Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Line Fault Detection of DC Distribution Networks Using the Artificial Neural Network

ADC distribution network is an effective solution for increasing renewable energy utilization with distinct benefits,such as high efficiency and easy control.However,a sudden increase in the current after the occurrence of faults in the network may adversely affect network stability.This study proposes an artificial neural network(ANN)-based fault detection and protection method for DC distribution networks.The ANN is applied to a classifier for different faults ontheDC line.The backpropagationneuralnetwork is used to predict the line current,and the fault detection threshold is obtained on the basis of the difference between the predicted current and the actual current.The proposed method only uses local signals,with no requirement of a strict communication link.Simulation experiments are conducted for the proposed algorithm on a two-terminal DC distribution network modeled in the PSCAD/EMTDC and developed on the MATLAB platform.The results confirm that the proposed method can accurately detect and classify line faults within a few milliseconds and is not affected by fault locations,fault resistance,noise,and communication delay.

Study on the Detection Methods of DC Series Arc Fault

It is difficult to detect and extinguish direct current(DC)arc in power electronics systems,and the arc could easily lead to a fire and cause great damage to surrounding equipment.A DC arc generation simulation unit is established,in which DC series arcs are generated by dragging the moving electrode away from the fixed one with the help of the stepper motor.In addition,a ferrite rod antenna is used to receive the electromagnetic radiation signals induced by the arcs.Based on experiments using the unit,the general characteristics of DC arc,including the pulse characteristics of arc current and source output in corresponding time window,and the frequency-domain characteristics of arc current,are studied.With discussion on three detection methods,it is concluded that the variation of current and voltage of arc,the spectrum of the arc current during the discontinuous intervals and the radiating electromagnetic signal are all features that can be adopted for detecting DC series arc.Therefore,a synthetic judgment method is suggested for further study.

Simulation Research of Fault Model of Detecting Rotor Dynamic Eccentricity in Brushless DC Motor Based on Motor Current Signature Analysis

基于Ansoft／Maxwell设置动态偏心故障，建立求解电机电感和磁链的有限元模型，通过仿真，证明了将感应电机动态偏心故障的特征频率经过简化后，同样适用于无刷直流电动机。基于Ansoft／Simplorer建立无刷直流电动机系统的仿真模型。在电机稳态运行下，对定子电流进行傅里叶分析，研究并建立基于定子电流监测动态偏心故障的仿真模型：动态偏心故障与特征频率的关系、动态偏心故障程度与特征频率幅值的关系。进而研究了无刷直流电动机稳态运行时转速波动对偏心故障监测的影响。仿真结果表明，转子偏心程度加大，特征频率的幅值增加。

基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断

目的为了解决双向DC-DC电力变换器的软故障诊断精度不高的问题,方法提出基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断模型。首先,分析双向DC-DC电力变换器中电容、电感和MOSFET管的故障机理,通过仿真实验模拟各元件失效后变换器的输出电气参数变化,从而确定变换器不同元件故障时对应的故障特征参数;其次,构建改进的LSTM-SVM双向DC-DC电力变换器故障诊断组合模型,在LSTM中添加Mogrifier门机制,提高LSTM提取时间序列原始数据中微弱特征的能力;最后,由于传统LSTM的末端分类器为Softmax,其主要解决单一元件诊断问题,变换器故障类型较多,维数较高,所以采用麻雀搜索算法优化的SVM代替原有的Softmax函数,对LSTM输出的数据进行故障分类,提高故障诊断的准确率。设置双向DC-DC电力变换器充放电两种状态下,包含电解电容、电感和MOSFET单双管故障在内的24组故障,分别采用本文构建的改进的LSTM-SVM和原始的LSTM-SVM双向DC-DC变换器故障诊断模型进行诊断。结果结果表明,改进的LSTM-SVM故障诊断模型诊断准确率平均值为99.71%,原始的LSTM-SVM故障诊断模型诊断准确率平均值为88.48%,改进的LSTM-SVM故障诊断模型对各元件的故障诊断正确率均高于原始的LSTM-SVM故障诊断模型的。结论基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断模型实现了对双向DC-DC电力变换器中的电解电容、电感和MOSFET单双管故障的准确诊断。

基于电磁时间反演的VSC-HVDC系统架空线-电缆混合线路故障定位方法

针对传统故障定位方法无法应用于架空线-电缆混合直流线路的问题,提出了一种架空线-电缆混合直流线路的故障定位方法,该方法采用先确定故障点所在区段、后在区段中定位的原理。利用故障附加网络的直流分量定义了故障区段判别函数,通过该判别函数在不同区段发生故障时数值不同实现故障区段的判定。然后,分析了电磁时间反演理论在故障定位中的可行性,分析确定了故障特征谐波。利用线路两端特征谐波的电流行进波作为时间反演电路的电流源,设置与原线路拓扑结构镜像的线路,利用时间反演电流源与镜像线路构建时间反演电路。在此基础上,根据时间反演的能量聚焦特性列写故障区段内的故障定位方程并实现区段内故障定位。利用PSCAD/EMTDC搭建的电压源型换流器高压直流输电(VSC-HVDC)系统仿真证明了所提方法能实现线缆混合直流输电线路的故障定位。

