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INTELLIGENT BASED power system stabilizer for two area power systemProject Guide: V.Santosh Kumar (Y10EE3259) T. Swapna Priya SD.Abdul Mujeer (Y10EE3256)

Department of EEE K.Gayathri Teja (Y10EE3226)

Introduction: The design simulation and validation of an Adaptive Neuro Fuzzy Inference System(ANFIS) based Power System Stabilizer(PSS) for a two area two machine system and investigate its performance in low frequency power system oscillations. The design employs a conventional input pairs like speed deviation( ) and change in tie line power ( Ptie) to the Neuro Fuzzy PSS. In this paper a first order sugeno Fuzzy Model, whose parameters are tuned off line through hybrid learning algorithm is used. This algorithm is a combination of least square estimation and error back propagation method. The advantage of this ANFIS based PSS is that it can be fed to each of machines in the two areas of the power system which reduces cost, scanning time and thus simplifies the structure. It is observed that ANFIS 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 MATLAB/SIMULINK.

STABILITY:In general, stability describes the tendency of an alternating current (a.c.) power system to maintain a synchronous and balanced operating state.Power System Stability: It is the ability of an electric power system, for a given initial operating condition, to regain a state of operating equilibrium after being subjected to a physical disturbance, with most of the system variables bounded so that practically the entire system remains intact . The disturbances mentioned in the definition could be faults, load changes, generator outages, line outages, voltage collapse or some combination of these.

Rotor angle stability: It is the ability of the system to remain in synchronism when subjected to a disturbance. The rotor angle of a generator depends on the balance between the electromagnetic torque due to the generator electrical power output and mechanical torque due to the input mechanical power through a prime mover.

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Need of pss: The synchronous machines since long time are equipped with fast acting static excitation with higher ceiling voltages which contribute to the improvement of synchronising torques and hence to the improvement of transient stability. However, these excitation systems introduce negative damping at the electromechanical oscillation of machines (usually in the range of 0.2 Hz to 2Hz) and endanger steady state stability as well as contribute to the sustained or growing oscillations at the tail end of a large transient. This has resulted in a search for providing additional supplementary stabilizing signal in the voltage regulator circuit to compensate for the phase lag, eliminate completely the negative damping and improve oral dynamic stability if the system.Power System Stabilizer :The basic function of power system stabilizer is to add damping to generator rotor oscillations by controlling its excitation using supplementary control signals. In order to provide damping, the stabilizer will produce electrical torque in phase with rotor speed deviations. These are added to each individual generator to compensate the local and inter area mode of frequency oscillations. These oscillations could go up to 1Hz off the nominal value.

VARIOUS TYPES OF PSS: The various types of PSS suggested by different authors and one suggested in the present work are: 1) Conventional PSS 2) Optimal PSS 3)Fuzzy Logic PSS 4) Neuro Fuzzy Based PSS CONVENTIONAL LEAD-LAG PSS:The generalised transfer function of CONVENTIONAL LEAD-LAG PSS contains three blocks-1) Phase Compensation Block2) Washout Block3) Gain Block

PHASE COMPENSATION: The phase compensation block provides the appropriate phase lead to compensate for the phase lag between the exciter input and the generator electrical torque. The lead compensation can be provided using either passive or active netwok. In practice, two or more first order blocks may be used to achieve the desired phase compensation. Generally some under compensation is desirable so that the PSS, in addition to significantly increasing damping torque, results in a slight increase of the synchronising torque component.WASHOUT: The signal washout block serves as a high pass filter, with the time constant Tw High enough to allow signal associated with oscillations in Wr to pass unchanged. Without the washout state, steady changes in speed to modify the terminal voltage. The washout time constant assures no permanent offset in the terminal voltage due to prolonged error in frequency which may occur in case of over loading and islanding.

GAIN:The stabilizer gain block determines the amount of damping introduced by the PSS. Ideally, the gain should be set at a value corresponding to maximum damping.

SIMULATION MODEL WITH PSS:

Change in rotor angle(rad) Vs time (secs)

Change in speed (rad/sec) Vs time (secs)

FUZZY MODEL:TheSugeno Fuzzy model(also known as theTSK fuzzy model) was proposed by Takagi, Sugeno, and Kang in an effort to develop a systematic approach to generating fuzzy rules from a given input-output dataset. A typical fuzzy rule in a Sugeno fuzzy model has the form:where A and B are fuzzy sets in the antecedent, while z=f(x,y) is a crisp function in the consequent. Usually f(x, y) is a polynomial in the input variables x and y, but it can be any function as long as it can appropriately describe the output of the model within the fuzzy region specified by the antecedent of the rule. When f(x, y) is a first-order polynomial, the resulting fuzzy inference system is called a first-order Sugeno fuzzy model, which was originally proposed in [1, 2]. Whenfis a constant, we then have azero-order Sugeno fuzzy model, which can be viewed either as a special case of theMamdani Fuzzy inference system, in which each rules consequent is specified by a fuzzy singleton (or a pre-defuzzified consequent), or a special case of theTsukamoto fuzzy model, in which each rules consequent is specified by an MF of a step function centre at the constant. Moreover, a zero-order Sugeno fuzzy model is functionally equivalent to a radial basis function network under certain minor constraints, as discussed in Chapter 12 in Neuro-fuzzy and soft computing.

conclusion:

We performed fuzzy logic technique to design pss on two area power system.

From the results obtained, we observed that fuzzy logic gives better performance than conventional pss.