Kesab Bhattacharya

Gravitational Search Algorithm Based Optimal Reactive Power Dispatch for Voltage Stability Enhancement

Abstract In this article, an efficient and reliable optimization procedure based on the behaviors of swarm in nature, namely the gravitational search algorithm, is proposed for solving multi-objective optimal reactive power dispatch problems, which minimizes transmission loss while maintaining the quality of voltages. The gravitational search algorithm is based on Newtons law of gravity and interaction of masses. In the proposed algorithm, the searcher agents which are a collection of masses interact with each other using Newtons laws of gravity and motion. In order to investigate the performance of the proposed scheme, multi-objective optimal reactive power dispatch problems are solved. This new gravitational search algorithm method is tested on IEEE 57-bus and IEEE 118-bus power systems. Results obtained by the gravitational search algorithm are compared with two versions of genetic algorithms, three versions of differential evolution algorithms, four versions of particle swarm optimization algorithms and the seeker optimization algorithm It is observed from the test results that the proposed gravitational search algorithm approach converges to better solutions much faster than the earlier reported approaches.

Rough-Set-Based Feature Selection and Classification for Power Quality Sensing Device Employing Correlation Techniques

In this paper, we present a scheme of rough-set-based minimal set of feature selection and classification of power quality disturbances that can be implemented in a general-purpose microcontroller for embedded applications. The developed scheme can efficiently sense the power quality disturbances by the features extracted from the cross-correlogram of power quality disturbance waveforms. In this paper, a stand-alone module, employing microcontroller-based embedded system, is devised for efficiently sensing power quality disturbances in real time for in situ applications. The stand-alone module is developed on a PIC24F series microcontroller. Results show that the accuracy of the proposed scheme is comparable to that obtained in offline analysis using a computer. The method stated here is generic in nature and can be implemented for other microcontroller-based applications for topologically similar problems.

Detection, Classification, and Estimation of Fault Location on an Overhead Transmission Line Using S-transform and Neural Network

Abstract This article demonstrates a technique for diagnosis of fault type and faulty phase on overhead transmission lines. A method for computation of fault location is also incorporated in this work. The proposed method is based on the multi-resolution S-transform, which is used for generating complex S-matrices of the current signals measured at the sending and receiving ends of the line. The peak magnitude of the absolute value of every S-matrix is noted. The phase angle corresponding to every peak component is obtained from the argument of the relevant S-matrix. These features are used as input vectors of a probabilistic neural network for fault detection and classification. Detection of faulty phase(s) is followed by estimation of fault location. The voltage signal of the affected phase is processed to generate the S-matrix. The frequency components of the S-matrices for different fault locations are used as input vectors for training a back-propagation neural network. The results are obtained with satisfactory accuracy and speed. All the simulations have been done in MATLAB (The MathWorks, Natick, Massachusetts, USA) environment for different values of fault locations, fault resistances, and fault inception angles. The effect of noise on both the current and voltage signals has been investigated.

Cross-Spectrum Analysis-Based Scheme for Multiple Power Quality Disturbance Sensing Device

This paper presents a methodology of cross-wavelet transform aided Fischer linear discriminant analysis (FLDA)-based feature selection and classification for sensing simultaneous occurrence of multiple power quality disturbances. A linear support vector machine is used for classification of the extracted features as it suits well with FLDA. This scheme is implemented in a general purpose microcontroller as a standalone module and the performance of the standalone module for sensing simultaneous occurrence of multiple power quality disturbances is judged by both online and offline testing. Results show that the performance is comparable with the results reported in the literatures. Moreover, the scheme is immune to real life uncorrelated noises due to incorporation of cross spectrum analysis in the feature extraction phase. The present method is generic in nature and can be implemented for any other microcontroller-based applications addressing topologically similar problems.

Time-frequency analysis of multiple fringe and nonsinusoidal signals obtained from a fiber-optic vibration sensor using an extrinsic Fabry-Pe/spl acute/rot interferometer

In the case of multiple fringes and complex frequency measurements, the frequency of the output signal changes rapidly when the vibration changes and frequency breakdown takes place at the turning point. For a particular vibration signature containing many frequency components at different time intervals, it is often difficult to trace the direction of the vibration as well as individual frequency peaks. In such cases, advanced signal-processing scheme is necessary to decode the vibration signature. This paper investigates the data interrogation technique for multifrequency and complex signals of surface vibration obtained from an extrinsic Fabry-Pe/spl acute/rot interferometric sensor. In this paper, wavelet transform (WT)-based signal processing methodology has been employed to count of optical fringes with special reference to signals having subfringes. A WT-based tool has also been developed for unambiguous identification of frequency components from a nonsinusoidal vibration. The results of such WT-based analyses are presented, and merits as well as demerits of the proposed methods are discussed.

