Gas Turbine Control Research Articles

The primary frequency modulation control strategy based on fuzzy active disturbance rejection control for gas turbines

Gas turbines play a core role in clean energy supply and construction of comprehensive energy system, and the control performance of primary frequency modulation (PFM) of gas turbine has a great impact on the frequency control of the power grid. However, there are some control difficulties in the PFM control of gas turbines, such as the coupling effect of fuel control loop and speed control loop, slow tracking speed and large variation of operating conditions. To relieve the above difficulties, a control strategy based on the fuzzy-modified active disturbance rejection control (F-MADRC) proposed in this paper. Based on the analyses of the parameter stability region for active disturbance rejection control (ADRC), fuzzy tuning rules of parameters for F-MADRC are designed. Finally, the proposed F-MADRC is applied to the PFM system of MS6001B heavy-duty gas turbine. The simulation results show that the gas turbine unit with the proposed method can obtain the best control performance of the PFM with strong ability to deal with system uncertainties. The proposed method shows good engineering application potential.

Desired Dynamic Equation for Primary Frequency Modulation Control of Gas Turbines

Desired Dynamic Equation for Primary Frequency Modulation Control of Gas Turbines

IFCN-BIASN Based Built-In Test Signal State Recognition for Heavy-Duty Gas Turbine Controller

IFCN-BIASN Based Built-In Test Signal State Recognition for Heavy-Duty Gas Turbine Controller

Закони керування авіаційним газотурбінним двигуном з турбовентиляторною приставкою

The main aspects of the general task of integrating an aviation engine (AE) with a turbofan additional unit (TAU) of a multi-mode aircraft are the selection of the scheme and design parameters of the AE with the TAU (including parameters of the work process), as well as methods and means of automatic control of the AE with the TAU for better matching its characteristics with certain flight modes. These two tasks are closely interrelated: on the one hand, when determining the appearance of the aircraft and its power plant (PP), it is necessary to consider what means will ensure the adaptation of its characteristics to the variable flight mode, and on the other hand, the purpose of the aircraft, its parameters and parameters of the PP, and the flight modes mostly determine the choice of control laws. The subject of the research is the formation of the laws of control of the aviation gas turbine engine with TAU. The goal is to improve the dynamic characteristics of the aviation gas turbine engine by applying adaptive control of the gas turbine engine with the use of wireless information exchange technologies. Objectives: to generalize the concept of adaptive control at the stage of determining the appearance of an engine with TAU; to determine the methods of regulating the GTE with TAU and their influence on the speed characteristics; to describe the process of formation of the laws governing AE with TAU; to investigate thermal processes in order to find functional dependencies in the optimal control of gas turbines with TAU. Research methods: system analysis, mathematical and computer modeling were used in the formation of control laws; the methods of philosophical knowledge were used to build an approach to the design of adaptive control systems of gas turbines with TAU. The theory of aircraft engines, the theory of differential equations, difference grids, and numerical methods were used to optimize the control of gas turbines with TAU. Results: methods of regulating the GTE with TAU and their influence on the speed characteristics of the aviation engine; formulas for researching the thermal processes of the working blades of the GTE with TAU in order to form the influence of the regulator on the executive mechanisms. Scientific and practical novelty: formation of a paradigm for the development of models of adaptive control of GTE with TAU, considering different flight modes of the aircraft and engine operation modes. The selection of control laws using multi-parameter optimization methods for finding the relationship between structural and component schemes of the gas generator and turbofan additional unit. The character of the engine thrust change depending on the input temperature is shown, which in turn, will allow to increase the efficiency of the fan and obtain a thrust reserve. The research directions of the temperature field and stress field were formed.

A novel optimization control of gas turbine based on a hybrid method using the BELBIC and adaptive multi input multi output feedback control

In recent years, the use of Gas Turbines (GTs) to generate electricity has grown exponentially. Therefore, for the optimal performance of gas power plants, a lot of research has been done on modeling different parts of GTs, estimating model parameters, and controlling them. But most of the available methods are not accurate enough, like most linear methods, or are model-based, which require an accurate model of the system (like most nonlinear methods), or there is a constant need to adjust the controller parameters. To address these shortcomings, this study uses a new hybrid method including the brain emotional learning-based intelligent controller, the nonlinear multivariate method in the form of feedback linearization, and an adaptive control method of mode predictive reference model used to quickly control the GT. The Rowen model is used to simulate the nonlinear model of the GT. Owing to the influence of exhaust temperature on the speed of GT and the multivariate system model, nonlinear multivariate controller design is considered. First, the adaptive control method of the state-predictive reference model for a multi-output multi-input system, in general, is presented, and then, the proposed method for a GT with real dynamic values is implemented. The simulation results show the ability of the proposed controller to control the GT. In order to prove the efficiency of the proposed method, the obtained results are compared with the PID industrial controller method and the classical reference model method.

