Machine learning solution for regional landslide susceptibility based on fault zone division strategy

Landslide susceptibility assessment is an essential tool for disaster prevention and management. In areas with multiple fault zones, the impact of fault zone on slope stability cannot be disregarded. This study performed qualitative analysis of fault zones and proposed a zoning method to assess the landslide susceptibility in Chengkou County, Chongqing Municipality, China. The region within a distance of 1 km from the faults was designated as sub-zone A, while the remaining area was labeled as sub-zone B. To accomplish the assessment, a dataset comprising 388 historical landslides and 388 non-landslide points was used to train the random forest model. 10-fold cross-validation was utilized to select the training and testing datasets for the model. The results of the models were analyzed and discussed, with a focus on model performance and prediction uncertainty. By implementing the proposed division strategy based on fault zone, the accuracy, precision, recall, F-score, and AUC of both two sub-zones surpassed those of the whole region. In comparison to the results obtained for the whole region, sub-zone B exhibited an increase in AUC by 6.15%, while sub-zone A demonstrated a corresponding increase of 1.66%. Moreover, the results of 100 random realizations indicated that the division strategy has little effect on the prediction uncertainty. This study introduces a novel approach to enhance the prediction accuracy of the landslide susceptibility mapping model in areas with multiple fault zones.

Deep Residual Joint Transfer Strategy for Cross-Condition Fault Diagnosis of Rolling Bearings

Rolling bearings are key components of the drivetrain in wind turbines,and their health is critical to wind turbine operation.In practical diagnosis tasks,the vibration signal is usually interspersed with many disturbing components,and the variation of operating conditions leads to unbalanced data distribution among different conditions.Although intelligent diagnosis methods based on deep learning have been intensively studied,it is still challenging to diagnose rolling bearing faults with small amounts of samples.To address the above issue,we introduce the deep residual joint transfer strategy method for the cross-condition fault diagnosis of rolling bearings.One-dimensional vibration signals are pre-processed by overlapping feature extraction techniques to fully extract fault characteristics.The deep residual network is trained in training tasks with sufficient samples,for fault pattern classification.Subsequently,three transfer strategies are used to explore the generalizability and adaptability of the pre-trained models to the data distribution in target tasks.Among them,the feature transferability between different tasks is explored by model transfer,and it is validated that minimizing data differences of tasks through a dual-stream adaptation structure helps to enhance generalization of the models to the target tasks.In the experiments of rolling bearing faults with unbalanced data conditions,localized faults of motor bearings and planet bearings are successfully identified,and good fault classification results are achieved,which provide guidance for the cross-condition fault diagnosis of rolling bearings with small amounts of training data.

Coordinated Rotor-Side Control Strategy for Doubly-FedWind Turbine under Symmetrical and Asymmetrical Grid Faults

In order to solve the problems of rotor overvoltage,overcurrent and DC side voltage rise caused by grid voltage drops,a coordinated control strategy based on symmetrical and asymmetrical low voltage ride through of rotor side converter of the doubly-fed generator is proposed.When the power grid voltage drops symmetrically,the generator approximate equation under steady-state conditions is no longer applicable.Considering the dynamic process of stator current excitation,according to the change of stator flux and the depth of voltage drop,the system can dynamically provide reactive power support for parallel nodes and suppress the rise of DC side voltage and rotor over-current.When the grid voltage drops asymmetrically,the positive and negative sequence components are separated in the rotating coordinate system.The doubly fed generator model is established to suppress the rotor positive sequence current and negative sequence current respectively.At the same time,the output voltage limit of the converter is discussed,and the reference value is adjusted within the allowable output voltage range.In order to adapt to the occurrence of different types of power grid faults and complex operating conditions,a fast switching module of fault type detection and rotor control mode is designed to detect the type of power grid faults and voltage drop depth in real time and switch the rotor side control mode dynamically.Finally,the simulation model of the doubly fed wind turbine is constructed in Matlab/Simulink.The simulation results verify that the proposed control strategy can improve the low-voltage ride through performance of the system when dealing with the symmetrical and asymmetric voltage drop of the power grid and identify the power grid fault type and provide the correct control strategy.

