Thermoeconomic Diagnosis: Zooming Strategy Applied to Highly Complex Energy Systems: Part 1 — Detection and Localization of Anomalies

2002 ◽  
Author(s):  
Vittorio Verda ◽  
Luis Serra ◽  
Antonio Valero
Keyword(s):  
Inverse Problem ◽  
Complex Systems ◽  
Reference Conditions ◽  
Energy Systems ◽  
Combined Cycle ◽  
Operating Conditions ◽  
The Other ◽  
Complex Energy ◽  
Operational Diagnosis ◽  
Detection And Localization

This paper presents a summary of our most recent advances in Thermoeconomic Diagnosis, developed during the last three years [1–3], and how they can be integrated in a zooming strategy oriented towards the operational diagnosis of complex systems. In fact, this paper can be considered a continuation of the work presented at the International Conference ECOS’99 [4–6] in which the concepts of malfunction (intrinsic and induced) and dysfunction [7] were analyzed in detail. These concepts greatly facilitate and simplify the analysis, the understanding and the quantification of how the presence of an anomaly, or malfunction, affects the behavior of the other plant devices and of the whole system. However, what remains unresolved is the so-called inverse problem of diagnosing [3], i.e. given two states of the plant (actual and reference operating conditions), find the causes of deviation of the actual conditions with respect to the reference conditions. The present paper tackles this problem and describes significant advances in addressing how to locate the actual causes of malfunctions, based on the application of procedures for filtering induced effects that hide the real causes of degradation. In this paper a progressive zooming thermoeconomic diagnosis procedure, which allows one to concentrate the analysis in an ever more specific zone is described and applied to a combined cycle. In an accompanying paper (part 2 [8]) the accuracy of the diagnosis results is discussed, depending on choice of the thermoeconomic model.

Thermoeconomic Diagnosis: Zooming Strategy Applied to Highly Complex Energy Systems. Part 1: Detection and Localization of Anomalies*

2005 ◽  
Vol 127 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Vittorio Verda ◽  
Luis Serra ◽  
Antonio Valero
Keyword(s):  
Inverse Problem ◽  
Complex Systems ◽  
Reference Conditions ◽  
Energy Systems ◽  
Combined Cycle ◽  
Operating Conditions ◽  
The Other ◽  
Complex Energy ◽  
Operational Diagnosis ◽  
Detection And Localization

This paper presents a summary of our most recent advances in Thermoeconomic Diagnosis, developed during the last three years, and how they can be integrated in a zooming strategy oriented toward the operational diagnosis of complex systems. In fact, this paper can be considered a continuation of the work presented at the International Conference ECOS’99 in which the concepts of malfunction (intrinsic and induced) and dysfunction were analyzed in detail. These concepts greatly facilitate and simplify the analysis, the understanding, and the quantification of how the presence of an anomaly, or malfunction, affects the behavior of the other plant devices and of the whole system. However, what remains unresolved is the so-called inverse problem of diagnosing, i.e., given two states of the plant (actual and reference operating conditions), find the causes of deviation of the actual conditions with respect to the reference conditions. The present paper tackles this problem and describes significant advances in addressing how to locate the actual causes of malfunctions, based on the application of procedures for filtering induced effects that hide the real causes of degradation. In this paper a progressive zooming thermoeconomic diagnosis procedure, which allows one to concentrate the analysis in an ever more specific zone is described and applied to a combined cycle. In an accompanying paper the accuracy of the diagnosis results is discussed, depending on choice of the thermoeconomic model.

scholarly journals Hydrothermal Energy Systems Development in the USA

1997 ◽  
Author(s):  
Radon Tolman ◽  
Ronald C. Timpe
Keyword(s):  
Activated Carbon ◽  
Steam Generator ◽  
Low Cost ◽  
Energy Systems ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Federal State ◽  
Supercritical Conditions ◽  
Wide Range ◽  
The Usa

A revolutionary hydrothermal steam generator is being developed by a federal, state university and industry partnership in the US to enhance economic growth and trade. The new generator is designed to accept solutions and slurries without corrosion and deposition on heat transfer surfaces up to the supercritical conditions of water, above 221 bar (3205 psia) and 374 C (705 F). The generator will produce steam from low quality water, such as from geothermal sources, for increased electric power generation. Water treatment costs and effluents will be eliminated for “zero discharge.” To improve efficiency and limit carbon dioxide and other emissions, the new steam generator will be tested for converting wastewater slurries of low-cost fuels and “negative value” wastes such as hazardous wastes, composted municipal wastes and sludges, to clean gas turbine fuel, hydrocarbon liquids, and activated carbon. Bench-scale results at sub- and supercritical conditions for lignite, refuse derived fuel, tire rubber and activated carbon are presented. An advanced continuous-flow pilot plant is being designed to test the generator over a wide range of operating conditions, including slurry feed up to 30 percent solids. Demonstration of the hydrothermal steam generator will be followed by design and construction of combined-cycle energy systems.

