The feasibility of the application of One Step Ahead Adaptive (OSAA) Control technique to a gas turbine power plant is investigated. The OSAA technique is a control algorithm especially suitable for non-linear and time-varying systems. This technique uses the Least Square algorithm to estimate in real-time a linear model of the controlled system, and, uses the estimated linear model to evaluate the feedback control variables. The proposed technique allows to control the Gas Turbine power plant in a wide range of electric loads due to its intrinsic adaptive capabilities. Moreover, the OSAA control does not require the knowledge of the dynamic characteristics (e.g. state space systems or transfer functions) in order to design the control system. The OSAA control system has been applied to a single shaft Gas Turbine power plant, which is numerically simulated. The proposed control technique has been tested both in Single-Input Single Output (SISO) mode and in Multi-Input Multi-Output (MIMO) mode. Starting from a steady-state condition, the power plant has been supposed to undergo a step reduction of the electric load. 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.

The One Step Ahead Controllers represent a branch of the Minimum Prediction Error Adaptive Controllers. They combine the parameter estimation of the controlled system model with a particular control scheme; therefore, they are especially suitable for non-linear and time-varying systems. Since the estimated parameters are updated at each time step (by using the sampled data), these methods can be adopted for real-time applications. Consequently, the One Step Ahead Controllers do not require the knowledge of the dynamic characteristics of the controlled system (e.g. state space systems or transfer functions). The One Step Ahead Adaptive (OSAA) algorithm combines the Least Square Algorithm (LSA) parameter estimator with a Deterministic Auto-Regressive Moving Average (DARMA) control scheme. The DARMA model can be characterized with a different number of time steps in the past (order of the estimated model) in relation to the dynamic feature of the controlled system. Sometimes, an excessive control effort could arise, caused by sudden variations of the electric load. In order to reduce this control action, the OSAA control technique has been applied also in Weighted fashion. The Weighted One Step Ahead Adaptive (WOSAA) control algorithm considers a penalty associated with the control effort by use an appropriate cost function. In this way, the control variable does not assume too large values, even when the Gas Turbine undergoes sudden changes in the external load. As a consequence, the robustness and the stability features of the WOSAA control system are increased with respect to the OSAA algorithm. The proposed techniques have been applied to a single shaft heavy-duty gas turbine (WOSAA) and to a double-shaft aero-derivative gas turbine (OSAA). They have been tested in Single-Input Single Output (SISO) mode. In the simulation tests, the plant is assumed to undergo sudden variations of the electric load. Second order schemes of the OSAA estimated model have been derived and applied to the double-shaft aero-derivative gas turbine. The results show that the OSAA control technique, applied to the double-shaft aero-derivative gas turbine, effectively counteracts the load reduction with limited overshoot in the controlled variables and, introducing an integral correction, with a negligible static error. On the other hand, the WOSAA control algorithm is able to efficiently regulate the single shaft heavy-duty gas turbine, and to counteract the sudden variations of the electric load, with reduced control effort.

A diagnostic tool based on Feed Forward Neural Networks (FFNN) is proposed to detect the origin of performance degradation in a Combined Cycle Gas Turbine (CCGT) power plant. In such a plant, due the connection of the steam cycle to the gas turbine, any deterioration of gas turbine components affects not only the gas turbine itself but also the steam cycle. At the same time, fouling of the heat recovery boiler may cause the increase of the turbine back-pressure, reducing the gas turbine performance. Therefore, measurements taken from the steam cycle can be included in the fault variable set, used for detecting faults in the gas turbine. The interconnection of the two parts of the CCGT power plant is shown through the fingerprints of selected component fault models for a power plant composed of a heavy-duty gas turbine and a steam plant with a single pressure recovery boiler. The diagnostic tool is composed of two FFNN stages: the first network stage is addressed to pre-process fault data in order to evaluate the influence of the single fault variable on the single fault condition. The second FFNN stage detects the fault conditions. Tests with simulated data show that the the diagnostic tool is able to recognize single faults of both the gas turbine and the steam plant, with a high rate of success, in case of full fault intensity, even in presence of uncertainties in measurements. In case of partial fault intensity, faults concerning gas turbine components and the superheater, are well recognized, while false alarms occur for the other steam plant component faults, in presence of uncertainties in data. Finally, some combinations of faults, belonging either to the gas turbine or the steam plant, have been examined for testing the diagnostic tool on double fault detection. In this case, the network is applied twice. In the first step the amount of the fault parameters that originate the primary fault are estimated. In the second step, the diagnostic tool curtails the contribution of the main fault to the fault parameters, and the diagnostic process is reiterated. In the examined fault combinations, the diagnostic tool was able to detect at least one of the two faults in about 60% of the cases, even in presence of uncertainty in measurements and partial fault intensity.