Fault Ride-Through Capability Enhancement of PV System with Voltage Support Control Strategy

With continuously increasing of photovoltaic (PV) plant’s penetration, it has become a critical issue to improve the fault ride-through capability of PV plant. This paper refers to the German grid code, and the PV system is controlled to keep grid connected, as well as inject reactive current to grid when fault occurs. The mathematical model of PV system is established and the fault characteristic is studied with respect to the control strategy. By analyzing the effect of reactive power supplied by the PV system to the point of common coupling (PCC) voltage, this paper proposes an adaptive voltage support control strategy to enhance the fault ride-through capability of PV system. The control strategy fully utilizes the PV system’s capability of voltage support and takes the safety of equipment into account as well. At last, the proposed control strategy is verified by simulation.

Exploration Strategies for Complex Fault Block Reservoirs in the Subei Basin

The geological background of the Subei basin is that of small relief subsidence, low geothermal gradient, multi-sedimentary hiatuses, intense reconstruction of the basin, frequent magmatic activity, and a unique combination of source rock and reservoir. This geological background resulted in characteristics such as many small fault blocks, multiple oil-bearing formations, scattered oil distribution, mini- to small-sized reservoirs, and difficulties in exploration. Aimed at such characteristics, an effective exploration strategy was adopted, and the oil reserves, production and economic benefits of the Jiangsu oilfield were significantly increased. This exploration strategy included understanding the hydrocarbon generation mechanism of source rocks, progressive evaluation of oil resources, comprehensive research on the faulted systems, the distribution of oil reservoirs and their controlling factors. The techniques used included integration of acquisition, processing and interpretation with 3-D seismics as the core technology, trap description and evaluation, directional drilling and cluster drilling, integration of cuttings logging, gas chromatographic logging and geochemical logging, and integration of early reservoir description and progressive exploration and development. This strategy could be guidance for other complex fault blocks.

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.

Application of Rollout Strategy to Test Points Selection for Integer-Coded Fault Wise Table

Test points selection for integer-coded fault wise table is a discrete optimization problem. The global minimum set of test points can only be guaranteed by an exhaustive search which is eompurationally expensive. In this paper, this problem is formulated as a heuristic depth-first graph search problem at first. The graph node expanding method and rules are given. Then, rollout strategies are applied, which can be combined with the heuristic graph search algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the greedy heuristic algorithms. The proposed rollout-based test points selection algorithm is illustrated and tested using an analog circuit and a set of simulated integer-coded fault wise tables. Computa- tional results are shown, which suggest that the rollout strategy policies are significantly better than other strategies.

Stator Winding Turn Faults Diagnosis for Induction Motor by Immune Memory Dynamic Clonal Strategy Algorithm

Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the motor model equations are described.The fault related features are extracted.An immune memory dynamic clonal strategy(IMDCS)system is applied to detecting the stator faults of induction motor.Four features are obtained from the induction motor,and then these features are given to the IMDCS system.After the motor condition has been learned by the IMDCS system,the memory set obtained in the training stage can be used to detect any fault.The proposed method is experimentally implemented on the induction motor,and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of stator winding turn faults in induction motors.

Fault Ride-Through(FRT)Behavior in VSC-HVDC as Key Enabler of Transmission Systems Using SCADA Viewer Software

The world’s energy consumption and power generation demand will continue to rise.Furthermore,the bulk of the energy resources needed to satisfy the rising demand is far from the load centers.The aforementioned requires long-distance transmission systems and one way to accomplish this is to use high voltage direct current(HVDC)transmission systems.The main technical issues for HVDC transmission systems are loss of synchronism,variation of quadrature currents,amplitude,the inability of station 1(rectifier),and station 2(inverter)to either inject,or absorb active,or reactive power in the network in any circumstances(before a fault occurs,during having a fault in network and after a fault cleared),and the variations of power transfer capabilities.Additionally,faults impact power quality such as voltage dips and power line outage time.This paper presents a method of overcoming the aforementioned technical issues using voltage-source converter(VSC)based HVDC transmission systems with SCADA VIEWER software and dynamic grid simulator.The benefits include having a higher capacity transmission system and proposed best method for control of active and reactive power transfer capabilities.Simulation results obtained using MATLAB validated the experimental results from SCADA Viewer software.The results indicate that the station’s rectifier or inverter can either inject or absorb either active power or reactive power in any circumstance.Also,the reverse power flow under different modes of operation can ride through faults.At a 100.0%power transfer rate,the rectifier injected 775.0 W into the network.At a 0.0%power transfer rate,the rectifier injected 164.0 W into the network.At a-100.0%rated power,the rectifier injected 1264.0 W into the network and direction was also changed.

