Modelling and simulation of power system stabilizer using fuzzy logic

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 08, 2015 | ISSN (online): 2321-0613

Modelling and Simulation of Power System Stabilizer using Fuzzy Logic D.H. Patel1 Prof. A.M. Upadhyay2 1 M.E. Student 2Associate Professor 1,2 Shantilal Shah Engineering Collage, Bhavnagar Abstract—This dissertation work is mainly emphasized on Power system stabilizer which enhances the power system stability. Conventional PSS gives satisfactory operation at specific operating condition for what they designed. If operating condition change, PSS not perform well as before.

Fuzzy logic is one Artificial intelligence technique that will be improving the work of PSS in different operating conditions. Initially the thesis gives the detail of power system condition, synchronous generator, AVR, PSS. It will give the basic information of fuzzy logic controller. Detail design of fuzzy logic based PSS will be done. Finally, it will compare the results of FPSS with CPSS in MATLAB SIMULATION. Key words: Power System Stabilizer, Fuzzy Logic I. INTRODUCTION The stability of power systems are dependent on the capability of the generators to work in synchronism and to return to the synchronous speed when subjected to the disturbances and various operating environment.

Low frequency oscillations are related to small signal stability which are decrease the power transfer capacity and power system stability. Using damper winding in generator rotor was satisfactory reducing the oscillations.

If power system began to operate near to stability margin weakness of synchronising torque create instability in power system which can reduce by use of AVR. Some times AVR magnifying the negative damping which cause low frequency oscillation. Power system stabilizer is use to damp out the power swing. But problem with conventional PSS is that they satisfactory operate at specific operating condition for what they designed. If operating condition change, PSS not perform well as before.

Main disadvantage of CPSS is that they can’t satisfactory operate in different operating conditions. To overcome this drawback of CPSS we can use Fuzzy Logic based PSS which can operate in different operating conditions.

II. SYNCHRONOUS GENERATOR WITH INFINITE BUS In this paper system consider as synchronous generator connected to the infinite bus.

Tm

Synchronous Generator Transformer

Ef0

∆P +

Exciter +

âˆ†Ď‰

CPSS

Et

FLPSS

-

AVR

+ +

Vref

Fig. 1: SMIB with both PSS

Infinite Bus

A. Swing Equation: đ?‘‘đ?›ż = đ?œ” đ?‘‘đ?‘Ą đ?‘‘đ?œ” 1 [đ?‘‡ − đ?‘‡đ?‘’ − đ??ˇđ?œ”] = đ?‘‘đ?‘Ą 2đ??ť đ?‘š B. Rotor Equation: đ?‘‘đ?œ“â„Ž 1 = ′′ [−đ?œ“h + đ?œ“d ] đ?‘‘đ?‘Ą Td đ?‘‘đ?œ“đ?‘“ đ?‘Ľđ?‘‘′ đ??¸đ?‘“đ?‘‘ 1 = ′ [−đ?œ“f + đ?œ“d + ] (đ?‘Ľđ?‘‘ − đ?‘Ľđ?‘‘′ ) đ?‘‘đ?‘Ą Td đ?‘‘đ?œ“đ?‘” 1 = ′ [−đ?œ“g + đ?œ“q ] đ?‘‘đ?‘Ą Tq đ?‘‘đ?œ“đ?‘˜ 1 = ′′ [−đ?œ“k + đ?œ“q ] đ?‘‘đ?‘Ą Tq C. Stator Equation: đ?‘Łđ?‘‘ = − đ?‘–đ?‘‘ đ?‘…đ?‘Ž − đ?‘Ľđ?‘žâ€˛â€˛ đ?‘–đ?‘ž + đ??¸đ?‘‘′′ đ?‘Łđ?‘ž = − đ?‘–đ?‘ž đ?‘…đ?‘Ž − đ?‘Ľđ?‘‘′′ đ?‘–đ?‘‘ + đ??¸đ?‘žâ€˛â€˛ đ?‘Łđ?‘ž − đ??¸đ?‘žâ€˛â€˛ đ?‘–đ?‘‘ = đ?‘Ľđ?‘‘′′ ′′ đ??¸đ?‘‘ − đ?‘Łđ?‘‘ đ?‘–đ?‘ž = đ?‘Ľđ?‘žâ€˛â€˛ III. CONVENTIONAL POWER SYSTEM STABILIZER A dual input PSS where speed and electrical power deviations are used as input. This PSS is referred to as Delta-P-Omega PSS. The objective of this PSS is to derive an equivalent speed signal âˆ†Ď‰ so that it does not contain torsional modes.

Washout Circuit âˆ†Ď‰

sTw1 1+ sTw1

Delay 1 1+ sT6

Filter +

1 (1+ sT9)2 +

Te

sTw2 1+ sTw2

T7/H 1+sT7

Washout Circuit

Integrator

Phase Compensator +

1 + sT1 1+ sT2

Gain Vs Kpss

-

Limiter

Fig. 2: Detail Model of CPSS The washout circuit acts essentially as a high pass filter and it must pass all frequencies that are of interest. If only the local modes are of interest, the time constant Tw can be chosen in the range of 1 to 2. However, if inter area modes are also to be damped, then Tw must be chosen in the range of 10 to 20.

Two lead lag stage dynamic compensator has following type of transfer function đ??žđ?‘ (1 + đ?‘ đ?‘‡1 )(1 + đ?‘ đ?‘‡3 ) đ?‘‡(đ?‘ ) = (1 + đ?‘ đ?‘‡2 )(1 + đ?‘ đ?‘‡4 ) Where, đ??žđ?‘ is gain of PSS and time constants, đ?‘‡1 to đ?‘‡4 are chosen to provide a phase lead for the input signal in the range of frequencies that are of interest (0.1 to 3.0 Hz).

