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Path: Home » Indice Pubblicazioni » Convegni ATI - Accesso riservato soci CTI » CA - 54 - L Aquila 1999 » One step ahead adaptive control for h...

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One step ahead adaptive control for heavy-duty and aero-derivative gas turbines

Pubblicazione


Autore: L. Dambrosio, B. Fortunato, S.M. Camporeale

Collana: CA - 54 - L Aquila 1999

Note:
The One Step Ahead Adaptive (OSAA) technique is a control algorithm especially suitable for non-linear and time-varying systems. This technique uses the Least Square Algorithm (LSA) to estimate a discrete-time linear model of the controlled system. The estimated parameters are update at each time step on the basis of the sampled data. Then, the estimated model is used to evaluate the feedback control variables. The specific features of this method make it suitable for real time applications.

Due to its adaptive capability, the OSAA control does not require the knowledge of the dynamic characteristics of the controlled system (e.g. state space systems or transfer functions). However, an appropriate scheme should be given to the OSAA estimated model in relation to the dynamic feature of the controlled system.

The proposed technique has been applied to a single shaft heavy-duty gas turbine and to a double-shaft aero-derivative gas turbine. It has been tested both in Single-Input Single Output (SISO) mode and in Multi-Input Multi-Output (MIMO) mode. In the simulation tests, the plant is assumed to undergo a sudden variations of the electric load. A first and a second order schemes of the OSAA estimated model have been derived and applied.

The results show that the OSAA control technique effectively counteracts the load reduction with limited overshoots in the controlled variables and, introducing a integral correction, a negligible static error. Specifically, the first order scheme of the OSAA estimated model has given good results for the single-haft heavy-duty gas turbine. On the other hand, the aero-derivative double-shaft gas turbine needs a second order scheme for the OSAA estimated model.


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