Neural Stabilization/Excitation Control of a High-Order Power System by Adaptive Feedback Linearization

نویسندگان

  • Kingsley Fregene
  • Diane Kennedy
چکیده

This paper discusses the systematic design of an adaptive feedback linearizing neurocontroller for a high-order model of the synchronous machine/infinite bus power system. The power system is first modelled as an input-output nonlinear discrete-time system approximated by two neural networks. The approach allows a simple linear pole-placement controller (which is itself not a neural network) to be designed. The control law is specified such that the controller adaptively calculates an appropriate feedback linearizing control law at each sampling instant by utilizing plant parameter estimates provided by the neural system model. The control system also adapts itself on-line. This avoids the requirement for exact knowledge of the power system dynamics and full state measurement as well as other difficulties associated with implementing analytical input-output feedback linearizing control for a complex power system model. Furthermore, a departure is made from the ‘ad hoc’ manner in which many neural controllers have been designed for power systems; the approach used here has foundations in control theoretic concepts of adaptive feedback linearization and pole-placement control design. Simulation results demonstrate the performance of this controller for a representative example of a single-machine/infinite bus power system configuration under various operational conditions. ∗This work was supported by the Natural Sciences and Engineering Research Council of Canada via a research grant. 1

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

adaptive control of two-link robot manipulator based on the feedback linearization method and the proposed neural network

This paper proposes an adaptive control method based on the feedback linearization technique and a proposed neural network,  for tracking and position control of an industrial manipulator. At first, it is assumed that the dynamics of the system are known and the control signal is constructed  by the feedback linearization method. Then to eliminate the effects of the uncertainties and external d...

متن کامل

Robust Control of Encoderless Synchronous Reluctance Motor Drives Based on Adaptive Backstepping and Input-Output Feedback Linearization Techniques

In this paper, the design and implementation of adaptive speed controller for a sensorless synchronous reluctance motor (SynRM) drive system is proposed. A combination of well-known adaptive input-output feedback linearization (AIOFL) and adaptive backstepping (ABS) techniques are used for speed tracking control of SynRM. The AIOFL controller is capable of estimating motor two-axis inductances ...

متن کامل

Design of an Adaptive-Neural Network Attitude Controller of a Satellite using Reaction Wheels

In this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. Then, an attitude control based on feedback linearization control is designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. In order to eliminate the effect of these uncertainties, ...

متن کامل

A Current-Based Output Feedback Sliding Mode Control for Speed Sensorless Induction Machine Drive Using Adaptive Sliding Mode Flux Observer

This paper presents a new adaptive Sliding-Mode flux observer for speed sensorless and rotor flux control of three-phase induction motor (IM) drives. The motor drive is supplied by a three-level space vector modulation (SVM) inverter. Considering the three-phase IM Equations in a stator stationary two axis reference frame, using the partial feedback linearization control and Sliding-Mode (SM) c...

متن کامل

Real-Time Output Feedback Neurolinearization

 An adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. Another key feature of this structure is the fact that, it does not need model of the system. In this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  Online training of neuroline...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2000