弧对触头侵蚀的问题,提出了智能无弧交流接触器全新的控制方法.该接触器以传统交流接触器为主体,实现三相电路的灵活控制,在主电路中每相触头并联一个单向晶闸管,以此来实现接触器在吸合和分断过程中的无弧控制,大大提高了接触器电寿命.并且将智能结构的理念带入了传统交流接触器中,采用ATMEGA 16单片机作为控制核心,使智能无弧交流接触器集数据采集,故障诊断与一体,实现了交流接触器状态的实时监测. 相似文献
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为了克服传统低压开关电动控制系统中所存在的诸如接触器主触头被烧、短路情况下断路器无法正常将电路分断、电机保护功能难以充分发挥等问题,可以通过永磁控制技术的应用加以实现.本文重点就低压开关的电磁控制技术进行探讨,以供参考. 相似文献
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浅谈交流接触器的节能与长寿命 总被引:3,自引:0,他引:3
通过分析传统电磁式交流接触器的不足,分别指出交流接触器节电器、固态交流接触器、永磁式交流接触器、智能型交流接触器和混合交流接触器的特点。通过比较表明,提出永磁智能混合式交流接触器将具有节能效果明显,闭合过程和分断过程无电弧的显著优点,从而将更好地实现交流接触器的性能指标与工作可靠性。 相似文献
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《Mechatronics》2001,11(1):95-117
In this study, the dynamic responses of an adaptive fuzzy neural network (FNN) controlled toggle mechanism is described. The toggle mechanism is driven by a permanent magnet (PM) synchronous servo motor. First, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the motor-toggle servomechanism. Since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the motor-toggle servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Moreover, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed adaptive and adaptive FNN controllers are robust with regard to uncertainties. 相似文献
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《Mechatronics》2000,10(1-2):145-167
A quick-return mechanism, which is driven by a field-oriented control permanent magnet (PM) synchronous servomotor, with fuzzy neural network (FNN) control is proposed in this study. The rod and crank of the quick-return mechanism are assumed to be rigid. First, the machine design of the motor-quick-return mechanism is developed. Next, the kinematic analysis of the quick-return mechanism is introduced. Then, an FNN controller with varied learning rates is implemented to control the motor-quick-return servomechanism without using the complex mathematical model of the motor–mechanism coupled system. Finally, experimental results are provided to demonstrate the effectiveness of the FNN controller. 相似文献
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A hybrid computed torque controller using fuzzy neural network formotor-quick-return servo mechanism
The dynamic response of a hybrid computed torque controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this paper. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque control, a position controller is designed to control the position of a slider of the motor-quick-return servo mechanism. In addition, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a fuzzy neural network (FNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Moreover, a hybrid control system, which combines the computed torque controller, the FNN uncertainty observer, and a compensated controller, is developed based on Lyapunov stability to control the motor-quick-return servo mechanism. The computed torque controller with FNN uncertainty observer is the main tracking controller, and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rule numbers of the FNN. Finally, simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed hybrid computed torque control system are robust with regard to parametric variations and external disturbances 相似文献
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针对各种矿用永磁机构真空断路器的控制需求,实现了基于CPLD的新型智能矿用真空永磁控制器。采用全电子电路无触点软件一体化设计,电源采用宽范围输入、恒压恒流输出的开关电源设计,电压和电流连续可调,具有较高的可靠性;用三极管和IGBT组成的电子开关通断来控制合/分闸的能量,使输出能量可控、可调,保证了矿井的安全和断路器可靠工作。 相似文献
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A speed control of a permanent magnet (PM) synchronous motor using the boundary layer state observer which is insensitive to the nonlinearities and the flux linkage variation of a permanent magnet is derived. This state observer reconstructs the electrical and mechanical states of the motor from the current and voltage measurements. The observer states converge at least to some arbitrary small neighbourhood of the true states.<> 相似文献
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《Mechatronics》2001,11(2):227-250
A supervisory fuzzy neural network (FNN) controller is proposed to control a nonlinear slider-crank mechanism in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive coupled with a slider-crank mechanism and a supervisory FNN position controller. The supervisory FNN controller comprises a sliding mode FNN controller and a supervisory controller. The sliding mode FNN controller combines the advantages of the sliding mode control with robust characteristics and the FNN with on-line learning ability. The supervisory controller is designed to stabilize the system states around a defined bound region. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results are provided to show that the proposed control system is robust with regard to plant parameter variations and external load disturbance. 相似文献