Analysis Of Self-excited Induction Generator Research Articles

A simplified computational technique for the analysis of self-excited induction generators considering the core loss resistance

A simplified computational technique for the analysis of self-excited induction generators considering the core loss resistance

Application of generalized hopfield neural network for the steady state analysis of self-excited induction generators

This study proposes a generalized Hopfield neural network (GHNN) for solving the non-linear equations involved in the steady state analysis of Self-Excited Induction Generators (SEIGs). Unlike other neural network architectures, the generalized Hopfield neural network can convert a non-linear equation solving problem into an optimization problem. Moreover, the weights and bias of GHNN can easily be obtained from the coefficients of the non-linear equations to be solved without additional training dataset. Motivated by its simplicity, faster convergence, and global stability properties, this study proposes a GHNN for solving the non-linear equations involved in the steady state analysis of Self-Excited Induction Generators (SEIGs). Moreover, unlike the heuristic algorithms, this approach does not require the boundary values for the unknown parameters (per unit frequency, magnetizing reactance Xm and core loss resistance Rm) of the SEIG to be given as inputs, for initiating the optimization process and it ensures guaranteed convergence. Symbolic programming technique has been employed to form the non-linear equations required for the analysis, thereby eliminating the lengthy derivations involved. The formulated equations are then used to form an energy function and a set of differential equations describing the dynamic behavior of the Hopfield network is obtained from this function. These equations are then solved until the energy function minimizes to zero to arrive at the unknown parameters of the SEIG. Using the energy function equation, the stability analysis of the proposed network has been carried out according to the Lyapunov’s second method of stability, which assures the convergence of the proposed method leading to the solutions. The proposed method has been found to be more efficient than other conventional optimization techniques, in terms of accuracy, number of iterations required and computation time. Experiments have been conducted on a 3.5 kW, 415 V SEIG and the results are shown to be in close agreement with the values predetermined using GHNN. It is also concluded that wind driven SEIGS, will find increased deployment in hybrid renewable energy sources, being applied in several emerging areas.

A new simplified approach for the steady state analysis of self-excited induction generators employing the concepts of co-ordinate geometry

A new simplified approach for the steady state analysis of self-excited induction generators employing the concepts of co-ordinate geometry

A New Simplified Approach for the Steady State Analysis of Self-Excited Induction Generators Employing the Concepts of Co-Ordinate Geometry

A New Simplified Approach for the Steady State Analysis of Self-Excited Induction Generators Employing the Concepts of Co-Ordinate Geometry

Grey wolf optimizer algorithm for the performance predetermination of variable speed self-excited induction generators

PurposeThis paper aims to apply grey wolf optimizer (GWO) algorithm for steady state analysis of self-excited induction generators (SEIGs) supplying isolated loads.Design/methodology/approachTaking the equivalent circuit of SEIG, the impedances representing the stator, rotor and the connected load are reduced to a single loop impedance in terms of the unknown frequency, magnetizing reactance and core loss resistance for the given rotor speed. This loop impedance is taken as the objective function and minimized using GWO to solve for the unknown parameters. By including the value of the desired voltage as a constraint, the formulated objective function is also extended for estimating the required excitation capacitance.FindingsThe experimental results obtained on a three phase 415 V, 3.5 kW SEIG and the corresponding predetermined performance characteristics agree closely, thereby validating the proposed GWO method. Moreover, a comparative study of GWO with genetic algorithm and particle swarm optimization techniques reveals that GWO exhibits much quicker convergence of the objective function.Originality/valueThe important contributions of this paper are as follows: for the first time, GWO has been introduced for the SEIG performance predetermination and computation of the excitation capacitance for attaining the desired terminal voltage for the given load and speed; the predicted performance accuracy is improved by considering the variable core loss of the SEIG; and GWO does not require derivations of lengthy equations for calculating the SEIG performance.

