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

Experimental analysis of a modified scheme for supplying single-phase remote loads from micro-hydro based three-phase self-excited induction generator

Induction machine (IM) plays significant role in rural electrification, acting as a generator for small kilowatt to large megawatt applications. These IMs are therefore used for power generation, gaining a huge acceptance within the researchers because of its brushless system, robust design and cheap market rate. In off-grid operation, the induction generator (IG) also known as self-excited IG (SEIG), requires supply of external reactive power (RP) for building up its terminal voltage and satisfying the electrical load demand. A small kilowatt level renewable-driven three-phase SEIG is generally deployed for generating single-phase power in off-grid mode for remote areas. Generation of single-phase power and supplying the load from a three-phase SEIG in off-grid operation, results in the imbalance of the SEIG's stator current and phase voltage. In this regard, various capacitor-based excitation (CBE) schemes have been reported for supplying the required RP to the three-phase SEIG and generating single-phase power, while reducing the mentioned difficulties. The current paper emphasizes on the steady-state mathematical formulation of a proposed modified CBE scheme for generating single-phase power and supplying remote hilly load from a hydro-driven three-phase SEIG under the fixed speed of the generator. Further, a state-of-the-art experimental test-bench of the proposed scheme, where a hydro-driven three-phase SEIG supplying single-phase resistive-inductive (RL) load has been showcased with the same being simulated in the MATLAB environment. The result indicates that the proposed CBE scheme can effectively supply single-phase RL load of different power factors (p.f.), having promising voltage regulation with balanced SEIG operation.

An improved and simplified approach for the steady-state analysis of self-excited induction generators using binary search algorithm

Purpose This paper aims at developing an improved method, based on binary search algorithm (BSA) for the steady-state analysis of self-excited induction generators (SEIGs), which are increasingly used in wind energy electric conversion systems. The BSA is also compared with linear search algorithm (LSA) to bring out the merits of BSA over LSA. Design/methodology/approach All the parameters of SEIG, including the varying core loss of the machine, have been considered to ensure accuracy in the predetermined performance values of the set up. The nodal admittance method has been adopted to simplify the equivalent circuit of the generator and load. The logic and steps involved in the formulation of the complete procedure have been illustrated using elaborate flowcharts. Findings The proposed approach is validated by the experimental results, obtained on a three-phase 240 V, 5.0 A, 2.0 kW SEIG, which closely match with the corresponding predicted performance values. The analysis is shown to be easy to implement with reduced computation time. Originality/value A novel improved and simplified technique has been formulated for estimating the per unit frequency (a), magnetizing reactance (Xm) and core loss resistance (Rm) of the SEIG using the nodal admittance of its equivalent circuit. The accuracy of the predetermined performance is enhanced by considering the SEIG’s varying core loss. Only simple MATLAB programming has been used for adopting the algorithms.

Performance Evaluation and Dynamic Characteristics of a Self-Excited Induction Generator for Pico Hydro Power Plants

The dynamic performance of an isolated three-phase squirrel cage self-excited induction generator (SEIG) in a Pico Hydro Power Plant (PHPP) is examined in this work. The investigation is carried out with the help of MATLAB/Simulink for mathematical modeling and simulation of the proposed system under various operational situations. The SEIG model, which was created using the steady-state equivalent circuit approach, included the electrical, magnetic, and mechanical components of the SEIG and PHPP. The dynamic behavior of the SEIG is explored under a variety of operating situations. The effects of load variations, speed fluctuations, and other disturbances on the voltage and frequency of the generator are examined. The experiment results were used to validate the simulation results. This research has implications for the design and optimization of PHPP using SEIGs.

Experimental determination of minimum capacitor for self-excitation of induction generators

This article addresses the issue related to determining the minimum capacitor required for the self-excitation of an induction generator. The determination of the minimum capacitance required for the self-excitation of a self-excited induction generator has already been the subject of several previous studies. It has been shown that the minimum capacitance depends on the rotation speed and the remanent magnetism. The study carried out in this paper shows that, in addition to the rotation speed and the remanent magnetism, there is a third parameter that has an influence on the self-excitation process, which is the acceleration or, in other words, the rotation speed ramp-up. In this paper, several experimental self-excitation tests for different values of the rotation speed ramp-up are carried out, leading to new characteristics of the minimum self-excitation capacitance as a function of the rotation speed. The results obtained from simulation and experimental studies prove the efficacy of the proposed approach.

Control of Self-Excited Induction generator based wind turbine using by IM controller

Control of Self-Excited Induction generator based wind turbine using by IM controller

Integration of a proton exchange membrane fuel cell system for voltage and frequency stabilization in a micro hydro system

This work discusses the frequency and voltage stabilization in a stand-alone self-excited induction generator system (SEIGs) using a fuel cell (FC)-based generation unit. The proposed SEIG acts as the power generator in a microhydro system. Variations in load create fluctuations in voltage and frequency, which have to be stabilized. In this work, authors have proposed integrating a FC-based generation unit with the existing SEIGs to improve its voltage and frequency profiles. For this work, a proton exchange membrane fuel cell (PEMFC) has been considered. Modeling of a 36 kW PEMFC is taken for the proposed work, which has to be integrated with the 22 kW self-excited induction generator. The design of the system and converter parameters have also been included in the work. Authors have proposed an orthogonal current component-based controlstructure that provides independent control of active and reactive power in the system. Considering the FC system working conditions, this article aims to assure optimum power transfer capability between the self-excited induction generator, fuel cell system, and the time-varying loads. The system is exposed to time-varying RL loads dynamic load switching, and the performance is evaluated in real-time. OP 4510 real-time simulator verifies the proposed system's dynamic model created using Matlab/Simulink.

