Modeling of the magnetizing phenomena of doubly fed induction generator using neuro-fuzzy algorithm considering non-linearity

Doubly fed Induction Generators (DFIGs) are quite common in wind energy conversion systems because of their variable speed nature and the lower rating of converters. Magnetic flux saturation in the DFIG significantly affect its behavior during transient conditions such as voltage sag, sudden change in input power and short circuit. The effect of including saturation in the DFIG modeling is significant in determining the transient performance of the generator after a disturbance. To include magnetic saturation in DFIG model, an accurate representation of the magnetization characteristics is inevitable. This paper presents a qualitative modeling for magnetization characteristics of doubly fed induction generator using neuro-fuzzy systems. Neuro-fuzzy systems with one hidden layer of Gaussian nodes are capable of approximating continuous functions with arbitrary precision. The results obtained are compared with magnetization characteristics obtained using discrete fourier transform, polynomial and exponential curve fitting. The error analysis is also done to show the effectiveness of the neuro fuzzy modeling of magnetizing characteristics. By neuro-fuzzy algorithm, fast learning convergence is observed and great performance in accuracy is achieved

Modeling and Linearization of DFIG Based Wind Turbine

Usage level of wind units in power systems is increasing rapidly. There are different kinds of wind turbine generator. The Doubly-Fed Induction Generator (DFIG), is one of the most widely used electrical machines in the megawatt-class wind turbines. In a DFIG-based wind turbine, the stator is connected to grid directly while the rotor is connected a back-to-back converter via slip rings. Current sensor fault diagnosis for renewable power of wind turbine based on DFIG has gained serious importance. In this work, mathematical modeling of DFIG is presented. Nonlinear state equations are linearized with Takagi-Sugeno (T-S) Local Models for current sensor fault diagnosis. Modelling error between linear and nonlinear model is minimized by heuristic approach on membership functions. A bank of observer-based residual generator system for fault diagnosis is created, so additive and gain faults of stator current sensors can be detected and isolated

Control of Doubly Fed Induction Generator with Maximum Power Point Tracking for Variable Speed Wind Energy Conversion Systems

In this paper, a Direct Power Control (DPC) based on the switching table and Artificial Neural Network-based Maximum Power Point Tracking control for variable speed Wind Energy Conversion Systems (WECS) is proposed. In the context of wind energy exploitation, we are interested in this work to improve the performance of the wind generator by controlling the continuation of the Maximum Power Point Tracking (MPPT) using the Artificial Neural Network (ANN). The results obtained show the interest of such control in this system. The proposed Direct Power Control strategy produces a fast and robust power response, also the grid side is controlled by Direct Power Control based a grid voltage position to ensure a constant DC- link voltage. The THD of the current injected into the electric grid for the Wind Energy Conversion Systems with Direct Power Control is shown in this paper, the THD is lower than the 5 % limit imposed by IEEE STANDARDS ASSOCIATION. This approach Direct Power Control is validated using the Matlab/Simulink software and simulation results can prove the excellent performance of this control as improving power quality and stability of wind turbine

Adaptive fractional order terminal sliding mode control of a doubly fed induction generator- based wind energy system

The dynamic model of a doubly fed induction generator (DFIG)-based wind energy system is subjected to nonlinear dynamics, uncertainties, and external disturbances. In the presence of such nonlinear effects, a high-performance control system is required to guarantee the smooth and maximum power transfer from the wind energy system to the ac grids. This paper proposes a novel fractional order adaptive terminal sliding mode control system for both the rotor and grid side converters of the DFIG system. The stability of the closed loop is ensured using the fractional order Lyapunov theorem. Numerical results are presented to show the superiority of the proposed control method over the classical sliding mode control system and the proportional integral controllers