Load Capacity Analysis of Self-Excited Induction Generators Based on Routh Criterion

In this paper, the Routh criterion has been used to analyze the stability of a self-excited induction generator-based isolated system which is regarded as an autonomous system. Special focus has been given to the load capacity of the self-excited induction generator. The state matrix of self-excited induction generators with resistor-inductor load has been established based on transient equivalent circuits in the stator stationary reference-frame. The recursive Routh table of self-excited induction generators is established by the characteristic polynomial coefficients of the state matrix. According to the Routh stability criterion, the necessary and sufficient condition to predict the critical loads of self-excited induction generators is deduced, from which the critical load impedance can be calculated. A simple self-excited induction generator-based isolated power system has been built up with a 2.2 kW self-excited induction generator. The theoretical analysis and experiments were all carried out based on this platform. In the range determined by the minimum excitation capacitance (Cmin) and the maximum excitation capacitance (Cmax), the critical loads under various power factors have been calculated. The agreement of the calculated theoretical results and experimental results demonstrate the effectiveness and accuracy of the proposed analysis method. The conclusions achieved lay a foundation for further application of Routh stability criterion in self-excited induction generator-based power systems analysis.

Voltage Build-Up Analysis of Self-Excited Induction Generator With Multi-Timescale Reduced-Order Model

Self-excited induction generator (SEIG) has received a lot of attentions for its increasing application in distributed generation systems with the essential feature of low cost. To analysis, the dynamic and transient performance of SEIG, several modifications of the mathematical models have been developed for improving the regulation of voltage and frequency. But these models are still complicated to be used in practice. Based on the transient equivalent circuit, a reduced-order model of SEIG with complex transformation in the two-phase stationary reference frame is realized for the transient analysis of voltage build-up. In this simplified model, the coefficients of the characteristic polynomial with multi-timescale time constants are proposed. Moreover, the physical interpretation of system transient behavior with the reconstructed time constants is established and visualized. Particularly, the upper and lower limits of the capacitance and speed for the SEIG with different parameters variation are simulated and analyzed respectively. The validation and the accuracy of the SEIG model are verified for the transient analysis of the voltage build-up. It is proved that the reduced-order model can be effectively used to insight the dynamic stability of SEIG voltage build-up with the multi-timescale.

An iterative solution approach for steady state analysis of self-excited induction generator

The steady state analysis of self-excited induction generator is widely used for stand-alone renewable energy system. The impedance values of equivalent circuit vary depending on frequency. The frequency changes according to load, excitation capacitance and rotor speed. If the values of load and capacitance are known, only the frequency and slip are the two main unknowns left for analysis of the single-phase equivalent circuit. The simulation is generally performed for a desired slip value to obtain the frequency. However, with the proposed approach in this study, the values of rotor slip, and frequency of terminal voltage are both directly determined in shorter time. The proposed algorithm is verified by the experiments of three phase 1.1 kW squirrel cage induction machine.

An iterative solution approach for steady state analysis of self-excited induction generator

The steady state analysis of self-excited induction generator is widely used for stand-alone renewable energy system. The impedance values of equivalent circuit vary depending on frequency. The frequency changes according to load, excitation capacitance and rotor speed. If the values of load and capacitance are known, only the frequency and slip are the two main unknowns left for analysis of the single-phase equivalent circuit. The simulation is generally performed for a desired slip value to obtain the frequency. However, with the proposed approach in this study, the values of rotor slip, and frequency of terminal voltage are both directly determined in shorter time. The proposed algorithm is verified by the experiments of three phase 1.1 kW squirrel cage induction machine.

Optimization Techniques for the Analysis of Self-excited Induction Generator

Nowadays the renewable energy systems are developing constantly around the whole world especially in backward regions the installation of wind firms increases day by day. Self-excited induction generators are the proper choice to be implemented in these applications because of its several merits. This paper includes the steady state performance of self-excited induction generator (SEIG) feeding balanced resistive load. Three optimization techniques have been adopted to determine the magnetizing reactance and generated frequency of the machine. Simulated results as obtained have been compared with experimental data. Comparative analysis as discussed proves the superiority of GA and PSO.