Smart plant propagation algorithm for the improvement of self-excited induction generator performance

Smart plant propagation algorithm for the improvement of self-excited induction generator performance

Dynamic Performance Enhancement of a Self-Excited Induction Generator Connected to the Grid Using an Effective Control Algorithm

The current study aims to present a comprehensive analysis of a control technique used for improving the dynamic performance of a grid connected self-excited induction generator (SEIG). All components in the control system are modelled and explained in details. The management of SEIG operation is achieved through controlling the machine side converter using a new formulated predictive voltage control scheme (PVC). The proposed (PVC) is compared to field oriented control (FOC), model predictive current control (MPCC) and model predictive direct torque control (MPDTC) systems. MPDTC and MPCC have several drawbacks like high ripple, high load commutation, and using a weighting factor in their cost function, While FOC systems depend on machine parameters for variable estimation factors, need to use PI regulators and co-ordinate transformations, which complicate the system and slow down the dynamic response. The results obtained indicate that the proposed PVC is effective, as the ripples are reduced by 43% when compared to the MPDTC-used one and by 30% when compared to MPCC. Additionally, PVC's time response is less than that of the FOC by 15%, MPCC by 9%, and MPDTC by 4%. Furthermore, PVC produces 38% fewer commutations than MPCC and 40% fewer commutations than MPDTC. Consequently, the generator's efficiency and dependability both increased as a result of its better performance.

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.

Optimization algorithms for steady state analysis of self excited induction generator

The current publication is directed to evaluate the steady state performance of three-phase self-excited induction generator (SEIG) utilizing particle swarm optimization (PSO), grey wolf optimization (GWO), wale optimization algorithm (WOA), genetic algorithm (GA), and three MATLAB optimization functions (<em><span lang="EN-US">fminimax</span></em><span lang="EN-US">, </span><em><span lang="EN-US">fmincon</span></em><span lang="EN-US">, </span><em><span lang="EN-US">fminunc</span></em><span lang="EN-US">). The behavior of the output voltage and frequency under a vast range of variation in the load, rotational speed and excitation capacitance is examined for each optimizer. A comparison made shows that the most accurate results are obtained with GA followed by GWO. Consequently, GA optimizer can be categorized as the best choice to analyze the generator under various conditions.</span>

DC-Bus Voltage Control of Self-Excited Induction Generator for Variable-Speed Wind Turbine Generation Including Dynamic Saturation

DC-Bus Voltage Control of Self-Excited Induction Generator for Variable-Speed Wind Turbine Generation Including Dynamic Saturation

Stator current signal crossing for fault diagnosis of self-excited induction generators

This paper presents a novel method for modelling and diagnosis of electrical and mechanical faults in fixed-Speed Self-Excited Induction Generators (SEIGs) operating in autonomous mode in a small-scale wind energy system. The proposed method is validated using the finite element method. After the selection of the magnetising capacitors, the self-excitation process is performed under no-load conditions. Once the stator voltage is established, a symmetrical three-phase load is connected. The fault detection method introduced here is called Stator Current Signal Crossing (SCSC). The SCSC extracts a new signal from the stator currents, that enables the detection of stator inter turn shortcircuits, broken rotor bars, and dynamic eccentricity faults in SEIGs. A spectral analysis of SCSC using the Fast Fourier Transform (FFT) algorithm is used to precisely locate the induced fault components. What sets this fault-tracking method apart from its predecessors is its exceptional ability to detect faults of any magnitude by analysing the modulation of the SCSC signal. These faults are directly identified by the presence of distinct harmonics, each indicative of a specific type of fault. This study also focuses on the SEIG in a wind energy system, whereas previous works have mainly addressed the induction machine in motor mode. In contrast, previous methods involved analysing a single current signal and isolating specific harmonics from a wide frequency range. The effectiveness of the proposed fault detection method and the self-excitation process are illustrated by simulation results and spectral analysis.

Control of self-excited induction generator based wind turbine using current and voltage control approaches

The self-excited induction generator (SEIG) is widely applied in the wind energy conversion system (WECS) to enhance power generation. The power generation from WECS is varied in terms of varying wind speed. Hence, to improve the working of SEIG-based WECS, the multi-stage coati optimized proportional integral (CPI) fractional order proportional integral derivative (FOPID) controller is proposed in this work. The proposed coati optimized proportional integral - fractional order proportional integral derivative controls the SEIG’s grid side converter and generator side converter. The coati optimization algorithm optimizes the controller parameters of a proposed multi-state controller. The proposed CPI-FOPID controls both the voltage at the grid side converter and the current at a generator side converter. Moreover, the pitch angle of the wind turbine (WT) is controlled by the fuzzy-based tilt integral derivative (F-TID) controller. The proposed work will be implemented on the Matlab/Simulink platform, and the THD of 0.63% has proven the efficacy of the proposed methodology.

