A Sequential Approach for Gas Turbine Power Plant Preventative Maintenance Scheduling

2005 ◽  
Vol 128 (4) ◽  
pp. 796-805 ◽  
Author(s):  
Yongjun Zhao ◽  
Vitali Volovoi ◽  
Mark Waters ◽  
Dimitri Mavris
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Preventive Maintenance ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Maintenance Scheduling ◽  
Plant Performance ◽  
Optimization Approach ◽  
Sequential Approach ◽  
Gas Turbine Power Plant

Traditionally, gas turbine power plant preventive maintenance schedules are set with constant intervals based on recommendations from the equipment suppliers. Preventive maintenance is based on fleet-wide experience as a guideline as long as individual unit experience is not available. In reality, the operating conditions for each gas turbine may vary from site to site and from unit to unit. Furthermore, the gas turbine is a repairable deteriorating system, and preventive maintenance usually restores only part of its performance. This suggests a gas turbine needs more frequent inspection and maintenance as it ages. A unit-specific sequential preventive maintenance approach is therefore needed for gas turbine power plant preventive maintenance scheduling. Traditionally, the optimization criteria for preventive maintenance scheduling is usually cost based. However, in the deregulated electric power market, a profit-based optimization approach is expected to be more effective than the cost-based approach. In such an approach, power plant performance, reliability, and the market dynamics are considered in a joint fashion. In this paper, a novel idea that economic factors drive maintenance frequency and expense to more frequent repairs and greater expense as equipment ages is introduced, and a profit-based unit-specific sequential preventive maintenance scheduling methodology is developed. To demonstrate the feasibility of the proposed approach, a conceptual level study is performed using a base load combined cycle power plant with a single gas turbine unit.

A Sequential Approach for Gas Turbine Power Plant Preventive Maintenance Scheduling

2005 ◽  
Author(s):  
Yongjun Zhao ◽  
Vitali Volovoi ◽  
Mark Waters ◽  
Dimitri Mavris
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Preventive Maintenance ◽  
Gas Turbines ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Maintenance Scheduling ◽  
Plant Performance ◽  
Optimization Approach ◽  
Gas Turbine Power Plant

Traditionally the gas turbine power plant preventive maintenances are scheduled with constant maintenance intervals based on recommendations from the equipment suppliers. The preventive maintenances are based on fleet wide experiences, and they are scheduled in a one-size-fit-all fashion. However, in reality, the operating conditions for each gas turbine may vary from site to site, and from unit to unit. Furthermore, the gas turbine is a repairable deteriorating system, and preventive maintenance usually restores only part of its performance. This suggests the gas turbines need more frequent inspection and maintenance as it ages. A unit specific sequential preventive maintenance approach is therefore needed for gas turbine power plants preventive maintenance scheduling. Traditionally the optimization criteria for preventive maintenance scheduling is usually cost based. In the deregulated electric power market, a profit based optimization approach is expected to be more effective than the cost based approach. In such an approach, power plant performance, reliability, and the market dynamics are considered in a joint fashion. In this paper, a novel idea that economics drive maintenance expense and frequency to more frequent repairs and greater expense as the equipment and components age is introduced, and a profit based unit specific sequential preventive maintenance scheduling methodology is developed. To demonstrate the feasibility of the proposed approach, this methodology is implemented using a base load combined cycle power plant with single gas turbine unit.

Bayesian Calibration for Power Splitting in Single-Shaft Combined Cycle Plant Diagnostics

2015 ◽  
Vol 138 (5) ◽  
Author(s):  
Xiaomo Jiang ◽  
TsungPo Lin ◽  
Eduardo Mendoza
Keyword(s):  
Bayesian Inference ◽  
Power Plant ◽  
Gas Turbine ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Plant Performance ◽  
Sensor Data ◽  
Power Splitting ◽  
Plant Level ◽  
Operational Data

