A NEW ALGORITHM FOR SCHEDULING CONDITION-BASED MAINTENANCE OF GAS TURBINES

Date

2015-06-19

Author

Yilmaz, Yavuz
Kurz, Rainer
Ozmen, Ayse
Weber, Gerhard Wilhelm

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

145
views

0
downloads

In developed electricity markets, the deregulation boosted competition among companies participating in the electricity market. Therefore, the enhanced reliability and availability of gas turbine systems is an industry obligation. Not only providing the available power with minimum operation and maintenance costs, but also guaranteeing high efficiency are additional requisites and efficiency loss of the power plants leads to a loss of money for the electricity generation companies. Multivariate Adaptive Regression Spline (MARS) is a modern methodology of statistical learning, data mining and estimation theory that is significant in both regression and classification is a form of flexible non-parametric regression analysis capable of modeling complex data. In this study, single shaft, 6MW class industrial gas turbines located at various sites have been monitored. The performance monitoring of a gas turbine consisted of hourly measurements of various input variables over an extended period of time. Using such measurements, predictive models for gas turbine heat rate and the gas turbine axial compressor discharge pressure values have been generated. The measured values have been compared with the values obtained as a result of the MARS models. The MARS-based models are obtained with the combination of gas turbine performance input and target variables and the complementary meteorological data. The results are presented, discussed, and conclusions are drawn for modern energy and cost efficient gas turbine and power plant maintenance management as the outcomes of this study.

URI

https://hdl.handle.net/11511/52603

Conference Name

ASME Turbo Expo: Turbine Technical Conference and Exposition

Collections

Graduate School of Applied Mathematics, Conference / Seminar

Suggestions

OpenMETU
Core

A soft computing approach to projecting locational marginal price Nwulu, Nnamdi I.; Fahrioglu, Murat (2013-05-01) The increased deregulation of electricity markets in most nations of the world in recent years has made it imperative that electricity utilities design accurate and efficient mechanisms for determining locational marginal price (LMP) in power systems. This paper presents a comparison of two soft computing-based schemes: Artificial neural networks and support vector machines for the projection of LMP. Our system has useful power system parameters as inputs and the LMP as output. Experimental results obtained...
A panel data analysis on the costs of Turkish electricity distribution companies Sirin, Selahattin Murat (2017-12-01) Turkey initiated electricity market reforms in 2001, and privatization of distribution companies was one of the main pillars of the electricity market reform. However, many problems have been encountered during the reform process, and both the regulator and the regulated parties are still struggling with major issues such as high loss ratio, financial soundness of the companies, and service quality problems. Moreover, there are concerns about the financial sustainability of these companies due to the recent...
A Cost-Based Approach to Elicit Ancillary Service Tariffs from Unified Capacity and Energy Tariffs Sezer, Hikmet; Güven, Ali Nezih; Tör, Osman Bülent; Cebeci, Mahmut Erkut; Teimourzadeh, Saeed (2022-01-01) Establishing separated markets for capacity, energy, and ancillary services (AS) is one of the key steps to liberalize electricity sector and realize electricity markets. This study aims at eliciting AS tariffs from unified capacity and energy tariffs through a cost-based approach. In the proposed framework, maintaining annual revenue requirements of the power plants is considered as the main objective while separating the AS tariff from unified tariffs. The conducted study considers two types of unified ta...
Incorporation of DSSC in real time congestion management Günay, Ramazan; Göl, Murat; Department of Electrical and Electronics Engineering (2018) Congestion became an inseparable part of power system operation, after deregulation of the monopolistic electric market. Presence of congestion causes rise of local market powers, and hence it is avoided during day-ahead planning. However, it is possible to encounter congestion during real-time operation because of the uncertain behavior of the loads. Although, the congestion can be detected in real-time, its management is not trivial as it requires change of topology or generation dispatch. This work propo...
Application of a Hybrid Machine Learning model on short term electricty demand prediction Assar, Ahmed Khaled Ahmed Farouk; Fahrioğlu, Murat; Sustainable Environment and Energy Systems (2022-2) Electricity demand forecasting is an important procedure in the electricity market and plays a great role in assuring a sustainable and efficient operation chain. By accurately forecasting the demand, one can see a considerable reduction in production costs as well as saving energy resources. Therefore, optimizing the demand forecasting techniques became an inseparable goal of power economics, leading to the introduction of machine learning to this sector that proved to be superior to other pre-defined alte...

Citation Formats

Y. Yilmaz, R. Kurz, A. Ozmen, and G. W. Weber, “A NEW ALGORITHM FOR SCHEDULING CONDITION-BASED MAINTENANCE OF GAS TURBINES,” presented at the ASME Turbo Expo: Turbine Technical Conference and Exposition, Montreal, CANADA, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52603.