Fuzzy decoupled-states multi-model identification of gas turbine operating variables through the use of their operating data

Aissat, Sidali; Hafaifa, Ahmed; Iratni, Abdelhamid; Hadroug, Nadji; Chen, Xiaoqi

doi:10.1016/j.isatra.2022.07.005

Cited by 11 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy logic has been extensively applied in industrial applications, including system control, modeling and diagnosis, process monitoring, speech and image processing, data classification, decisionmaking, and more. Notable research studies in these areas include the works of Hafaifa et al [4], Ahmed Zohair Djeddi et al [5], Bezdek et al [11], Boon Chiang et al [12], Choayb Djeddi et al [13], Gustafson et al [17], Hakim Bagua et al [18], Jang et al [21], Merouane Alaoui et al [27][28], Nadji Hadroug et al [35][36] and Sidali Aissat et al [41][42]. The advantage of using fuzzy logic lies in its ability to incorporate human reasoning into analytical models.…”

Section: Nonlinear Modeling Systemmentioning

confidence: 99%

A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control

2023

ijSmartGrid

View full text Add to dashboard Cite

This paper focuses on the identification and modeling of gas turbine dynamics, specifically those used in power generation plants. The approach utilizes experimental data and employs fuzzy reasoning systems. The resulting model serves the purpose of approximating nonlinear gas turbine systems and ensuring reliable system control. By incorporating uncertainties associated with human reasoning, such as fuzzy systems based on Takagi-Sugeno reasoning, it is possible to achieve highly reliable control systems. The primary goal of this paper is to increase the effective monitoring system by employing nonlinear identification techniques, namely fuzzy systems and neuro-fuzzy systems, based on real-time on-site experimental data. Additionally, the proposed identification approaches are evaluated through a comparative study, where the results obtained using the Nonlinear Autoregressive Exogenous Neural Networks (NARX-NN) modeling technique are compared with those obtained using the Adaptive Inference System combined with the techniques of Neuro-Fuzzy renowned ANFIS concept. The obtained investigation results further facilitate the comprehension and analysis of the nonlinearities present in these complex systems, ultimately aiding in the prediction of their dynamic behavior.

show abstract

Section: Nonlinear Modeling Systemmentioning

confidence: 99%

A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control

2023

ijSmartGrid

View full text Add to dashboard Cite

show abstract

“…Sanjay Barad et al [25] developed an intelligent configuration for monitoring the mechanical state of a gas turbine, based on the use of neural networks, leading to improved monitoring performance and precision. Sidali Aissat et al [28] identified the operating parameters of a gas turbine using a fuzzy multi-model approach and exploited operating data for the prediction of the operating state dynamics of the machine. However, Sadough Vanini Z.N.…”

Section: Introductionmentioning

confidence: 99%

ARMAX-based identification and diagnosis of vibration behavior of gas turbine bearings

Mahroug,

Khaldi,

Guemana

et al. 2023

Diagnostyka

View full text Add to dashboard Cite

Parametric identification approaches play a crucial role in the control and monitoring of industrial systems. They facilitate the identification of system variables and enable the prediction of their evolution based on the input-output relationship. In this study, we employ the ARMAX approach to accurately predict the dynamic vibratory behavior of MS5002B gas turbine bearings. By utilizing real input-output data obtained from their operation, this approach effectively captures the vibration characteristics of the bearings. Additionally, the ARMAX technique serves as a valuable diagnostic tool for the bearings, enhancing the quality of identification of turbine variables. This enables continuous monitoring of the bearings and real-time prediction of their behavior. Furthermore, the ARMAX approach facilitates the detection of all potential vibration patterns that may occur in the bearings, with monitoring thresholds established by the methodology. Consequently, this enhances the availability of the bearings and reduces turbine downtime. The efficacy of the proposed ARMAX approach is demonstrated through comprehensive results obtained in this study. Robustness tests are conducted, comparing the real behavior observed through various probes with the reference model, thereby validating the approach.

show abstract

“…In addition, several works were dedicated to the improvement of the performance of this type of rotating machine, such as Djeddi et al (2022), Hadroug et al (2022) and Choayb et al (2021). Also, the operating variables of this type of machine were identified by Aissat et al (2022), Asgari et al (2013) and Bagua et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Gas Turbine Speed Monitoring Using a Generalized Predictive Adaptive Control Algorithm

ALAOUI¹,

Alshammari²,

Iratni³

et al. 2022

STUD INFORM CONTROL

Self Cite

View full text Add to dashboard Cite

The outstanding performance of modern gas turbine power plants requires an efficient and robust control strategy in order to recover the losses caused by the undesirable effects and the instabilities related to their operation. Also, these losses can be the cause of their unstable dynamic behaviour during full-load and partial load operation in the unplanned shutdown state. Hence, choosing the right control strategy can improve the operation of this type of machine by increasing its efficiency by up to 60%. This paper proposes the implementation of an innovative control strategy based on a generalized predictive adaptive control algorithm for monitoring the rotation speed of a Solar Titan 130 gas turbine, with the purpose of considering the constraints related to the nonlinear behavior of a turbine during large variations in machine load. The aim is to automatically adjust the parameters of the regulator of the turbine's control loop in real time, with a recursive estimation of these parameters. Using the experimental input/output measurements in order to study its behavior, by integrating the predictive control estimators, ensures the superior performance of the analyzed gas turbine in terms of energy, efficiency and robustness regarding the parametric uncertainties and the variation of the turbine load.

show abstract

Fuzzy decoupled-states multi-model identification of gas turbine operating variables through the use of their operating data

Cited by 11 publications

References 27 publications

A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control

A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control

ARMAX-based identification and diagnosis of vibration behavior of gas turbine bearings

Gas Turbine Speed Monitoring Using a Generalized Predictive Adaptive Control Algorithm

Contact Info

Product

Resources

About