Alessandro Sorce

Gross Error Detection Based on Serial Elimination: Applications to an Industrial Gas Turbine

Gross Error Detection (GED) is a technique used to identify possible systematic errors in measurements and validate data for a further diagnostic phase. It is always applied along with Data Reconciliation (DR), a technique to improve the accuracy of process data by adjusting the measured values to fit the process equations describing the physical phenomena. They have been applied for a long time to chemical plants with balance equations (mass and composition) and recently extended to industrial power plants [1,2,3]. In this paper a well-known GED technique based on serial elimination has been applied in a gas turbine plant operating in a combined cycle power plant. In a first analysis errors have been imposed manually in the field data to understand the minimum error amplitude avoiding the smearing effect: at the beginning a single gross error has been imposed on the fuel flow rate and on the compressor discharge temperature respectively, then multiple gross errors have been imposed simultaneously on the same measurements. The single gross error tests showed a high capacity of detection and localization, while the multiple gross error analysis highlighted the problems due to the smearing effect (the minimum error intensity to detect and locate errors increased with respect to the single error case). In a second analysis the GED technique has been used to detect and locate a gross error among the three sensors measuring the compressor discharge temperature. The main objective was to analyze the ineffectiveness in error detection and localization of using the mean for redundant measurements.Copyright

A Simplified Hybrid Approach to Dynamic Model a Real HRSG

This paper proposes a dynamic simplified approach to model a Heat Recovery Steam Generator of a Gas Turbine Combined Cycle (GTCC) and its validation against field data. The adopted framework begins with some physical considerations on global HRSG structure, and then focuses on a specific application for a real plant, i.e. a 390 MW multi-shaft combined cycle based on the AEN94.3 A4 frame. Moreover the model embodies some parameters, which are easily derived from historical data to enhance the forecasting capabilities of the software, resulting in a hybrid model which covers a high range of working conditions. The whole model is designed to run in Excel/Visual Basic environment to allow for extended use by people who have limited experience in advanced modelling software. The model so created has been handled through a training process based on 10 days of experimental data, in order to create the basis for true system flexibility. Therefore, the feasibility of this approach has been verified using a Gas Turbine (GT) load profile accomplished in everyday working operations and validating the results against field data.Copyright

Heat Recovery Steam Generator Health Assessment Basing on Reconciled Measurement

Long-term monitoring and diagnostic of power plants is a permanent issue for the energy companies. In particular with the increase of flexible operation (e.g. daily start-up and shutdown cycles, part load operations) the definition of proper diagnostic indicators becomes mandatory. Different monitoring strategies were developed, implemented and tested for the main components of a combined cycle power plant (e.g. Gas Turbine, Heat Recovery Steam Generator, Steam Turbine, Pumps) to prevent fault/failure or to plan/evaluate the maintenance activities.This work focuses on the first principles health assessment of the Heat Recovery Steam Generator (HRSG). The impact of ambient conditions on the gas turbine outlet temperature and mass flow rate and thus on the HRSG behavior is presented referring to the control strategies of the Gas Turbine (GT). To validate the measurements a preprocessing phase basing on Data Reconciliation was performed, aimed at improving the accuracy of the estimation of exhaust mass flow rate entering the HRSG. Gas Turbine and HRSG nergy balances are exploited to reduce the uncertainties of the results, eliminate the outlier data sets and obtain consistent data. Moreover an evaluation of the sensitivity of the indicator will be made basing on field measurements before and after a maintenance intervention.Copyright

A New Sensor Diagnostic Technique Applied to a Micro Gas Turbine Rig

This paper describes the development and testing of a new algorithm to identify faulty sensors, based on a statistical model using quantitative statistical process history. Two different mathematical models were used and the results were analyzed to highlight the impact of model approximation and random error. Furthermore, a case study was developed based on a real micro gas turbine facility, located at the University of Genoa. The diagnostic sensor algorithm aims at early detection of measurement errors such as drift, bias, and accuracy degradation (increase of noise). The process description is assured by a database containing the measurements selected under steady state condition and without faults during the operating life of the plant. Using an invertible statistical model and a combinatorial approach, the algorithm is able to identify sensor fault. This algorithm could be applied to plants in which historical data are available and quasi steady state conditions are common (e.g. Nuclear, Coal Fired, Combined Cycle).Copyright

Experimental Investigation of the Dynamic Performance of a Micro Gas Turbine Recuperator Including Innovative Cycle Configurations

The aim of this work is the experimental analysis of steady-state and transient behavior of a primary surface recuperator installed in a 100 kW commercial micro gas turbine. The machine is integrated in an innovative test rig for high temperature fuel cell hybrid system emulation. It was designed and installed by the Thermochemical Power Group (TPG), at the University of Genoa, within the framework of the Felicitas and LARGE-SOFC European Integrated Projects. The high flexibility of the rig was exploited to perform tests on the recuperator operating in the standard cycle. Attention is mainly focused on its performance in transient conditions (start-up operations and load rejection tests). Start-up tests were carried out in both electrical grid-connected and stand-alone conditions, operating with different control strategies. Attention is focused on system response due to control strategy and on boundary temperature variation because of its influence on component life consumption. Further tests were carried out using the valves installed on the test rig to bypass the air side of the unit. Different operative conditions were analyzed to show the effect of different mass flow rates on recuperator behavior. Attention is mainly focused on recuperator performance when it operates in unbalanced flow rate conditions (i.e. different mass flow rate values in recuperator sides), as well as during advanced cycle start-up and shutdown operations.© 2010 ASME