**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**30069

##### Estimation of Real Power Transfer Allocation Using Intelligent Systems

**Authors:**
H. Shareef,
A. Mohamed,
S. A. Khalid,
Aziah Khamis

**Abstract:**

This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation.

**Keywords:**
Artificial intelligence,
Power tracing,
Artificial
neural network,
ANFIS,
Power system deregulation.

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1328670

**References:**

[1] H. Shareef, and M. W. Mustafa, “Real and reactive power allocation in
a competitive market,” WSEAS Trans. Power Systems, vol. 1, pp. 1088-
1094, June 2006

[2] R. Reta, and A. Vargas, “Electricity tracing and loss allocation methods
based on electric concepts,” IEE Generation, Transmission and
Distribution, vol. 148, pp. 518-522, Nov. 2001.

[3] Y. C. Chang, and C. N. Lu, “An electricity tracing method with
application to power loss allocation,” Int. J. Electrical Power and
Energy Systems, vol. 23, pp. 13-17, Jan.2001.

[4] J. H. Teng, “Power flow and loss allocation for deregulated transmission
systems,” Int. J. Electrical Power and Energy Systems, vol. 27, pp. 327-
333, May 2005.

[5] H. Shareef, M. W. Mustafa, S. N. Khalid, A. Khairuddin, A. Kalam, and
A. M. T. Oo, “Real and reactive power transfer allocation utilizing
modified nodal equations,” Int. J. Emerging Electric Power Systems,
vol. 9, 2008, pp. 1-14, Dec.2008.

[6] S.N. Khalid, H. Shareef, M.W. Mustafa, A. Khairuddin, and A. M. T.
Oo. “Evaluation of real power and loss contributions for deregulated
environment,” Int. J. Emerging Electric Power Systems, vol. 38, pp. 63-
71, June 2012.

[7] J. Bialek “Tracing the flow of electricity,” IEE Proceedings Generation
Transmission and Distribution, vol. 143, pp. 313–320, July 1996.

[8] F. F. Wu, Y. Ni, and P. Wei, “Power transfer allocation for open access
using graph theory – fundamentals and applications in systems without
loop flows,” IEEE Trans. Power Systems, vol. 15, pp. 923-929, Aug.
2000.

[9] S. Abdelkader, “Efficient computation algorithm for calculating load
contributions to line flows and losses,” IEE Proc. Generation,
Transmission and Distribution, vol. 153, pp. 391-398, July 2006.

[10] D. Kirschen, R. Alian, and G. Strbac, “Contributions of individual
generators to loads and flows,” IEEE Trans. Power Systems, vol. 12, pp.
1312-1319, Feb. 1997.

[11] M. W. Mustafa, S. N. Khalid, H. Shareef, and A. Khairuddin, "Reactive
power transfer allocation method with the application of artificial neural
network," IET Generation, Transmission and Distribution, vol. 2, pp.
402-413, May 2008.

[12] N. B. D. Choudhury, and S. K. Goswami, “Artificial intelligence
solution to transmission loss allocation problem” Int. J. Expert Syst. with
Appl., vol. 38, pp. 3757–64, Apr. 2011.

[13] A. R. Abhyankar, S. A. Soman, and S. A. Khaparde, "Optimization
approach to real power tracing: an application to transmission fixed cost
allocation," IEEE Trans. Power Systems, vol. 21, pp. 1350-1361,
Aug.2006.

[14] M. H. Sulaiman, M. W. Mustafa, and O. Aliman, "Transmission loss
and load flow allocations via genetic algorithm technique," in proc.
TENCON 2009 - 2009 IEEE Region 10 Conf.,Singapore, 2009, pp. 1-5.

[15] J. A. K. Suykens, and J. Vandewalle, "Least Squares Support Vector
Machine Classifiers," Neural Processing Letters, vol. 9, pp. 293-300,
June 1999.

[16] J. Nagi, K. S. Yap, S. K. Tiong, S. K. Ahmed, and A. M. Mohammad,
"Detection of abnormalities and electricity theft using genetic Support
Vector Machines," in proc. TENCON 2008 - 2008 IEEE Region 10
Conf.,Hyderabad, India, 2008, pp. 1-6.

[17] M. W. Mustafa, M. H. Sulaiman, H. Shareef, S. N. A. Khalid, “Reactive
power tracing in pool-based power system utilising the hybrid genetic
algorithm and least squares support vector machine,” IET Generation,
Transmission and Distribution, vol. 6, pp. 133-141, Feb. 2012.

[18] M. J. Reddy, and D. K. Mohanta, “Adaptive-neuro- fuzzy inference
system approach for transmission line fault classification and location
incorporating effects of powering,” IET Generation, Transmission and
Distribution, vol. 2, pp. 235-244, Mar. 2008.

[19] H. Shareef, S. N. Khalid, M. W. Mustafa, and A. Khairuddin, “An
ANFIS approach for real power transfer allocation,” J. Applied
Mathematics,vol. 2011 , pp. 1-14, 2011.

[20] H. Shareef, A.Mohamed, S. N. Khalid, and M. W. Mustafa, “A method
for real power flow transfer allocation using multivariable regression
analysis,” J. Central South University, vol. 19, pp. 179-186, Jan. 2012.

[21] W. W. Hines, and D. C. Montgomery, “Probability and statistics in
engineering and management science,” 3rd ed., New York: John Wiley
and Sons, 1990, pp: 732.

[22] D. S. Broomhead, and D. Lowe, “Multivariable functional interpolation
and adaptive networks,” Complex System, vol. 2, pp. 321-355, 1988.

[23] F.Girosi, and T. Poggio, “Network and the best approximation
property,” Biological Cybernetics, vol. 63, pp. 169-176, 1990.

[24] P. Simon, Oscillatory stability assessment of power system using
computational intelligence, Ph.D thesis, Universit Duishdurg-Essen,
Germany.

[25] J. Shing, and R. Jang, “ANFIS: adaptive-network-based fuzzy inference
system,” IEEE Trans. systems, man, and cybernetics, vol. 23, pp.665-
685, May/June 1993.

[26] S. Bateni, S. Borghei, and D. Jeng, “Neural network and neuro-fuzzy
assessments for scour depth around bridge piers,” Engineering
Applications of Artificial Intelligence, vol. 20, pp. 401-414, Apr. 2007.

[27] V. N. Vapnik, “The nature of statistical learning theory,” 2nd ed. New
York: Springer-VerlagNew York, 1995, pp.1-311.

[28] C. A. Jensen, M. A. El-Sharkawi, and R. J. Marks, “Power system
security assessment using neural networks: feature selection using
Fisher discrimination,” IEEE Trans. Power System, vol. 16, pp. 757-
763, Nov. 2001.

[29] B. Scholkopf, A. Smola, and K. R. Muller, Kernel Principal Component
Analysis, in Advances in Kernel Methods - SV Learning. Cambridge,
MA: MIT Press, 1999, pp. 327–352.