An Improved Thermoeconomic Diagnosis Procedure for the Detection of Different Malfunctions of Complex Energy Systems

2003 ◽  
Author(s):  
Vittorio Verda
Keyword(s):  
Complex System ◽  
Neural Networks ◽  
Control System ◽  
Energy Systems ◽  
Operating Conditions ◽  
Fuel Quality ◽  
System Efficiency ◽  
Complex Energy ◽  
Ambient Conditions ◽  
Economic Implications

Diagnosis of energy systems mainly consists of detecting and locating anomalies that cause reduction in the system efficiency or can cause major failures. This is an important task due to its economic implications. The attention is here focused on the anomalies that affect the system efficiency. The problem of their location is not easy to solve, due to some ‘disturbs’ that make propagate the effects of an anomaly throughout the system. These effects are caused by the dependence of the components’ behavior on their operating conditions. Moreover they can be amplified by the intervention of the control system and the variations in ambient conditions, fuel quality and plant load. A technique for highly complex system has been proposed in [1]. This procedure, based on the hypothesis of small malfunctions, consists of the progressive elimination of the disturbs, so that the anomalies could be more clearly highlighted. In this paper, a procedure particularly suitable for the application to operating plants is adopted to overcome the hypothesis of small malfunction. It consists of a combination of two techniques: 1) the use of neural networks for the elimination of the malfunctions [2] induced by the dependence of efficiency of components on the operating conditions and 2) the successive application of the analysis to several operating conditions selected within the plant case history.

A Comparative Study of Different Methods of Correcting Combined Cycle Thermal Performance

2015 ◽  
Author(s):  
Jagadish Nanjappa
Keyword(s):  
Power Plant ◽  
Boundary Conditions ◽  
Thermal Performance ◽  
Power Output ◽  
Correction Term ◽  
Reference Conditions ◽  
Heat Rate ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Correction Curve

The power output and heat rate (or efficiency) of a combined cycle power plant are expressed in the Power Industry at a specified set of “reference conditions”. Some of these reference conditions pertain to the test boundary (eg. ambient air temperature, barometric pressure etc.) while some others pertain to the operating condition (eg. baseload, evaporative cooler status, etc.) within the plant boundary. The process of measuring the actual thermal performance of a combined cycle plant involves conducting a test wherein the plant is operated at the pre-determined set of operating conditions that enable minimizing deviations from the “reference conditions”. It is a well-known fact that despite all efforts made during such a test, the actual boundary and operating conditions that prevail at the time of the test will not necessarily be identical to the pre-defined set of “reference conditions”. Hence, in order to evaluate the performance levels of the plant, one of the essential steps in the testing process is to “correct” the measured power output and heat consumption (or heat rate) for differences that persist between the actual test conditions and the corresponding set of “reference conditions”. This “correction” can be performed by using either a correction curve-based approach or a model-based approach. When a correction curve-based approach is used, the effects of the boundary conditions on the relevant performance parameter (output, heat consumption or heat rate) can be depicted as an additive correction term or as a multiplicative correction term. As such, the corrections to the boundary conditions can be applied as either a) additive or b) multiplicative or c) a combination of additive and multiplicative referred to as “hybrid”. The prevailing industry code for testing combined cycle power plants, ASME PTC 46, has adopted the “hybrid” method while the codes for testing individual equipment (such as PTC 22, PTC 6.2, PTC 6) have adopted either the additive philosophy or the multiplicative philosophy or a “hybrid” philosophy similar to PTC 46. The purpose of this paper is to present the outcome of a study that compares the three different correction methods utilizing the correction curve approach for a combined cycle power plant. The studies were based on thermodynamic simulations performed on different plant configurations. A key result will be the quantification of the errors associated with the different methods, which are primarily a function of the ability of the different methods to inherently capture the interactions between the various boundary parameters in the correction process and are a representation of the uncertainty associated with the particular correction method. Furthermore, the paper will introduce a new calculation method and provide recommendations that will help improve the accuracies of test results.

