Smoke detection in open areas with texture analysis and support vector machines

Early and certain fire detection is one of the important issues to keep infrastructures safe. Especially, it becomes an urgent problem for open places such as port facilities, large factories, and power plants, due to its large harmful effect to the surrounding areas. In these places, direct detection of fire or flame has some difficulties because they are open and hence have problem to set sensor devices. Therefore, smoke is an important and useful sign to detect fire or flames robustly even in such cases. In this paper, we present a novel smoke detection method based on image information. First, we extract moving objects in an image sequence as smoke candidate regions in a preprocessing step. Since smoke has a characteristic pattern as image information, we focus on the texture pattern of smoke. Here, we use texture analysis to extract feature vectors of the images. To classify extracted areas of moving objects to smoke or nonsmoke, we use support vector machines (SVMs) with texture features as an input feature vector. Extraction of moving objects is sometimes easily and greatly affected by environmental conditions such as wind, background objects, and so forth. It obviously causes bad classification results. To solve this problem, we additionally accumulate the results of classification with SVM about time to obtain accurate extraction result of smoke regions under these conditions. Experimental results using real‐scene data show that our method works effectively under several different environmental conditions. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Wavelet and PDD as fault detection techniques

Motor current signature analysis has been successfully used for fault diagnosis in induction machines. However, this method does not always achieve good results with variable load torque. This paper proposes a different signal processing method, which combines wavelet and power spectral density techniques giving the power detail density as a fault factor. The method shows good theoretical and experimental results.     

Decentralised fault detection,identification, and mitigation in excitation control systems

A new intelligent, decentralised, fault detection, location identification and mitigation approach for the excitation control systems of multi-generator power grids is presented in this paper. The proposed approach detects and locates faults in basic components of the excitation systems with a high accuracy and in almost real-time using interval type 2 fuzzy set theory. Once a fault is detected and the location of the fault is identified, a mitigation action follows, where the signal out of the faulty component is automatically replaced by a stabilising signal generated by the fault mitigation system. The mitigation action ensures that the field excitation voltage (thus the terminal voltage of the affected generator) is controlled such that the affected generator and the whole system maintain their pre-fault operating condition. The high accuracy and fast response time aspects of the proposed approach are verified through case studies performed on the IEEE 30 bus test system.     

A critical fault detection analysis & faulttime in a UPFC transmission line

This paper discusses a critical study of fault detection and fault time analysis in a Unified Power Flow Controller (UPFC)transmission line. Here the Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT) approach are used forprocessing the faulty current signal to obtain fundamental current signal. The extracted fault current signals from thecurrent transformer are fed to DWT and DFT approach for computing spectral energy (SE). The differential spectral energy(DSE) of phase currents are evaluated by taking the difference of SE obtained at sending and receiving end. The DSE isthe key factor for deciding the fault in any of the phase or not. The Daubechy mother wavelet (db4) is used here becauseof its high accuracy of detection with less processing time. The novelty of the scheme is that it can accurately detect thecritical fault variation of the line. Number of simulations are validated at the extreme condition of the line and comparedto other conventional existing scheme. Multi-phase fault in double circuit line, CT saturation, UPFC operating condition(series voltage and angle), UPFC location and wind speed variation including wind farm simulation are validated to verifythe performance of the scheme. The advantages of the scheme is that it works effectively to detect the fault at any stageof critical condition of the line and fault detection time remains within 20 msec (less than one cycle period). This schemeprotects both internal and external zone including parameter variation of the line.     

An artificial immune system approach for fault detection in the stator and rotor circuits of induction machines

In this paper, an artificial immune system approach to the detection and diagnosis of faults in the stator and rotor circuits of an induction machine is presented. The proposed technique requires the measurement of two stator currents to compute their αβ representation before and after a fault condition. It is verified that for different faults, different patterns are generated by the vector αβ representation, helping to construct a characteristic image of the operating condition of the induction machine.A pattern recognition algorithm inspired by how the immune system operates throughout the body is proposed to identify and classify the fault condition. According to the proposed methodology, there is no need to know the details of machine operation in a certain regime and all phenomena and effects resulting from the machine operating in this regime are taken into account. Several experimental results obtained on 2.2 kW and 3.2 kW three-phase induction machines are presented and discussed to validate the methodology, verifying its good performance in preventive fault detection.     

