Fetching the paper…
Reading the bibliography…
We develop DeepOPF as a Deep Neural Network (DNN) approach for solving security-constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for reliable and cost-effective power system operation.DeepOPF is inspired by the observation that solving SC-DCOPF problems for a given power network is equivalent to depicting a high-dimensional mapping from the load inputs to the generation and phase angle outputs.
J. Carpentier, “Contribution to the economic dispatch problem,” Bulletin de la Societe Francoise des Electriciens , vol. 3, no. 8, pp. 431–447, 1962
1962
Earlier work this paper cites.
D. E. Johnson, J. R. Johnson, J. L. Hilburn, and P. D. Scott, Electric Circuit Analysis . Prentice Hall Englewood Cliffs, 1989, vol. 3
1989
Earlier work this paper cites.
Y. Ye and E. Tse, “An extension of karmarkar’s projective algorithm for convex quadratic programming,” Mathematical Programming , vol. 44, no. 1, pp. 157–179, May 1989
1989
Earlier work this paper cites.
J. A. Momoh, “A generalized quadratic-based model for optimal power flow,” in Proceedings of IEEE International Conference on Systems, Man and Cybernetics , vol. 1, Cambridge, MA, USA, Nov 1989, pp. 261–271
1989
Earlier work this paper cites.
P. M. Vaidya, “Speeding-up linear programming using fast matrix multiplication,” in IEEE FOCS , 1989, pp. 332–337
1989
Earlier work this paper cites.
S.-G. Chen and P. Hsieh, “Fast computation of the nth root,” Computers & Mathematics with Applications , vol. 17, no. 10, pp. 1423–1427, 1989
1989
Earlier work this paper cites.
K. Hornik, “Approximation capabilities of multilayer feedforward networks,” Neural networks , vol. 4, no. 2, pp. 251–257, 1991
1991
Earlier work this paper cites.
M. Leshno, V. Y. Lin, A. Pinkus, and S. Schocken, “Multilayer feedforward networks with a nonpolynomial activation function can approximate any function,” Neural networks , vol. 6, no. 6, pp. 861–867, 1993
1993
Earlier work this paper cites.
J. H. Park, Y. S. Kim, I. K. Eom, and K. Y. Lee, “Economic load dispatch for piecewise quadratic cost function using hopfield neural network,” IEEE Transactions on Power Systems , vol. 8, no. 3, pp. 1030–1038, Aug 1993
1993
Earlier work this paper cites.
N. Qian, “On the momentum term in gradient descent learning algorithms,” Neural networks , vol. 12, no. 1, pp. 145–151, 1999
1999
Earlier work this paper cites.
R. D. Christie, B. F. Wollenberg, and I. Wangensteen, “Transmission management in the deregulated environment,” Proceedings of the IEEE , vol. 88, no. 2, pp. 170–195, Feb 2000
2000
Earlier work this paper cites.
H. Wang, C. E. Murillo-Sanchez, R. D. Zimmerman, and R. J. Thomas, “On computational issues of market-based optimal power flow,” IEEE Transactions on Power Systems , vol. 22, no. 3, pp. 1185–1193, 2007
2007
Earlier work this paper cites.
C. H. Liang, C. Y. Chung, K. P. Wong, and X. Z. Duan, “Parallel Optimal Reactive Power Flow Based on Cooperative Co-Evolutionary Differential Evolution and Power System Decomposition,” IEEE Transactions on Power Systems , vol. 22, no. 1, pp. 249–257, Feb 2007
2007
Earlier work this paper cites.
P. E. O. Yumbla, J. M. Ramirez, and C. A. C. Coello, “Optimal power flow subject to security constraints solved with a particle swarm optimizer,” IEEE Transactions on Power Systems , vol. 23, no. 1, pp. 33–40, 2008
2008
Earlier work this paper cites.
B. Stott, J. Jardim, and O. Alsac, “DC Power Flow Revisited,” IEEE Transactions on Power Systems , vol. 24, no. 3, pp. 1290–1300, Aug 2009
2009
Earlier work this paper cites.
V. J. Gutierrez-Martinez, C. A. Cañizares, C. R. Fuerte-Esquivel, A. Pizano-Martinez, and X. Gu, “Neural-network security-boundary constrained optimal power flow,” IEEE Transactions on Power Systems , vol. 26, no. 1, pp. 63–72, 2010
2010
Earlier work this paper cites.
Q. Zhai, X. Guan, J. Cheng, and H. Wu, “Fast identification of inactive security constraints in scuc problems,” IEEE Transactions on Power Systems , vol. 25, no. 4, pp. 1946–1954, 2010
2010
Earlier work this paper cites.
F. Capitanescu, J. M. Ramos, P. Panciatici, D. Kirschen, A. M. Marcolini, L. Platbrood, and L. Wehenkel, “State-of-the-art, challenges, and future trends in security constrained optimal power flow,” Electric Power Systems Research , vol. 81, no. 8, pp. 1731–1741, 2011
2011
Earlier work this paper cites.
