A Dynamic Performance Model for Hybrid Wind/Gas Power Plants

The scope of this chapter is to assess the performance of hybrid power plants and more specifically demonstrate the challenges of partnering the wind turbines with gas turbines. A dynamic engine model of a gas turbine along with a wind turbine model is developed to simulate plethora of scenarios for optimizing their operation in terms of efficiency, fuel consumption and NOx emissions. Moreover, a comparison between the hybrid power plant and a twin gas turbine power plant is carried out to assess the improvement in both NOx emissions and fuel consumption. The results demonstrate and illustrate the significant impact that dynamic performance modeling has in the optimization and controller design of hybrid power plant

Performance assessment of classical and fractional controllers for transient operation of gas turbine engines

The nonlinear behaviour of gas turbine engines has motivated the development of advanced controllers for ensuring their safe and reliable operation. In this paper, the problem of controller design for a two-shaft industrial gas turbine is addressed. Specifically, a transient dynamic engine model has been developed in MATLAB/Simulink for assessing the performance behavior of the engine. Observed engine behavior during transient manoeuvres has enabled the development of a PI controller capable of ensuring a smooth gas turbine operation. The performance of the gas turbine engine implementing the developed PI controller has been also compared to a fractional PI controller. Results demonstrate and illustrate the remarkable impact that transient engine simulation has in the development of robust controller

Derivative-driven window-based regression method for gas turbine performance prognostics

The domination of gas turbines in the energy arena is facing many challenges from environmental regulations and the plethora of renewable energy sources. The gas turbine has to operate under demand-driven modes and its components consume their useful life faster than the engines of the base-load operation era. As a result the diagnostics and prognostics tools should be further developed to cope with the above operation modes and improve the condition based maintenance (CBM). In this study, we present a derivative-driven diagnostic pattern analysis method for estimating the performance of gas turbines under dynamic conditions. A real time model-based tuner is implemented through a dynamic engine model built in Matlab/Simulink for diagnostics. The nonlinear diagnostic pattern is then partitioned into data-windows. These are the outcome of a data analysis based on the second order derivative which corresponds to the acceleration of degradation. Linear regression is implemented to locally fit the detected deviations and predict the engine behavior. The accuracy of the proposed method is assessed through comparison between the predicted and actual degradation by the remaining useful life (RUL) metric. The results demonstrate and illustrate an improved accuracy of our proposed methodology for prognostics of gas turbines under dynamic modes. © 2017 Elsevier Lt

A component map tuning method for performance prediction and diagnostics of gas turbine compressors

In this paper, a novel compressor map tuning method is developed with the primary objective of improving the accuracy and fidelity of gas turbine engine models for performance prediction and diagnostics. A new compressor map fitting and modeling method is introduced to simultaneously determine the best elliptical curves to a set of compressor map data. The coefficients that determine the shape of the compressor map curves are analyzed and tuned through a multi-objective optimization scheme in order to simultaneously match multiple sets of engine performance measurements. The component map tuning method, that is developed in the object oriented Matlab Simulink environment, is implemented in a dynamic gas turbine engine model and tested in off-design steady state and transient as well as degraded operating conditions. The results provided demonstrate and illustrate the capabilities of our proposed method in refining existing engine performance models to different modes of the gas turbine operation. In addition, the excellent agreement between the injected and the predicted degradation of the engine model demonstrates the potential of the proposed methodology for gas turbine diagnostics. The proposed method can be integrated with the performance-based tools for improved condition monitoring and diagnostics of gas turbine power plants. © 2014 Elsevier Ltd

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

A dynamic prognosis scheme for flexible operation of gas turbines

The increase in energy demand has led to expansion of renewable energy sources and their integration into a more diverse energy mix. Consequently the operation of thermal power plants, which are spearheaded by the gas turbine technology, has been affected. Gas turbines are now required to operate more flexible in grid supporting modes that include part-load and transient operations. Therefore, condition based maintenance should encapsulate this recent shift in the gas turbine's role by taking into account dynamic operating conditions for diagnostic and prognostic purposes. In this paper, a novel scheme for performance-based prognostics of industrial gas turbines operating under dynamic conditions is proposed and developed. The concept of performance adaptation is introduced and implemented through a dynamic engine model that is developed in Matlab/Simulink environment for diagnosing and prognosing the health of gas turbine components. Our proposed scheme is tested under variable ambient conditions corresponding to dynamic operational modes of the gas turbine for estimating and predicting multiple component degradations. The diagnosis task developed is based on an adaptive method and is performed in a sliding window-based manner. A regression-based method is then implemented to locally represent the diagnostic information for subsequently forecasting the performance behavior of the engine. The accuracy of the proposed prognosis scheme is evaluated through the Probability Density Function (PDF) and the Remaining Useful Life (RUL) metrics. The results demonstrate a promising prospect of our proposed methodology for detecting and predicting accurately and efficiently the performance of gas turbine components as they degrade over time. © 2015 Elsevier Ltd