A versatile volume-based modeling technique of distributed local quadratic convergence for aeroengines

For advanced aero-engine design and research, modeling and simulation in a digital environment is indispensable, especially for engines of complicated configurations, such as variable cycle engines (VCE) and adaptive cycle engines (ACE). Also, in the research of future smart engines, reliable real-time digital twins are paramount. However, the 2 dominant methods that used in solving the simulation models, Newton-Raphson (N-R) method and volume-based method, are not fully qualified for the study requirements, because neither of them reaches the satisfactory balance of convergence rate and calculating efficiency. In this study, by deeply analyzing the mathematical principle of these 2 methods, a novel modeling and solving method for aero-engine simulation, which integrates the advantages of both N-R and volume-based methods, is established. It has distributed architecture and local quadratic convergence rate. And a novel modeling method for variable area bypass injectors (VABI) is put forward. These facilitate simulation of various configurations of aero-engines. The modeling cases, including a high bypass-ratio (BPR) turbofan and an ACE, illustrate that the novel technique decreases the iterations by about two-thirds comparing with volume-based method, while the success rate of convergence remains over 99%. This proves its superiority in both convergence and calculating efficiency over the conventional ones. This technique can be used in advanced gas turbine engine design and control strategy optimization, and study of digital twins.

GAS turbine sensor fault diagnostic system in a real-time executable digital-twin

In this study, a sensor fault diagnostic system to detect/isolate and accommodate faults in sensors from an industrial gas turbine has been developed. The sensor fault diagnostic module is integrated with a gas turbine real-time executable digital-twin (RT xDT) reported in a previous study. The sensor fault diagnostic module of the digital-twin considers analytical sensor redundancy using a reference engine model to provide redundant estimates of measured engine variables. A Software-in-the-Loop (S-i-L) architecture and Hardware-in-the-Loop (H-i-L) facility are constructed to assess the sensor diagnostic module (fault detection/ fault isolation) during failure in sensors from the engine. The results demonstrated that if the discrepancy between virtual measurement (provided by digital-twin) and sensor measurement exceeds the prescribed tolerance levels, the sensor fault diagnostic logic determines the state of switching between the virtual and engine sensor measurements in a dual lane control configuration of the gas turbine control system. The sensor fault detection system implemented in the gas turbine RT xDT can be deployed onto a distributed control system of industrial gas turbines to diagnose sensor deficiencies and ensure continuous and safe operation of the gas turbine. Consequently, the developed system will increase engine availability and reliability by diagnosing engine operational deficiencies before severe failure.

Multistate Prediction for In-Service Gas Turbine via Adaptive Diffusion Graph Network

State estimation is integral to the continuous monitoring and management of the energy consumption in gas turbines. Recent research about multistate prediction adopts separate predictions and uses few connections between signals. The complex physical mechanism of the gas turbine makes it difficult to extract and obtain the spatial information between states as prior knowledge, and the spatial information has irregularities as the operating conditions change. To overcome these limitations, a graph structure with a fusion strategy was adopted to solve the irregularity of multisource sensor data, and then the adaptive adjacency-matrix diffusion graph network (AdapDGN) was proposed. First, an adaptive adjacency matrix module learned through a query and key and based only on the input data can capture the temporal trends of spatial information. Second, the diffusion graph recurrent module can remember the history sequences. The two aforementioned modules are combined in a unified framework, and the entire framework is learned end to end. The performance of AdapDGN for multistate prediction is demonstrated on a real-world sensor network of a gas turbine control system under variable working conditions. The results show a 2% improvement in prediction accuracy of the fusion strategy to the separate prediction, and the learned connections between signals under diverse working conditions are also visualized.

