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This paper presents adaptive neural control of a superconducting magnetic energy storage system (SMES) in a power system for improved load frequency control (LFC). The proposed scheme for SMES control is a new one, tackles the deficiencies in the existing SMES control schemes and is clear about its implementation aspects. The power conversion system (PCS) of SMES used in this paper comprises of a voltage source converter (VSC) and a two-quadrant chopper. The control in each control area is implemented through a neural estimator and a neural controller with both of them operating online. The neural estimator extracts the control area dynamics around an operating point, and the neural controller then generates the power command for the corresponding SMES unit on the basis of a newly introduced variable which is a function of area control error and the change in stored energy in the SMES coil. This feature leads to a suitable and pure adaptive control of SMES. Moreover, in this paper the supplementary controller associated with the automatic generation control (AGC) is made to act on the area control error, with a modified gain setting, which is obtained by the integral square error (ISE) criterion. Simulation studies based on MATLAB are presented on a two-area power system after carrying out the necessary modeling exercise. 

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