Fault Ride-Through Study of Wind Turbines

The installation of wind energy has increased rapidly around the world. The grid codes about the wind energy require wind turbine (WT) has the ability of fault (or low voltage) ride-through (FRT). To study the FRT operation of the wind farms, three methods were discussed. First, the rotor short current of doubly-fed induction generator (DFIG) was limited by introducing a rotor side protection circuit. Second, the voltage of DC bus was limited by a DC energy absorb circuit. Third, STATCOM was used to increase the low level voltages of the wind farm. Simulation under MATLAB was studied and the corresponding results were given and discussed. The methods proposed in this paper can limit the rotor short current and the DC voltage of the DFIG WT to some degree, but the voltage support to the power system during the fault largely depend on the installation place of STATCOM.

A fault injection model-oriented testing strategy for component security

A fault injection model-oriented testing strategy was proposed for detecting component vulnerabilities.A fault injection model was defined,and the faults were injected into the tested component based on the fault injection model to trigger security exceptions.The testing process could be recorded by the monitoring mechanism of the strategy,and the monitoring information was written into the security log.The component vulnerabilities could be detected by the detecting algorithm through analyzing the security log.Lastly,some experiments were done in an integration testing platform to verify the applicability of the strategy.The experimental results show that the strategy is effective and operable.The detecting rate is more than 90%for vulnerability components.

不对称短路故障下永磁直驱风电机组并网控制策略研究

电网侧发生不对称故障时,永磁直驱风电机组并网电流会发生三相不平衡的问题。针对这一问题,提出一种基于自适应陷波器的网侧换流器控制策略。该控制策略基于解决机组并网电流二倍频分量的思想,在电网侧发生不对称短路故障时,将进入逆变器的dq坐标系下的二倍频电压分量去除,从而使得网侧换流器输出的电流达到三相平衡。本文首先分析了电网侧发生不对称短路故障后永磁直驱风力发电机组的动态响应;然后建立了机侧和网侧换流器的模型,并分别采用了双闭环矢量控制技术和基于自适应陷波器的控制策略;最后进行仿真分析,验证了改进后的控制策略相较于传统控制策略,有效解决了不对称短路故障下并网电流三相不平衡问题。

考虑风电机组故障电压穿越特性的连锁故障关键线路辨识

新能源的接入提高了电网连锁故障发生的概率,给故障传播中关键线路的辨识增加了难度。为此,建立了考虑风电机组故障电压穿越特性和线路可靠性的连锁故障仿真模型,从线路失负荷严重程度、失负荷不均匀性和结构脆弱性3个角度建立了线路综合风险指标评价集。基于状态故障网络计及线路失负荷程度和线路失负荷不均匀性,分别定义了失负荷风险指标和不均匀风险指标;基于电气介数提出了考虑分布式新能源接入的脆弱结构指标;同时,考虑电网的网络结构和状态转移特性,采用模糊熵权法定义综合风险指标,以衡量线路开断的综合影响。通过IEEE 39节点和IEEE 118节点系统算例验证所提方法用于关键线路辨识的有效性,且针对关键线路的缓解措施能显著降低大停电风险。

基于数据映射和胶囊网络的轴承故障诊断方法

传统深度学习模型自适应提取振动信号故障特征,实现端到端轴承故障诊断。然而,振动监测信号为非常复杂的非稳态时序信号,若深度网络直接以原始振动信号为输入,数据之间的非线性耦合作用会极大影响模型对故障特征的提取效率。目的为降低故障信号之间强非线性耦合作用,解决卷积神经网络对空间约束信息丢失的问题,提升轴承故障诊断性能,方法本文提出一种基于数据映射和胶囊网络(capsule network,CapsNet)的轴承故障诊断方法。首先,将图像处理领域中具有细化颜色特征能力的颜色空间模型(color names,CN)引入故障数据预处理中,将原始低维空间数据映射至高维空间,提升故障数据空间区分度;其次,针对映射后数据维度较高且具有一定冗余,影响故障诊断效率的问题,引入主成分分析(principal compo⁃nent analysis,PCA)法提取故障数据主元信息,降低数据维度;最后,考虑到胶囊网络有效提取空间约束信息的能力,将CapsNet作为故障诊断的骨干网络对故障特征进行识别和分类。结果使用CWRU、XJTU-SY数据集对该方法进行验证,实验结果表明,该方法在两种数据集上故障诊断准确率均达98%以上,与其他基于深度学习的故障诊断方法进行对比,该方法的诊断性能具有一定优势。结论本文方法可对故障数据进行有效解耦,提升数据之间的空间区分度,获得较高的轴承故障诊断精度。