Power-heat conversion coordinated control of combined-cycle gas turbine with thermal energy storage in district heating network

Thermal energy storage, with its low energy storage cost and wide distribution in industrial processes, is an effective way to improve the operational flexibility of power plants. Due to colossal energy storage capacity and small deployment costs, this article proposes connecting district heating networks to combined cycle gas turbine (CCGT) plants as a thermal energy storage capacity, improving the flexibility of CCGTs. The main focus here is on developing an appropriate control strategy to effectively control the power-heat conversion, meet the heat and power demands of the connected network, and the operational flexibility of the plant. The major problem is that the intrinsic static and dynamic conversion relationship of power and heat in the CCGT and district heating network and the buildings are multi-factor interactive and unknown. Therefore, the CCGT bottom cycle and district heating network, and building models were built to obtain the power-heat conversion parameters and the dynamic model for control design. Then, the energy storage coefficient of 0.105 MW/kg/s is obtained through the model simulation instead of a complex thermodynamic calculation, corresponding to the 113.22 GJ energy storage capacity of the district heating network. Based on the obtained conversion rules, a new control strategy called ‘conversion coordinated control’ is designed and applied, using load signal decomposition and synergistic load response of flue gas mass flow rate and steam extraction valve. The simulation results show that the proposed method can promote a ramping load rate of 8.6 MW/min in the first 30 s with only 0.3 °C building temperature variation. The control strategy can effectively reduce the gap between the grid demand and CCGT power and ensure grid stability without compromising thermal users’ comfort.

Model predictive control for gas turbine shaft speed based on model compensation using extended state observer

Gas turbine engines have offered advanced power generation with relatively small size on marine vessels. The operational flexibility has high control requirements for shaft speed regulation. Model predictive control provides a methodology for safe and efficient operation of gas turbines. However, when working at partial load, the controller degrades due to the mismatch between predictive model in model predictive control and dynamics of gas turbine. To deal with the issue of model adaptability in model predictive control, model compensation–based model predictive control is proposed for the gas turbine operation. The dynamics of the plant is modified by the extended state observer to get close to the pure integrator. The model predictive control is devised based on the modified plant. The wide-range load regulation and disturbance rejection of gas turbine have been improved by leveraging the benefits of model compensation–based model predictive control.

Fault signal reconstruction for multi-sensors in gas turbine control systems based on prior knowledge from time series representation

Improving efficiency through intelligence is the current development trend of industrial gas turbines. Among the fault statistics of gas turbines, the number of sensor fault is the highest during use. The fault signal diagnosis and reconstruction are of great significance to the efficient and safe operation of gas turbines. In order to eliminate the sensor fault signal and transmit the normal signal to the control system, a multivariate fault signal reconstruction method based on the prior knowledge of the time-series representation was proposed in this work. The proposed multivariate signal reconstruction method can reconstruct almost all fault cases with high accuracy by training only one model. Firstly, the prior knowledge is applied to improve the conventional time series data representation. Secondly, three steps are employed to utilize spatial or temporal information and obtain three intermediate data. The masks combine the third intermediate data and the original time series to obtain the final reconstruction results. Then, reconstruction data sets are built based on exhaust gas temperatures (EGTs) from real-world power plant to verify the effectiveness of the proposed method. Different evaluation metrics and visualization reveal the high accuracy results. Compared results with different reconstruction algorithms reflect both the robustness and high speed of this reconstruction method. Finally, three typical fault signal reconstruction cases reveal the generalizability of this model.

Lightweight methane-air gas turbine controller and simulator

• Lightweight CH4/air gas turbine simulator. • System identification, cycle, and sensitivity analyses CH4/air gas turbines. • Developing and calibrating a CH4/air gas turbine controller. • The potential of utilizing waste heat utilization mechanisms. • Control measures to eliminate turbine overheating. Due to the uncertainties of determining stable, efficient, and safe operating conditions of methane-air gas turbines and the complexity of involving multivariable parameters, the task of controlling and analyzing methane-gas turbines remains a challenge despite the existing methods of control and analyses in the literature. In fact, as far as the methane-air gas turbines are concerned, there is still much room for improvement within the criterion of (computation effort, settle time, its capacity to adjust with a variable range of set-points, as well as its stable, steady, and dynamic outputs can all be enhanced). Furthermore, an accurate system-identification phase has been adopted in this paper to initiate the process of advancing an efficient gas turbine controller by determining the system's sensitivities towards the involved multivariable parameters. Therefore, this paper presents a control and analysis tool for methane-air gas turbines. This tool is a ‘light code (the time to reach the desired set-point (i.e., settle period reaches 1 s)) to serve as a methane-air gas turbine controller that can be used for experimentation or applied on an industrial scale. Moreover, the code is designed with a user interface that enables the simulations and system identification for the methane-fueled gas turbine, thus; ensuring that the governing parameters are calibrated precisely. The code was created using LabVIEW and GASEQ and is driven by conventional gas turbine cycle assessments. A simulation case study has been utilized as a system identification phase to determine the system's sensitivity of temperatures and works at the three primary stages of a simple cycle methane-air gas turbine (compressor, combustion chamber, and turbine) toward the multivariable input parameters of the compression stage (compressor pressure ratio, efficiency, inlet pressure, and temperature), the combustion phase (methane, airflow rates) and the expansion phase (turbine ratio of pressures and isentropic efficiency). Based on the sensitivity analysis performed through the developed code, the turbine output temperature is shown to be very sensitive to the turbine's parameters ( P 04 / P 03 and ε t ). This essentially emphasizes the possibility of including waste heat utilization mechanisms (i.e., heat exchangers, combined cycles) for turbines with high P 04 / P 03 or/and low ε t , resulting in a high turbine outlet temperature. Based on the performed system identification phase and sensitivity analyses, the intervals of the Proportional controller gain ( K c ) , Integral time ( t i ) , and Derivative time ( t d ) have been identified to guarantee a settling period ( T s ) within [1 s-60 s] to achieve the desired temperature set-point which eliminates turbine overheating.

