An Algorithm for Short-Circuit Current Interval in Distribution Networks with Inverter Type Distributed Generation Based on Affine Arithmetic

During faults in a distribution network,the output power of a distributed generation(DG)may be uncertain.Moreover,the output currents of distributed power sources are also affected by the output power,resulting in uncertainties in the calculation of the short-circuit current at the time of a fault.Additionally,the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources.Thus,it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network.In this study,an affine arithmetic algorithm for calculating short-circuit current intervals in distribution networks with distributed power sources while considering power fluctuations is presented.The proposed algorithm includes two stages.In the first stage,normal operations are considered to establish a conservative interval affine optimization model of injection currents in distributed power sources.Constrained by the fluctuation range of distributed generation power at the moment of fault occurrence,the model can then be used to solve for the fluctuation range of injected current amplitudes in distributed power sources.The second stage is implemented after a malfunction occurs.In this stage,an affine optimization model is first established.This model is developed to characterizes the short-circuit current interval of a transmission line,and is constrained by the fluctuation range of the injected current amplitude of DG during normal operations.Finally,the range of the short-circuit current amplitudes of distribution network lines after a short-circuit fault occurs is predicted.The algorithm proposed in this article obtains an interval range containing accurate results through interval operation.Compared with traditional point value calculation methods,interval calculation methods can provide more reliable analysis and calculation results.The range of short-circuit current amplitude obtained by this algorithm is slightly larger than those obtained using the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Therefore,the proposed algorithm has good suitability and does not require iterative calculations,resulting in a significant improvement in computational speed compared to the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Furthermore,the proposed algorithm can provide more reliable analysis and calculation results,improving the safety and stability of power systems.

Data-driven short circuit resistance estimation in battery safety issues

Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.

A Fault Feature Extraction Model in Synchronous Generator under Stator Inter-Turn Short Circuit Based on ACMD and DEO3S

This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.

Low switching loss and increased short-circuit capability split-gate SiC trench MOSFET with p-type pillar

A split-gate SiC trench gate MOSFET with stepped thick oxide, source-connected split-gate(SG), and p-type pillar(ppillar) surrounded thick oxide shielding region(GSDP-TMOS) is investigated by Silvaco TCAD simulations. The sourceconnected SG region and p-pillar shielding region are introduced to form an effective two-level shielding, which reduces the specific gate–drain charge(Q_(gd,sp)) and the saturation current, thus reducing the switching loss and increasing the short-circuit capability. The thick oxide that surrounds a p-pillar shielding region efficiently protects gate oxide from being damaged by peaked electric field, thereby increasing the breakdown voltage(BV). Additionally, because of the high concentration in the n-type drift region, the electrons diffuse rapidly and the specific on-resistance(Ron,sp) becomes smaller.In the end, comparing with the bottom p~+ shielded trench MOSFET(GP-TMOS), the Baliga figure of merit(BFOM,BV~2/R_(on,sp)) is increased by 169.6%, and the high-frequency figure of merit(HF-FOM, R_(on,sp) × Q_(gd,sp)) is improved by310%, respectively.

星-三角接法的分数槽永磁电机匝间短路故障分析

为了分析永磁电机绕组采用星-三角(Y-△)接法时发生匝间短路故障后对电机的影响,建立了绕组在Y-△接法下分别在星形(Y)接法部分和角形(△)接法部分发生匝间短路故障的电路模型,推导得到故障发生后电机三相电流。以10极12槽永磁电机为例进行二维有限元仿真,对传统的Y接法双层绕组永磁电机和Y-△接法4层绕组永磁电机发生匝间短路故障的情况进行对比分析。仿真结果表明:在Y-△接法下匝间短路故障位置的不同对电流会有很大影响;在发生匝间短路故障后Y-△接法能降低电机的转矩脉动,降低故障发生后故障相电流中的3次谐波幅值。

基于Hotelling T2-BiLSTM的同步调相机故障诊断方法

同步调相机故障会严重影响特高压直流输电工程的稳定性。针对特高压电网中不对称电压条件下同步调相机励磁绕组匝间短路故障难以识别问题,提出基于霍特林T2-双向长短期记忆网络(Hotelling T2-BiLSTM)的同步调相机故障诊断方法。首先,从三相电压、三相电流、励磁电流、转子振动和无功功率中提取故障特征,计算Hotelling T2统计量以构建故障检测指标;然后,采用BiLSTM模型对特征进行分类,并利用逻辑回归层进行故障诊断;最后,在动模实验室中进行实验验证其有效性。实验结果表明,所提方法具有较高的故障诊断精度。

