The effective usage of the power facilities can be realized by leveling the fluctuating active power and compensating the reactive power. The fuzzy control strategy of superconducting magnet energy storages (SMES) was proposed for this purpose. The control results depend on the values of scaling factors in fuzzy reasoning. Therefore, it is desired to obtain better control results that the scaling factors are successively adjusted according to the load power fluctuation. In this paper, the control strategy based on an auto-tuning of scaling factors and a fuzzy singleton-type of reasoning method using the back propagation of neural network is proposed for leveling load fluctuation. The prediction and revision of teaching signal with energy of SMES is proposed. The coefficients of learning rate and the revision of the teaching signal is discussed. Then, we can achieve the better leveling of load power fluctuation by using fuzzy logic and neural network.
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering