共查询到20条相似文献,搜索用时 15 毫秒
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Tripathy M. Maheshwari R.P. Verma H.K. 《Generation, Transmission & Distribution, IET》2008,2(1):43-52
Protection of medium- and large-power transformers has always remained an area of interest of relaying engineers. Conventionally, the protection is done making use of magnitude of various frequency components in differential current. A novel technique to distinguish between magnetising inrush and internal fault condition of a power transformer based on the difference in the current wave shape is developed. The proposed differential algorithm makes use of radial basis probabilistic neural network (RBPNN) instead of the conventional harmonic restraint- based differential relaying technique. A comparison of performance between RBPNN and heteroscedastic-type probabilistic neural network (PNN) is made. The optimal smoothing factor of heteroscedastic-type PNN is obtained by particle swarm optimisation technique. The results demonstrate the capability of RBPNN in terms of accuracy with respect to classification of differential current of the power transformer. For the verification of the developed algorithm, relaying signals for various operating conditions of the transformer, including internal faults and external faults, were obtained through PSCAD/EMTDC. The proposed algorithm has been implemented in MATLAB. 相似文献
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Tripathy M. Maheshwari R.P. Verma H.K. 《Generation, Transmission & Distribution, IET》2007,1(2):218-222
Investigations towards the applicability of probabilistic neural networks (PNNs) as core classifiers to discriminate between magnetising inrush and internal fault of power transformer are made. An algorithm has been developed around the theme of conventional differential protection of transformer. It makes use of the ratio of the voltage-to-frequency and the amplitude of differential current for the detection of the operating condition of the transformer. The PNN has a significant advantage in terms of a much faster learning capability because it is constructed with a single pass of exemplar pattern set and without any iteration for weight adaptation. For the evaluation of the developed algorithm, transformer modelling and simulation of fault are carried out in power system computer-aided designing PSCAD/EMTDC. The operating condition detection algorithm is implemented in MATLAB 相似文献
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针对滚动轴承不同运行状态振动信号具有不同复杂性的特点,提出一种新的基于多尺度熵(multiscale entropy, MSE)和概率神经网络(probabilistic neural networks, PNN)的滚动轴承故障诊断方法。该方法首先利用MSE方法对滚动轴承振动信号进行特征提取,并将其作为PNN神经网络的输入,再利用PNN自动识别轴承故障类型及故障程度。实验数据包括不同故障类型和不同故障程度样本,结果表明,相比于小波包分解和PNN结合的诊断方法,提出的方法具有更高的诊断精度,能有效实现滚动轴承故障类型及程度的诊断。 相似文献
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Samantaray S.R. Panigrahi B.K. Dash P.K. 《Generation, Transmission & Distribution, IET》2008,2(2):261-270
An intelligent approach for high impedance fault (HIF) detection in power distribution feeders using advanced signal-processing techniques such as time-time and time-frequency transforms combined with neural network is presented. As the detection of HIFs is generally difficult by the conventional over-current relays, both time and frequency information are required to be extracted to detect and classify HIF from no fault (NF). In the proposed approach, S- and TT-transforms are used to extract time-frequency and time-time distributions of the HIF and NF signals, respectively. The features extracted using S- and TT-transforms are used to train and test the probabilistic neural network (PNN) for an accurate classification of HIF from NF. A qualitative comparison is made between the HIF classification results obtained from feed forward neural network and PNN with same features as inputs. As the combined signal-processing techniques and PNN take one cycle for HIF identification from the fault inception, the proposed approach was found to be the most suitable for HIF classification in power distribution networks with wide variations in operating conditions. 相似文献
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针对煤矿井下掘进机截割岩壁硬度识别难度大的问题,利用其悬臂振动信号、升降油缸和回转油缸压力信号、截割电机电流信号,提出了一种基于多源数据融合的截割岩壁硬度识别方法。该方法首先对各类信号进行小波包分解,单支重构各频带信号并组建时频矩阵,通过奇异值分解得到包含时频信息的若干特征奇异值,以构造特征向量;再利用LDA算法实现数据特征级融合,得到类可分性更好的低维特征。为解决概率神经网络(PNN)平滑参数无法确定和网络结构复杂的问题,提出了基于差分进化算法(DE)和QR分解的PNN优化方法,并通过优化PNN对低维特征进行硬度识别。实验结果表明:所提出的特征量提取和模式识别方法是有效的,与目前常用的其它模式识别算法相比,优化PNN在掘进机三种工况下均有更高的硬度识别准确率。 相似文献
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Kang Y.C. Yuri J.S. Lee B.E. Kang S.H. Jang S.I. Kim Y.G. 《Generation, Transmission & Distribution, IET》2008,2(1):100-109