面向高比例新能源电网短路计算的机电-电磁融合电源模型

为解决新型电力系统计算与分析中传统电源模型难以继续适用的问题,提出一种面向高比例新能源电网短路计算的机电-电磁融合电源模型。一方面,考虑新能源电源通用控制策略作用下的外部输出特性,采用单一压控电流源等效电源侧海量新能源电源的整体输出;另一方面,根据短路电流故障的时间划分尺度,保留传统发电机工频基波意义下的外部输出特性,并采用3个大小不一且衰减速度不同的电压源等效电源侧所有发电机的整体输出。进而采用3个电压源和1个压控电流源并联的形式作为融合电源模型的结构,并采用内点法对融合电源模型参数进行在线辨识。通过短路仿真数据和电网实测故障录波数据验证了融合电源模型的适用性。

基于d-q变换及WOA-LSTM的异步电机定子匝间短路故障诊断方法

为了实现对异步电机定子绕组匝间短路故障的可靠在线诊断,提出一种基于d-q变换及鲸鱼优化算法(WOA)优化的长短期记忆网络(LSTM)的故障诊断方法。通过理论推导可知,d-q变换可有效提取定子电流中的特征频谱数据。采用鲸鱼优化算法对长短期记忆网络中的3个关键参数进行优化,建立WOA-LSTM故障分类模型。为了验证基于d-q变换和WOA-LSTM故障诊断方法的有效性,分别以小波变换、快速傅里叶变换及d-q变换提取电流频谱数据作为输入数据集,以一台YE2-100L1-4型异步电机为实验对象进行实验验证。研究结果表明:相比于小波变换及快速傅里叶变换,采用d-q变换能更准确的提取出定子电流中的故障特征,更精确地反映电机故障状态,有助于提高故障分类准确率;相比于传统的LSTM算法,经WOA优化后的LSTM算法分类准确率可达98.3%,能可靠地实现不同程度匝间短路故障的诊断。

The Transformer Short-Circuit Test and the High Power Laboratory in China-the Past,Present,and Future

We review the short-circuit testing of distribution and power transformers, and include a list of 110-220kV power transformers tested up to February 2002.

级联式气-液旋流分离器流动特性数值研究

为探究级联式气-液旋流分离器流场特性,了解短路流这一特殊流型对其性能影响,采用雷诺应力模型和离散颗粒模型对气、液两相流动特征进行数值模拟,通过Q准则对涡流特征进行识别。研究结果表明,级联式气-液旋流分离器一级旋流管内流动特性与传统旋流分离器相似,增加二级旋流管使级联式气-液旋流分离器内形成了大量的局部二次涡流。当入口气速为7 m/s时,级联式气-液旋流分离器中一级旋流区内短路流流量高达入口流量的70.28%;增设二级旋流管对改善分离器性能具有重要作用,两级分离后级联式气-液旋流分离器对直径6μm以上的液滴能够实现完全分离。本研究为气-液旋流分离器的结构开发和级联式气-液旋流分离器的应用提供了思路和基础数据。

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。

基于d-q理论的双馈异步电机定子匝间短路故障诊断方法

为实现双馈异步电机定子绕组匝间短路的早期故障诊断,提出一种基于转子电流特征频率分量的故障诊断方法 .首先,通过分析故障后电机的电磁特性,得到故障后转子电流中会产生特征频率分量的结论 .然后,通过对转子单相电流进行特殊的相移构造出三相电流,并对该组电流进行d-q变换,利用理论推导证明d-q变换后的直流分量幅值能够反映特征谐波的大小,将直流分量作为故障特征量进行双馈异步电机匝间短路故障的诊断.最后,依据有限元法建立故障前后电机的仿真模型,对不同工况下故障特征量的变化进行研究.结果表明:基于d-q理论提取的故障特征量与定子绕组的短路匝数呈正相关,且在转速变化和电压不平衡时也可有效诊断出匝间短路故障;通过设置合理的故障阈值可实现对双馈异步电机定子绕组匝间短路故障的精确辨识.研究结果可以为电机绕组匝间短路故障诊断提供参考.

基于FFT-LSTM的抽水蓄能发电机定子匝间短路故障诊断方法

定子短路故障是抽水蓄能发电机常见的故障之一,其会对发电机的性能和安全性产生严重影响,为确保抽水蓄能发电机安全稳定运行,提出基于FFT-LSTM的抽水蓄能发电机定子短路故障诊断方法。建立抽水蓄能发电机定子绕组匝间短路故障模型,分析定子绕组匝间短路故障时,发电机定子电、磁相关状态。以抽水蓄能发电机定子绕组匝间短路故障时的三相电流信号为依据,基于磁势相等原理将三相电流变换成两相电流后,利用FFT转换定子两相电流的时域信号为频域信号,获取故障电流频谱图输入LSTM网络中进行处理,输出抽水蓄能发电机定子绕组匝间短路故障诊断结果。实验结果表明,该方法可以更好地区分抽水蓄能发电机正常与故障状态,实现抽水蓄能发电机定子绕组匝间短路故障诊断,且故障诊断的交叉熵损失低。

Effects of current waveform parameters during droplet transfer on spatter in high speed waveform controlled Short-circuiting GMAW

Aim at improving the stability of the Short-circuiting Gas Metal Arc Welding （GMAW-S） process for the enhanced speed usage, effects of current waveform parameters during short-term on the welding stability have been investigated by experimental method. The welding power source used for the research is an inverter with a special current waveform control. It is shown that the spatter decreases at first then increases with each increase of the low current period, current increase rate and the maximum current limit. The test results are provided for welding of 1 mm and 3 mm mild steel at speed of 1.2 m/min. The stable GMA W-S process under high speed welding condition has been achieved by optimizing the parameters.

