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In this article, an innovative online adaptive high-efficiency control strategy is proposed to improve the power generation efficiency of a marine micro gas turbine under partial load. Firstly, a mathematical model of the micro-gas turbine is established, and a control strategy consisting of an on-board prediction model and an online update model is proposed. To evaluate the performance changes of the gas turbine, we applied deep learning techniques to enhance the extreme learning machine (ELM) algorithm, resulting in the development of a high-precision, high-real-time deep extreme learning machine (DL_ELM) prediction model. This model effectively monitors changes in the gas turbine's performance. Furthermore, an online time-series deep extreme learning machine with a dynamic forgetting factor (DFF_DL_OSELM) model is designed to achieve the real-time tracking of performance variations. When the DL_ELM model detects a gas turbine's performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. To validate the superiority of the proposed control strategy, a comparison is made with traditional high-efficiency control strategies based on polynomial fitting and BP neural networks. The results demonstrate that although all three strategies can achieve efficient operation under constant conditions, traditional strategies fail to identify and adjust to performance changes in real time, leading to decreased control performance and potential engine damage as engine characteristics degrade. In contrast, the proposed online adaptive control strategy dynamically adjusts the speed control plan based on performance degradation, ensuring that the gas turbine operates efficiently while keeping the turbine inlet and exhaust temperatures within safe limits. [ABSTRACT FROM AUTHOR]

To address the challenge of measuring the dynamic characteristic parameters of the gas turbine rotor system affected by hull excitation, a vibration transmission model integrating a ship model slice, test data, and a three-dimensional entity is proposed, based on the two-dimensional slice theory, scaled ship model, and finite element model of the turbine rotor system. The transient dynamic responses of the front and rear bearing points were calculated and analyzed. Vibration response tests with significant wave heights of 0.5 m, 1.25 m, 2.5 m, and 4 m were carried out in the towing tank of the ship model to obtain the dynamic characteristic parameters of the deck position. Techniques including wavelet denoising, Fast Fourier Transform (FFT), and signal resampling were employed to filter out and reconstruct high-frequency noise, overcoming the technical challenges of a high sampling frequency and a low computational efficiency. The experimental data and simulation results were compared and analyzed, validating the accuracy of the vibration transmission model of the turbine rotor system with data and entity integration. By comparing the vibration signal values in the X and Z directions at the front and rear bearing points after vibration transmission, it is evident that the effective values of the vibration signals at the front bearing point are 0.03% to 0.1% greater than those at the rear bearing point. This model provides a theoretical basis and reference for the design of the gas turbine rotor system. [ABSTRACT FROM AUTHOR]

Vibration analysis is crucial for studying rotor dynamics. The gas turbine rotor system is subjected to complex alternating loads during navigation, resulting in vibrations transmitted to the bearings that alter the system's dynamic characteristics. Based on the similarity law of the wave resistance test, a hull model was established. Beam sea and head sea tests were conducted in the towing pool to measure the acceleration response at the key positions. A finite element model of the turbine rotor system was established, and the test data were imported into the model after wavelet noise reduction and resampling to calculate the vibration response at the front and rear bearing points. The vibration responses transmitted to different locations and directions caused by beam sea and head sea conditions were analyzed. A comparison and analysis were conducted on the acceleration responses in various locations and directions under beam sea or head sea conditions. The equivalent von Mises stress distribution of the gas turbine rotor system under beam sea and head sea loads was obtained. The vibration transfer model was verified for accuracy and can be used to quickly analyze the vibration response of bearings under wave load transfer. This study provides a theoretical basis and reference for enhancing the stability of the gas turbine rotor system. [ABSTRACT FROM AUTHOR]