R. D. Zimmerman, C. E. Murillo-Sánchez, R. J. Thomas et al. , “MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on Power Systems , vol. 26, no. 1, pp. 12–19, 2011
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the International Conference on Neural Information Processing Systems , vol. 1, Lake Tahoe, Nevada, USA, 2012, pp. 1097–1105
2012
Cited alongside, same era.
S. Frank, I. Steponavice, and S. Rebennack, “Optimal power flow: a bibliographic survey i,” Energy Systems , vol. 3, no. 3, pp. 221–258, Sep 2012
2012
Cited alongside, same era.
——, “Optimal power flow: a bibliographic survey ii,” Energy Systems , vol. 3, no. 3, pp. 259–289, Sep 2012
2012
Cited alongside, same era.
M. B. Cain, R. P. O’neill, and A. Castillo, “History of optimal power flow and formulations,” Federal Energy Regulatory Commission , vol. 1, pp. 1–36, 2012
2012
Cited alongside, same era.
2017
Later among the works it cites.
D. Yarotsky, “Error bounds for approximations with deep ReLU networks,” Neural Networks , vol. 94, pp. 103–114, 2017
2017
Later among the works it cites.
I. Safran and O. Shamir, “Depth-width tradeoffs in approximating natural functions with neural networks,” in International Conference on Machine Learning , 2017, pp. 2979–2987
2017
Later among the works it cites.
F. Wan, L. Hong, A. Xiao, T. Jiang, and J. Zeng, “NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions,” Bioinformatics , vol. 35, no. 1, pp. 104–111, Jul 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2012
Cited alongside, same era.
L. A. Wehenkel, Automatic learning techniques in power systems . Springer Science & Business Media, 2012
2012
Cited alongside, same era.
A. J. Ardakani and F. Bouffard, “Identification of umbrella constraints in dc-based security-constrained optimal power flow,” IEEE Transactions on Power Systems , vol. 28, no. 4, pp. 3924–3934, 2013
2013
Cited alongside, same era.
A. Toshev and C. Szegedy, “Deeppose: Human pose estimation via deep neural networks,” in Proceeding of IEEE Conference on Computer Vision and Pattern Recognition , Columbus, OH, USA, June 2014, pp. 1653–1660
2014
Cited alongside, same era.
S. H. Low, “Convex relaxation of optimal power flow—part i: Formulations and equivalence,” IEEE Transactions on Control of Network Systems , vol. 1, no. 1, pp. 15–27, March 2014
2014
Cited alongside, same era.
G. F. Montufar, R. Pascanu, K. Cho, and Y. Bengio, “On the number of linear regions of deep neural networks,” in Advances in Neural Information Processing Systems 27 , 2014, pp. 2924–2932
2014
Cited alongside, same era.
N. Chiang and A. Grothey, “Solving security constrained optimal power flow problems by a structure exploiting interior point method,” Optimization and Engineering , vol. 16, no. 1, pp. 49–71, 2015
2015
Cited alongside, same era.
I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep Learning . MIT Press Cambridge, 2016, vol. 1
2016
Cited alongside, same era.
2018
Later among the works it cites.
V. H. Hinojosa and F. Gonzalez-Longatt, “Preventive Security-Constrained DCOPF Formulation Using Power Transmission Distribution Factors and Line Outage Distribution Factors,” Energies , vol. 11, no. 6, 2018
2018
Later among the works it cites.
“IEEE case300 topology,” 2018, https://www.al-roomi.org/power-flow/300-bus-system
2018
Later among the works it cites.
2018
Later among the works it cites.
2019
Closest in time.
L. Zhang, G. Wang, and G. B. Giannakis, “Real-time power system state estimation and forecasting via deep unrolled neural networks,” IEEE Transactions on Signal Processing , vol. 67, no. 15, pp. 4069–4077, 2019
2019
Closest in time.
L. A. Roald and D. K. Molzahn, “Implied constraint satisfaction in power system optimization: The impacts of load variations,” in 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton) . IEEE, 2019, pp. 308–315
2019
Closest in time.
X. Pan, T. Zhao, and M. Chen, “DeepOPF: Deep Neural Network for DC Optimal Power Flow,” in IEEE SmartGridComm , Oct. 2019
2019
Closest in time.
2019
Closest in time.
L. Gurobi Optimization, “Gurobi optimizer reference manual,” 2019. [Online]. Available: http://www.gurobi.com
2019
Closest in time.
2019
Closest in time.
F. Hasan, A. Kargarian, and A. Mohammadi, “A Survey on Applications of Machine Learning for Optimal Power Flow,” in 2020 IEEE Texas Power and Energy Conference (TPEC) . IEEE, 2020, pp. 1–6
2020
Closest in time.
L. Duchesne, E. Karangelos, and L. Wehenkel, “Recent Developments in Machine Learning for Energy Systems Reliability Management,” Proceedings of the IEEE , vol. 108, no. 9, pp. 1656–1676, 2020
2020
Closest in time.