基于MMC闭锁与负直流电压无闭锁的综合直流故障限流策略

基于模块化多电平换流器(MMC)的柔性直流电网存在抗直流故障扰动能力弱的问题。为此,在无闭锁和闭锁限流的基础上,提出了一种适用于柔性直流电网的无闭锁与闭锁的综合直流故障限流策略。故障发生后,各换流站首先根据直流侧出口电流进行基于负直流电压控制的无闭锁限流,若某换流站由于无闭锁降压限流失效而发生过流,则主动将该换流站闭锁。故障消失或隔离后,无闭锁换流站第一时间恢复正常工作模式,而无须等待重启过程较长的闭锁换流站,以尽量减小系统功率损失。基于MATLAB/Simulink的仿真结果表明,所提综合直流故障限流策略可快速阻断故障电流,并兼顾限流过程的安全性和快速性,最大限度保证系统功率传输,提高了直流电网的抗故障扰动能力。

改进贝叶斯网络在变压器故障诊断中的应用

针对变压器故障诊断精度低的问题,文中提出一种基于改进黏菌优化算法(Improved Slime Mould Algorithm,ISMA)优化贝叶斯网络(Bayesian Network,BN)的变压器故障诊断方法。通过爬山算法对定向最大支撑树搜索得到贝叶斯网络初始结构即初始种群,在改进黏菌优化算法中引入反向学习策略,增加种群多样性。添加正弦-余弦算法(Sine Cosine Algorithm,SCA),更新解的位置以避免种群陷入局部最优。根据改良的无编码比值法选取变压器故障状态的特征,利用改进黏菌优化算法优化贝叶斯网络结构,提高基于贝叶斯网络的变压器故障诊断的准确率,并利用不同种类的测试函数验证了改进黏菌优化算法具有收敛速度快、收敛精度高的优良性能。仿真结果表明,ISMA-BN诊断模型的训练集和测试集准确率分别为98.2%和97.14%,具有一定的研究价值。

低压直流微电网新型主动限流器及控制策略

低压直流微电网中直流母线发生短路故障后,现有的被动式保护措施难以有效保护电力电子装置,并且当计及直流线路电感与变流器直流母线侧电容时,常规主动限流器会出现限流电感电流振荡的特性。详细分析了该电流振荡特性产生的机理,探究了系统参数变化对限流电感电流振荡峰值的影响。并提出了一种新型主动限流器拓扑结构及控制策略,该拓扑结构通过增加能量耗散支路,在限流器直流母线侧电容电压产生负压时导通,以此耗散能量,解决了电感电流振荡的问题。通过仿真与实验,验证了所提新型主动限流器对电感电流振荡特性抑制的有效性,且在不同工况下具有良好的鲁棒性。

基于等效序阻抗差异特征的低频输电线路差动保护优化方案

低频输电线路故障时,两侧短路电流均由模块化多电平矩阵变换器提供,两侧短路电流相角均受控且幅值相当,导致差动保护灵敏度严重下降。分析了低频输电系统定电压控制侧和功率控制侧的等效正、负序阻抗特征,指出因控制策略不同,系统故障时两侧等效正、负序阻抗特征存在明显差异。分析了低频输电线路区内和区外故障两侧保护装置测量到的等效序阻抗之间的差异性和相似性,基于此特征构建了两侧等效序阻抗差异指标,提出了基于等效序阻抗差异指标的差动保护制动系数优化方法。仿真结果表明,所提方案能够显著提升低频输电线路区内故障时差动保护的动作速度和动作灵敏性。