Nonlinear control algorithm for a self-excited induction generator for wind power applications

A non-linear state-feedback input-output linearizing control with tracking controllers for an isolated Induction Generator (IG) driven by a wind turbine and supplying an AC load is presented. For this purpose, an appropriate mathematical model of the IG based on the so-called stator-flux field oriented is detailed. Simulation results show that the input-output linearizing control provides an excellent control performances and a perfect tracking for both IG stator voltage magnitude and DC bus voltage. The proposed system can be directly explored for wind power generation purposes. Simulation results are curried out to demonstrate the feasibility of the proposed approach.

Fuzzy Logic PI controller based Direct Torque control of a Self-Excited Induction Generator through a three-level Rectifier

The main goal of this work is to control the terminal voltage of a Self-excitation induction generator (SEIG) that supplies autonomous load and is driven by a variable speed wind turbine. More advanced strategies of control have been suggested and applied these through a two-level converter. However, in this paper, we propose to study a modified DTC approach which based on the use of three-level converter (NPC structure) and instead of the traditional PI controller, we use a PI fuzzy controller to reduce torque and flux ripples, the induction generator's stator flux and electromagnetic torque are controlled using voltage space vector selection. The proposed control strategy has to maintain constant DC bus voltage regardless of load or wind speed variations. Finally, it should be noted that this control accounts for the induction generator's magnetic saturation phenomenon.

Analysis of a Self-excited Induction Generator with Fuzzy PI Controller for Supporting Domestic Loads in a Microgrid

This study provides a technical analysis of integrating a microgrid structure for power generation with a wind energy conversion system. This study's primary objective is to identify solutions to issues that arise when isolated induction generator is utilized in a microgrid. A closed loop electronic load connector is used to control the loads of the induction generator-based system. The load controller is adjusted using an adaptive fuzzy based proportional-integral controller for better control. Additionally, by employing a straightforward variable capacitor design to inject reactive power during voltage dips, it provides a way to sustain the voltage profile. The suggested approach and its control are investigated in MATLAB/Simulink and verified with the aid of a lab experimental setup. The outcomes clearly demonstrate the practicality of the suggested plan to be implemented in grid-isolated places.

Coordinated Power Management of a Laboratory Scale Wind Energy Assisted LVDC Microgrid With Hybrid Energy Storage System

The present work proposes a coordinated power management scheme (CPMS) of wind energy-fed self-excited induction generator (SEIG) based low-voltage direct current (LVDC) autonomous microgrid. The battery and supercapacitor (SC) based hybrid energy storage system (HESS) is interfaced with the common 48 V LVDC bus using a parallel-active configuration through two bidirectional converters. The central idea of hybridization is to mitigate the instantaneous surge current demand and alleviate the charge/discharge stress during transients, thus ultimately enhancing the cycle life of the battery. The experimental validation of the autonomous LVDC microgrid equipped with the CPMS is conducted for various operating modes. The dSPACE DS1103 digital controller is used for the real-time implementation of the complete system. The effectiveness of the proposed CPMS is supported by the analysis of comparative experimental results demonstrates its ability to incorporate seamless mode transition. The developed 48 V LVDC microgrid can be a promising solution for powering consumer electronics in homes/office spaces or other small-scale applications.

Optimal Frequency and Voltage Control of Self-Excited Induction Generator for Wind Power Generation Using PWM Converter and Load Management

Optimal Frequency and Voltage Control of Self-Excited Induction Generator for Wind Power Generation Using PWM Converter and Load Management

Grid Integration of Livestock Biogas Using Self-Excited Induction Generator and Spark-Ignition Engine

This study developed a grid-connected biogas power generation system for a rural community using a 3-phase 2.2 kW squirrel-cage induction machine as the self-excited induction generator. The generator was driven by a 196 cc single-cylinder spark-ignition engine fueled by biogas. We developed a back-to-back converter that consists of a 3-phase PWM rectifier as the generator-side converter and a single-phase LCL-filtered grid-connected inverter. The generator-side converter transferred the active power to the grid-side converter and supplied the reactive power control back to the generator. The notch filter-based bus voltage control on the generator side mitigated the inter-harmonics in the generator current. The injected grid current complied with the IEEE 1547 standard because of the multi-frequency unbalanced synchronous reference frame control. The proposed system was validated with biogas produced from pig manure at a pig farm in central Thailand, which found a maximum output of 1.2 kW with a thermal system efficiency of 10.7%. The proposed system was scheduled to operate at 1.2 kW for 8 h per day with a levelized cost of 0.07 US$/kWh, 42% cheaper than the retail electricity price, and a payback period of 2.76 years. The proposed system is suitable for a farm with a minimum of 34 pigs.