Fuzzy Expert Systems for the Diagnosis of Component and Sensor Faults in Complex Energy Systems

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Andrea Toffolo
Keyword(s):  
Expert Systems ◽  
Numerical Models ◽  
Energy Systems ◽  
Combined Cycle ◽  
Sensor Data ◽  
Complex Energy ◽  
Fuzzy Expert Systems ◽  
Mass Flow Rates ◽  
Expected Values ◽  
Fault Mode

Locating the causes of malfunctions in complex energy systems is an extremely difficult task, since more than one fault mode may produce similar and possibly undistinguishable patterns of effects. This paper shows how fuzzy expert systems can exploit the available measurements from the data acquisition system to identify different component and sensor fault modes. Real sensor data (mass flow rates, pressures, temperatures, and key operating parameters) are compared with the expected values of the same quantities that are calculated using numerical models of local subsystems. This comparison simply determines if the differences between measured and expected values are “negative,” “zero,” or “positive” in fuzzy logic terms. The final objective is to verify the existence of some patterns of these attributes that univocally identify the considered fault modes. These patterns are then implemented as the set of rules forming the knowledge base of a fuzzy expert system. The proposed diagnostic methodology is tested on the gas section of a real combined-cycle cogeneration plant, and the effect of measurement noise is also discussed.

Thermoeconomic Diagnosis: Zooming Strategy Applied to Highly Complex Energy Systems. Part 2: On the Choice of the Productive Structure*

2005 ◽  
Vol 127 (1) ◽  
pp. 50-58 ◽  
Author(s):  
Vittorio Verda ◽  
Luis Serra ◽  
Antonio Valero
Keyword(s):  
Energy Systems ◽  
Complex Energy ◽  
Number Of Components ◽  
Productive Structure ◽  
Control Volumes ◽  
Detection And Localization ◽  
Specific Part ◽  
Different Levels

The thermoeconomic diagnosis strategy introduced in the accompanying paper [Verda, V., Serra, L., Valero, A. 2004. Thermoeconomic Diagnosis: Zooming Strategy Applied to Highly Complex Energy Systems. Part 1: Detection and Localization of Anomalies. Part 1: The diagnosis procedure. ASME J. Energy Resour. Technol. 127(1), pp. 42–49. This issue.] is a zooming technique consisting of a successive localization of anomalies. At each step the required productive structure to be adopted becomes even more detailed, focusing the analysis on a more specific part of the system. The detail of a productive structure has two different levels: the number of components and the number of productive flows. The first one is selected according to the precision desired in locating the anomalies. A larger number of components (or subsystems) allows one to locate the anomalies in smaller control volumes, providing more precise indications for maintenance. The number of flows is partially dependent on the number of components. Once the number of components is fixed, the productive flows can be increased by separating exergy into its components or introducing fictitious flows, such as negentropy [see, for example, C. A. Frangopoulos, Energy, The International Journal 12(7), pp. 563–571 (1987)]. This decision also affects the results of the thermoeconomic analysis when it is adopted for diagnosis purposes. In this paper, the effects of the productive structure on the diagnosis results are carefully analyzed. Depending on the selected productive structure, the accuracy of the diagnosis results can be significantly improved.

Fuzzy Expert Systems for the Diagnosis of Component and Sensor Faults in Complex Energy Systems

2007 ◽  
Author(s):  
Andrea Toffolo
Keyword(s):  
Expert Systems ◽  
Numerical Models ◽  
Energy Systems ◽  
Combined Cycle ◽  
Sensor Data ◽  
Complex Energy ◽  
Fuzzy Expert Systems ◽  
Mass Flow Rates ◽  
Expected Values ◽  
Fault Mode

Locating the causes of malfunctions in complex energy systems is an extremely difficult task, since more than one fault mode may produce similar and possibly undistinguishable patterns of effects. This paper shows how fuzzy expert systems can exploit the available measurements from the data acquisition system to identify different component and sensor fault modes. Real sensor data (mass flow rates, pressures, temperatures, and key operating parameters) are compared to the expected values of the same quantities that are calculated using numerical models of local subsystems. This comparison simply determines if the differences between measured and expected values are “negative”, “zero” or “positive” in fuzzy logic terms. The final objective is to verify the existence of some patterns of these attributes that univocally identify the considered fault modes. These patterns are then implemented as the set of rules forming the knowledge base of a fuzzy expert system. The proposed diagnostic methodology is tested on the gas section of a real combined-cycle cogeneration plant and the effect of measurement noise is also discussed.