Performance analysis of self-excited induction generator for different excitation and loading condition in wind turbine application

To ensure the complete control of AC machine, state analysis is highly required during the dynamic modelling. In this paper, a mathematical model of the self-excited induction generator (SEIG) is developed to analyse its operation in wind energy systems. A generalised steady state model using d-q stationary reference frame of such a three phase SEIG has been developed. The model has been analysed for balanced and unbalanced excitation condition. Moreover, the effect of wind speed and pitch angle in generator response is observed. Eventually, the model is tested for static and dynamic loads. The analysis of the results could help to determine the efficiency solutions for the system to supply isolated areas even when the loads are unbalanced. The derived equations and developed model are verified through the simulation results of MATLAB simulator.

Performance analysis of self-excited induction generator for different excitation and loading condition in wind turbine application

To ensure the complete control of AC machine, state analysis is highly required during the dynamic modelling. In this paper, a mathematical model of the self-excited induction generator (SEIG) is developed to analyse its operation in wind energy systems. A generalised steady state model using d-q stationary reference frame of such a three phase SEIG has been developed. The model has been analysed for balanced and unbalanced excitation condition. Moreover, the effect of wind speed and pitch angle in generator response is observed. Eventually, the model is tested for static and dynamic loads. The analysis of the results could help to determine the efficiency solutions for the system to supply isolated areas even when the loads are unbalanced. The derived equations and developed model are verified through the simulation results of MATLAB simulator.

Simplified methods for the analysis of self‐excited induction generators

In this study, two simple methods for the steady-state analysis of self-excited induction generators (SEIGs) are proposed. These methods neither require lengthy mathematical derivations nor any advanced optimisation techniques to solve the equivalent circuit of SEIGs. First method involves a simple linear search algorithm and the second method employs a binary search algorithm to find the operating frequency at any given rotor speed, leading to the performance predetermination of SEIGs. To start the search techniques, a systematic approach has been formulated for fixing the boundary values required for the unknown pu frequency. The efficacy of the proposed methods has been demonstrated by presenting the predetermined performance characteristics of a three-phase, 230 V, four-pole, 50 Hz SEIG. The same SEIG was also tested in the laboratory using a DC motor as the prime mover. The closeness observed between the predetermined values and the experimental results further confirms the validity of the search algorithms. It has also been shown that the proposed methods can be extended with the same simplicity, for carrying out the performance predetermination of the short-shunt configuration of SEIGs, used for obtaining improved voltage regulation with lagging power factor loads.

A cuckoo search algorithm optimizer for steady-state analysis of self-excited induction generator

Abstract Renewable energy systems especially the wind energy grows worldwide continuously. Also, installations of the wind farms increase in remote area applications. The self-excited induction generator (SEIG) is an effective choice for using in these applications due to its merits. This paper presents a cuckoo search algorithm (CSA) optimizer for steady state analysis of the SEIG. The mathematical model of the SEIG is based on equivalent circuit loop impedance method. The target of optimization is to minimize the total equivalent circuit impedance of the generator in order to obtain the frequency and any other unknown parameter of the SEIG. In this study, the frequency and magnetizing reactance define the search space for the optimization problem. The MATLAB CSA optimization toolbox is used to solve optimization problem. The effectiveness of the proposed optimizer is evaluated on a 220 V, 0.75 kW induction generator for various operating conditions. The effectiveness of the CSA model is then compared with other conventional optimization models. The validity of the proposed optimizer is verified by experimental results. With this proposed CSA optimization technique, a faster and an accurate prediction for steady state analysis of the SEIG are achieved.