Generalized ESO based stable predictive tracking control for micro gas turbine cogeneration system

Control of micro gas turbine combined heating and power (MGT-CHP), i.e., cogeneration systems, is challenging because of large inertia, strong coupling, strict input constraints, nonlinearity, and complex disturbances. To overcome these problems, this paper develops an extended state observer (ESO) based stable predictive tracking control (SPTC) for MGT-CHP. Unlike traditional ESO-based control methods, ESO-SPTC guarantees overall optimality by using disturbance feedback compensation. A new discrete-time generalized ESO is developed for the ESO-SPTC to surmount higher-order disturbances and its bounded stability is demonstrated. Besides, the designed SPTC fully guarantees that the infinite horizon inputs fully satisfy the amplitude and rate constraints. The resulting ESO-SPTC can eliminate the impact of matched and unmatched disturbances in the output channel at a steady state. Simulation results on a numerical example and an 80 kW MGT-CHP verify the effectiveness of the control scheme.

MoHydroLib: An HMU Library for Gas Turbine Control System with Modelica

Modelica is an open-source, object-oriented equation-based modeling language. It is suitable for describing sophisticated dynamic systems (symmetry/asymmetry) as it uses mathematical acausal equations to express physical characteristics. The hydraulic mechanical units (HMU) of gas turbine engine control systems couple the contents of mechanical, hydraulic, symmetry, and other multidisciplinary fields. This paper focuses on the Modelica description method of those HMU models. The content of this work is threefold: firstly, the division form of basic elements in HMU is defined, and the method for describing these element models with Modelica is proposed; secondly, the organization of the element models is defined by using the inheritance characteristics of Modelica, and a lightweight (small code scale) component model is designed; and finally, the causal/acausal connections are designed according to bond graph theory, and the elements and components are integrated into a prototype modeling library. In this paper, the modeling library is verified by comparing simulation results of five typical HMU subsystem models with commercial modeling and simulation software.

Ammonia-hydrogen-air gas turbine cycle and control analyses

Blending ammonia with hydrogen has the potential of replacing conventional hydrocarbon fuels to reduce carbon emissions. However, the uncertainties of determining safe, stable, and efficient operating conditions remain a great challenge. Furthermore, although control technologies have been in the scope of interest for gas turbine manufacturers, those are not yet specialized for NH 3 –H 2 /air gas turbines. Therefore, this paper identifies the cycle performance of an NH 3 –H 2 /air gas turbine in comparison to a CH 4 /air gas turbine, thus highlighting the operating conditions in which the NH 3 –H 2 /air gas turbines show higher performance compared to the CH 4 /air gas turbines. The results have been obtained by developing a LabVIEW code which has also been utilized to serve as a control code for the NH 3 –H 2 /air and CH 4 /air gas turbines. The system identification stage of calibrating the controller has been achieved for both gas turbines by determining the system's sensitivities, thus providing an accurate calibration of the controlling parameters. • Higher NH3–H2/air gas turbines performance compared to CH4/air gas turbines. • System identification, cycle, and sensitivity analyses of NH3–H2/air gas turbines. • Developing and calibrating an NH3–H2/air gas turbines controller.