A Second-Law-Based Optimization: Part 1—Methodology

1996 ◽  
Vol 118 (4) ◽  
pp. 693-697 ◽  
Author(s):  
Y. M. El-Sayed
Keyword(s):  
Objective Function ◽  
Energy Systems ◽  
Second Law Of Thermodynamics ◽  
Energy System ◽  
Combined Cycle ◽  
Second Law ◽  
Complex Energy ◽  
System A ◽  
Decision Variables ◽  
Further Development

This paper deals with the optimization of complex energy systems given a cost objective function. The optimization uses a decomposition strategy based on the second law of thermodynamics and a concept for costing the components of a system. A large number of nonlinear decision variables can be optimized with enhanced convergence to an optimum. The paper is in two parts. In this part, the methodology is described. In Part 2, the methodology is applied to a simple energy system of 10 components and 19 manipulated decision parameters. The system is treated once as a single purpose combined cycle and once as a power-heat cogenerating system. The results of the application are summarized and evaluated. Further development is encouraged.

Inlet Air Cooling Applied to Combined Cycle Power Plants: Influence of Site Climate and Thermal Storage Systems

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Nicola Palestra ◽  
Giovanna Barigozzi ◽  
Antonio Perdichizzi
Keyword(s):  
Power Output ◽  
Parametric Analysis ◽  
Power Plants ◽  
Cooling System ◽  
Thermal Storage ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Air Cooling ◽  
Ambient Conditions ◽  
Cooling Systems

The paper presents the results of an investigation on inlet air cooling systems based on cool thermal storage, applied to combined cycle power plants. Such systems provide a significant increase of electric energy production in the peak hours; the charge of the cool thermal storage is performed instead during the night time. The inlet air cooling system also allows the plant to reduce power output dependence on ambient conditions. A 127MW combined cycle power plant operating in the Italian scenario is the object of this investigation. Two different technologies for cool thermal storage have been considered: ice harvester and stratified chilled water. To evaluate the performance of the combined cycle under different operating conditions, inlet cooling systems have been simulated with an in-house developed computational code. An economical analysis has been then performed. Different plant location sites have been considered, with the purpose to weigh up the influence of climatic conditions. Finally, a parametric analysis has been carried out in order to investigate how a variation of the thermal storage size affects the combined cycle performances and the investment profitability. It was found that both cool thermal storage technologies considered perform similarly in terms of gross extra production of energy. Despite this, the ice harvester shows higher parasitic load due to chillers consumptions. Warmer climates of the plant site resulted in a greater increase in the amount of operational hours than power output augmentation; investment profitability is different as well. Results of parametric analysis showed how important the size of inlet cooling storage may be for economical results.

Preliminary Study on the Effect of Operating Conditions on the Measurement Accuracy of Electronic Type Electric Energy Meter

2013 ◽  
Vol 834-836 ◽  
pp. 1246-1250
Author(s):  
Hai Bo Yu ◽  
Jia Liu ◽  
Chun Yu Wang ◽  
Li Li ◽  
Rui Ming Tong
Keyword(s):  
Measurement Accuracy ◽  
Reference Conditions ◽  
Electric Energy ◽  
Operating Conditions ◽  
Impulsive Load ◽  
Energy Meter ◽  
Power Frequency ◽  
Working Principle ◽  
Preliminary Study ◽  
Electric Energy Meter

The verification of electronic type electric energy meter is under strict reference conditions, but some special operating conditions at locale will exceed the standard scopes. In order to improve the calculation accuracy and operating reliability of electric energy meter under operating conditions, it is necessary to study the operating conditions. According to the working principle and the standard verification regulation of electronic type electric energy meter, this paper summarizes these operating conditions: low power factor, harmonic waves, dynamic impulsive load, voltage fluctuation and outside magnetic field of power frequency, then analyzes the causes of operating conditions and their influence on electronic type electric energy meter, eventually lays foundation for thorough and systematic study of electric energy meter in the operating conditions, and establishes measurement center database of operating conditions.

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