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Generation, Transmission & Distribution, IET

Issue 4 • Date April 2010

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Displaying Results 1 - 8 of 8
  • Efficient calculation of critical eigenvalues in large power systems using the real variant of the Jacobi-Davidson QR method

    Publication Year: 2010 , Page(s): 467 - 478
    Cited by:  Papers (2)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (681 KB)  

    The real variant of the Jacobi-Davidson QR (RJDQR) method is a novel and efficient subspace iteration method to find a selected subset eigenvalues of a real unsymmetric matrix and is favourable to eigenanalysis for the power system small signal stability. In this study, the RJDQR method in conjunction with a flexible selection strategy of critical eigenvalue detection criteria for the small signal stability analysis is presented. Compared with the original Jacobi-Davidson QR (JDQR) method, the RJDQR method keeps the search subspace real and constructs a partial real Schur form iteratively to improve the overall performance. These strategies significantly accelerate iteration convergence and completely avoid repeated computation of the detected eigenvalues. Numerical examples demonstrate the efficiency of the RJDQR method adopting the proposed selection strategy in pursuing eigenanalysis tasks of 89- and 120-machine systems. The results show that it is capable of effectively finding critical eigenvalues in large power systems. View full abstract»

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  • Effective insulator maintenance scheduling using artificial neural networks

    Publication Year: 2010 , Page(s): 479 - 484
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (223 KB)  

    One of the most frequent causes of failure of overhead high- and medium-voltage transmission and distribution lines is contamination of the insulators with diverse substances such as saline and industrial substances. The contamination mechanically degrades the insulators and affects the electrical characteristics of the insulating material, leading to flashovers. Periodic maintenance of insulators can reduce or even prevent the outages caused by contamination. The maintenance scheduling is planned based either on measurements, which are quite expensive and time consuming processes or on experience, a definitely inaccurate process. The current work presents a new approach for the assessment of contamination of insulators on the basis of artificial intelligence and, more specifically, artificial neural networks (ANNs). An ANN model is defined and when applied on operating voltage insulators it presented results similar to experimental results. The proposed approach can be useful in the work of electrical maintenance engineers, reducing the time and cost of insulator maintenance. View full abstract»

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  • Optimum fault current limiter placement with search space reduction technique

    Publication Year: 2010 , Page(s): 485 - 494
    Cited by:  Papers (7)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (419 KB)  

    A fault occurring in power networks normally results in a large short-circuit current flow in the system, which may exceed the rating of existing circuit breakers and can damage system equipments. Because of difficulty in power network reinforcement and the interconnection of more distributed generations, fault current level has become a serious problem in transmission and distribution system operations. The utilisation of fault current limiters (FCLs) in power systems could provide an effective way to suppress fault currents. In a loop transmission or distribution system, the advantages would greatly depend on the number and locations of FCL installations. The authors propose a method to determine the optimum number and locations for FCL placement in terms of installing smallest FCLs circuit parameters to restrain short-circuit currents under circuit breakers' interrupting ratings. In the proposed approach, the sensitivity factor, defined as the reduction of bus fault currents because of a given variation in the branch parameter, is derived and used to choose better candidates for active FCL installations. The search space for FCL installations can be reduced by using the proposed sensitivity factor calculation; therefore the computational efficiency and accuracy can be improved. A genetic-algorithm-based method is then designed to include the sensitivity information in searching for the best locations and parameters of FCLs. The test results demonstrate the efficiency and accuracy of the proposed method. View full abstract»

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  • Method for determining the maximum allowable penetration level of distributed generation without steady-state voltage violations

    Publication Year: 2010 , Page(s): 495 - 508
    Cited by:  Papers (22)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (978 KB)  

