静止无功发生器（SVG）无功电流检测及控制策略研究

发布时间：2018-04-04 03:09

本文选题：静止无功发生器（SVG）　切入点：无功补偿　出处：《辽宁工业大学》2014年硕士论文

【摘要】：随着全球经济的发展，冲击性无功负荷对电网的影响愈加严重。传统的无功补偿装置，由于不能快速动态补偿和补偿范围小的原因无法满足用户需要。静止无功发生器（SVG）作为一种新型静止无功补偿装置，克服了上述不足，显现出了巨大的优越性。该项技术已成为国内外争相研究的重要课题之一。所以，本论文对SVG的关键技术进行了研究。本论文首先介绍了SVG的发展历史、最新发展方向；分析了考虑损耗和不考虑损耗两种情况的等效电路和工作原理；引入了基于三相瞬时无功功率理论（基于旋转矢量积、基于0坐标变换、基于dq0坐标变换）的数学模型。在比较了电流间接控制、电流直接控制两类SVG控制策略优劣的基础上，选择了基于dq0坐标变换的直接电流控制方法。其次，分析比较了各种无功电流检测方法的局限性和适用性；重点介绍了基于神经网络的无功电流检测方法，并研究了其在本论文中的实现原理。然后，对SVG系统总体结构和总体流程进行了研究。并重点设计了主要硬件电路（主电路部分、主控制器部分、信号调理电路部分）和主要软件模块（过零检测模块、PI控制模块）。最后，在MATLAB环境下对用于无功检测的神经网络进行了训练，得到了训练过程图、训练状态图、训练误差曲线和仿真拟合曲线；在Simulink仿真环境下搭建了SVG的系统模型，对感性补偿、容性补偿两种情况进行了仿真分析。
[Abstract]:With the development of global economy, the impact of reactive power load on power grid is becoming more and more serious.The traditional reactive power compensation device can not meet the needs of users because it can not compensate quickly and dynamically and has a small compensation range.Static Var Generator (SVG), as a new static Var compensator, overcomes these disadvantages and shows great superiority.This technology has become one of the most important research topics at home and abroad.Therefore, this paper studies the key technologies of SVG.This paper first introduces the development history and the latest development direction of SVG, analyzes the equivalent circuit and working principle of considering loss and not considering loss, and introduces the theory of three-phase instantaneous reactive power (based on rotating vector product).The mathematical model based on 0 coordinate transformation and dq0 coordinate transformation.On the basis of comparing the advantages and disadvantages of two kinds of SVG control strategies, indirect current control and direct current control, the direct current control method based on dq0 coordinate transformation is selected.Secondly, the limitations and applicability of various reactive current detection methods are analyzed and compared, and the reactive current detection method based on neural network is mainly introduced, and its realization principle in this paper is studied.Then, the overall structure and flow of SVG system are studied.The main hardware circuit (the main circuit part, the main controller part, the signal conditioning circuit part) and the main software module (the zero-crossing detection module and Pi control module) are designed.Finally, the neural network for reactive power detection is trained in MATLAB environment, and the training process diagram, training state diagram, training error curve and simulation fitting curve are obtained, and the system model of SVG is built in the Simulink simulation environment.The inductive compensation and capacitive compensation are simulated and analyzed.
【学位授予单位】：辽宁工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM761.12

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