Cellular computational generalized neuron network with cooperative PSO for power systems

To enhance the reliability of the power system, newer technologies are being incorporated day by day. Predictions of different states can significantly increase the reliability of the system. To predict the frequency, a cellular computational generalized neuron network (CCGNN) that is trained with particle swarm optimization (PSO) is proposed recently. However, with the size of the system, the dimension of PSO grows that in turn, increases the complexity of the training. To solve the problem, a special version of PSO named cooperative PSO (CPSO) is applied for training the CCGNN in this paper. Through simulation on a 68-dimensional problem of a two-area four-machine system, it is shown that the CPSO performs significantly better than the canonical one.

[1]  S. J. Kiartzis,et al.  A neural network short term load forecasting model for the Greek power system , 1996 .

[2]  R Langridge,et al.  Improvements in protein secondary structure prediction by an enhanced neural network. , 1990, Journal of molecular biology.

[3]  Xiaodong Li,et al.  Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization , 2014, IEEE Transactions on Evolutionary Computation.

[4]  Ganesh K. Venayagamoorthy,et al.  Cellular computational networks - A scalable architecture for learning the dynamics of large networked systems , 2014, Neural Networks.

[5]  Xiaodong Li,et al.  Cooperatively Coevolving Particle Swarms for Large Scale Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[6]  Le-Ren Chang-Chien,et al.  Online estimation of system parameters for artificial intelligence applications to load frequency control , 2011 .

[7]  Xiaodong Li,et al.  Cooperative Co-evolution with delta grouping for large scale non-separable function optimization , 2010, IEEE Congress on Evolutionary Computation.

[8]  Saman K. Halgamuge,et al.  Quantifying Variable Interactions in Continuous Optimization Problems , 2017, IEEE Transactions on Evolutionary Computation.

[9]  Ganesh K. Venayagamoorthy,et al.  A lite cellular generalized neuron network for frequency prediction of synchronous generators in a multimachine power system , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[10]  Ramesh Sharda,et al.  A neural network model for bankruptcy prediction , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[11]  Michael J Demetsky,et al.  SHORT-TERM TRAFFIC FLOW PREDICTION: NEURAL NETWORK APPROACH , 1994 .

[12]  Ganesh K. Venayagamoorthy,et al.  Decentralized Asynchronous Learning in Cellular Neural Networks , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[14]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer with local search , 2005, 2005 IEEE Congress on Evolutionary Computation.

[15]  Xin Yao,et al.  Large scale evolutionary optimization using cooperative coevolution , 2008, Inf. Sci..

[16]  Muhammed Fazlur Rahman,et al.  Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks , 2009, Adv. Fuzzy Syst..

[17]  M. Pavella,et al.  Dynamic state prediction and hierarchical filtering for power system state estimation , 1988, Autom..

[18]  Ganesh K. Venayagamoorthy,et al.  Generalized neuron: Feedforward and recurrent architectures , 2009, Neural Networks.