    One of the main factors that may limit the penetration level of distributed generation (DG) in typical distribution systems is the steady-state voltage rise. The maximum amount of active power supplied by distributed generators into each system bus without causing voltage violations can be determined by using repetitive power flow studies. However, this task is laborious and usually time-consuming, since different loading level and generation operation modes have to be evaluated. Therefore this article presents a method that, based on only one power flow solution and one matrix operation, can directly determine the maximum power that can be injected by distributed generators into each system bus without leading to steady-state voltage violations. This method is based on the determination of voltage sensitivities from a linearised power system model. In addition, this article proposes a numerical index to quantify the responsibility of each generator for the voltage level rise in a multi-DG system. Based on this index, utility managers can decide which generators, and in which degree, should be penalised by the voltage rise or rewarded by not depreciating the voltage profile. The method is applied to a 70-bus distribution network. The results are compared with those obtained by repetitive power flow solutions in order to validate the proposed method. View full abstract»

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  • Transient stability prediction of power systems by a new synchronism status index and hybrid classifier

    Publication Year: 2010 , Page(s): 509 - 518
    Cited by:  Papers (3)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (295 KB)  

    In this study, a new transient stability prediction method is proposed. The measured rotor angles of generators are first processed by a new non-linear transformation based on hyperbolic functions to construct a novel synchronism status index. The transformed rotor angles are then applied as input data to a hybrid classifier composed of an array of parallel probabilistic neural networks in which one probabilistic neural network is assigned to each unit of the power system. The proposed hybrid classifier can predict transient stability status of power system and determine tripped machines. The efficiency of the proposed solution method for transient stability prediction is studied based on the IEEE 162-bus and IEEE 145-bus test systems. Moreover, the effectiveness of the method under varied configurations of the power system is also shown. View full abstract»

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  • Adaptive wavelet neural network-based fast dynamic available transfer capability determination

    Publication Year: 2010 , Page(s): 519 - 529
    Cited by:  Papers (3)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (474 KB)  

    An adaptive wavelet neural network (AWNN)-based method has been proposed to determine dynamic available transfer capability (DATC) in the electricity markets, having bilateral as well as multilateral contracts. Mexican hat wavelet basis function has been used as the activation function in the hidden layer of the network. Wavelet parameters, that is, translations and dilations of the AWNN, have been initialised using Euclidean distance-based clustering method. The AWNN has been trained using back propagation gradient descent training algorithm. The relevant features to be used, as input to the AWNN, are identified using a random forest technique. To demonstrate the effectiveness of the proposed AWNN-based method for the DATC determination, it has been tested on 39-bus New England system and a 246-bus Indian system and its results have been compared to the radial basis function neural network (RBFNN). View full abstract»

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  • Voltage stability analysis based on probabilistic power flow and maximum entropy

    Publication Year: 2010 , Page(s): 530 - 537
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (302 KB)  

    Methods of determination of voltage stability margin index had been well established. This study adopts a new method to determine the probabilistic distribution of margin index taking into account the random variations of bus loads. First, the probabilistic technique and the Jacobian method are combined to determine the probabilistic characteristics of stability margins and nodal voltages at the maximum load points. Then, according to these probabilistic characteristics, maximum entropy method is adopted to determine the probabilistic distribution of stability margin. Last, the proposed method is investigated on two test systems with random active and reactive loads. Monte Carlo simulations are used as a reference solution to evaluate the accuracy of the proposed method. View full abstract»

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  • Decision tree-initialised fuzzy rule-based approach for power quality events classification

    Publication Year: 2010 , Page(s): 530 - 537
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (496 KB)  

    The proposed method develops a decision tree (DT)-initialised fuzzy rule base for Power Quality (PQ) event classification. Power system suffers from different PQ events such as sag, swell, momentary interruptions, impulsive transients, flicker, notch, spike, harmonics and so on. The above-mentioned events comprise high-frequency and low-frequency components. Thus, it is difficult to classify these PQ events using traditional approaches. This approach derives various statistical parameters using advanced signal processing technique such as S-transform. After the required features are extracted, the DT is used to build up the classification tree. From the DT classification boundaries, the fuzzy membership functions and corresponding fuzzy rule base are developed for final classification. The proposed DT-fuzzy method provides more accurate results for PQ events classification compared to heuristic fuzzy rule-based approach. Also, a qualitative comparison is made between S-transform and wavelet transform, where S-transform-based DT-fuzzy provides highly improved results compared to the later including noisy environment. View full abstract»

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IET Generation, Transmission & Distribution is intended as a forum for the publication and discussion of current practice and future developments in electric power generation, transmission and distribution.

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