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.

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.

scholarly journals Improving the energy efficiency of oil production using identification and prediction of operating modes of production wells based on data analysis methods, machine learning and neural networks

2019 ◽  
Vol 124 ◽  
pp. 05031 ◽  
Author(s):  
A.M. Sagdatullin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control System ◽  
Oil And Gas ◽  
Gas Production ◽  
Operating Conditions ◽  
Identification System ◽  
Oil And Gas Production ◽  
The Neural Network ◽  
And Control

Currently, there is a need to improve the systems and control of pumping equipment in the oil and gas production and oil and gas transport industries. Therefore, an adaptive neural network control system for an electric drive of a production well was developed. The task of expanding the functional capabilities of asynchronous electric motors control of the oil and gas production system using the methods of neural networks is solved. We have developed software modules of the well drive control system based on the neural network, an identification system, and a scheme to adapt the control processes to changing load parameters, that is, to dynamic load, to implement the entire system for real-time control of the highspeed process. In this paper, based on a model of an identification block that includes a multilayered neural network of direct propagation, the control of the well system was implemented. The neural network of the proposed system was trained on the basis of the error back-propagation algorithm, and the identification unit works as a forecaster of system operation modes based on the error prediction. In the initial stage of the model adaptation, some fluctuations of the torque are observed at the output of the neural network, which is associated with new operating conditions and underestimated level of learning. However, the identification object and control system is able to maintain an error at minimum values and adapt the control system to a new conditions, which confirms the reliability of the proposed scheme.

Long-Term Degradation-Based Modeling and Optimization Framework

2018 ◽  
pp. 192-220
Author(s):  
Tarannom Parhizkar
Keyword(s):  
Energy Systems ◽  
Optimization Procedure ◽  
Energy System ◽  
Operating Conditions ◽  
Energy Prices ◽  
Ambient Conditions ◽  
Term Operation ◽  
Optimization Framework ◽  
Degradation Models

Energy systems degrade during long-term operation. Thus, performance profile of the system deteriorates over time. To optimize energy system parameters more reliably and accurately, it is necessary to consider degradation models of the system in the optimization procedure. In this chapter, a novel degradation-based optimization framework is proposed. This framework optimizes design and operation parameters of energy systems while accounting for the degradation effects on system performance. Therefore, this framework is beneficial for long-term analysis and optimization of energy systems. Validity and usefulness of the proposed methodology are demonstrated by optimizing the operating conditions and maintenance intervals of a gas turbine power plant, under different seasonal ambient conditions and energy prices. The case study results effectively meet all the positive expectations that are placed on the proposed degradation-based optimization framework.

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 Analysis of Control System of Fuel Combustion in Boilers with Oxygen Sensor

2019 ◽  
Vol 63 (4) ◽  
pp. 241-248 ◽  
Author(s):  
Artur Zaporozhets
Keyword(s):  
Control System ◽  
Oxygen Sensor ◽  
Experimental Studies ◽  
Combustion Products ◽  
Fuel Combustion ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Fuel Quality ◽  
Combustion Control ◽  
Boiler Operation

The method of fuel quality control is considered, which is based on the using of the oxygen sensor (without sensors of incomplete fuel combustion products). An algorithm for the electric drive of a fan is proposed, which is based on a step changing in the rotation speed. The choice of broadband oxygen sensor as a basis for the development of a fuel combustion control system is determined. In the course of experimental studies, the possibility of reconstructing the boiler by replacing a burner with an installed control system was demonstrated. The commissioning works were carried out with the installed system, the optimum operating conditions of the boiler were determined (with the formation of CO in the flue gases at a minimum level <50 ppm). The technical characteristics of the boiler operation under different loads (from 10 % to 100 %) are considered. Ecological and economic analysis of the developed fuel combustion control system was carried out.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

Algorithms structural and parametric synthesis of remote control and diagnostics systems of mobile measuring points

2018 ◽  
pp. 60-65
Author(s):  
A. I. Tatarinov
Keyword(s):  
Complex System ◽  
Control System ◽  
Remote Control ◽  
Structural Features ◽  
Information Protection ◽  
Parametric Synthesis ◽  
Unauthorized Access ◽  
Operating Modes ◽  
Remote Control System ◽  
Made In

With the help of the general and structurally-information schemes of remote control, an analysis was made in the course of which the requirements for protection against unauthorized access of the complex system were clarified and established. In the article structural features of the remote control system of mobile measuring points of rocket and space equipment are considered. These features are represented by the requirements for information protection, as well as the operating modes of this system. The list of these regimes was obtained as a result of studies of structural and functional schemes of a remote control system for mobile measuring points.

scholarly journals A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 6388
Author(s):  
Karim M. El-Sharawy ◽  
Hatem Y. Diab ◽  
Mahmoud O. Abdelsalam ◽  
Mostafa I. Marei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Distributed Generation ◽  
Control Strategy ◽  
Current Control ◽  
Operating Conditions ◽  
Pi Controllers ◽  
Artificial Neural ◽  
The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

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