Optimal Load Dispatch of Gas Turbine Power Generation Units based on Multiple Population Genetic Algorithm

In this paper, a multiple population genetic algorithm (MPGA) is proposed to solve the problem of optimal load dispatch of gas turbine generation units. By introducing multiple populations on the basis of Standard Genetic Algorithm (SGA), connecting each population through immigrant operator and preserving the best individuals of every generation through elite strategy, MPGA can enhance the efficiency in obtaining the global optimal solution. In this paper, MPGA is applied to optimize the load dispatch of 3×390MW gas turbine units. The results of MPGA calculation are compared with that of equal micro incremental method and AGC instruction. MPGA shows the best performance of optimization under different load conditions. The amount of saved gas consumption in the calculation is up to 2337.45m3N/h, which indicates that the load dispatch optimization of gas turbine units via MPGA approach can be effective.

Hybrid component and configuration model for combined-cycle units in unit commitment problem

This letter proposes a novel hybrid component and configuration model for combined-cycle gas turbines(CCGTs) participating in independent system operator(ISO) markets. The proposed model overcomes the inaccuracy issues in the current configuration-based model while retaining its simple and flexible bidding framework of configuration-based models. The physical limitations—such as minimum online/offline time and ramping rates—are modeled for each component separately, and the cost is calculated with the bidding curves from the configuration modes. This hybrid mode can represent the current dominant bidding model in the unit commitment problem of ISOs while treating the individual components in CCGTs accurately. The commitment status of the individual components is mapped to the unique configuration mode of the CCGTs. The transitions from one configuration mode to another are also modeled. No additional binary variables are added, and numerical case studies demonstrate the effectiveness of this model for CCGT units in the unit commitment problem.

Design point analysis of two-shaft gas turbine engines topped by four-port wave rotors for power generation systems

Wave rotors are rotating equipment designed to exchange energy between high and low enthalpy fluids by means of unsteady pressure waves.In ground power plants,they can be used as topping devices to existing gas turbines aiming to improve their performance characteristics.A four-port wave rotor is an attractive configuration to be integrated into the gas generator of a two-shaft gas turbine,typical for power generation and propulsion systems,by slightly modifying the architecture of existing engines.In particular,in the present article the wave rotor-topped engine utilizes the same compressor,combustion chamber and turbine inlet temperature of the baseline engine.Cycle analysis for two-shaft gas turbine engines topped with four-port wave rotors is studied and their performance at design point is compared to the performance of the baseline engines accordingly.It is concluded that important benefits are obtained with respect to the ones of the baseline engines in terms of specific work and specific fuel consumption.Furthermore calculations by varying the pressure ratio within the wave rotor and the pressure losses in the ducts connecting the wave rotor to the engine’s components indicate the effect on performance,for engines with different compressor pressure ratios and turbine inlet temperatures.

工业燃气涡轮发动机变工况性能直接预测（英文）

The modern gas turbine engine has been used in current power generation industry for almost half a century. Gas turbines are designed to operate with the best efficiency during normal operating conditions and at specific operating points. However, the real world is non-optimal and the engine may have to operate at off-design conditions due to load requirements, different ambient temperatures, fuel types, relative humidity and driven equipment speed.Also more and more base-load gas turbines have to work today on partial load too, which can affect the hot gas path condition and life expectancy. At these off-design conditions, gas turbine's efficiency and life deterioration rate might significantly deviate from the design specifications. During a gas turbine's life, power generation providers might need to perform several overhauls or upgrades for their engines. Thus, the off-design performance after the overhaul also might be changed. Prediction of gas turbine's off-design performance is essential to economical operation of power generation equipment. In this paper, an integrated system for complex design and off-design performance prediction(Ax STREAM? Platform) is presented. It allows to predict gas turbine engine's design and off-design performance almost automatically. Each component's performance such as turbine, compressor, combustor and entire secondary flow(cooling) system is directly and simultaneously calculated for every off-design performance request, making possible to build an off-design performance map including cooling system. The example of off-design performance estimation of industrial gas turbine engine is presented. The presented approach provides wide capabilities for optimization of operation modes of industrial gas turbine engines and other complex turbomachinery systems for every specific operation conditions(environment, grid demands and other factors).

A Self-Learning Diagnosis Algorithm Based on Data Clustering

The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.

Assessment of converting approach to efficient design of micro-turboprop engines

The design,manufacture and experiment of a shaft power unit for converting a microturbojet engine to a micro-turboprop in the class of less than 20 kW with the aim of obtaining maximum shaft power were described in this study.For this purpose,a Wren 100 micro-turbojet engine was used as the gas generator,and the specifications of its outflow were measured.The optimal configuration of the inter-stage diffuser,which was an annular S-type diffuser,was selected based on its small total pressure drop and outlet flow uniformity.The power turbine was a single stage axial turbine that was designed based on the fixed nozzle angle assumption without any taper or twist in its stator.The turbine rotor was a bladed disk(Blisk)in which its unique blade profiles were designed based on the Wilson method.Subsequently,the shaft power unit was completed by designing and manufacturing an exhaust complex and gearbox.Finally,the micro-turboprop engine was tested with an overloading propeller.The results show a significant increase in the extracted power,an acceptable efficiency of the power turbine,and a significant reduction in the Specific Fuel Consumption(SFC)compared to other engines that use similar gas generators.