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.

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 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.

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。

Fault-tolerant Strategies and Their Design Methods for Application Software

The key to software reliability is fault-tolerant design ofapplication software.New fault-tolerant strategies andtheir design methods for application software under vari-ous computer system are introduced.It has such advan-tages as simple hardware platform,independent fromapplication,stable reliability.lastly,some technicalproblems are discussed in details.

多间隔信息融合的母线保护电流互感器断线再开放策略

对于电流互感器(CT)断线后发生金属性故障的情景,现有母线保护采取的闭锁差动保护动作方式将会引发多个变电站停电。此外,当母线区内发生高阻接地故障时,现有母线保护判据可能会将其误判为CT断线故障,不利于电力系统的安全稳定运行。针对这一问题,提出了一种母线保护CT断线再开放策略,该策略基于断线间隔与非断线间隔的零序电流变化量对负荷波动和故障进行区分,并基于非断线间隔的差流有效值和间隔失灵保护信息对区内外故障进行判别。实时数字仿真系统仿真结果验证了所提CT断线识别判据和再开放策略的正确性。

基于多卷积和结构搜索的电弧故障检测模型

串联型电弧故障主要由电路中电气接触点接触不良引起,是引发电动汽车电气火灾的主要原因之一,直接威胁着车内人员的生命安全。为对其进行研究,论文搭建了电动汽车直流串联型电弧故障实验平台,获取了系统处于不同工作状态下的电源端电压信号,分析了电弧故障对电源端电压的影响。在构建检测模型时,论文使用了卷积神经网络,引入轻量型的卷积操作并考虑了其在实际应用中的局限性。将常规卷积和轻量化卷积操作结合,构建了电弧故障检测的初步模型。接着以网络的规模和准确率为评估指标,通过具有精英保留策略的遗传算法对模型的外部结构和内部参数进行搜索。最终建立了适合电动汽车的电弧故障检测(arc fault detection,AFD)的检测模型AFDNet。该模型的检测准确率达到93.73%,在嵌入式设备Jetson Nano(JN)中的运行时间为10.82 ms。模型建立后,论文在网络的规模、准确性及实时性方面,将搜索算法的搜索结果与其他的网络结构进行比较,验证了搜索算法所得结果的合理性。并通过与其他检测方法对比,证明了电动汽车电弧故障检测模型AFDNet性能的优越。

MMC直流侧故障有源限流策略

为了避免直流短路故障威胁到模块化多电平换流器(modular multilevel converter,MMC)型柔性直流电网的安全可靠运行,该文提出了一种适用于半桥型MMC直流侧故障的有源限流策略,限制直流短路电流和桥臂电流的峰值及上升速率。首先,该文阐述了故障期间系统控制策略对故障电流的影响,然后通过反馈桥臂电流分别调整差、共模调制波,进而改变上、下桥臂中子模块的数量及分配,达到快速抑制交、直流侧故障电流的目的。其次,推导了考虑有源限流策略下的故障电流解析式,并分析了该限流策略对控制系统稳定性的影响,为有源阻尼参数选取提供理论依据。最后,在PSCAD/EMTDC中搭建四端柔性直流电网模型,仿真结果表明:有源限流策略不仅能够显著降低直流断路器开断故障电流,且能保证桥臂内器件的安全。

刀库式加工中心自动换刀系统故障诊断与维修

我国是制造大国,数控机床作为工业生产的重要设备,能够有效提升工业生产自动化水平。主要探讨了刀库式加工中心自动换刀系统的故障诊断与维修策略,详细介绍了自动换刀系统的组成,包括自动换刀装置和自动换刀控制系统,并分析了刀库故障、换刀机械手故障以及主轴松刀故障等多种常见问题。在自动换刀控制系统故障方面,着重分析了换刀点漂移、刀库乱刀和换刀超时等问题,并提出了相应的诊断和维修策略。希望本文的研究能为相关技术人员提供参考和借鉴。

特高压直流输电工程中性母线开关故障处理策略优化

特高压直流输电工程采用双极共用接地极设计,一个极故障闭锁后需通过极隔离操作实现故障极与运行极的隔离。极隔离操作过程中,如果极中性母线开关发生故障无法分开导致极隔离中断,需配置极中性母线开关故障处理策略继续完成极隔离。根据不同直流故障时对应的工况,分析了中性母线开关故障处理策略存在的不足,基于故障特性优化了故障处理策略,最后通过RTDS仿真试验验证了策略优化的有效性。

考虑储能系统控保协同体系的光伏场站送出线路突变量选相能力恢复策略

针对新能源接入电网后,突变量选相元件动作性能显著劣化,尤其在弱电源侧难以保证选相正确性的问题,提出考虑储能系统控保协同体系的突变量选相能力恢复策略。通过理论分析,得出逆变电源侧负序阻抗基本恒定这一实施选相能力恢复举措的前提条件。在此基础上,提出将光伏场站中储能侧逆变电源控制策略中的负序抑制短时屏蔽,并将故障线路两侧的正序分配系数控制到与负序分配系数相等,以此营造令传统突变量选相元件实现动作的最理想条件。该控制可以通过控制储能设备电流内环中的d轴和q轴电流实现。基于PSCAD/EMTDC的仿真结果表明,在该控制策略作用下,线路两侧正序分配系数与负序分配系数几乎相等,极大程度恢复了弱电源侧的选相特征,在不同故障工况下均有较好的适应性,且不会干扰系统侧选相元件的正常动作,具有很高的可靠性。