Model predictive control of a heat recovery steam generator during cold start-up operation using piecewise linear models

Sindareh-Esfahani, Peyman; Tabatabaei, S. Sepehr; Pieper, J.K.

doi:10.1016/j.applthermaleng.2017.03.041

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…Reference [65] classifies the models used for determining the reliability as follows: failure occurrence according to the probability distribution of the time to failure (black box), deterioration process until failure (gray box) and physical process of the deterioration (white box). Sindareh et al [66] classify the models used for a heat recovery steam generator during cold start-up operation into the use of thermodynamic equations with known parameters (white box), models extracted from thermodynamic equations with unknown parameters (gray box), and neural and fuzzy networks based on data collection (black box).…”

Section: Maintenance Types and Associated Mathematical Modelsmentioning

confidence: 99%

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

et al. 2019

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Wind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models.

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Section: Maintenance Types and Associated Mathematical Modelsmentioning

confidence: 99%

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

et al. 2019

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“…Next, on the basis of the now‐classified data, the parameters θ i ( i = 1, …, s ) for each submodel are identified. Different techniques can be used for this purpose including gradient‐based and heuristic methods . In this paper, the least square approach is employed (see Appendix A).…”

Section: Piecewise Affine Model Identification Of a Wind Turbinementioning

confidence: 99%

“…Different techniques can be used for this purpose including gradient-based and heuristic methods. [60][61][62] In this paper, the least square approach is employed 63 (see Appendix A).…”

Section: Piecewise Affine Model Identification Of a Wind Turbinementioning

confidence: 99%

Machine learning–based piecewise affine model of wind turbines during maximum power point tracking

Sindareh-Esfahani

Pieper

2019

Wind Energy

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In this paper, a discrete-time piecewise affine (PWA) model of a wind turbine during Maximum Power Point Tracking (MPPT) region is identified. A clustering-based identification method is utilized to create PWA maps for nonlinear aerodynamic torque and thrust force functions. This method exploits the combined use of clustering, pattern recognition, and parameter identification techniques. The well-known K-means clustering method is employed along with a perceptron-based multiclassifier for pattern recognition and the least squared technique for parameter estimation. The identified maps are approximated the nonlinear static functions of the dynamic model of the wind turbine. Characteristics of a 5-MW wind turbine are considered and the resulting model, which consists of 25 subregions is compared with the nonlinear dynamic model. Two test cases are studied in order to validate the presented model. Simulation results demonstrate the effectiveness and accuracy of the PWA model such that the response of the identified PWA model is fitted well to the nonlinear one. The PWA model identified in this paper can be widely used for advanced control systems design and long-term performance and security assessment of the power grid.

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“…Different strategies were developed by means of exhaust gas turbine bypass to reduce the start-up time and maintaining the steam drum stress in allowable ranges. Sindareh-Esfahani et al [11] studied the cold startup of an HRSG and optimized the start-up time based on a model predictive control approach. In order to assure the lifetime of the HRSG components, the model considered the thermo-mechanical constraints of the steam drum, superheater and economizer.…”

Section: Introductionmentioning

confidence: 99%

Dynamic performance and stress analysis of the steam generator of parabolic trough solar power plants

González-Gómez

Gómez-Hernández

Ferruzza

et al. 2019

Applied Thermal Engineering

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show abstract

Model predictive control of a heat recovery steam generator during cold start-up operation using piecewise linear models

Cited by 11 publications

References 37 publications

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

Machine learning–based piecewise affine model of wind turbines during maximum power point tracking

Dynamic performance and stress analysis of the steam generator of parabolic trough solar power plants

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