基于限流电抗器两侧特定频段暂态能量比的MTDC电网单端量保护方案

柔性直流输电技术有利于未来大量分布式能源接入电网,而配置快速、可靠的保护方案是柔性直流技术发展的关键。基于直流电网等效模型,提出一种基于限流电抗器两侧电压特定频段暂态能量比差异性的单端量故障区域识别方法。该方法依据贝瑞隆等效模型计算出限流电抗器两侧电压比,通过广义S变换提取限流电抗器两侧特定频段电压,根据故障时正负极限流电抗器的电压幅值差的差异性实现故障选极。最后在PSCAD/EMTDC平台搭建仿真模型,分析出发生区内外故障时差异性明显,具备较强的耐过渡电阻与抗噪声干扰能力。

基于CRS-LMD和SVD的MMC-HVDC线路故障测距方法

直流输电线路故障行波波速不确定、波头提取困难以及噪声干扰等因素制约了直流电网中故障测距技术的应用。为了降低上述因素对定位准确性的影响,提出一种基于局部特征有理样条插值均值分解(LMD based on characteristic rational spline,CRS-LMD)和奇异值分解(singular value decomposition,SVD)的故障测距方法。首先,利用特征尺度选取最优极点系数,结合有理样条插值调节拟合曲线的松紧程度,实现对故障电压行波的局部均值分解。其次,采用奇异值分解对故障行波波头进行准确提取。最后,在PSCAD/EMTDC中搭建了张北±500 kV柔性直流电网的仿真模型,模拟各种故障情况并输出故障数据,利用Matlab对故障数据进行处理并验证定位算法。最后,仿真结果表明,所提故障测距算法在不同故障距离和故障类型下均能实现故障测距,且在叠加噪声和过渡电阻的情况下也能保障较高的精确性。

基于MMC主动限流的VSC-HVDC双极短路故障控保协同策略

直流故障保护是柔性直流输电系统发展面临的主要挑战之一,利用模块化多电平换流器(MMC)的灵活控制实现限流已成为当前研究的热点。MMC的主动限流降低了故障电流变化率和故障电流幅值,易引起故障判断以及保护选择困难。文中以对称单极接线的柔性直流系统为研究对象,采用控保协同思想,将主动限流控制与故障识别相结合,提出了基于主动限流因子的故障区域识别与区分方法实现双极短路故障的区域识别与切除。首先,通过在MMC电流内环控制器中附加故障电流目标预设控制器,在故障发生后可快速跟踪故障电流的变化,自适应地调整MMC输出的直流电压将故障电流控制在预设目标参考值附近;其次,根据不同故障位置时故障电流目标预设控制器输出的限流因子快速确定故障区域。最后,在PSCAD/EMTDC中对四端柔性直流输电系统的不同故障场景进行了仿真分析,验证了所提控保协同策略的可行性和有效性。

Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks

In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.

含主动限流控制的MMC-HVDC电网直流短路故障电流解析计算

传统的MMC-HVDC电网直流短路故障电流计算一般采用等效RLC电路方法。但是该方法没有考虑主动限流控制(ACLC)对等效电路RLC参数的影响,因此无法直接用于含ACLC的MMC-HVDC电网的直流短路故障电流的解析计算问题。该文通过将ACLC的控制效果等效为虚拟阻抗产生的压降,建立了体现ACLC的控制影响的等效RLC电路,以包含电流变化率限流环节的典型ACLC算法为例,推导了直流双极短路故障条件下ACLC的控制参数与虚拟阻抗的映射关系以及相应的直流故障电流解析表达式。最后,以基于含ACLC的半桥型四端MMC-HVDC电网为例,建立PSCAD/EMTDC仿真模型,对采用ACLC的直流双极短路故障电流解析计算结果进行了验证。结果表明:故障后10ms内直流短路故障电流的PSCAD仿真结果与所提出的含ACLC的等效RLC电路解析计算结果的平均误差在8.29%以内,最大误差在10.99%。因此,使用所提出的方法具有工程实用性。

Fault Tolerant Neuro-Robust Position Control of DC Motors

DC motors are widely used in industry such as mechanics, robotics, and aerospace engineering. In this paper, we present a high performance control method for position control of DC motors. Fault-tolerant control model are also addressed to combine with neuro-robust control approach. It is shown that with the proposed control algorithms, external disturbances and coupled dynamics inherent in the system are effectively compensated using neural network unit in which no analytical estimation on the upper bound of the reconstruction error and uncertainties is needed. Simulations on various flight conditions also confirm the effectiveness of the proposed methods.

