A novel energy feedback control method of flywheel energy storage system based on radial basis function neural network

A novel energy feedback control method based on radial basis function neural network (RBFNN) for flywheel energy storage system (FESS) driven by brushless DC motor (BLDCM) is proposed and applied in a wind power conversion. The RBFNN is trained off-line by adding new hidden neurons in the process of learning. Compared with the traditional PID controller, the proposed method can further reduce the ripple of DC bus voltage to improve the stability of output power when wind speed changes, which enhances the reliability and safety of the power system. A stand-alone wind energy generation system including a FESS driven by a BLDCM is simulated by Matlab/Simulink. The simulation results verify the effectiveness and correctness of the proposed method.

[1]  Cao Binggang,et al.  Study on regenerative braking of electric vehicle , 2004, The 4th International Power Electronics and Motion Control Conference, 2004. IPEMC 2004..

[2]  Narasimhan Sundararajan,et al.  An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  R. S. Weissbach,et al.  A combined uninterruptible power supply and dynamic voltage compensator using a flywheel energy storage system , 2001 .

[4]  Abderrazak Ouali,et al.  A comparative study of three different sensorless vector control strategies for a Flywheel Energy Storage System , 2010 .

[5]  Abderrazak Ouali,et al.  A fuzzy logic supervisor for active and reactive power control of a variable speed wind energy conversion system associated to a flywheel storage system , 2009 .

[6]  Benoit Robyns,et al.  FLYWHEEL ENERGY STORAGE SYSTEM TO IMPROVE THE INTEGRATION OF WIND GENERATORS INTO A NETWORK , 2003 .

[7]  D. Vinnikov,et al.  Control basics of a flywheel-powered uninterruptible motor drive , 2008, 2008 11th International Biennial Baltic Electronics Conference.

[8]  Mikihiko Matsui,et al.  Fuzzy-Logic-Based $V/f$ Control of an Induction Motor for a DC Grid Power-Leveling System Using Flywheel Energy Storage Equipment , 2009, IEEE Transactions on Industrial Electronics.

[9]  Chen Quan-shi.  Fuzzy Neural Network Control Strategy of Wind Generation and Flywheel Energy Storage Combined System , 2007 .

[10]  H.L. Hess,et al.  Modeling and analysis of a flywheel energy storage system for Voltage sag correction , 2006, IEEE Transactions on Industry Applications.