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|Title: ||A Novel Approach for Tuning of Power System Stabilizer Using Genetic Algorithm|
|Authors: ||Singh, Ravindra|
|Advisors: ||Sen, Indraneel|
|Submitted Date: ||Jul-2004|
|Publisher: ||Indian Institute of Science|
|Abstract: ||The problem of dynamic stability of power system has challenged power system engineers since over three decades now. In a generator, the electromechanical coupling between the rotor and the rest of the system causes it to behave in a manner similar to a spring mass damper system, which exhibits an oscillatory behaviour around the equilibrium state, following any disturbance, such as sudden change in loads, change in transmission line parameters, fluctuations in the output of turbine and faults etc. The use of fast acting high gain AVRs and evolution of large interconnected power systems with transfer of bulk power across weak transmission links have further aggravated the problem of low frequency oscillations. The oscillations, which are typically in the frequency range of 0.2 to 3.0 Hz, might be excited by the disturbances in the system or, in some cases, might even build up spontaneously. These oscillations limit the power transmission capability of a network and, sometimes, even cause a loss of synchronism and an eventual breakdown of the entire system.
The application of Power System Stabilizer (PSS) can help in damping out these oscillations and improve the system stability. The traditional and till date the most popular solution to this problem is application of conventional power system stabilizer (CPSS). However, continual changes in the operating condition and network parameters result in corresponding change in system dynamics. This constantly changing nature of power system makes the design of CPSS a difficult task.
Adaptive control methods have been applied to overcome this problem with some degree of success. However, the complications involved in implementing such controllers have restricted their practical usage.
In recent years there has been a growing interest in robust stabilization and disturbance attenuation problem. H∞ control theory provides a powerful tool to deal with robust stabilization and disturbance attenuation problem. However the standard H∞ control theory does not guarantee robust performance under the presence of all the uncertainties in the power plants.
This thesis provides a method for designing fixed parameter controller for system to ensure robustness under model uncertainties. Minimum performance required of PSS is decided a priori and achieved over the entire range of operating conditions.
A new method has been proposed for tuning the parameters of a fixed gain power system stabilizer. The stabilizer places the troublesome system modes in an acceptable region in the complex plane and guarantees a robust performance over a wide range of operating conditions. Robust D-stability is taken as primary specification for design. Conventional lead/lag PSS structure is retained but its parameters are re-tuned using genetic algorithm (GA) to obtain enhanced performance. The advantage of GA technique for tuning the PSS parameters is that it is independent of the complexity of the performance index considered. It suffices to specify an appropriate objective function and to place finite bounds on the optimized parameters. The efficacy of the proposed method has been tested on single machine as well as multimachine systems. The proposed method of tuning the PSS is an attractive alternative to conventional fixed gain stabilizer design as it retains the simplicity of the conventional PSS and still guarantees a robust acceptable performance over a wide range of operating and system condition.
The method suggested in this thesis can be used for designing robust power system stabilizers for guaranteeing the required closed loop performance over a prespecified range of operating and system conditions. The simplicity in design and implementation of the proposed stabilizers makes them better suited for practical applications in real plants.|
|Appears in Collections:||Electrical Engineering (ee)|
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