In mini-grids and marine-isolated grids, power generation gas turbines are subjected to rapid start-up, shutdown, and acceleration/deceleration. This sudden load change can pose a significant impact on the power grid, severely affecting the operational characteristics of gas turbines. To understand the dynamic characteristics of the gas turbine in the transitional processes, this testing takes twin-shaft medium-sized power generation gas turbines as the test object, and goes through the process of startup, acceleration, deceleration, acceleration, shutdown in one hour, and repeats this process 40 times continuously. With fuel flow as the control parameter and power turbine outlet temperature and high-pressure turbine speed as the controlled parameters, the parameter response rate of the gas turbine under various transition processes is analyzed and the effect of thermal inertia on the gas turbine mass temperature as well as speed is studied. Research findings: During the transition processes, the gas temperature exhibited an axial gradient distribution in the channel. In both the acceleration and deceleration processes, the working fluid temperature gradually decreased along the flow direction. And thermal inertia posed different extents of impact on the dynamic characteristics of the gas turbine under different transitional processes. In the same transition process, the impacts of thermal inertia on the response speeds of temperature and rotational speed varied. The results of this study help to more accurately predict the operating state of the gas turbine during the transition process and lay the foundation for the dynamic simulation model of the non-adiabatic gas turbine. [ABSTRACT FROM AUTHOR]

Due to high greenhouse gas emissions, countries worldwide are stepping up their emission reduction efforts, and the global demand for new, carbon-free fuels is growing. Ammonia (NH3) fuels are popular due to their high production volume, high energy efficiency, ease of storage and transportation, and increased application in power equipment. However, their physical characteristics (e.g., unstable combustion, slow flame speed, and difficult ignition) limit their use in power equipment. Based on the structural properties of the power equipment, NH3 fuel application and emissions characteristics were analyzed in detail. Combustion of NH3 fuels and reduction measures for NOx emissions (spark plug ignition, compression ignition, and gas turbines) were analyzed from various aspects of operating conditions (e.g., mixed fuel, fuel-to-exhaust ratio, and equivalence ratio), structure and strategy (e.g., number of spark plugs, compression ratio (CR), fuel injection, and ignition mode), and auxiliary combustion techniques (e.g., preheating, humidification, exhaust gas recirculation, and secondary air supply). The performance of various NH3 fuel cell (FC) types was analyzed, with a focus on the maximum power achievable for different electrolyte systems. Additionally, the application and NOx emissions of indirect NH3 FCs were evaluated under flame and catalytic combustion conditions. The system efficiency of providing heat sources by burning pure NH3, anode tail gas, and NH3 decomposition gas was also compared. Based on a comprehensive literature review, the key factors influencing the performance and emissions of NH3-powered equipment were identified. The challenges and limitations of NH3-powered equipment were summarized, and potential strategies for improving efficiency and reducing emissions were proposed. These findings provide valuable insights for the future development and application of NH3 FCs. [ABSTRACT FROM AUTHOR]

Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data are often time-sensitive and have measurement noise. This article proposes an online sequential regularization extreme learning machine algorithm based on the forgetting factor (FOS_RELM) to improve gas turbine identification performance. The proposed FOS_RELM not only retains the advantages of the extreme learning machine algorithm but also enhances the learning effect by rapidly discarding obsolete data during the learning process and improves the anti-interference performance by using the regularization principle. A detailed performance comparison of the FOS_RELM with the extreme learning machine algorithm and regularized extreme learning machine algorithm is carried out in the model identification of a gas turbine. The results show that the FOS_RELM has higher accuracy and better robustness than the extreme learning machine algorithm and regularized extreme learning machine algorithm. All in all, the proposed algorithm provides a candidate technique for modeling actual gas turbine units. [ABSTRACT FROM AUTHOR]

• A PSO-based optimization method for VRCD coordination control parameters of shipboard MVDC system is proposed. • Models of gas turbine and MVDC power system are established, leading to the quantification of evaluation indicators considering gas turbine capacity and HESS configuration. • The simulation results validate the effectiveness of the proposed control strategy and optimization method, contributing to improved transient power flow. Supplying power for large pulsed power load with shipboard MVDC system has continued to be a challenge. Due to limited grid capacity and power ramp rate of prime movers, the high-power load mutation will cause intolerable impact, which is an essential issue to be solved. In this paper, hybrid energy storage system is configured for transient power compensation, and the distributed virtual resistive capacitive droop is introduced to power coordination for MVDC sources. The transient flow evaluation function considering the speed state of the genset and the configuration cost of hybrid energy storage system is defined, models of the gas turbine and MVDC power system are established, and the optimization method of control parameters and system flow based on PSO and model simulation is proposed. The effectiveness of the proposed control strategy and optimization method is verified by MATLAB/Simulink simulations. [ABSTRACT FROM AUTHOR]