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    • 2. 发明申请
    • SUPERVISION OF A NEUTRON DETECTOR IN A NUCLEAR REACTOR
    • 在核反应堆中监测中子探测器
    • WO1995034077A1
    • 1995-12-14
    • PCT/SE1995000634
    • 1995-06-02
    • ABB ATOM ABOCIECZEK, GrzegorzTIUSANEN, Mika
    • ABB ATOM AB
    • G21C17/108
    • G21C17/108G21Y2002/204G21Y2002/206G21Y2004/403Y10S706/915
    • A method for supervision of a neutron flux detector in a nuclear reactor comprising a plurality of neutron flux detectors. During a learning phase, connection weights of a neural network are determined, which network comprises at least one input layer (31) and one output layer (33). The input layer comprises a number of processing elements adapted to receive a number of input signals (S(t) - S(t-49 DELTA t)), and the output layer comprises a number of process elements adapted to deliver a number of state signals (N1, N2, N3, N4). The determination of the connection weights is done by supplying to the network a plurality of sets of input signals with known state signals, by comparing the state signals calculated by the network with the known state signals, and by successively correcting the connection weights to reduce the deviation between the known and the calculated state signals. During a supervision phase the neural network is supplied with input signals corresponding to measured values at different times from the supervised detector, and based on the connection weights determined during the learning phase, the state signals are calculated. In dependence of the calculated state signals and the state signals of a number of selected reference detectors, it is determined whether the detector is defective.
    • 一种用于在包括多个中子通量检测器的核反应堆中监测中子通量检测器的方法。 在学习阶段,确定神经网络的连接权重,哪个网络包括至少一个输入层(31)和一个输出层(33)。 输入层包括适于接收多个输入信号(S(t)-S(t-49 DELTA t))的多个处理元件,并且输出层包括适于递送多个状态的多个处理元件 信号(N1,N2,N3,N4)。 通过将由网络计算的状态信号与已知状态信号进行比较,并且通过连续地校正连接权重以减少连接权重来确定连接权重是通过向网络提供具有已知状态信号的多组输入信号 已知和计算出的状态信号之间的偏差。 在监督阶段,神经网络被提供有与监督检测器不同时间的测量值对应的输入信号,并且基于在学习阶段确定的连接权重,计算状态信号。 根据计算出的状态信号和多个选择的参考检测器的状态信号,确定检测器是否有故障。
    • 3. 发明申请
    • EXPERT SYSTEM FOR DETECTING HIGH IMPEDANCE FAULTS
    • 用于检测高阻抗故障的专家系统
    • WO1995010815A1
    • 1995-04-20
    • PCT/US1994011117
    • 1994-09-28
    • THE TEXAS A & M UNIVERSITY SYSTEM
    • THE TEXAS A & M UNIVERSITY SYSTEMRUSSELL, B., Don
    • G06F17/50
    • G06F11/2257H02H1/0015H02H1/0092Y10S706/907Y10S706/915
    • An expert detection system (10) includes a method and apparatus for detecting high impedance faults occurring on a distribution circuit (12) coupled to an AC power source (14). Based upon an expert's knowledge of high impedance fault behavior and the performance of various fault detection techniques, the expert forms a belief (120) as to whether a high impedance fault has indeed occurred. The expert's beliefs may be adjusted (130) with an elliptic formula, and then used to weight the status output of each technique. The weighted multiple technique outputs are combined to determine whether a high impedance fault has occurred. The expert's beliefs are calibrated during initial start-up (160) by comparison with a confirmed performance history of the detector using a scoring rule (170). The belief calibration may be included during operation to provide on-line adaption of the expert detector (10) to the changing situations of the distribution circuit (12).
    • 专家检测系统(10)包括用于检测耦合到AC电源(14)的分配电路(12)上出现的高阻抗故障的方法和装置。 根据专家对高阻抗故障行为的知识和各种故障检测技术的性能,专家对确定是否发生高阻抗故障形成了一种信念(120)。 专家的信念可以用椭圆公式进行调整(130),然后用于加权每项技术的状态输出。 加权多技术输出被组合以确定是否发生高阻抗故障。 通过使用评分规则(170)与检测器的确认性能历史进行比较,在初始启动(160)期间校准专家的信念。 在操作期间可以包括置信校准以提供专家检测器(10)在分配电路(12)的变化情况下的在线适应。
    • 6. 发明申请
    • DYNAMIC METHOD FOR PREVENTING VOLTAGE COLLAPSE IN ELECTRICAL POWER SYSTEMS
    • 用于在电力系统中预防电压波动的动态方法
    • WO1996033544A1
    • 1996-10-24
    • PCT/US1996005424
    • 1996-04-18
    • CORNELL RESEARCH FOUNDATION, INC.
    • CORNELL RESEARCH FOUNDATION, INC.
    • H02J03/14
    • H02J3/14Y02B70/3225Y04S20/222Y10S706/915Y10T307/391Y10T307/406Y10T307/461
    • Given the current operating condition of a power-generating system (obtained from real-time data), the near-term (e.g., after 25 min.) load demand at each bus (obtained from short-term load forecast) and the generation dispatch (based on economic dispatch), a load margin measure (MW and/or MVAR) is determined in order to assess the system ability to withstand the forecasted load and generation variations. The invention has a method to predict near-term (e.g., after 25 min.) system voltage profiles. The proposed look-ahead measure and/or the system voltage profiles are then applied to contingency selections for the near-term power system, in terms of load margins to collapse and bus voltage magnitudes. The proposed load margin measure and the voltage profiles are based on the power flow technique and the saddle-node bifurcation theory.