Condition monitoring and diagnostics of a combined cycle gas turbine (CCGT) power plant has become an important tool to improve its availability, reliability, and performance. However, there are two major challenges in the diagnostics of performance degradation and anomaly in a single-shaft combined cycle (CC) power plant. First, since the gas turbine (GT) and steam turbine (ST) in such a plant share a common generator, each turbine's contribution to the total plant power output is not directly measured, but must be accurately estimated to identify the possible causes of plant level degradation. Second, multivariate operational data instrumented from a power plant need to be used in the plant model calibration, power splitting, and degradation diagnostics. Sensor data always contain some degree of uncertainty. This adds to the difficulty of both estimation of GT to ST power split (PS) and degradation diagnostics. This paper presents an integrated probabilistic methodology for accurate power splitting and the degradation diagnostics of a single-shaft CC plant, accounting for uncertainties in the measured data. The method integrates the Bayesian inference approach, thermodynamic physics modeling, and sensed operational data seamlessly. The physics-based thermodynamic heat balance model is first established to model the power plant components and their thermodynamic relationships. The model is calibrated to model the plant performance at the design conditions of its main components. The calibrated model is then employed to simulate the plant performance at various operating conditions. A Bayesian inference method is next developed to determine the PS between the GT and the ST by comparing the measured and expected power outputs at different operation conditions, considering uncertainties in multiple measured variables. The calibrated model and calculated PS are further applied to pinpoint the possible causes at individual components resulting in the plant level degradation. The proposed methodology is demonstrated using operational data from a real-world single-shaft CC power plant with a known degradation issue. This study provides an effective probabilistic methodology to accurately split the power for degradation diagnostics of a single-shaft CC plant, addressing the uncertainties in multiple measured variables.

scholarly journals Thermodynamic and Economic Evaluation of Gas Turbine Power Plants

2020 ◽  
Vol 7 (1) ◽  
pp. G1-G8
Author(s):  
T. Oyegoke ◽  
I. I. Akanji ◽  
O. O. Ajayi ◽  
E. A. Obajulu ◽  
A. O. Abemi
Keyword(s):  
Power Plant ◽  
Combustion Chamber ◽  
Gas Turbine ◽  
Payback Period ◽  
Operating Conditions ◽  
Plant Performance ◽  
Second Law ◽  
Profitability Analysis ◽  
Second Law Analysis ◽  
Gas Turbine Power Plant

Thermodynamic analysis and economic feasibility of a gas turbine power plant using a theoretical approach are studied here. The operating conditions of Afam Gas Power Plant, Nigeria are utilized. A modern gas turbine power plant is composed of three key components which are the compressor, combustion chamber, and turbine. The plants were analyzed in different control volumes, and plant performance was estimated by component-wise modeling. Mass and energy conservation laws were applied to each component, and a complete energy balance conducted for each component. The lost energy was calculated for each control volume, and cumulative performance indices such as thermal efficiency and power output were also calculated. The profitability of the proposed project was analyzed using the Return on Investment (ROI), Net Present Worth (NPW), Payback Period (PBP), and Internal Rate of Return (IRR). First law analysis reveals that 0.9 % of the energy supplied to the compressor was lost while 99.1 % was adequately utilized. 7.0 % energy was generated within the Combustion Chamber as a result of the combustion reaction, while 33.2 % of the energy input to the Gas Turbine was lost, and 66.8 % was adequately converted to shaft work which drives both compressor and electric generator. Second law analysis shows that the combustion chamber unit recorded lost work of 248.27 MW (56.1 % of the summation), and 77.33 MW (17.5 % of the summation) for Gas Turbine, while air compressor recorded 11.8 MW (2.7 %). Profitability analysis shows that the investment criteria are sensitive to change in the price of natural gas. Selling electricity at the current price set by the Nigerian Electricity Regulation Commission (NERC) at zero subsidies and an exchange rate of 365 NGN/kWh is not profitable, as the analysis of the investment gave an infinite payback period. The investment becomes profitable only at a 45 % subsidy regime. Keywords: energy conversion system, gas turbine, economic analysis, second law analysis, power plant.

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2021 ◽  
Vol 143 (3) ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract Conventional physics-based or experimental-based approaches for gas turbine combustion tuning are time consuming and cost intensive. Recent advances in data analytics provide an alternative method. In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor vibrational acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

Performance Analysis of a Combined Cycle Power Plant Through Exergetic and Environmental Indices

2017 ◽  
Author(s):  
Edgar Vicente Torres González ◽  
Raúl Lugo Leyte ◽  
Martín Salazar Pereyra ◽  
Helen Denise Lugo Méndez ◽  
Miguel Toledo Velázquez ◽  
...  
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Combined Cycle ◽  
Environmental Indices ◽  
Combined Cycle Power Plant ◽  
Combustion Gases ◽  
Combined Cycle Plant ◽  
Exergetic Analysis ◽  
Gas Turbine Power Plant

