Abstract- The design simulation and validation of an Fuzzy Controller based Power System Stabilizer (PSS) for a multi area system and investigate its performance in damping low frequency power system oscillations. The design employs a conventional input pairs like speed deviation () and rotor angle deviation () to the Fuzzy PSS. The advantage of this Fuzzy based PSS is that it can be fed to each of machines in the each areas of the Power System which reduces cost, scanning time and thus simplifies the structure. It is observed that Fuzzy based PSS yields a more satisfactory role in damping low frequency power system oscillations to improve the stability of power system. The performance of the system model is done using MATLB/SIMULINK. INTELLIGENT BASED POWER SYSTEM STABILIZER FOR A MULTI AREA POWER SYSTEM

INTRODUCTIONPower systems are complex nonlinear systems and often exhibit low frequency power oscillations due to insufficient damping, especially in large, weakly coupled systems. Power System Stabilizers (PSS) are widely used to provide the supplementary stabilizing signal introduced in the excitation system to suppress the electromechanical oscillations and enhance the overall stability of power systems. Signals such as machine speed and acceleration can be chosen as PSS inputs. Conventional PSS (CPSS) provides a positive damping torque in phase with the speed signal to cancel the effect of the system negative damping torque. However, since CPSS design is based on the use of a transfer function designed for a linear model representing the generator at a certain operating point it may not produce adequate damping in system when operating conditions changes.

Since PSSs are tuned at nominal operating point, the damping is only adequate in the vicinity of those operating points. But power systems are highly nonlinear systems, therefore the machine parameters change with loading and time. The dynamic characteristics also vary at different points. For the PSS to continue to provide optimal or near optimal performance as operating condition changes, it is desired to track the changes in the system parameters on-line and determine the control most appropriate for the current operating conditions.

Recently, artificial intelligence-based approaches such as fuzzy logic (FL) , adaptive neuro fuzzy inference system (ANFIS) have been proposed to design adaptive PSS. The parameters associated with the membership functions and rules change through the learning process. The computation of these parameters, or their adjustment, is facilitated by a gradient vector, which provides a measure of how well the fuzzy inference system is modeling the input/output data for a given set of parameters. Once the gradient vector is obtained, an optimization routine is applied in order to adjust the parameters so as to reduce the sum of the squared difference between actual and desired outputs. CONVENTIONAL POWER SYSTEM STABILIZER

The action of a PSS is to extend the angular stability limits of a power system by providing supplemental damping to the oscillation of synchronous machine rotors through the generator excitation. Dynamics of a synchronous machine represented by the following linearized equations around the operating point. The damping of generator oscillations is mainly dependent on the damping coefficient However, this depends on generator construction and cannot be changed once a machine is manufactured. The use of these damping components is add a torque component on rotor shaft, which is proportional to generator speed deviation. If it is possible to induce a torque component on the shaft, which is proportional to generator speed deviation, the damping of the oscillations can be improved. The simplest way of doing this to add a signal (proportional to speed deviation) to the voltage reference of excitation system, which will induce a torque component proportional to speed deviation. This added feedback loop to the excitation system to improve the damping of oscillations is known as the Power System Stabilizer (PSS).

Phase compensation block is to improve the phase lag between the exciter input and electrical torque. Normally, the frequency range of small oscillations is 0.1 to 2.0 Hz and the phase compensation block should provide compensation over this entry frequency range.

The signal washout block as a high-pass filter with a time constant high to allow signals associated with oscillations in to pass unchanged. Otherwise, steady change in speed would modify the terminal voltage. Therefore, it allows the PSS to respond only to changes in speed at frequency of interest and avoid undesirable voltage excursion during in system steady-state operating conditions

PROBLEMS WITH CPSS: CPSS are designed based on linear control theory. The power system model is linearized around a nominal operating point, and the structure and parameters of the PSS are determined to provide optimal performance at this point. However, power systems are dynamics systems and their performances are non-linear. A CPSS with fixed parameters cannot provide optimal performance over the whole operating range. Power system configuration also change with time and the parameters of the PSS must be retuned so that it can continue to provide the desired performance. Hence, CPSSs fail to give the optimum performance over the whole operating range because it cannot tolerate the changes are very complex.

Fuzzy logic is one of the most promising tools which address the above issue. Unlike the classical logic approach, which requires a deep understanding of a system, exact equations and precise numeric values, fuzzy logic incorporate an alternative way of thinking which allows one to, model complex systems using a higher level of abstraction originated from accumulated knowledge and experience.

CASE STUDIES

CASE STUDY 1: SINGLE MACHINE INFINITE BUS (SMIB)SYSTEM MODEL AND DATA: A single machine connected to infinite bus is considered here for analysis. The synchronous machine is assumed to be equipped with solid state acting exciter. The synchronous machine is represented by the standard model involving k-parameters regulator by the first order model.

K1=1.4479 K2=1.3174 K3=0.3072 K4=1.8050 K5=0.0294 K6=0.5257 Ka=400.00 Ta=0.05

=5.9 D=0.0 M=2H=4.74 P=1.0

Without any Power System Stabilizer the responses of and with time for the given system are observed and plotted.

Simulation model without PSS Change in rotor angle (rad) vs time (sec)

Change in speed (rad/sec) vs time (sec)

CONVENTIONAL POWER SYSTEM STABILIZER:The conventional Power System Stabilizer model is given by transfer function- G(s)=1.8050*(1+0.04435*s/1+0.01*s)*(10*s/10*s+1)

The performance of this model is studied for step increase of 5% in load torque. The responses of and with time are observed and plotted.

Simulation model of Conventional PSS

Change in rotor angle (rad) vs time (sec) Change in speed (rad/sec) vs time (sec)

FUZZY BASED POWER SYSTEM STABILIZER: Using speed and derivative of speed as the controller input variables, a fuzzy logic based controller has been developed to perform the function of power system stabilizer and to provide a supplementary signal to the excitation system of a synchronous machine. The completed range for the variation of each of the two controller inputs is represented by a 7 by 7 decision table i.e. 49 rules.

Set of 7x7 rules

The performance of this model is studied for a step increase of 5% in load torque. The response of with time and are observed and plotted.

Simulation model of Fuzzy logic PSS

Change in rotor angle (rad) vs time (sec) Change in speed (rad/sec) vs time (sec)

CASE STUDY 2: TWO MACHINE SYSTEM

The two machine system considered consists of two identical machines connected through tie line. In this two machines system, various types PSSs controller techniques is provided on both machines. A small signal disturbance has been taken in first machine. The performance of these two machines system of various type PSSs for 5% step load disturbance in one machine. The performance of of various types of PSSs are shown below.

Single Line Diagram of Two Machines System

Simulation model of two area system

Change in (rad) vs Time (sec) with CPSS

Change in (rad) vs Time (sec) with FLPSS

` CASE STUDY 3: THREE MACHINE SYSTEM

Speed deviation in area 1

Rotor angle deviation in area 1

Speed deviation in area 2

Rotor angle deviation in area 2

Rotor angle deviation in area 3

Speed deviation in area 3

CONCLUSION The performance of the given system is observed for Conventional and Fuzzy based power system stabilizers. The responses of change in speed and change in rotor angle with time are observed. The response with fuzzy based stabilizer shows smaller overshoot and quicker damping out of small frequency oscillations than Conventional stabilizer. The system without stabilizer is highly oscillatory. Although the CPSS is effective in damping the oscillations, the load angle settles to its new value very smoothly and quickly with FLPSS.