Saeed Jazebi

Magnetizing inrush current identification using wavelet based gaussian mixture models

Abstract The paper presents a novel differential protection approach based on Gaussian Mixture Models (GMM). This method has the advantages of high accuracy and low computational burden. Two common transients such as magnetizing inrush current and internal fault which their mis-identification may lead to mal-operation of differential relays are considered. GMM, the powerful probabilistic pattern classifier is trained with the features extracted by discrete wavelet transform to reduce the computational time of training procedure and enhance the discrimination accuracy. Training data sets are achieved using k -means clustering algorithm. Based on the proposed algorithm, a high speed differential relaying (a quarter of a cycle) without dependency on a specific threshold is performed. The suitable performance of this method is demonstrated by simulation of different faults and switching conditions on a power transformer in PSCAD/EMTDC software. Sympathetic and recovery inrush currents were also simulated and investigated. The proposed algorithm is also evaluated by the data collected from a prototype laboratory power transformer. It provides a high operating sensitivity for internal faults and remains stable for inrush currents even in noisy conditions. Since the discrimination method is done with stochastic characteristics of signals without application of any deterministic index, more reliable and accurate classification is achieved.

Power System Security Improvement by Using Differential Evolution Algorithm Based FACTS Allocation

The fast development of restructuring, the problem of power system security has become a matter of grave concern in deregulated power industry. To maintain security of such systems, it is desirable to plan suitable measures to improve power system security margins. FACTS devices can regulate the active and reactive power as well as voltage-magnitude. Placement of these devices in suitable location can lead to control in line flow and maintain bus voltages in desired level and so improve security margins. This paper presents a differential evolution algorithm (DEA) based allocation of FACTS devices considering cost function of FACTS devices and power system losses. Proposed algorithm is tested on IEEE 30 bus power system for optimal allocation of multi-type FACTS devices and the results are presented and discussed.

Optimal multi-type FACTS allocation using genetic algorithm to improve power system security

As power transfer increases, operation of power system become gradually more complex. Short circuit level increases and so power system will become less secure. Moreover, the problem of power system security has become a mater of grave concern in the deregulated power industry. FACTS devices can control power flow because of their flexibility and fast control characteristics. Placement of these devices in suitable location can lead to control in line flow and maintain bus voltages in desired level and so improve power system security. This paper presents a novel algorithm for allocation of FACTS devices based on genetic algorithm (GA). Cost function of FACTS devices and power system losses are considered in this algorithm. Proposed algorithm is tested on IEEE 30 bus power system for optimal allocation of multi-type FACTS devices and results are presented.

Dual Reversible Transformer Model for the Calculation of Low-Frequency Transients

This paper presents a physically consistent dual model applicable to single-phase two-winding transformers for the calculation of low-frequency transients. First, the topology of a dual electrical equivalent circuit is obtained from the direct application of the principle of duality. Then, the model parameters are computed considering the variations of the transformer electromagnetic behavior under various operating conditions. Current modeling techniques use different topological models to represent diverse transient situations. The reversible model proposed in this paper unifies the terminal and topological equivalent circuits. The model remains invariable for all low-frequency transients including deep saturation conditions driven from any of the two windings. The proposed model is tested with a single-phase transformer for the calculation of magnetizing inrush currents, series ferroresonance, and geomagnetic-induced currents (GIC). The electromagnetic transient response of the model is compared to the π model and to laboratory measurements for validation.

Accurate Measurement of the Air-Core Inductance of Iron-Core Transformers With a Non-Ideal Low-Power Rectifier

The air-core inductance of power transformers is measured using a nonideal low-power rectifier. Its dc output serves to drive the transformer into deep saturation, and its ripple provides low-amplitude variable excitation. The principal advantage of the proposed method is its simplicity. For validation, the experimental results are compared with 3-D finite-element simulations.

A Comparative Study on

The performance of the T and the π equivalent models used to represent transformers are tested under ferroresonance. Comparisons between simulations and laboratory experiments show the superiority of the π equivalent circuit.

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Abstract In this article, a combination method based on S-transform and the hidden Markov model is presented for power quality disturbance classification. S-transform is a new method that has been used in signal processing and power quality disturbance classification applications. S-transform not only has the advantages of both wavelet transform and fast Fourier transform but also characteristics superior than both mentioned transform methods. Besides, the hidden Markov model is a powerful and effective method on signal processing applications. This method computes the maximum likelihood probability between training and testing data signals for identification. The proposed method makes the classification more simple and accurate. The identification procedure has two major steps. First, features are extracted by S-transform, and in the second step, classification is done using a decision tree structure of hidden Markov models. Simulation results by simulated and experimental test signals reveal the robustness of proposed method and the possibility of using it for real applications.

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In this paper, a previously published model for the representation of the leakage inductance of multiwinding transformers is enhanced to support accurate calculations of low-frequency transients, including inrush currents, series ferroresonance, and geomagnetic-induced currents. The new circuit is obtained from the principle of duality and, therefore, is physically consistent. The unique characteristic of the improved model is that the very deep saturation behavior of the iron core is properly represented for each winding simultaneously (reversible model) without changing parameters. The hysteresis cycle and iron-core losses are also included. In addition to its reversible terminal behavior coupled with physical consistency, the proposed model can be built with circuit elements available in Electromagnetic Transients Program-type programs, and all of the parameters can be computed from terminal tests. The model is validated by comparing computer simulations versus laboratory measurements for three- and four-winding transformers.

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In this paper we present a combinatorial scheme based on hidden Markov models (HMM) and wavelet transform (WT) to discriminate between magnetizing inrush currents and internal faults in power transformers. HMMs are powerful tools for transient classification which compute the maximum likelihood probability between training and testing data signals for identification. The WT is employed to extract certain features which reduce the computation burden of HMMs and enhance detection accuracy. The newly extracted feature efficiently discriminates between faults by different trends. The k-means clustering technique is applied to reduce the training procedure time investment. Since the discrimination method is based on the probabilistic characteristics of the signals without application of any deterministic index, more reliable and accurate classification is achieved. This method is independent of the selection thresholds. Based on the proposed algorithm a highspeed relay response (a quarter of a cycle) can be achieved. The suitable performance of this method is demonstrated by simulation of different faults and switching conditions on a power transformer using PSCAD/EMTDC software.

Equivalent Models for the Analysis of Transformer Ferroresonance

This paper presents a novel method to obtain an equivalent circuit for the modeling of eddy current effects in the windings of power transformers. The circuit is derived from the principle of duality and, therefore, matches the electromagnetic physical behavior of the transformer windings. It properly models the flux paths and current distribution from dc to MHz. The model is synthesized from a nonuniform concentric discretization of the windings. Concise guidelines are given to optimally calculate the width of the subdivisions for various transient simulations. To compute the circuit parameters only information about the geometry of the windings and their material properties is needed. The calculation of the circuit parameters does not require an iterative process. Therefore, the parameters are always real, positive, and free from convergence problems. The results are compared with conventional synthesis methods and finite elements for validation.