In this paper is carried out a comparison between a gas turbine power plant and a combined cycle power plant through exergetic and environmental indices in order to determine performance and sustainability aspects of a gas turbine and combined cycle plant. First of all, an exergetic analysis of the gas turbine and the combined is carried out then the exergetic and environmental indices are calculated for the gas turbine (case A) and the combined cycle (case B). The exergetic indices are exergetic efficiency, waste exergy ratio, exergy destruction factor, recoverable exergy ratio, environmental effect factor and exergetic sustainability. Besides, the environmental indices are global warming, smog formation and acid rain indices. In the case A, the two gas turbines generate 278.4 MW; whereas 415.19 MW of electricity power is generated by the combined cycle (case B). The results show that exergetic sustainability index for cases A and B are 0.02888 and 0.1058 respectively. The steam turbine cycle improves the overall efficiency, as well as, the reviewed exergetic indexes. Besides, the environmental indices of the gas turbines (case A) are lower than the combined cycle environmental indices (case B), since the combustion gases are only generated in the combustion chamber.

scholarly journals Operation and Test of a New 37 MW Gas Turbine Package Power Plant

1980 ◽  
Author(s):  
J. Jermanok ◽  
R. E. Keith ◽  
E. F. Backhaus
Keyword(s):  
Power Generation ◽  
Power Plant ◽  
Electric Power ◽  
Gas Turbine ◽  
Combined Cycle ◽  
Simple Cycle ◽  
Initial Operation ◽  
Design Features ◽  
Electric Power Generation ◽  
Gas Turbine Power Plant

A new 37-MW, single-shaft gas turbine power plant has been designed for electric power generation, for use in either simple-cycle or combined-cycle applications. This paper describes the design features, instrumentation, installation, test, and initial operation.

Modeling and Performance Analysis of Combined-Cycle Based Ship Power Plant

2010 ◽  
Vol 44-47 ◽  
pp. 1240-1245 ◽  
Author(s):  
Hong Zeng ◽  
Xiao Ling Zhao ◽  
Jun Dong Zhang
Keyword(s):  
Diesel Engine ◽  
Power Plant ◽  
Performance Analysis ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Fuel Oil ◽  
Plant Performance ◽  
Oil Consumption ◽  
Combined Cycle Power Plant ◽  
Simulation Performance

For combined-cycle power plant performance analysis, a ship power plant mathematical model is developed, including diesel engine, controllable pitch propeller, exhaust gas boiler, turbine generator and shaft generator models. The simulation performance characteristic curves of diesel engine under various loads are given. Comparison of simulation results and experimental data shows the model can well predict the performance of diesel engine in various operating conditions. The specific fuel oil consumption contours of combined-cycle power plant and the relations between engine operating conditions and steam cycle parameters are given. The influence of diesel engine operating conditions to the overall performance of combined-cycle power plant is discussed.

Thermo-Economic Assessment Under Electrical Market Uncertainties of a Combined Cycle Gas Turbine Integrated With a Flue Gas-Condensing Heat Pump

2020 ◽  
Author(s):  
Alberto Vannoni ◽  
Andrea Giugno ◽  
Alessandro Sorce
Keyword(s):  
Power Plant ◽  
Power Systems ◽  
Gas Turbine ◽  
Heat Pump ◽  
Flue Gas ◽  
Power Plants ◽  
Greenhouse Gas Emission ◽  
Capital Expenditure ◽  
Combined Cycle ◽  
Gas Turbine Power Plant

Abstract Renewable energy penetration is growing, due to the target of greenhouse-gas-emission reduction, even though fossil fuel-based technologies are still necessary in the current energy market scenario to provide reliable back-up power to stabilize the grid. Nevertheless, currently, an investment in such a kind of power plant might not be profitable enough, since some energy policies have led to a general decrease of both the average price of electricity and its variability; moreover, in several countries negative prices are reached on some sunny or windy days. Within this context, Combined Heat and Power systems appear not just as a fuel-efficient way to fulfill local thermal demand, but also as a sustainable way to maintain installed capacity able to support electricity grid reliability. Innovative solutions to increase both the efficiency and flexibility of those power plants, as well as careful evaluations of the economic context, are essential to ensure the sustainability of the economic investment in a fast-paced changing energy field. This study aims to evaluate the economic viability and environmental impact of an integrated solution of a cogenerative combined cycle gas turbine power plant with a flue gas condensing heat pump. Considering capital expenditure, heat demand, electricity price and its fluctuations during the whole system life, the sustainability of the investment is evaluated taking into account the uncertainties of economic scenarios and benchmarked against the integration of a cogenerative combined cycle gas turbine power plant with a Heat-Only Boiler.

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