A Novel Smart Grid State Estimation Method Based on Neural Networks

Authors

DOI:

https://doi.org/10.9781/ijimai.2018.01.004

Keywords:

Renewable energies, Neural Network, Smart Grid, Power Loss, Voltage Profile

Abstract

The rapid development in smart grids needs efficient state estimation methods. This paper presents a novel method for smart grid state estimation (e.g., voltages, active and reactive power loss) using artificial neural networks (ANNs). The proposed method which is called SE-NN (state estimation using neural network) can evaluate the state at any point of smart grid systems considering fluctuated loads. To demonstrate the effectiveness of the proposed method, it has been applied on IEEE 33-bus distribution system with different data resolutions. The accuracy of the proposed method is validated by comparing the results with an exact power flow method. The proposed SE-NN method is a very fast tool to estimate voltages and re/active power loss with a high accuracy compared to the traditional methods.

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References

C. González García, D. Meana Llorián, C. Pelayo G-Bustelo, and J. M. Cueva-Lovelle, “A review about Smart Objects, Sensors, and Actuators,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, no. 3, pp. 7-10, 2017.

G. Jeon, Yun Bae Kim, and Jinsoo Park, “Agent based smart grid modelling,” in 2015 Winter Simulation Conference (WSC), 2015, pp. 3114–3115.

T. Halder, “A smart grid,” in 2014 6th IEEE Power India International Conference (PIICON), 2014, pp. 1–6.

V. C. Gungor et al., “Smart Grid Technologies: Communication Technologies and Standards,” IEEE Trans. Ind. Informatics, vol. 7, no. 4, pp. 529–539, Nov. 2011.

H. Farhangi, “The path of the smart grid,” IEEE Power Energy Mag., vol. 8, no. 1, pp. 18–28, Jan. 2010.

M. Abdel-Nasser and K. Mahmoud, “Accurate photovoltaic power forecasting models using deep LSTM-RNN,” Neural Comput. Appl., pp. 1–14, Oct. 2017.

H. Mortazavi, H. Mehrjerdi, M. Saad, S. Lefebvre, D. Asber, and L. Lenoir, “A Monitoring Technique for Reversed Power Flow Detection With High PV Penetration Level,” IEEE Trans. Smart Grid, vol. 6, no. 5, pp. 2221–2232, Sep. 2015.

A. P. Kenneth and K. Folly, “Voltage Rise Issue with High Penetration of Grid Connected PV,” IFAC Proc. Vol., vol. 47, no. 3, pp. 4959–4966, 2014.

G. K. Ari and Y. Baghzouz, “Impact of high PV penetration on voltage regulation in electrical distribution systems,” in 2011 International Conference on Clean Electrical Power (ICCEP), 2011, pp. 744–748.

W. Tinney and C. Hart, “Power Flow Solution by Newton’s Method,” IEEE Trans. Power Appar. Syst., vol. PAS-86, no. 11, pp. 1449–1460, Nov. 1967.

K. Mahmoud and M. Abdel-Nasser, “Efficient SPF approach based on regression and correction models for active distribution systems,” IET Renew. Power Gener., vol. 11, no. 14, pp. 1778–1784, Dec. 2017.

Y. Zhang, R. Madani, and J. Lavaei, “Power system state estimation with line measurements,” in 2016 IEEE 55th Conference on Decision and Control (CDC), 2016, pp. 2403–2410.

H. Hooshyar and L. Vanfretti, “Power flow solution for multiphase unbalanced distribution networks with high penetration of photovoltaics,” in 2013 8th International Conference on Electrical and Electronics Engineering (ELECO), 2013, pp. 167–171.

R. Dugan, U. S. A. Epri, and G. Ramos, “Harmonics Analysis Using Sequential-Time Simulation for Addressing,” 23th International Conference on Electricity Distribution (CIRED), pp. 15–18, 2015.

T. Adefarati and R. C. Bansal, “Integration of renewable distributed generators into the distribution system: a review.,” IET Renew. Power Gener., vol. 10, no. 7, pp. 873–884, 2016.

Danling Cheng, B. Mather, R. Seguin, J. Hambrick, and R. P. Broadwater, “PV impact assessment for very high penetration levels,” in 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC), 2015, pp. 1–6.

C. D. López, “Thesis: Shortening time-series power flow simulations for cost-benefit analysis of LV network operation with PV feed-in,” 2015.

K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Networks, vol. 1, no. 1, pp. 4–27, Mar. 1990.

M. Al Shamisi, A. Assi, and H. Hejase, “Using MATLAB to Develop Artificial Neural Network Models for Predicting Global Solar Radiation in Al Ain City–UAE,” Intechopen.Com, pp. 219–238, 2009.

P. Siano, C. Cecati, H. Yu, and J. Kolbusz, “Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow,” IEEE Trans. Ind. Informatics, vol. 8, no. 4, pp. 944–952, Nov. 2012.

H. Le Nguyen, “Newton-Raphson method in complex form [power system load flow analysis],” IEEE Trans. Power Syst., vol. 12, no. 3, pp. 1355–1359, 1997.

J.-H. Teng, “A modified Gauss–Seidel algorithm of three-phase power flow analysis in distribution networks,” Int. J. Electr. Power Energy Syst., vol. 24, no. 2, pp. 97–102, Feb. 2002.

B. N. Rao and R. Inguva, “Power System State Estimation Using Weighted Least Squares ( WLS ) and Regularized Weighted Least Squares ( RWLS ) Method,” International Journal of Engineering Research and Applications, vol. 6, no. 5, pp. 1–6, 2016.

A. Primadianto and C.-N. Lu, “A Review on Distribution System State Estimation,” IEEE Trans. Power Syst., vol. 32, no. 5, pp. 3875-3883, 2016.

C. Muscas, M. Pau, P. A. Pegoraro, S. Sulis, F. Ponci, and A. Monti, “Multiarea Distribution System State Estimation,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 5, pp.1140-1148, 2015.

J. Deboever, X. Zhang, M. J. Reno, R. J. Broderick, S. Grijalva, and F. Therrien “Challenges in reducing the computational time of QSTS simulations for distribution system analysis,” Sandia National Laboratories, SAND2017-5743, 2017.

H. Ramchoun, M. Amine, J. Idrissi, Y. Ghanou, and M. Ettaouil, “Multilayer Perceptron: Architecture Optimization and Training,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, no. 1, pp. 26-30, 2016.

J. R. Machado Fernández and J. C. Bacallao Vidal, “Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 3, no. 7, pp. 96-103, 2016.

K. Haddouch, K. Elmoutaoukil, and M. Ettaouil, “Solving the Weighted Constraint Satisfaction Problems Via the Neural Network Approach,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, no. 1, pp. 56-60, 2016.

S. C. Huang and Y.-F. Huang, “Learning algorithms for perceptions using back-propagation with selective updates,” IEEE Control Syst. Mag., vol. 10, no. 3, pp. 56–61, Apr. 1990.

N. Yorino and K. Mahmoud, “Robust quadratic-based BFS power flow method for multi-phase distribution systems,” IET Gener. Transm. Distrib., vol. 10, no. 9, pp. 2240–2250, 2016.

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Published

2018-06-01
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How to Cite

Abdel-Nasser, M., Mahmoud, K., and Kashef, H. (2018). A Novel Smart Grid State Estimation Method Based on Neural Networks. International Journal of Interactive Multimedia and Artificial Intelligence, 5(1), 92–100. https://doi.org/10.9781/ijimai.2018.01.004