Lorenzo Dambrosio

Estimator based adaptive fuzzy logic control technique for a wind turbine–generator system

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.

VGT turbocharger controlled by an adaptive technique

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.

One step ahead adaptive control technique for wind systems

The control of a wind system considered as an isolated source of power and composed of a horizontal-axis wind-turbine connected to an induction generator is analyzed. Appropriate mathematical models for both the horizontal-axis wind-turbine and the induction generator are used. The one-step-ahead adaptive control technique is presented and used to regulate the wind system. A sensitivity analysis of the induction generator performances with respect to control and disturbance variables is presented in order to evaluate the control flexibility. The results of three control problems are finally shown in order to prove the reliability of the suggested control technique.

Fault Diagnosis of Combined Cycle Gas Turbine Components Using Feed Forward Neural Networks

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.Copyright

Performance of Gas Turbine Power Plants Controlled by the Multiagent Scheme

In recent years the idea of artificial intelligence has been focused around the concept of rational agent. An agent is an (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. Seldom 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 multiagent system. In the present work, a multiagent system was chosen as a control system of a single-shaft heavy-duty gas turbine in the multi input multi output mode. The shaft rotational speed (power frequency) and stack temperature (related to the overall gas turbine efficiency) represent the controlled variables; on the other hand, the fuel mass flow (VCE) and the variable inlet guide vanes (VIGV) have been chosen as manipulating variables. The results show that the multiagent approach to the control problem effectively counteracts the load reduction (including the load rejection condition) with limited overshoot in the controlled variables (as other control algorithms do) while showing a good level of adaptivity, readiness, precision, robustness, and stability.

Performance of Gas Turbine Power Plants Controlled by Multiagent Scheme

In latter years the idea of artificial intelligence has been focused around the concept of a rational agent. An agent is a (software or hardware) entity that can receive signals from the environment and act upon that environment through output signals. In general an agent always tries to carry out an appropriate task. Seldom agents are considered as stand-alone systems. Their main strength can be found in the interaction with other agents in several different ways in a multiagent system. In the present work, multiagent system approach will be used to manage the control process of a single-shaft heavy-duty gas turbine in Multi Input Multi Output mode. The results will show that the multiagent approach to the control problem effectively counteracts the load reduction (including the load rejection condition) with limited overshoot in the controlled variables (as other control algorithms do) while showing good level adaptivity readiness, precision, robustness and stability.© 2006 ASME

Feed Forward Neural Network-Based Diagnostic Tool for Gas Turbine Power Plant

A diagnostic tool able to detect faults that may occur in a gas turbine power plant at an early stage of their emergence is of a great importance for power production. In the present paper, a diagnostic tool, based on Feed Forward Neural Networks (FFNN), has been proposed for gas turbine power plants with a condition monitoring approach. The main aim of the proposed diagnostic tool is to reliably detect not only every considered single fault, but also two or more faults that may occur contemporarily. Two different FFNNs compose the proposed diagnostic tool. The first network, that is not-fully connected, operates a fault pre-processing in order to evaluate the influence of the single fault variable on the single fault condition. The second FFNN detects the fault conditions by means of an iterative process. Such a diagnostic tool has been applied to a mathematical model of a single shaft gas turbine for power generation, resulting able to detect the 100% of single faults and the 80% of combined faults.Copyright