The International Daily journal
ISSN 2278 – 5469 EISSN 2278 – 5450
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ANALYSIS
Publication History
Received: 24 August 2015
Accepted: 26 September 2015
Published: 17 October 2015
Citation
Hemisha P Patel, Ami T Patel, Jay S Tandel. Development of Intelligent controller for Power System stabilization for Single
Machine Infinite Bus system.
Discovery , 2015, 44(202), 46-51

The need of power system stabilizer (PSS) or supplementary excitation control is to apply a stabilizing signal through the excitation system to produce additional damping torque of the generator in a power system at all operating and system conditions.
This paper presents a design of FPSS and NPSS over a broad range of operating condition. The proposed intelligent design is evaluated against CPSS.The
analysis shows the effectiveness of the proposed technique.
Keywords: synchronous machine, excitation, voltage regulator, Power system Stabilizer, low frequency oscillation, Damping, small signal stability, Fuzzy logic controller, Artificial Neural network
generator loading condition. To overcome these limitations, different intelligent techniques like ANN,
FLC, GA, and PSO have been proposed. [1-3]
In this paper, a rule based FPSS and ANN based
PSS is designed. and the system response with the neural network based PSS are compared with a FPSS and CPSS.[8]
The block diagram of a single machine infinite bus
(SMIB) system with PSS is shown in fig. The figure presents the transfer function block diagram of Heffron
Phillips model.[17]
Power system is a dynamic system.
Nowadays, the electric power systems are not operated as isolated system, but as interconnected systems which having thousands of electrical elements and be spread over wide areas. Excitation control is very useful for maintaining stability of power system. Excitation systems constitute the fast acting AVR. However, it produces a negative damping at higher values of system reactance high generator output. Thus, it is very important to increase the damping torque in order to reduce the rotor angle oscillations. The Power system stabilizer (PSS) is added to damp the Generator rotor oscillations by controlling its excitation by providing supplementary signal in the excitation systemto damp out low frequency Oscillations.[4] The use of
become very common in operation of large power system. The Conventional Power system stabilizer
(CPSS), which uses lead lag compensation, where gain settings designed for specific operating condition is giving poor performance under different synchronous
Fig.1 Block diagram of Heffron Phillips model for
SMIB system

Control algorithms based on fuzzy logic have been implemented in many processes. The application of such control techniques has been motivated by the following reasons:
• Improved robustness over the conventional linear control algorithms
• Simplified control design for diff icult system models
Fuzzy Logic was initiated in 1965 by Lotfi A. Zadeh.
Basically, Fuzzy Logic (FL) is a multivalued logic, that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc.[7]
A typical fuzzy system consists of a rule base, membership functions and an inference system. The design of a fuzzy logic controller which consists of a Fuzzification interface, a knowledge base, decision making logic, and a
Defuzzification interface.[7-9]
Fig.2 Fuzzy logic controller
Fuzzy controller used in power system stabilizer is normally a two-input and a single-output component. It is usually a MIS0 system. The two inputs are change in angular speed and rate of change of angular speed whereas output of fuzzy logic controller is a voltage signal. The two inputs, speed and acceleration, result in 49 rules for each machine.[9] The typical rules are having the following structure:
Table 1. Rule based for Fuzzy Power system stabilizer
Here , MAMDANI method is used for Fuzzification and centre of area method is used for Defuzzification. And the range for both input and output is taken [-1,1].
Artificial neural networks can be most adequately characterized as „computational models
‟ with particular properties such as the ability to adapt or learn, to generalize, or to cluster or organize data, and which operation is based on parallel processing. However, many of the above mentioned properties can be attributed to existing (non-neural) models; the intriguing question is to which extent the neural approach proves to be better suited for certain applications than existing models. An artificial network consists of a pool of simple processing units which communicate by sending signals to each other over a large number of weighted connections.[12]
The basic processing elements of a neural network are often called a neuron. The function of neurons is to perform summing and nonlinear mapping.
The neural network can be trained using backpropogation algorithm. The simple four steps are required in training process:[12-13]
1. Assemble the training data
2. Create the network object
3. Train the network
4. Simulate the corresponding network with given new input
Feed forward networks have either one or more hidden layers of neurons. Multiple layer neurons with nonlinear transfer function allow the given network to learn relation between linear and nonlinear inputs and output vectors.[13]
Fig 3. The three layers of feed forward neural network[12]

