Energy storage is becoming increasingly important for isolated power systems having overall low inertia. Among many energy storage devices, superconducting magnetic energy storage (sMEs) is most suited for improved fr...
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Energy storage is becoming increasingly important for isolated power systems having overall low inertia. Among many energy storage devices, superconducting magnetic energy storage (sMEs) is most suited for improved frequency control in isolated power systems, due to its outstanding advantages. However, a small rating sMEs device has operational constraints, therefore a suitable control strategy is required for its profitable and constrained operation. An adaptive controller which encapsulates on-line identification with model predictive control is proposed in this paper. A recursive least-squares algorithm is used to identify a reduced-order model of wind-diesel power system on-line. Based on the identified model and a simple discrete time model of sMEs unit, an adaptive generalized predictive control scheme (AGPC) considering constraints on sMEs current level and converter rating is formulated. The scheme yields a control signal which on one hand keeps the system frequency deviations to minimum and on the other hand forces the sMEs device to operate within and near its operational constraints, for profitable operation. simulation studies are performed to illustrate the potency of the proposed strategy in achieving all the control objectives.
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