For design purposes, the PSS transfer function is approximated to T(s),the transfer function of the dynamic

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Modelling and Simulation of Power System Stabilizer using Fuzzy Logic (IJSRD/Vol. 3/Issue 08/2015/142)

compensator. The effect of the washout circuit and torsional filter may be neglected in the design but must be considered in evaluating performance of PSS under various operating conditions.

The torsional filter in the PSS is essentially a band reject or a low pass filter to attenuate the first torsional mode frequency.

The output of the PSS must be limited to prevent the PSS acting to counter the action of AVR.

Fig. 4(a): Response of Rotor angle with CPSS

IV. FUZZY LOGIC BASED POWER SYSTEM STABILIZER The basic structure of the fuzzy logic controller is shown in Figure 3. Here the inputs to the fuzzy logic controller are the Speed and Electric Power. Normalization is done to limit the universe of discourse of the inputs between -1 to 1 such that the controller can be successfully operated within a wide range of input variation. Here Kw and Kp are normalization factor for speed signal and power signal respectively and output parameter Ku. The selection of these parameters is usually subjective and requires previous knowledge of the fuzzy control variables (input and output signals). For this fuzzy logic controller design, the normalization factors are taken as constants. Table 1 represents the fuzzy rules. In this paper triangular membership function is used.

B. Case: II Three Phase fault occurs at 10 sec, fault clear in 4 cycle with reclosing of line 2 with CPSS and FLPSS. The response of rotor angle δ is plotted. Figure 5(a) and 5(b) shown the oscillation of rotor angle with CPSS and FLPSS respectively.

Fuzzy Rules

Defuzzification

Kw Fuzzification

∆ω

Fig. 4(b): Response of Rotor angle with FLPSS

Vs Ku

∆P Kp

Fig. 5(a): Response of Rotor angle with CPSS Fig. 3: Schematic Diagram of FLPSS

Fig. 5(b): Response of Rotor angle with FLPSS C. Case: III Change in Mechanical Torque with CPSS and FLPSS. The response of rotor angle δ is plotted. Figure 6(a) and 6(b) shown the oscillation of rotor angle with CPSS and FLPSS respectively.

Table 1: Fuzzy Rules V. RESULTS AND DISCUSSION The comparison analysis of CPSS and FLPSS are carried out under different operating condition and disturbance.

These disturbances are the three phase short circuit, outage of transmission line, suddenly changes in mechanical input.

A. Case: I Three Phase fault occurs at 10 sec, fault clear in 4 cycle with tripping of line 2 with CPSS and FLPSS. The response of rotor angle δ is plotted. Figure 4(a) and 4(b) shown the oscillation of rotor angle with CPSS and FLPSS respectively.

Fig. 6(a): Response of Rotor angle with CPSS

Fig. 6(b): Response of Rotor angle with FLPSS

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Modelling and Simulation of Power System Stabilizer using Fuzzy Logic (IJSRD/Vol. 3/Issue 08/2015/142)

VI. CONCLUSION In this paper effectiveness of power system stabilizer to damping power system oscillations reviewed. By adding the PSS in Excitation system oscillations are damp out and system become stable in short time of periods for different conditions.

Then fuzzy logic based power system stabilizer introduced with two input variable speed deviation and electrical power and voltage as output variable of FLPSS. As results shows proper fuzzy rules and normalization factors will be needed to improve the robust operation of FLPSS. It can be concluded that choice of membership functions are important.

REFERENCES [1] P.Kundur, Power System Stability and Control. New York: McGraw-Hill, 1996.

[2] K.R. Padiyar, Power System Dynamics - Stability and Control, BS Publications, Hyderabad, India, 2002.

[3] N.P.Padhy, Artificial Intelligence And Intelligent Systems ,India: Oxford Press, 2005 [4] P. Kundur, M. Klein, G. Rogers, and M. Zywno, “Application of power system stabilizers for enhancement of overall system stability,” IEEE Trans.

Power Syst., vol. 4, no. 2, pp. 614–626, May 1989.

[5] A. Dysko, W. Leithead, and J. O'Reilly, “Enhanced power system stability by coordinated pss design," Power Systems, IEEE Transactions on, vol. 25, no. 1, pp. 413-422, feb. 2010.

[6] Y. Lin,”Systematic approach for the design of a fuzzy power system stabiliser," in Power System Technology, 2004. Power Con 2004. 2004 International Conference on, vol. 1, nov. 2004, pp. 747 -752 Vol.1.

[7] A. Roosta, H. Khorsand, and M. Nayeripour, “Design and analysis of fuzzy power system stabilizer," in Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2010 IEEE PES, oct. 2010, pp.

1-7.

[8] H. Behbehani, J. Bialek, and Z. Lubosny, “Enhancement of power system stability using fuzzy logic based supervisory power system stabilizer," in Power and Energy Conference, 2008. PECon 2008.

IEEE 2nd International, dec. 2008, pp. 479-484.

[9] D.K. Sambariya, Rajendra Prasad, “Robust Power System Stabilizer Design for Single Machine Infinite Bus System with Different Membership Functions for Fuzzy Logic Controller”, in 7th International Conference on Intelligent Systems and Control (ISCO) 2013, pp.13-19.

[10] Mohamed E. El- Hawary, Electric power system applications of fuzzy system, IEEE Press, New York, 1998.

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