A comparative study of single-loop control and multi-loop control of gas turbine

Gas turbine is widely used for generation in power plants and propulsion of ships. The gas turbine is required to operate at different conditions on request. However, its complex dynamics such as multivariable coupling, unknown disturbance and strong nonlinearity, makes it a challenging task to control the gas turbine system. In this paper, a linear offset-free model predictive control algorithm is designed for the gas turbine. Two control modes including single-loop and multi-loop are investigated under the designed control method. Simulations are used to compare the two control mode. From the significant differences, multi-loop mode is preferred because of better performance of reference tracking and disturbance rejection.

Система обработки данных для диагностирования системы управления газотурбинным двигателем

A system for processing diagnostic data of a gas turbine control system is considered. The procedures for obtaining diagnostic signs, identifying the symptoms of faults, and diagnosing the technical state of the control system have been implemented. It is proposed to sequentially complicate the structure of the neural network data processing system to achieve the required quality of classification of the diagnosed object technical state. An example of diagnostics of a model of a gas turbine control system proposed by W. Rowen is given.

Hybrid improved particle swarm optimization-cuckoo search optimized fuzzy PID controller for micro gas turbine

In order to improve the control performance of the micro gas turbine in the full range of operating conditions, this paper proposes a new control technique based on the hybridization of improved particle swarm optimization algorithm and cuckoo search algorithm (HIPSO_CS) to tune the fuzzy PID controller parameters. Firstly, the traditional particle swarm optimization algorithm is improved by linearly decreasing the number of particles and the value of the inertia weight. Secondly, the cuckoo algorithm’s local random walk strategy is introduced into the particle swarm optimization algorithm to enhance particles’ diversity. Through comparing with traditional optimization algorithms, the proposed HIPSO_CS algorithm is verified to have high convergence accuracy and fast iteration speed. To improve the dynamic response performance of the micro gas turbine, controllers with different structures are designed, and a comparative study of HIPSO_CS optimized Fuzzy PID/PID/PI is presented. The simulation results show that the micro gas turbine controlled by the fuzzy PID controller has a rapid response to fuel flow, minor speed overshoot, and shorter stabilization time during load increase or decrease. In addition, the designed control method can also achieve significant control effects under load disturbance, model parameter changes, and extreme operating condition.

Study on Intelligent Control of Gas Turbines for Extending Service Life Based on Reinforcement Learning

Abstract Frequent changes in operating conditions can result in the great loss of the service life of gas turbines that work at high speed, high pressure, and high temperature. To improve the control system of gas turbines is of great significance for extending the service life, boosting the dynamic performance, and reducing the maintenance cost. Due to good dynamic response characteristics, the present control methods are neither capable of tackling the nonlinearity of the system nor adaptive to the frequent variations of operating conditions. As a powerful learning paradigm, reinforcement learning (RL) can explore the operation environment and make decisions adaptively. A novel intelligent control framework for the gas turbine control system is constructed by coupling a RL agent with a dynamic simulation model and a damage estimation model. Compared with the proportion, integral, differential (PID) and fuzzy control, the proposed method achieves better performance in both extending life and depicting dynamic characteristics, and reduces the overall damage to as low as 0.01% in the loading process. Besides, the overshoot and the adjusting time of the novel approach are lower and shorter than those of PID and fuzzy control by more than 90% and about 14%, respectively, but it takes longer to accelerate. Finally, the effect of different types of RL agents and their hyperparameters are investigated. The results show that the deep deterministic policy gradient (DDPG) agent can achieve the best performance. Furthermore, in addition to extending the life and improving the dynamic performance, the controlling framework presented is recommended to construct an intelligent dynamic control system for achieving various purposes.

Bumpless Transfer Control for Gas Turbine

This paper mainly studies a bumpless transfer controller which can be applied to a certain gas turbine. Due to the requirements of low exhaust emission and high combustion efficiency, the gas supply system of the gas turbine is composed of duty fuel, master fuel, and secondary fuel, which inevitably lead to the switching problem in the control system. In order to solve this problem, we have built a three-branch natural gas supply system in the Amesim software and established a gas turbine model in the Simulink software according to its mechanism. In addition, the gas turbine model and the natural gas system model are jointly simulated through the interface. Finally, in accordance with the traditional PID gas turbine controller, an active disturbance rejection controller (ADRC) is designed, which is combined with a bumpless transfer controller including differentiator and integrator to suppress the interference caused by fuel switching. The effectiveness and superiority of this strategy are verified by the complete joint simulations.