Combined heat and power economic dispatch by harmony search algorithm

This paper presents the modeling and control design for a wind energy conversion scheme using induction generators. The scheme consists of a three-phase induction generator driven by a horizontal axis wind turbine and interfaced to the utility through a double overhead transmission line. A static VAr compensator was connected at the induction generator terminals to regulate its voltage. The mechanical power input was controlled using the blade pitch-angle. Both state and output feedback controllers are designed using MATLAB software to regulate the generator output. From the simulation results, the response of closed loop system exhibited a good damping and fast recovery under different type of large disturbances.

Control design and dynamic performance analysis of a wind turbine-induction generator unit

Economic load dispatch (ELD) and economic emission dispatch (EED) have been applied to obtain optimal fuel cost and optimal emission of generating units, respectively. Combined economic emission dispatch (CEED) problem is obtained by considering both the economy and emission objectives. This biobjective CEED problem is converted into a single objective function using a price penalty factor approach. A novel modified price penalty factor is proposed to solve the CEED problem. In this paper, evolutionary computation (EC) methods such as genetic algorithm (GA), micro GA (MGA), and evolutionary programming (EP) are applied to obtain ELD solutions for three-, six-, and 13-unit systems. Investigations showed that EP was better among EC methods in solving the ELD problem. EP-based CEED problem has been tested on IEEE 14-, 30-, and 118-bus systems with and without line flow constraints. A nonlinear scaling factor is also included in EP algorithm to improve the convergence performance for the 13 units and IEEE test systems. The solutions obtained are quite encouraging and useful in the economic emission environment.

Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints

Distance protection of flexible ac transmission lines, including the thyristor-controlled series compensator (TCSC), static synchronous compensator, and static var compensator has been a very challenging task. This paper presents a new approach for the protection of TCSC line using a support vector machine (SVM). The proposed method uses postfault current samples for half cycle (ten samples) from the inception of the fault and firing angle as inputs to the SVM. Three SVMs are trained to provide fault classification, ground detection, and section identification, respectively, for the line using TCSC. The SVMs are trained with polynomial kernel and Gaussian kernel with different parameter values to get the most optimized classifier. The proposed method converges very fast with fewer numbers of training samples compared to neural-network and neuro-fuzzy systems which indicates fastness and accuracy of the proposed method for protection of the transmission line with TCSC

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Fault Classification and Section Identification of an Advanced Series-Compensated Transmission Line Using Support Vector Machine

Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients

A realistic simulation of fault arcs is extremely important in the successful design and development of alternative and improved transmission system equipment such as adaptive auto-reclosure and novel protection schemes It is also particularly important in relation to the design of single pole switched systems This paper discusses, in some detail, techniques that have been developed to model fault arcs more realistically than has been achieved hitherto A time dependent dynamic resistance representation of the primary arc is proposed with emphasis on an empirical approach which is used to determine the parameters concerned In particular, significant improvements have been made to the dynamic conducting characteristic of secondary arc models The paper concludes with a presentation of some interesting studies relating to typical 400 kV single circuit and double circuit systems when the new are models are incorporated into the electromagnetic transients program (EMTP)

Improved techniques for modelling fault arcs an faulted EHV transmission systems

Adaptive single-pole autoreclosure offers many advantages over conventional approaches. In this paper an adaptive single-pole autoreclosure technique is developed using artificial neural networks. The data generation, data preprocessing, and feature extraction process required to set up the training/test data for the neural network, and the implementation of the latter are described in detail. A nonfully connected three-layer perceptron is trained by the Extended-Delta-Bar-Delta learning algorithm. The test results demonstrate the ability of this network to distinguish reliably between permanent and transient faults, and in the latter case, the ability to determine the exact arc extinction time. The outcome of the study indicates that the neural network approach can be used as an attractive and effective means of realising an adaptive autoreclosure scheme.

Neural-network based adaptive single-pole autoreclosure technique for EHV transmission systems

This paper presents a new genetic approach for solving combined heat and power (CHP) economic dispatch problems The complication of CHP dispatch lies in the constraints imposed by the multi-demand and heat-power capacity mutual dependencies The paper proposes improved penalty function formulation for Genetic Algorithms (GAs) to effectively handle constraints The method has been tested and compared to demonstrate its effectiveness

Combined heat and power economic dispatch using genetic algorithm based penalty function method

Single-pole autoreclosure is quite extensively used in long-line applications and involves tripping only the faulted phase for single-phase earth faults. Reliable and fast phase selection is thus imperative in order to avoid potential problems of system insecurity and instability. Conventional phase selectors, primarily based on power frequency measurands, can suffer some impairment in performance because of their heavy dependency on varying system and fault conditions. However, the advent of artificial neural networks (ANNs), with their ability to map complex and highly nonlinear input/output patterns, provides an attractive potential solution to the long-standing problems of accurate and fast phase selection. This paper describes the design of a novel phase selector using ANNs. The technique is based on utilising fault generated high frequency noise (captured through the high voltage coupling capacitor of a conventional capacitor voltage transformer) to essentially recognise the various patterns generated within the frequency spectra of the fault generated noise signals on the three phases, for the purposes of accurately deducing the faulted phase. The paper demonstrates a new concept and methodology in phase selection which will facilitate single-pole autoreclosure applications in power systems.

A New Approach to Phase Selection Using Fault Generated High Frequency Noise and Neural Networks

Since die complex variation of line impedance measured is controlled by thyristors and is accentuated as the capacitor's own protection equipment operates randomly under fault conditions in controllable series compensated transmission systems (CSC), conventional distance protection schemes are limited to certain applications. The authors have extensively addressed the development of new protection techniques for such systems using multilayer percetrons. The basic idea of the method is to design a protection scheme using a neural network approach by catching die feature signals in a certain frequency range under fault conditions. This is different from conventional schemes that are based on deriving implicit mathematical equations based on the infoimation obtained by complex filtering techniques. This paper presents some recent results of employing radial basis function neural networks (RBFN) for this particular application. The use of RBFN is because it has a number of advantages over multilayer ...

Y.H. Song

Papers

Improved techniques for modelling fault arcs an faulted EHV transmission systems

Neural-network based adaptive single-pole autoreclosure technique for EHV transmission systems

Combined heat and power economic dispatch using genetic algorithm based penalty function method

A New Approach to Phase Selection Using Fault Generated High Frequency Noise and Neural Networks

Protection scheme for ehv transmission systems with thyristor controlled series compensation using radial basis function neural networks