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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. 0 = 16.17 kJ .

Summary Delivering reliable and adequate power to the consumer is essentially critical. Standard quality of power is measured by its frequency stability and power flow between different control areas. Therefore, power‐system‐control is generally attained with load‐frequency‐control (LFC). This paper presents the LFC of a hybrid power system comprising of conventional‐thermal, solar‐thermal and electric vehicle (EV). The inclusion of EVs into the utility grid, generation‐rate‐constraint of thermal plants and time‐delay in all three control areas makes the proposed power system more realistic and a practical one. This makes the system a bit complex and requires a robust controller to function optimally. An integral‐double‐derivative (IDD) controller is applied for this study and the system responses are compared with those of classical controllers. The controller gains are optimized using the powerful magnetotactic bacteria optimization (MBO) technique, which find its maiden application in power system studies. MBO optimized IDD controller performs better in contrast to other classical controllers. Further, a fuzzy logic control (FLC) is developed to optimize the gains of optimal IDD controller. System dynamic responses comparison of both fuzzy optimized IDD and MBO optimized IDD controller reveals an inclination towards the performance of fuzzy optimized IDD controller. This is validated with the help of demerit index. Robustness analysis is also done to highlight the strength of IDD controller optimized with both MBO and FLC for various system changes, such as load perturbation, system loading and solar irradiance. The critical review of all these analysis infers the effective performance of fuzzy optimized IDD controller.

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