Hierarchical Extreme Learning Machine-Polynomial Based Low Valued Capacitance Measurement Using Frequency Synthesizer–Vector Voltmeter

This present paper describes the development of a capacitance measurement system in the picofarad region. The system uses an universal serial bus port-based arrangement in conjunction with an indigenously developed Programmable Intelligent Computer microcontroller-based frequency synthesizer-vector voltmeter that can be used to measure the voltage in vector form and the capacitance can be determined using circuit solution technique. An intelligent two-layered, hierarchical reinforcement-based instrumentation scheme is proposed that can be integrated along with the original measurements to significantly improve the system performance. In layer 1, an extreme learning machine-based supervised phase reinforcement scheme is employed to improve the accuracy of the voltage measurement. Subsequently, in layer 2, local polynomial-based reinforcements are employed to improve both the resistive and reactive part measurements in the unknown capacitance. Three variants of ELM-based reinforcements are implemented for capacitance measurements in the range 100-10 000 pF and the utility of the hybrid ELM-polynomial-based reinforcements for such measurements is aptly demonstrated.

Identification and classification of fault using S-transform in an unbalanced network

In this paper, S-Transform (ST) based fault classification rules are introduced in case of overhead transmission line. The power system model is simulated in MATLAB Simulink environment with unbalanced loading. The proposed technique requires voltage and current signals to be extracted at the sending and receiving end of the network. The current and voltage signals are processed by ST to produce complex S-matrices. Four types of features are obtained from the absolute value of S-matrix involving simple calculation and less computational time. It is noticed from the simulations that these features enjoy a valuable advantage to be a prime choice as parameters for detection of the type of fault and the affected phase on the basis of some threshold values. The classification rules based on these parameters have been established on the basis of 5220 simulations of fault conditions. The multiple fault conditions have been obtained by changing fault resistance, fault location and fault inception angle. The rules are also tested with signals impregnated by synthetic noise. The proposed scheme has been conveniently programmed in MATLAB and the output result is obtained fast and accurate. A computationally fast version of ST is intended to be implemented in future.

Asynchronous operation of synchronous generators under field failure

A synchronous generator is forced to asynchronous operation under sudden field failure. Under such fault condition, the general practice is to switch off the faulted machine by the action of an off-set type mho-relay after an appropriate time delay to allow for recoverable swings. This practice is good enough for salient pole hydro-alternators for which both the reactive power burden and the pulsating power component are relatively large. But these quantities are not prohibitively high for a cylindrical pole turbo-alternator. Sustained operation under field failure may be continued for limited time for them without any risk of thermal injury, provided the reactive power requirement of the alternator can be met by the system, keeping the voltage profile within acceptable limits. In this paper, the performance variables under sudden field failure for both salient and non-salient pole generators have been computed by specially constructed programs and presented in tabular form. Taking the example of a 210 MW set of BHEL, it has been established that sustained operation of a cylindrical pole generator is not harmful, particularly when the field circuit is disconnected from the source and closed through a properly chosen value of discharge resistance.

Cross Hilbert-Huang transform based feature extraction method for multiple PQ disturbance classification

This paper presents a new methodology of Cross-Hilbert Huang transform based feature selection for sensing simultaneous occurrence of multiple power quality disturbances. Kernel PCA is used for feature selection because this method is well suited for non-linear and non-stationary multiple power quality disturbances. A linear support vector machine is used for classification of the extracted features. Results show that the performance is comparable with the results reported in the literatures. The present method is generic in nature and can be applicable for topologically similar problems.

Application of Support Vector Network for Power System Static Security Evaluation

This paper uses Support Vector Network (SVN) to examine whether the power system is secured under steady-state operating conditions. A system is considered operationally reliable if the load bus voltages do not fall below a certain limit and if the power flow through lines does not exceed the corresponding allowable values. SVN determines the minimum bus voltage and maximum ratio of line-flow to permissible line-flow. The input variables to the network are the active power of the load buses, power factor of the loads and the net generated powers of the generating buses. IEEE 14-bus system has been taken as an example. The proper kernel function and optimal value of C i. e. penalty parameter has been calculated. A comparison of the performance of SVN and ANN with those calculated by fast decoupled load flow is carried out. Results of the SVN closely agree with that obtained by fast decoupled load-flow and ANN in the case of proportional input vector. ANN is not suitable in the case of disproportionate input vector whereas SVN overcomes this disadvantage.