压电振动能量收集的拓频方法及阻抗分析

利用能量收集技术来为装置供电,比有线布网供电和电池供电节约成本,同时便于设备的无线部署和长期运营维护。振动能量作为自然界中最常见的机械能之一,收集振动能量可以促进装置微小型化。现有的振动能量收集技术主要利用了装置与环境振动产生共振,因此能量收集的频率带宽仅在共振频率附近的小范围内,这限制了技术的应用场景。文中基于目前的机械和电气等各种拓展振动能量收集技术,分析了电气方法的频率响应特性。此外,相位可变开关电感电路作为电气拓频方法,比同步开关电感电路具有更宽的振动频率响应,使得在环境振动频率远离共振频率时,整体装置保持高水平的能量收集效率和能力。     

Robust fault analysis in transmission linesusing Synchrophasor measurements

As more electric utilities and transmission system operators move toward the smart grid concept, robust fault analysis has become increasingly complex. This paper proposes a methodology for the detection, classification, and localization of transmission line faults using Synchrophasor measurements. The technique involves the extraction of phasors from the instantaneous three-phase voltages and currents at each bus in the system which are then decomposed into their symmetrical components. These components are sent to the phasor data concentrator (PDC) for real-time fault analysis, which is completed within 2–3 cycles after fault inception. The advantages of this technique are its accuracy and speed, so that fault information may be appropriately communicated to facilitate system restoration. The proposed algorithm is independent of the transmission system topology and displays high accuracy in its results, even with varying parameters such as fault distance, fault inception angle and fault impedance. The proposed algorithm is validated using a three-bus system as well as the Western System Coordinating Council (WSCC) nine bus system. The proposed algorithm is shown to accurately detect the faulted line and classify the fault in all the test cases presented.     

Improved modelling of power transformer winding using bacterial swarming algorithm and frequency response analysis

The paper discusses an improved modelling of transformer windings based on bacterial swarming algorithm (BSA) and frequency response analysis (FRA). With the purpose to accurately identify transformer windings parameters a model-based identification approach is introduced using a well-known lumped parameter model. It includes search space estimation using analytical calculations, which is used for the subsequent model parameters identification with a novel BSA. The newly introduced BSA, being developed upon a bacterial foraging behavior, is described in detail. Simulations and discussions are presented to explore the potential of the proposed approach using simulated and experimentally measured FRA responses taken from two transformers. The BSA identification results are compared with those using genetic algorithm. It is shown that the proposed BSA delivers satisfactory parameter identification and improved modelling can be used for FRA results interpretation.     

计及异步电机频率响应的电力系统最低惯量评估

随着大规模新能源等电力电子器件的并网,电力系统惯量水平逐渐降低,系统转动惯量需求产生新的变化,负荷侧频率支撑能力显得愈发重要。针对这一问题,系统分析了异步电机的频率响应特性。基于小信号分析法提出一种机电暂态下异步电机建模方法,构建计及异步电机频率响应的电力系统频率响应模型。在深入分析新型电力系统惯量特性的基础上,考虑系统最大频率变化率、最大频率偏差以及备用容量限制等频率安全约束,构建了电力系统最低惯量评估模型并通过遗传算法进行求解。最后,在Matlab和PSASP平台上进行算例仿真和分析,验证了所提异步电机频率响应模型以及最低惯量评估结果的正确性和有效性。     

同步测频法的误差分析及其在单片机测量系统中的实现

对同步测频法进行了详尽的误差分析并由此对该方法做了改进。以此为据，指出了在实际系统中应用此方法的要点和如何合理选择测量参数。同时提出了一种在单片机系统中实现这种测量的具体方法，并举出了一个应用于油井压力计的实例作为说明。     

频率响应法在变压器绕组变形测试中的应用

电力变压器是电力系统的主要设备之一,进行变压器绕组变形测试对正确判断变压器在受到冲击力后是否产生绕组变形,从而保障变压器的安全运行具有重要意义。本文介绍了应用频率响应法对变压器进行绕组变形测试的原理、方法,对现场开展变压器绕组变形测试、确保电网安全运行具有指导作用。     

计及励磁控制响应的含小水电集群配电网复杂接地故障分析方法

小水电机组在配电网中的大规模接入增大了配电网拓扑的复杂程度,在恶劣气象条件下易发生复杂故障。同时,小水电励磁系统较快的响应速度使其对小水电故障输出电流的影响不能忽略。为此,通过对小水电机组故障特征的研究和对含小水电集群配电网复杂故障的分析,计及小水电励磁控制特性的影响,将小水电机组等效为由公共连接点(PCC)电压控制的压控电流源(VCCS),并基于多口网络理论和理想变压器,构建出一种含小水电集群配电网复杂接地故障分析方法。经PSCAD/EMTDC全面仿真,结果证明该方法可精确求解复杂接地故障下小水电输出电流、系统各支路电流和各节点电压,可作为配电网复杂故障保护整定的理论依据,也可为含小水电集群配电网故障后孤岛划分和恢复重构提供理论支撑。     

Fault detection in HVDC-connected wind farm with full converter generator

In HVDC-connected wind farms with full converter generator (FCG) the short-circuit currents are mainly governed by the control systems of HVDC and wind generators’ converters. This peculiarity requires accurate studies for the design and evaluation of reliable fault detection methods. In line with this need three-phase faults analyses have been performed on a test-case system, using PSCAD/EMTDC, and protection strategies have been proposed and analyzed.     