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。

Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit

External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batteries under ESC conditions.Experiments were systematically performed under different battery initial state of charge and ambient temperatures.Based on the experimental results,we employed an extreme learming machine(ELM)-based thermal(ELMT)model to depict battery temperature behavior under ESC,where a lumped-state thermal model was used to replace the activation function of conventional ELMs.To demonstrate the effectiveness of the proposed model,wecompared the ELMT model with a multi-lumped-state thermal(MLT)model parameterized by thegenetic algorithm using the experimental data from various sets of battery cells.It is shown that the ELMT model can achieve higher computa-tional efficiency than the MLT model and better fitting and prediction accuracy,where the average root mean squared error(RMSE)of the fitting is 0.65℃ for the ELMT model and 3.95℃ for the MLT model,and the RMES of the prediction under new data set is 3.97℃ for the ELMT model and 6.11℃ for the MLT model.

Internal short circuit evaluation and corresponding failure mode analysis for lithium-ion batteries

Internal short circuit(ISC)is the major failure problem for the safe application of lithium-ion batteries,especially for the batteries with high energy density.However,how to quantify the hazard aroused by the ISC,and what kinds of ISC will lead to thermal runaway are still unclear.This paper investigates the thermal-electrical coupled behaviors of ISC,using batteries with Li(Ni_(1/3)CO_(1/3)Mn_(1/3))O_(2) cathode and composite separator.The electrochemical impedance spectroscopy of customized battery that has no LiPF6 salt is utilized to standardize the resistance of ISC.Furthermore,this paper compares the thermal-electrical coupled behaviors of the above four types of ISC at different states-of-charge.There is an area expansion phenomenon for the aluminum-anode type of ISC.The expansion effect of the failure area directly links to the melting and collapse of separator,and plays an important role in further evolution of thermal runaway.This work provides guidance to the development of the ISC models,detection algorithms,and correlated countermeasures.

Effect of welding speed and electrode extension on the approximate entropy of welding current in short-circuiting GMAW

Based on the phase space reconstruction of welding current in short-circuiting transfer arc welding under carbon dioxide, the approximate entropy of welding current and its standard deviation have been calculated and analyzed at different welding speeds and different electrode extensions respectively. The experimental and calculated results show that at a certain arc voltage, wire feeding rate and gas flow rate, welding speed and electrode extension have significant effects not only on the approximate entropy of welding current, but also on the stability of welding process. Further analysis proves that when the welding speed and electrode extension are in an appropriate range respectively, the welding current approximate entropy attains maximum and its standard deviation minimum. Just under such circumstances, the welding process is in the most stable state.

A fault warning for inter-turn short circuit of excitation winding of synchronous generator based on GRU-CNN

Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding of a synchronous generator,a gate recurrent unit-convolutional neural network(GRU-CNN)model whose structural parameters were determined by improved particle swarm optimization(IPSO)is proposed.The outputs of the model are the excitation current and reactive power.The total offset distance,which is the fusion of the offset distance of the excitation current and offset distance of the reactive power,was selected as the fault judgment criterion.The fusion weights of the excitation current and reactive power were determined using the anti-entropy weighting method.The fault-warning threshold and fault-warning ratio were set according to the normal total offset distance,and the fault warning time was set according to the actual situation.The fault-warning time and fault-warning ratio were used to avoid misdiagnosis.The proposed method was verified experimentally.

Analysis of Short-circuit Characteristics and Calculation of Steady-state Short-circuit Current for DFIG Wind Turbine

Large-scale doubly-fed induction generator(DFIG)wind turbines are connected to the grid and required to remain grid-connection during faults,the short-circuit current contributed by the generation has become a significant issue.However,the traditional calculation methods aiming at synchronous generators cannot be directly applied to the DFIG wind turbines.A new method is needed to calculate the short-circuit current required by the planning,protection and control of the power grid.The short-circuit transition of DFIG under symmetrical and asymmetric short-circuit conditions are mathematically deduced,and the short-circuit characteristics of DFIG are analyzed.A new method is proposed to calculate the steady-state short-circuit current of DFIG based on the derived expressions.The time-domain simulations are conducted to verify the accuracy of the proposed method.