Ranking of power Systems

1. ACADEMIC WRITING – SUBJECTIVE ASSIGNMENT 2
NAME: PAVITHRA M
E-MAIL ID: pavithramr1996@gmail.com
App. No.: 4100c3e8ee5611e994132df5d4dbdca6

2. ACKNOWLEDGEMENT
I acknowledge that I have understand the objectives of the Academic
Writing course completely. I would like to thank Dr. Ajay Semalty, H.N.B.
Garhwal University (A Central University) for his guidance throughout this
course.

3. Calculation of Weighting Factors of Static Security
Indices used in Contingency Ranking of Power
Systems based on Fuzzy Logic and Analytical
Hierarchy Process
Name: M. PAVITHRA
University Reg. No.: 120047009
Degree & Branch : II- M. TECH - POWER SYSTEMS
College: SASTRA Deemed To Be University, Thanjavur, Tamil Nadu.

4. ABSTRACT
 Contingency screening and ranking is one of the most important issues
for security assessment in the field of power system operation.
 The objective of contingency ranking is to quickly and accurately select a
short list of critical contingencies from a large list of potential
contingencies and rank them according to their severity.
 In this paper a novel approach is presented for contingency ranking based
on static security assessment.
 The fuzzy logic based analytical hierarchy process is used to select the
weighting factors.

5. INTRODUCTION
 Contingency analysis should be performed to ensure power system security
when unexpected and severe events or disturbances may occur.
 The result of contingency analysis can be used to save the power system by
preventing other cascade accidents.
 Contingency analysis leads to assess the two aspects of power system security.
(a) Static Security Assessment (SSA) which is mainly based on voltage
security and overloads
(b) Dynamic Security Assessment (DSA) which considers those criteria in
SSA accompanied with the dynamic and transient stability.

6. PERFORMANCE INDEX FOR CONTINGENCY RANKING
The Performance Index (PI) used for contingency ranking is,
Where,
Vi, Wi are voltage amplitude and associated weighting factor for ith bus;
Sj, Wj are apparent power and associated weighting factor for jth line;
Vref,i is the nominal voltage magnitude (PQ buses=1 p.u. and PV buses=
specified value) ;
Sj,max is apparent power nominal rate of jth line

7. ANALYTICAL HIERARCHY PROCESS
 In electric power systems, the assignment of weighting factors to each bus
and transmission line depends on importance of the specific bus and
transmission line during power system operation.
 The specific preference values are given in the Table 1,

8.  Consider a three bus power system as shown in fig.1.
 The preference matrix based on experts opinion related to voltage security

9. in all bus bars for this simple system is:
 In the preference matrix (WPM) it is clear that the importance of voltage
security in Bus 1 is twice respect to the related parameters in Bus 2.
 In other word, the importance of voltage security in Bus 2 is half of the
importance of voltage security in Bus 1.
 The diagonal elements of preference matrix is always equal to unity.

10. AHP METHOD- STEPS
 Step 1 : Add the columns of WPM, thus;
 Step 2 : Divide each element of preference matrix in specific column to
sum of its own column given in W1, thus;

11.  Step 3 : Calculate the mean value for each row in W2. Thus:
 The value of the fourth column in W3 provide appropriate and different
weighting factors based on AHP due to hypothesis experts opinion on voltage
security importance in power system shown in Fig. 1.
 Thus, the calculated weighting factors for the given power system is,
W1 = 0.593, W2 = 0.341, W3 = 0.066

12. FUZZY LOGIC BASED ANALYTICAL HIERARCHY PROCESS
 Fuzzy set theory is designed to extract the possible outcome from a great
variety of information expressed in vague and imprecise terms.
 In a universe X, a fuzzy subset A of X is expressed by a membership
function which maps each element of x of X to a real number in the interval
[0,1].
 The membership function of a triangular fuzzy number is defined as:

13.  This triangular fuzzy number can be denoted by triplet (c,a,b) in Fig.2.
 Moreover, c and b are lower and upper bond of available area for
evaluation data.
 Narrower interval [c,b] means the lower fuzziness of the evaluation data.

14.  Operational laws of the triangular fuzzy numbers are parameterized by the
triplets (a1,a2,a3) and (b1,b2,b3) are defined as:

15.  Using graded mean integration method, triangular fuzzy number can be
defuzzified as:
 In this paper graded mean integration method is applied to defuzzify
triangular fuzzy numbers.
 In proposed algorithm, each element of preference matrix WPM is a
linguistic variable obtained by experts opinion.

16.  So, main linguistic variables are selected and defined as:

17.  After completing preference matrix in fuzzy environment, each element is
divided to sum of its column by above arithmetic fuzzy procedure.
 Weighting factors are also obtained by defuzzification of the mean of each
row similar to previous section.

18. FLOW CHART OF PROPOSED ALGORITHM

19. CONCLUSION
 The proposed method is based on performance index associated with bus
bars and transmission lines or transformers weighting factors.
 The weighting factors were adjusted by newly implemented analytical
hierarchy process (AHP) and fuzzy logic based analytical hierarchy
process (FLAHP).
 Although contingency ranking is possible without the application of AHP
and FLAHP for weighting factors adjustment, the proposed method assures
the ranking to be more accurate, realistic and similar to natural
behavior of existing power systems.

20. THANK YOU