Islanded Wind Energy Management System Based on Neural Networks

Wind power, as the main renewable energy source, is increasingly deployed and connected into electrical networks thanks to the development of wind energy conversion technologies. This dissertation is focusing on research related to wind power system include grid-connected/islanded wind power systems operation and control design, wind power quality, wind power prediction technologies, and its applications in microgrids. The doubly fed induction generator (DFIG) wind turbine is popular in the wind industry and thus has been researched in this Dissertation. In order to investigate reasons of harmonic generation in wind power systems, a DFIG wind turbine is modeled by using general vector representation of voltage, current and magnetic flux in the presence of harmonics. In this Dissertation, a method of short term wind power prediction for a wind power plant is developed by training neural networks in Matlab software based on historical data of wind speed and wind direction. The model proposed is shown to achieve a high accuracy with respect to the measured data. Based on the above research work, a microgrid with high wind energy penetration has been designed and simulated by using Matlab/Simulink. Besides wind energy, this microgrid system is operated with assistance of a diesel generator. A three-layer energy management system (EMS) is designed and applied in this microgrid system, which is to realize microgrid islanded operation under different wind conditions. Simulation results show that the EMS can ensure stable operation of the microgrid under varying wind speed situations

PCA-ANN Based Algorithm for the Determination of Asymmetrical Network Failures of Network-Connected Induction Generators

Presented in this study is a principal component analysis - artificial neural network based hybrid failure determination system that can make failure determination selectively and rapidly in asymmetrical external failures that might occur on the network side of a grid-connected induction generator. By creating asymmetrical external failures in the developed simulation model, analysis of noisy and unbalanced fluctuations that carry effects of positive, negative and zero sequence in currents were realized. The suggested model depends on entering data taken from the simulation into the artificial neural network model as a training data by being simplified with principal component analysis, in phase-phase, phase-ground and two phase-ground failures. The protection model makes correct classification with acceptable errors in case of above stated failures. However, in current fluctuations caused by sudden load changes and operation under an unbalanced load, it may remain insensitive by behaving selectively

A comparative study between a simplified fuzzy PI and classic PI input-output linearizing controller for the wind-turbine doubly fed induction generator

International audienceThe paper presents a comparative study of a linearizing control with classic PI and fuzzy PI controllers of the active and reactive stator power of a doubly fed induction generator (DFIG) applied to a wind-energy conversion systems (WECS). The paper discusses the operating principles of the power-generation scheme. Simulation results show that the preented input-output linearizing control provides a decoupled control, perfect tracking of the generated active and reactive power and robustness the active- and reactive-power variations. Keywords: Doubly-Fed Induction Generator (DFIG), Input-Output linearization, Fuzzy Logic Controller (FLC)

Empowering the powerful: a critical discourse analysis of public discourse on graduate employability

This study explores the issue of graduate employability in Malaysia as construed in public discourse in English, a language of power in Malaysia. The term employability itself has many definitions depending on the requirements of government and industry, and in the case of Malaysia, the English-language ability of graduates is inseparable from graduate employability. Data were collected from three socially significant English-language publications: a mainstream newspaper (the New Straits Times), an alternative newspaper (The Malaysian Insider), and a government document outlining the national approach to improving graduate employability in universities (the Graduate Employability Blueprint). The data were collected between 2012 and 2013, a significant two-year period of time due to the publication of the Graduate Employability Blueprint in 2012, and the five-yearly Malaysian General Election in 2013. Applying Critical Discourse Analysis (Fairclough, 1995), the study employs Transitivity analysis (Halliday & Matthiessen, 2004) and Appraisal analysis (Martin & White, 2005) from Systemic Functional Linguistics. The analysis looks at the grammatical roles and evaluation of important social actor groups in the graduate employability issue (e.g. government, government link companies, employers, graduates, parents and teachers). The findings show that government, the government programs and the employers are construed favourably, while the graduates are depicted unfavourably. Parents and teachers are largely excluded from the discourse. Significant government expenditure and national resources from public and private organizations are dedicated to improving the employability of graduates in Malaysia. However,the public discourse on graduate employability in the powerful English language appears unlikely to contribute to a social context where the aims of the groups with a key interest in graduate employability will be achieved