一种基于频谱分析的可控整流电路故障诊断方法

以12脉波可控整流电路为例,简要介绍了大功率整流装置的工作原理,并对各种故障情况 下的整流波形进行详细分析和归类;定义了一种"面积",对故障波形进行逻辑预处理,建立了故障模 型,再利用DFT分析方法对模式向量进行谱分析,提出了故障诊断方法;最后归纳出故障诊断系统 的实验算法,总结了本方法的特点并将其推广至其他形式整流电路。     

Fault detection in induction motors using Hilbert and Wavelet transforms

In this work, a new on-line method for detecting incipient failures in electrical motors is proposed. The method is based on monitoring certain statistical parameters estimated from the analysis of the steady state stator current (for broken bars, saturation, eccentricities, and bearing failures) or the axial flux signal (for coil short-circuits in the stator windings). The approach is based on the extraction of the envelop of the signal by Hilbert transformation, pre-multiplied by a Tukey window to avoid transient distortion. Then a wavelet analysis (multi-resolution analysis) is performed, which makes the fault diagnosis easier. Finally, based on a statistical analysis, the failure thresholds are determined. Thus, by monitoring the mean value estimate it is possible to detect an incipient failure condition on the machine.     

阶跃响应法在小电流接地系统故障选线与测距中的应用

小电流接地系统发生单相接地后,分析对外施阶跃激励,所有线路在某一特定频率下幅值达到最大,故障线路幅频响应最大值等于所有非故障线路幅频响应最大值之和,相位相反;且幅频特性峰值及其对应频率与故障位置存在严格的解析映射关系,据此构造了基于注入信号的小电流接地系统单相接地故障选线和测距的"阶跃响应法".仿真实验表明,该方法受过渡电阻影响很小,能够实现准确选线和精确定位.     

A new approach of fast fault detection in HV-B transmission lines based on Detail Spectrum Energy analysis using oscillographic data

The main focus of this research is to develop an accurate real-time method for fault detection and analysis of HVB (High Voltage Class-B) transmission lines. The current and voltage signals of oscillographic records are acquired by the distance protections relays with minimum impedance GE D60-1 installed in the electrical network of SONELGAZ (Algerian Company of Electricity and GAS). This method deals on the evaluation of the Detail Spectrum Energy (DSE) calculated from the Discrete Wavelet Transform (DWT) applied on the current phases by moving data windows with length equivalent to one cycle of the fundamental power frequency. The fault detection algorithm is processed at first scale superimposed in the fault current signals (phases and ground) by the sharp variation of (DSE). Most of the existing methods treat the disturbances and faults simultaneously exist in transmission line as a single type. The proposed method has the ability to discriminate between the disturbances and the faults. This study is compared with the “Powerful Analysis of all Protection Fault Records” SIGRA software for determining the start fault inception and it’s clearing time. The performance of this method was tested and evaluated on a real data records and can accurately detect the fault within only half a cycle from the instant of fault occurrence.     

频率响应法在诊断绕组变形中的应用

介绍了应用频率响应分析法检测变压器绕组变形的两个实例。指出在检测中通过对前后频谱曲线的分析,能准确有效地发现变压器内部电气及机械事故隐患,对于变压器及电网的安全运行,有着重要意义。但频率响应分析法不一定能够有效反映出变压器绕组变性发生单一扭动变形这类故障,绕组变形的诊断仍需使用多种试验手段综合分析和判断。     

基于递推主元分析法的汽车衡称重传感器零点故障检测方法

汽车衡称重传感器零点故障是一种典型的微小故障,不易在线检测,利用递推主元分析(RPCA)与四类故障检测指标相结合的方法,提出一种汽车衡称重传感器零点故障在线检测方法。该方法首先利用基于秩1修正的主元递推算法在线更新主元模型,然后利用Hotelling’s T²统计量、平方预测误差(SPE)统计量、Hawkins T_H²统计量、主元相关变量残差(PVR)统计量及其控制限构建故障综合评判方法,最终完成称重传感器零点故障及微小故障在线检测。实验表明,采用这种基于递推主元分析和综合评判方法的称重传感器,零点故障检测准确率比传统方法(即仅采用T²、T_H²、SPE、PVR任何一类统计量进行判别),提高了一个数量级,证实了该方法的有效性。     

Sweep frequency response analysis (SFRA) for the assessment of winding displacements and deformation in power transformers

The sweep frequency response analysis (SFRA) is an analysis technique for detecting winding displacement and deformation (among other mechanical and electrical failures) on power and distribution transformers. Nowadays, there is an increasing interest in SFRA method because of its sensibility in detecting mechanical faults without opening the unit. SFRA as a diagnostic technique must integrate both the off-line measurements and the interpretation of the data in order to provide an assessment of the condition of the windings. However, guidelines for the measurement and record interpretation are not available. The evaluation is presently done by experts in the topic through the visual inspection or with the help of statistical parameters such as the correlation coefficient and the standard deviation. However, criteria like the limits of normal variation of the parameters, and the features observed in the records in the presence of a determined type of fault could not to coincide. Although, there are some proposals for making the interpretation more objective, neither of them integrate human expertise along with the different kind of parameters obtained from the evaluation of the records in a diagnostic model. This paper presents a survey on the alternatives in the measurement techniques and interpretation of SFRA measurements, describing some sources of uncertainty in applying this methodology.