Automatic Generation Control (AGC) is one of the most important issues in electric power system design and operation. The objective of the AGC in an interconnected power system is to maintain the frequency of each area and to keep tie-line power close to the scheduled values by adjusting the MW outputs the AGC generators so as to accommodate fluctuating load demands. The automatic generation controller design with better performance has received considerable attention during the past years and many control strategies have been developed for AGC problem.
The availability of an accurate model of the system under study plays a crucial role in the development of the most control strategies like optimal control. However, an industrial process, such as a power system, contains different kinds of uncertainties due to changes in system parameters and characteristics, loads variation and errors in the modeling. On the other hand, the operating points of a power system may change very much randomly during a daily cycle. Because of this, a fixed controller based on classical theory is certainly not suitable for AGC problem. Thus, some authors have suggested a variable structure and neural networks methods for dealing with parameter variations. All the proposed methods are based on the state-space approach and require information about the system states which are not usually known or available.
On the other hand, various adaptive techniques have been introduced for AGC controller design. Due to the requirement of a prefect model which has to track the state variables and satisfy system constraints, it is rather difficult to apply these adaptive control techniques to AGC in practical implementations. Recently, several authors have applied robust control methodologies to the solution of AGC problem. Although via these methods, the uncertainties are directly introduced to the synthesis. But models of large scalar power systems have several features that preclude direct application of robust control methodologies. Among these properties, the most prominent are: very high (and unknown) model order, uncertain connection between subsystems, broad parameter variation and elaborate organizational structure.
In this project, because of the inherent nonlinearity ofpower systems we address a new nonlinear Artificial Neural Network (ANN) controller based on µ-synthesis technique. The motivation of using the µ-based robust controller for training the proposed controller is to take the large parametric uncertainties and modeling error into account. To improve the stability of the overall system and also its good dynamic performance achievement, the ANN controller has been reconstructed with applying the µ- based robust controller to power systems in different op- erating points under different load disturbances by using the learning capability of the neural networks. Moreover, the proposed controller also makes use of a piece of information which is not used in the conventional and µ-based robust controllers (an estimate of the electric load perturbation, ie an estimate of the change in electric load when such a change occurs on the bus). The load perturbation estimate could be obtained either by a linear estimator or by a nonlinear neural network estimator in certain situations. It could also be measured directly from the bus.
We will show by simulation that when a load estimator is available, the ANN controller can achieve an extremely dynamic response. In the work, a two-area power system is considered as a test system. Each area of the power system consists of steam turbines, which include reheaters.Therefore, there are the effects of reheaters and generating rate boundaries in each area. For comparison, the considered system is controlled by using:
(i) Conventional integral controller
(ii) ANN controller
for different cases of the plant parameter changes under various step load disturbances. The simulation results show that the proposed controller is very effective and gives a good dynamic response compared to the conventional PI and µ-based robust controllers even in the presence of the plant parameters changes and Generation Rate Constraint (GRC).
No comments:
Post a Comment