Steady State Modeling and ANFIS Based Analysis of Self-Excited Induction Generator

Self-excited induction generator (SEIG) has the ability to generate power at varying speed, which facilitates its application for wind energy conversion in remote and windy areas. Estimation of the generator behavior under actual operating conditions is essential, but the SEIG analysis with conventional techniques take long time with fatigued mathematical procedures. This paper presents a new technique for the performance analysis of a three phase SEIG supplying an isolated resistive load using adaptive neuro-fuzzy inference system (ANFIS). Proposed ANFIS model has been implemented to predict the effect of prime mover speed, capacitance and load on generated voltage of SEIG. Experimental data is used for the training of ANFIS. Results obtained from the trained have been compared with the experimental results. The comparison confirms the validity and accuracy of the ANFIS based modeling of induction generator.

Behaviour Analysis of Self Excited Induction Generator Feeding Linear and No Linear Loads

Application of induction machines in wind turbine driven generators is a good alternative due to their good characteristics such as efficiency, reliability and low cost. Nevertheless, when isolated operation is required, the application of external capacitive bank, connected to the stator windings, to provide self-excitation results in a rather complex analysis. This paper presents an analysis of self-excited induction generator connected to a load either directly or by an intermediate of a power converter. At first a dynamic model of the induction generator accounting for magnetic saturation is developed. Then a number of balanced and unbalanced capacitors, passive and active loads are verified. Experimental results obtained from laboratory tests are compared to those simulated; the two are shown to be in good agreement.

Steady-state analysis of self-excited induction generators using genetic algorithm approach under different operating modes

The main focus of this paper is the steady-state analysis of self-excited induction generators (SEIGs). It employs the genetic algorithm approach (GAA) to estimate the steady-state performance of these machines. Further, the GAA is used for the solution of problems related to the operation of a number of SEIGs running in parallel. GA-based modelling is found to be effective to determine the generated voltage and frequency. Experimental results validate the proposed methodology.

Dynamic and pole-zero analysis of self-excited induction generator using a novel model with iron losses

In this paper, we propose and analyze a novel self-excited induction generator (SEIG) dynamic model with iron losses. The iron losses are represented by means of an equivalent iron loss resistance connected in parallel with the stator inductance. Moreover, the iron loss resistance is modeled as variable with respect to both synchronous frequency and magnetizing flux, whereas the magnetizing flux influence is expressed by means of the corresponding iron loss current. We determined this variation by performing a series of standard no-load tests over a wide range of frequencies and using sinusoidal supply. In order to validate the performance of the proposed model, we carried out a number of simulations and experiments. The results obtained by the proposed model are compared with the results obtained by other commonly used SEIG models, as well as with the results obtained by measurement. All SEIG models were built in the MATLAB/Simulink environment. It is shown that, of all the considered models, the proposed model is the best choice for a comprehensive SEIG analysis because of the high accuracy, high numerical stability and low computational demands. For the same reasons, the proposed model is also a very good candidate for control system design. Finally, the pole-zero maps of two SEIG models and the corresponding pole-zero analysis are given for the first time in this paper.

Performance Analysis of Self-Excited Induction Generator Using Simulated Annealing Algorithm

The utilization of simulated annealing algorithm (SAA) for the analysis of a self-excited induction generator (SEIG) is presented in this paper. The results obtained are compared with the corresponding results acquired by genetic algorithm approach. The SAA results are also validated by experimental test results. The present investigation proves that SAA is an accurate and simple technique to evaluate the performance of SEIG under different operating conditions. The other remarkable feature of the proposed approach is the possibility to obtain the performance of SEIG feeding a static or dynamic load. This is due to the capability of SAA to extract more than two unknown parameters using one optimization function.

Performance Analysis of Three-Phase Self-Excited Induction Generator Using Genetic Algorithm

This paper presents a new technique for the analysis of a three-phase self-excited induction generator feeding a three-phase balanced unity power factor load. A genetic algorithm has been proposed to find the generated frequency and magnetizing reactance simultaneously. This leads to a complete analysis of the self-excited induction generator. Computed results have been compared with experimental results. The comparison confirms the validity and accuracy of the proposed method.