    • 考虑到发电系统(从实时数据获得)的当前运行状况,每个总线(从短期负载预测获得)的近期(例如,25分钟后)负载需求和发电调度 (基于经济调度),确定负载裕度测量(MW和/或MVAR),以评估系统承受预测负载和发电变化的能力。 本发明具有预测近期(例如,25分钟之后)系统电压分布的方法。 然后,将所提出的预先测量和/或系统电压曲线应用于近期电力系统的紧急选择,就压缩负载余量和总线电压幅度而言。 提出的负载裕度测量和电压曲线基于功率流技术和鞍点分岔理论。
    • 7. 发明申请
    • PROCESS FOR ANALYSING PROCESS DATA IN AN INDUSTRIAL PLANT
    • 程序对技术系统的过程数据的分析
    • WO1996014609A1
    • 1996-05-17
    • PCT/DE1995001471
    • 1995-10-23
    • SIEMENS AKTIENGESELLSCHAFTMEDERER, Hans-GerdFÜHRING, ThorstenJACOBY, KonstantinPANYR, JiriMICHELIS, Rainer
    • SIEMENS AKTIENGESELLSCHAFT
    • G05B23/02
    • G05B23/0221G05B23/0272Y04S10/522Y10S706/915
    • In the proposed process for analysing process data in an industrial plant, in particular a power generation plant with automated control of the different sub-facilities (ai), the characteristic features (mi of the plant process are first determined and the parameters (Pi) relevant to the plant process are prepared, in order to ensure compression of data and filtering as well as prompt fault diagnosis. Thereafter, for each sub-facility (ai) of the plant, the presence of each feature (mi) is confirmed using the parameter (Pi); features (mi) common to different sub-facilities (ai) and different features (mi) within sub-facilities (ai) are used to produce correlations for combinations of sub-facilities (ai) and features (mi). The sub-facilities (ai) and/or the features (mi) are then displayed as data elements (Ii(ai), Ii(mi)) and positioned in such a way that the distance (L, W) between any two data elements (Ii(ai), Ii(mi)) represents the degree of correlation between them.
    • 在一个技术安装的过程数据的分析的方法,特别是功率植物,植物部分(AI)被自动根据第一投资过程表征特征(MI)预定的和相关的工厂工艺参数的本发明的控制,用于信息压缩和滤波以及用于早期故障诊断 (PI)设置。 此后,(AI)中,每个特征的存在(MI)是使用参数(PI)为每个系统部分检查,并且有不同的植物部分的装置(AI)的共同特征(MI)和各种特征(MI)共同(AI)相关之间 的植物部分(AI)和特征(MI)的组合被确定。 随后,如定位成的植物部分(AI)和/或特征(MI)被称为信息元素所示(II(AI),II(MI)),该距离(L,W)(每两个信息单元II(AI)之间, II(MI))代表其相关程度。
    • 8. 发明申请
    • METHOD OF GENERATING A FAULT-INDICATION SIGNAL
    • 程序为获得错误指示信号
    • WO1995009463A1
    • 1995-04-06
    • PCT/DE1994001137
    • 1994-09-20
    • SIEMENS AKTIENGESELLSCHAFTDALSTEIN, Thomas
    • SIEMENS AKTIENGESELLSCHAFT
    • H02H01/00
    • G05B23/024G01R31/025H02H1/0015H02H1/0092H02H1/04H02H3/50Y10S706/915
    • The invention concerns a method of generating, using a neural-net arrangement, a fault-indication signal in the event of a fault occurring in a power-supply network being monitored. In order to generate rapidly fault-indication signals which distinguish between a trnsient short-circuit due to arcing and a permanent one due to metal contact, the invention calls for a neural net (26) to be used for each phase in the power-supply network. Each neural net (26) is taught by simulating the voltages [UR(t)] for each phase during arcing and metal-contact short-circuits in such a way that, in the event of an arcing short, the output signal (S) has one predetermined value (0.8) and, in the event of a metal-contact short, another predetermined value (0.1). Sequentially sampled normal values [URn1(t)...URn20(t)] of the voltage [UR(t)] of each of the phases in the power-supply network are applied simultaneously to various neurons (31) in the input layer (27) of each neural net (26).
    • 本发明涉及一种方法,用于当在由神经网络装置的装置在所述供电网络的监视发生错误恢复的故障指示信号。 快速赢得故障,其区分永久金属短路的暂时短路电弧指示信号,神经网络(26)用于被监视的电源网络的每个阶段。 通过电压的模拟(UR(t))的每一个神经网络(26)中的电弧和金属短裤每个阶段训练,使得被监测当电弧短路时,输出信号(S)的预定值(0.8),并在一个 金属短路假定另一预定值(0.1)。 到的电压中的分别分配的阶段的每个神经元网络(26)的输入层(27)的各种神经元(31)同时连续地采样,归一化值(URn1(T)... URn20(t))的(UR(t))的 的电源系统被应用。