Performance adaptation of gas turbines for power generation applications

One of the greatest challenges that the world is facing is that of providing everyone access to safe and clean energy supplies. Since the liberalization of the electricity market in the UK during the 1990s many combined cycle gas turbine (CCGT) power plants have been developed as these plants are more energy efficient and friendlier to the environment. The core component in a combined cycle plant is the gas turbine. In this project the MEA’s Pulrose Power Station CCGT plant is under investigation. This plant cronsists of two aeroderivative LM2500+ gas turbines of General Electric for producing a total of 84MW power in a combined cycle configuration. Cont/d.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Non-linear model calibration for off-design performance prediction of gas turbines with experimental data

One of the key challenges of the gas turbine community is to empower the condition based maintenance with simulation, diagnostic and prognostic tools which improve the reliability and availability of the engines. Within this context, the inverse adaptive modelling methods have generated much attention for their capability to tune engine models for matching experimental test data and/or simulation data. In this study, an integrated performance adaptation system for estimating the steady-state off-design performance of gas turbines is presented. In the system, a novel method for compressor map generation and a genetic algorithm-based method for engine off-design performance adaptation are introduced. The methods are integrated into PYTHIA gas turbine simulation software, developed at Cranfield University and tested with experimental data of an aero derivative gas turbine. The results demonstrate the promising capabilities of the proposed system for accurate prediction of the gas turbine performance. This is achieved by matching simultaneously a set of multiple off-design operating points. It is proven that the proposed methods and the system have the capability to progressively update and refine gas turbine performance models with improved accuracy, which is crucial for model-based gas path diagnostics and prognostics

A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions

At present, aero engine fault diagnosis is mainly based on the steady-state condition at the cruise phase, and the gas path parameters in the entire flight process are not effectively used. At the same time, high quality steady-state monitoring measurements are not always available and as a result the accuracy of diagnosis might be affected. There is a recognized need for real-time performance diagnosis of aero engines operating under transient conditions, which can improve their condition-based maintenance. Recent studies have demonstrated the capability of the sequential model-based diagnostic method to predict accurately and efficiently the degradation of industrial gas turbines under steady-state conditions. Nevertheless, incorporating real-time data for fault detection of aero engines that operate in dynamic conditions is a more challenging task. The primary objective of this study is to investigate the performance of the sequential diagnostic method when it is applied to aero engines that operate under transient conditions while there is a variation in the bypass ratio and the heat soakage effects are taken into consideration. This study provides a novel approach for quantifying component degradation, such as fouling and erosion, by using an adapted version of the sequential diagnostic method. The research presented here confirms that the proposed method could be applied to aero engine fault diagnosis under both steady-state and dynamic conditions in real-time. In addition, the economic impact of engine degradation on fuel cost and payload revenue is evaluated when the engine under investigation is using hydrogen. The proposed method demonstrated promising diagnostic results where the maximum prediction errors for steady state and transient conditions are less than 0.006% and 0.016%, respectively. The comparison of the proposed method to a benchmark diagnostic method revealed a 15% improvement in accuracy which can have great benefit when considering that the cost attributed to degradation can reach up to $702,585 for 6000 flight cycles of a hydrogen powered aircraft fleet. This study provides an opportunity to improve our understanding of aero engine fault diagnosis in order to improve engine reliability, availability, and efficiency by online health monitoring

Techno-economic evaluation and optimization of CCGT power plant: a multi-criteria decision support system

A key objective of the power generation industry is to achieve maximum economic benefit without over-consuming the life of power plants and over-maintaining its assets. From a CCGT power plant operator’s perspective, the stand-alone performance analysis of the plant is not enough to support the decision-making process due to the plethora of possible scenarios characterized by variable ambient conditions, engine health (fouling, erosion), electricity prices, and power demand. This study proposes a novel methodology to support decision-making for a CCGT power plant’s operational optimization. The comprehensive techno-economic performance evaluation is conducted by multidisciplinary optimization and decision-making to enhance information integration for the combined cycle power plant operated by Manx Utilities in the Isle of Man, UK. The decision support system has the capability to recommend the optimal operation schedules to plant operators. It recommends that the more severely degraded engine should run at a relatively lower power setting to decrease creep life consumption. The established power plant optimization framework has the potential to assist power plant operators in deciding the total power output and power split between gas turbines based on optimization results that considers both immediate thermo-economic benefits and life consumption. Finally, the proposed system can facilitate similar power plants to adjust daily operations to achieve thermo-economic and lifing benefit