The present paper proposes an enhanced version of the One Step Ahead control algorithm applied to a wind system. The wind system is considered as an isolate source of power and it is composed of a horizontal-axis wind-turbine with pitch variable blades connected to a synchronous generator: consistent mathematical models for the horizontal-axis wind-turbine and the synchronous generator will be introduced. The main strength of the One-Step-Ahead control technique is its adaptivity, thus it represents a suitable control strategy. Nevertheless its convergence rate is shown to reduce very quickly. This aspect shows the primary algorithm weakness and is the reason for the proposed enhancement that, whist it allows a good level of algorithm adaptivity, effectively eliminates the local instabilities. Finally, results of control problems will prove the reliability of the suggested enhanced control technique.

This paper provides an innovative technique for the control of a variable-geometry turbine in a turbocharged compression ignition engine. Such a control algorithm is based on a multi-agent System scheme. It is seldom that agents are considered as stand-alone systems; on the contrary, their main strength can be found in the interaction with other agents, constituting the so-called multi-agent System. An agent is a software or hardware entity that can receive signals from the environment and act upon that environment through output signals, trying to carry out an appropriate task. In the present paper, a fuzzy logic system has been employed for the agents' internal structure. In order to test the performance of the proposed control technique, a numerical model of the engine has been used, which employs a thermodynamic (zero-dimensional) approach. The paper will show that the proposed control technique is able to take into account the non-linearity of the controlled system.

This article provides two robust and effective inverse design procedures based on fuzzy logic. The procedures benefits from the fuzzy logic non-analytical structure, which allows one to handle numerical map and data measurements, exploiting the designers' knowledge distributed in already existing systems. Based on such data, the fuzzy logic algorithm is able to promptly provide a first glance design which represents a good starting solution. The two proposed design algorithms differ in the optimisation process which improves the first glance design. The first one considers the optimisation as a false control problem, solved by,in adaptive control strategy which, treating the design error its the control error, tries to annihilate it by acting on the design variables. The second algorithm is based on it multiagent structure that, starting from the first glance design, performs the subsequent optimisation step by mapping, through it fuzzy logic structure, the relationship between the variation of the design variables and the design error. The effectiveness of the proposed methods is verified performing the design of it nozzle in different flow regimes, using both 1- and 2D flow solvers to perform the analyses.

This paper provides an adaptive technique for the control of a variable geometry turbine (VGT) in a turbocharged compression ignition engine. The adaptive control is based on a fuzzy logic control scheme and a least-squares parameter estimator algorithm. In order to test the performance of the proposed control technique, a numerical model of the engine has been used, which employs a thermodynamic (zero-dimensional) approach. The paper will show that the fuzzy logic control technique is able to take into account the non-linearity of the controlled system and to reject white noise affecting the measurement chain.

This paper provides an adaptive technique for the control of the variable geometry turbine in a turbocharged compression ignition engine. The adaptive control is based on a one-step-ahead (OSA) technique and a least-square parameter estimator algorithm. In order to test the performance of the proposed control technique, a numerical model of the engine has been developed, which employs a thermodynamic (zero-dimensional) approach. The paper will show that the OSA technique is able to improve dramatically the control performance with respect to that provided by a commonly applied proportional integral derivative control technique.

The control of a wind power plant, operating as an isolated power source, is analyzed. The plant consists of a wind turbine and a three phase synchronous electric generator, connected by means of a gear box. The mathematical models of the wind turbine and of the electrical generator are indicated. The use of an estimator based adaptive fuzzy logic control technique to govern the system is proposed. The results of a control test case are shown in order to demonstrate the reliability of the proposed control technique.

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.

The one-step-ahead controllers represent a branch of minimum prediction error adaptive controllers. They combine the parameter estimation of the controlled system model with the 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 non-linear applications. Consequently, the one-step-ahead controllers do not require knowledge of the dynamic characteristics of the controlled system (e.g. state-space systems or transfer functions). Sometimes, in the gas turbine field, the control system could produce an excessive control effort, due to sudden variations of the electric load. In order to reduce this control action, the weighted one-step-ahead adaptive (WOSAA) control algorithm considers a penalty associated with the control effort using an appropriate cost function. In this way, the control variable does not assume values that are too large, even when the gas turbine undergoes sudden changes in the external load. As a consequence, the robustness and stability features of the WOSAA control system are increased. The results show that the WOSAA control technique, applied to both the double-shaft aero-derivative gas turbine and the single-shaft heavy-duty gas turbine, effectively counteracts the load reduction with limited overshoot in the controlled variables with reduced control effort.

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.

This paper provides an adaptive technique for the control of a variable geometry turbine (VGT) in a turbocharged compression ignition engine. The adaptive control is based on a one-step-ahead technique and a least square parameter estimation algorithm. In order to test the performance of the proposed control technique, a numerical model of the engine has been used, which employs a zero-dimensional approach. The paper shows that the one-step-ahead technique is able to improve dramatically the control performance with respect to that provided by a commonly applied PID control technique.

Control of a wind power plant as an isolated power source is analyzed. The plant consists of a wind turbine (connected by means of a gear box to a three-phase synchronous electric generator) and a control system. Mathematical models of the wind turbine and electrical generator have been proposed. The one-step-ahead adaptive control technique has been adopted to govern the system. Results of a control test case are shown in order to demonstrate the reliability of the proposed control technique.