The design, evaluation and implementation of a busbar differential protection relay that operates in conjunction with a current transformer (CT) compensating algorithm are described. Prior to saturation, the secondary current of a CT is not compensated. The compensating algorithm detects the start of first saturation on the basis of the third-difference function of the current and estimates the core flux at the first saturation start by inserting the negative value of the third- difference function of the current into the magnetisation curve of a CT. Thereafter, it calculates the core flux and then the corresponding magnetising current in conjunction with the magnetisation curve. The calculated magnetising current is added to the measured secondary current to obtain the correct secondary current. The algorithm can estimate the correct current irrespective of the level of the remanent flux. In the proposed busbar protection scheme, a current differential relay with the single-slope operating characteristic is used on the basis of the compensated current of the saturated CT. Test results indicate that the relay shows satisfactory performance for the various external and internal faults with CT saturation, particularly in the case of a progressive fault from a feeder fault to a busbar fault. The algorithm is implemented in a prototype relay based on a digital signal processor. The relay achieves greater stability on external faults, enhanced sensitivity on internal faults and fast operation on internal faults with CT saturation. 相似文献
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《Electric Power Applications, IET》2009,3(1):1-7
An algorithm to identify the excitation inductances of three-phase power transformer with wye-delta connection is proposed. Existing methods of determining the excitation inductances of three-phase transformers require that all winding currents to be known, making them impractical on some wye-delta transformers where the delta winding currents are not measured. Based on the transformer equivalent circuit, the proposed algorithm eliminates the influence of the delta circulating current, allowing the excitation inductances to be calculated using the line currents of the delta connection side directly. The algorithm?s accuracy has been verified by electromagnetic transients program including direct current (EMTDC) simulations, which show that the proposed algorithm is able to differentiate accurately and sensitively between transformer inrush and fault conditions. This lends itself to applications in the area of transformer protection. 相似文献
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A simple self-adaptive version of the differential evolution algorithm was applied for simultaneous architectural and parametric optimization of feed-forward neural networks, used to classify the crystalline liquid property of a series of organic compounds. The developed optimization methodology was called self-adaptive differential evolution neural network (SADE-NN) and has the following characteristics: the base vector used is chosen as the best individual in the current population, two differential terms participate in the mutation process, the crossover type is binomial, a simple self-adaptive mechanism is employed to determine the near-optimal control parameters of the algorithm, and the integration of the neural network into the differential evolution algorithm is performed using a direct encoding scheme. It was found that a network with one hidden layer is able to make accurate predictions, indicating that the proposed methodology is efficient and, owing to its flexibility, it can be applied to a large range of problems. 相似文献
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Power transformer protection using S-transform with complex window and pattern recognition approach 总被引:1,自引:0,他引:1
Samantaray S.R. Panigrahi B.K. Dash P.K. Panda G. 《Generation, Transmission & Distribution, IET》2007,1(2):278-286
A new approach for power transformer protection using S-transform with complex window to distinguish between inrush current and internal fault is presented. The S-transform with complex window is used to extract patterns of transient current samples during inrush and faults. S-transform is a very powerful tool for non-stationary signal analysis giving the information of transient currents both in time and in frequency domains. The spectral energy is calculated for inrush and internal faults and an energy index is found out to distinguish between inrush magnetising current and internal faults. The simulation results and the results obtained using real-time data from a transformer in the laboratory environment indicate the robustness of the proposed technique 相似文献