Here, The same input as FPSS is given to the Neural network and performance is analyzed.
In this paper, analysis is done for three various loading condition and according to that the value of K1 to K6 constant are calculated. The disturbance is given by 5
% step change in mechanical input and the variation in speed, angular position and the electric torque are analyzed.
1
0.5
A.Comparison of system response for case 1:P=0.5 and Q=0.3
2
1.5
CPSS
FPSS
NPSS
0
0 2 4
Time,sec
6 8 10
Fig. 4 change in rotor angle for case 1 With CPSS,
FPSS,NPSS
0.015
0.01
0.005
0
-0.005
CPSS
FPSS
NPSS
-0.01
0 1 2 3 4 5
Time,sec
6 7 8 9
Fig. 5speed deviation for case 1 With CPSS,
FPSS,NPSS
10
1.4
1.2
1
0.8
0.6
0.4
0.2
0
CPSS
FPSS
NPSS
-0.2
0 1 2 3 4 5
Time,sec
6 7 8 9 10
Fig. 6 Torque variation for case 1 With CPSS,
FPSS,NPSS
1
0.8
0.6
0.4
B.
Comparison of system response for
Case 2: P=1 and Q=0.4
1.2
0.2
0
CPSS
FPSS
NPSS
-0.2
0 2 4
Time,sec
6 8 10
Fig.7change in rotor angle for case 2 With CPSS,
FPSS,NPSS
15x 10
-3
10
CPSS
FPSS
NPSS
5
0
-5
0 1 2 3 4 5
Time,sec
6 7 8 9
Fig. 8 speed deviation for case 2 With CPSS,
10
FPSS,NPSS
1.5
1
0.5
0
CPSS
FPSS
NPSS
-0.5
0 1 2 3 4 5
Time,sec
6 7 8 9 10
Fig. 9 Torque variation for case 2 With CPSS,
FPSS,NPSS
C. Comparision of system response for
Case 3: P= 1.5 and Q= 0.8
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
0 2 4
Time,sec
6 8
CPSS
FPSS
NPSS
10
Fig. 10 change in rotor angle for case 3 With CPSS,
FPSS,NPSS

0.015
0.01
0.005
0
-0.005
CPSS
FPSS
NPSS
-0.01
0 1 2 3 4 5
Time,sec
6 7 8 9 10
Fig. 11 speed deviation for case 3 With CPSS,
FPSS,NPSS
2
1.5
1
0.5
CPSS
FPSS
FPSS
0
0 1 2 3 4 5
Time,sec
6 7 8 9 10
Fig. 12 Torque variation for case 3 With CPSS,
FPSS,NPSS
From the above simulation results, We can see that The
CPSS design not proves better durina heavy loading condition.Thus, To achieve more better result, Its design is replaced by FPSS. But the main drawback of
FPSS, It takes more simulation computation time.Thus,
To overcome the above drawbacks of FPSS, Neural based PSS is developed.The table clearly shows the effectiveness of Neural based PSS compared to FPSS and CPSS.
PARAMETER TYPE SETTLING TIME
CASE 1 CASE 2 CASE 3
CPSS 4.8 s 4.7 s 7.4 s
Omega FPSS 3.2 s 3.2 s 3.1 s
NPSS 2.7 s 2.5 s 2.1 s
Rotor angle
Torque variation
CPSS 5.0 s 4.8 s 6.5 s
FPSS 3.5 s 3.3 s 3.0 s
NPSS 2.4 s 2.4 s 2.2 s
CPSS 5.2 s 4.9 s 7.1 s
FPSS 2.8 s 2.8 s 3.2 s
NPSS 2.2 s 2.2 s 2.0 s
In this paper, system performance of CPSS,FPSS and
NPSS under a variety of operating conditions is analyzed. But, as well as loading increases, the CPSS design not proves better, thus to achieve more better performance different intelligent techniques can be used. From the simulation results, we can see that neural based PSS takes 2 seconds, FPSS takes 3 seconds , But with CPSS it takes about 6 seconds to reach to final steady state for all three operating condition. The results shows the effectiveness of NPSS and FPSS over conventional PSS.
The Generator data:
Xd=1.6, Xq=1.55, x’d=0.32, T’d0=6.0, H=5, D=0,
F=50 Hz.
The Transmission line data:
Re=0, Xe=0.4
AVR data:
KA=200, TA=0.
PSS data:
T1=0.154 sec, T2=0.033 sec, TW=1.4 sec,Kstab=9.5
FPSS data:
Kin1= 1.8, Kin2=29.58 and Kout=1.05.
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