Research on DC Fault of Multi-Terminal Direct Current System

This paper introduces the characteristics of VSC and MMC-MTDC and discusses the effects of different kinds of faults in HVDC systems. Special attention is given to the comparison between a pole-to-pole fault and a pole-to-ground fault occurring in the middle of the line or at the terminal of a VSC. Simulations using MATLAB are provided in this article which show the difference effects clearly when faults occur in a VSC-MTDC system or in a MMC-MTDC system. Understanding of such fault characteristics and the influence of the control system on them are important prerequisites on the way to MTDC systems.

Research on Parameter Collaborative Configuration of DC Circuit Breaker and DC Fault Current Limiter

Fault current suppression is the key technology to ensure the safe operation of the DC power distribution system. In order to realize the parameter collabora-tive configuration of the DC circuit breaker and the DC current limiter and improve the fault current suppression capability, the fault current suppression mechanism of the DC power distribution system is revealed based on the circuit model. Then, based on the mathematical model of the DC breaker, the characteristic parameters of DC breaking are extracted, and then the influence of different characteristic parameters on the breaking characteristics of fault current is studied. Finally, the mathematical model of the collaborative process between DC circuit breaker and DC current limiter is established. The charac-teristic parameters of fault current collaborative suppression are extracted. The coupling effects of different characteristic parameters on the fault current col-laborative suppression are studied. The principle of collaborative configuration of DC circuit breaker and DC current limiter is proposed, and the collaborative suppression ability of DC circuit breaker and DC current limiter to fault current is fully exploited to ensure the safe and reliable operation of the DC power distribution system.

适用于MMC-MTDC的母线侧故障分析及保护策略

柔性直流输电系统中,直流故障的分析与判别是柔直系统中亟待解决的重要问题。为了更好地分析故障特征,推导出一种多端柔性直流输电网(modular multilevel converter-high voltage direct current,MMC-MTDC)的母线侧简化电路故障计算方法,并分析了母线侧单极接地故障和极间短路故障的故障特征,在此基础上设计了一种适用于多端柔性直流输电网的故障判别方案和保护措施。该判别方案通过故障启动判据、故障区域判别和故障类型判别3个模块进行故障判定,采用了故障电压瞬时跌落值与系统电压之差及故障线路电流值作故障特征量,整套判别方案不受其他元件参数、故障位置等因素的影响,将Spearman相关性分析与传统电气故障区域判别相结合,准确判定系统区内外故障的同时,解决了故障区域判别中受线路各参数干扰导致误判的问题,仿真测试验证了本保护方案的可行性。

混合级联型HVDC系统的后续换相失败预判方法

白鹤滩—江苏±800k V特高压直流输电工程于2022年7月1日在中国投运,在世界上首次创造性地研发应用了特高压混合级联直流输电新技术方案。基于此系统背景对逆变侧电网换相换流器(line commutated converter,LCC)进行具有一定时间裕度的后续换相失败预判。分析了LCC控制器对电气量的控制作用和模块化多电平换流器(modular multilevel converters,MMC)对LCC的电压支撑作用,明确了混合级联直流输电系统逆变侧LCC发生后续换相失败的机理。推导了逆变侧LCC在首次换相失败后恢复过程末期关断角与故障后线路传输功率最小值的关系,定义了阈值功率的概念。然后建立了关断角与阈值功率的定量关系,提出一种故障后直流传输功率最小值与功率阈值相比较的后续换相失败预判方法。最后在PSCAD/EMTDC中搭建了相应的仿真模型,验证了理论分析和预测方法的准确性。

DCS控制系统在乙二醇控制中的应用

文章研究了DCS控制系统在乙二醇生产中的应用,探讨了其在生产控制中的优势和应用策略。首先介绍了DCS控制系统的基本结构和特点,详细分析了其在乙二醇生产中的应用现状,包括高可靠性硬件设备的应用、智能化和网络化技术的发展以及系统架构的优化;然后阐述了DCS在乙二醇生产中的优势,包括可靠性、灵活性、自动化控制、故障处理能力等方面,提出了在乙二醇生产中采用DCS控制系统的应用策略,包括广泛应用高可靠性硬件设备、采用冗余技术、实现系统容错技术等;最后,通过实验数据分析展示了DCS控制系统在乙二醇生产中的有效性和优势,实验证明其能够提高生产效率和产品质量。

基于IAO-ELM的DC-DC电路软故障诊断

针对DC-DC电路软故障诊断精确度差的问题,提出了一种基于改进的天鹰算法(IAO)-优化极限学习机(ELM)的故障诊断方法。采用变分模态分解的方法对原始的故障信号进行分解重构,提取时频域特征作为特征向量。使用SPM混沌映射、透镜反向学习、自适应权重和柯西变异策略改进天鹰算法,提升其寻优性能。采用IAO对ELM的输入权值和隐含层偏置进行优化,得到IAO-ELM诊断模型,提高分类精度。结果显示,IAO-ELM诊断模型的准确率为99.375%,能有效地实现DC-DC电路软故障诊断。