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An improved probabilistic neural network (IPNN) algorithm for use in chemical sensor array pattern recognition applications is described. The IPNN is based on a modified probabilistic neural network (PNN) with three innovations designed to reduce the computational and memory requirements, to speed training, and to decrease the false alarm rate. The utility of this new approach is illustrated with the use of four data sets extracted from simulated and laboratory-collected surface acoustic wave sensor array data. A competitive learning strategy, based on a learning vector quantization neural network, is shown to reduce the storage and computation requirements. The IPNN hidden layer requires only a fraction of the storage space of a conventional PNN. A simple distance-based calculation is reported to approximate the optimal kernel width of a PNN. This calculation is found to decrease the training time and requires no user input. A general procedure for selecting the optimal rejection threshold for a PNN-based algorithm using Monte Carlo simulations is also demonstrated. This outlier rejection strategy is implemented for an IPNN classifier and found to reject ambiguous patterns, thereby decreasing the potential for false alarms. 相似文献
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应用概率神经网络诊断自行火炮发动机的故障 总被引:4,自引:0,他引:4
目的 研究概率神经网络模型 ,并应用于故障诊断 .方法 对基于概率统计思想和 Bayes分类规则的概率神经网络模型、网络结构、算法及其特点进行分析 ,利用其进行故障诊断 ,并提出一种优化估计平滑因子的方法 .结果 概率神经网络可很好地诊断自行火炮发动机进行中油路和气路的故障 .结论 概率神经网络在模式识别和故障诊断领域中可取得良好地应用效果 相似文献
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Dong Hyawn Kim Dookie Kim Seongkyu Chang Hie-Young Jung 《Probabilistic Engineering Mechanics》2008,23(1):45-50
A new neuro-control scheme for active control of structures having a basic structure similar to the Probabilistic Neural Network (PNN) is proposed. It utilizes the lattice pattern of state vector as the training data of PNN, and thus it is called the Lattice Probabilistic Neural Network (LPNN). Comparing the two schemes, PNN takes much time to obtain a control force in the application because it uses all the training patterns. This may delay the control action inevitably. However, in LPNN, the control force is calculated by using only the adjacent information of LPNN input, making the response of LPNN greatly faster than that of PNN. To investigate the general control capability of the proposed algorithm, one-story and three-story buildings under California, El Centro, and Northridge earthquakes are used as test models. Control results of the LPNN are compared with those of the conventional PNN, and these show that the structural responses have been suppressed effectively by the proposed algorithm. 相似文献
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P. Ilamathi V. Selladurai K. Balamurugan V. T. Sathyanathan 《Clean Technologies and Environmental Policy》2013,15(1):125-131
An artificial neural network (ANN) and genetic algorithm (GA) approach to predict NOx emission of a 210 MW capacity pulverized coal-fired boiler and combustion parameter optimization to reduce NOx emission in flue gas, is proposed. The effects of oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature, and nozzle tilt were studied. The data collected from parametric field experiments was used to build a feed-forward back-propagation neural net. The coal combustion parameters were used as inputs and NOx emission as outputs of the model. The ANN model was developed for full load conditions and its predicted values were verified with the actual values. The algebraic equation containing weights and biases of the trained net was used as fitness function in GA. The genetic search was used to find the optimum level of input operating conditions corresponding to low NOx emission. The results proved that the proposed approach could be used for generating feasible operating conditions. 相似文献
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Arpaia P. Lucariello G. Zanesco A. 《IEEE transactions on instrumentation and measurement》2007,56(5):1573-1582
An evolutionary algorithm with a cultural mechanism of evolution influence for effectiveness and efficiency higher than classical genetic algorithms is proposed for industrial fault isolation. Moreover, the evolution influence is based on a differential concept in order to move toward better zones of the solution space by sensing the fitness gradient. The proposed cultural algorithm is designed in order to be portable and easily configurable in different diagnostic applications. On-field results of an industrial application to motor-vehicle fleet remote monitoring and automatic fault isolation of vehicle wear, operating danger, and fraud in a company that transports dangerous goods are shown. 相似文献
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