This study examines the lubrication performance of the lubricating oil circuit in the planetary gear reducer of a steam turbine generator, focusing on the effects of oil supply pressure and rotation of the oil circuit. Computational fluid dynamics (CFD) simulations employing the Moving Reference Frame (MRF) method were implemented to simulate the rotational flow field of the oil circuit and analyze lubricant distribution across critical lubrication points. Analysis of the simulation results revealed that the influence of the oil circuit rotation on the lubrication performance is more significant than that of the oil supply pressure. As the rotational speed of the oil circuit increases, the lubricating oil flow rate at the axial oil nozzle of the main oil passage gradually decreases. Specifically, when the rotational speed reaches 750 r/min, the oil flow rate drops to 0 L/min. Experimental validation demonstrates strong agreement with simulations, showing maximum and minimum errors of 10.37% and 5.95%, respectively, confirming methodological reliability. Therefore, although a higher rotational speed of the oil circuit is conducive to the splash lubrication of the reducer, it has a substantial impact on the lubrication performance of the lubricating oil circuit. This establishes rotational speed as a critical design parameter for optimizing planetary gear reducer lubrication systems.

Numerical Investigation of Low Engine Order Excitations on the Last Rotor Blade of Steam Turbines

The lifespan penalties of a steam Turbine’s governing stage rotor blade during the rapid peak shaving of new power system

High-fidelity large eddy simulation of transonic wet steam flows through a steam turbine cascade

A structural optimization method for maximizing power output in multi-stage self-superheated systems.

Effect of superheat on non-equilibrium condensation in nuclear steam turbines

Electro-Thermal-Flow Coupled Analysis of Thermal Ageing Patterns in Steam Turbine Generator Insulation Under Deep Peak Shaving Conditions

Turbine blades are an important and critical component in a steam turbine, to convert thermal energy from pressurized steam into mechanical energy to drive a generator shaft to generate electricity. Generally, steam turbine blades are widely made of martensitic stainless steel (MSS) due to the material’s good mechanical properties at temperatures above 500°C. These experimental aims to study low-alloy MSS steel (LAMSS) and highalloy MSS steel (HAMSS) as steam turbine blade materials. Thus, the operation of a 250 MW steam turbine is referred to as a test parameter to evaluate tensile properties, fracture behavior and microstructure at 25°C, 250°C, 400°C and 550°C. In the preparation of LAMSS materials, the composition of C, Cr, Mn, Mo, Ni, V and W elements is different from HAMSS. Next, the quenching process of LAMSS material is performed at 980°C and tempered at 730°C, while HAMSS is performed at 1020°C and tempered at 700°C with oil and air cooling to study the effect of heat treatment on both materials. The tensile test results for LAMSS and HAMSS materials were studied in detail at 550°C, finding that HAMSS has better tensile properties than LAMSS, with a yield strength of 1531.3 MPa, ultimate tensile strength of 1722 MPa, and 16.2% elongation. After the specimen was analyzed, the fracture surface of LAMSS showed a dimple with 33.8% more ductility, and a rough martensite microstructure compared to HAMSS. With this, a study at a temperature of 550°C was carried out to produce the selection of suitable materials to extend the life of the turbine blades, in addition to increasing efficiency and better performance.

Steam turbines are critical components in power generation systems, requiring precise monitoring and control to ensure operational efficiency, reliability, and safety. The development of advanced Steam Turbine Monitoring Systems (STMS) integrates sensor technology, real-time data acquisition, signal processing, and intelligent diagnostic algorithms to detect performance anomalies, prevent failures, and optimize maintenance schedules. Recent studies highlight the application of vibration analysis, temperature and pressure monitoring, rotational speed tracking, and acoustic emission techniques as key indicators of turbine health. Integration with Industrial Internet of Things (IIoT) platforms and SCADA systems allows realtime remote monitoring, predictive maintenance, and data-driven decision-making. Artificial Intelligence (AI) and Machine Learning (ML) approaches are increasingly employed to analyze large datasets, predict fault conditions, and enhance turbine life-cycle management. Despite significant advances, challenges remain in terms of sensor accuracy, system integration, data security, and standardization. This review consolidates recent research trends, technological advancements, and practical implementations in STMS, identifying research gaps and future directions. The study emphasizes the potential of smart monitoring systems to improve turbine efficiency, reduce downtime, and enable sustainable energy generation, ultimately contributing to the reliability and profitability of power plants.

The purpose of this research is to design and fabricate a Pico Hydro-Steam Turbine that combines steam and rain-catching technologies for power generation, addressing identified issues and implementing modifications to enhance efficiency. The research used an experimental-developmental approach, involving the concept development, testing, and prototyping phases. It was revealed that in the water phase, the turbine requires a flow rate above 0.0000574 m³/s to produce electricity, and in the steam phase, it begins to spin when the pressure exceeds 42.8 PSI. Measured efficiencies reached 94.164% in the water phase and 81.549% in the steam phase at the highest water flow rate and pressure, indicating that the turbine could operate at even higher efficiency. It was also observed that the increased flow rate and pressure enhance turbine performance. Statistical analysis of Trial 1 showed significant differences in the independent variables water flow rate and pressure flow with values of 0.011 and 0.023, respectively, both below the 0.05 significance level. This confirms that these independent variables have an impact on the turbine's performance. The findings are informative for energy companies, utility providers, homeowners, and future researchers, offering insights into optimal operational thresholds. The study recommends the use of rainwater in the water phase, steam powered by solar energy in the steam phase, incorporating control elements, and adding battery storage. This research confirms that integrating water and steam technologies within the Pico Hydro-Steam Turbine for electricity generation is efficient and has the potential for further improvement.

Abstract A novel reactor dynamics methodology for passive autonomous load following (PALF) of small pressurized water reactors (small PWRs) is proposed. This methodology enhances steam generator (SG) modeling by subdividing the heat transfer process into two stages: “primary to metal” and “metal to secondary,” A secondary circuit model based on linear regression captures the steam pressure and turbine power, thus avoiding the need for detailed modeling of turbine or valve dynamics. To enhance the accuracy of the simulation, the methodology incorporates Mann's model and the delay time for coolant flow. The methodology is validated using experimental data from reference studies within a ±5% load disturbance range. The simulated values of indicators such as primary power and coolant temperature are highly consistent with experimental measurements, while the secondary circuit steam pressure and turbine power are estimated with reasonable accuracy. This work also provides new insights for future PALF feasibility studies, such as system reliability issues caused by the reduced heat transfer capacity of SGs. Overall, the proposed method balances accuracy, computational efficiency, and ease of implementation, laying the foundation for a wider range of load regulation scenarios and long-term safety assessments.

This study presents a comparative harmonic analysis of shaft voltage waveforms in large-scale steam turbine generators, emphasizing the influence of excitation system topology and generator design on spectral behavior. Using high-resolution Fast Fourier Transform (FFT) analysis of healthy-state data from five hydrogen-cooled turbo-generators (600–846 MW), this work identifies consistent harmonic patterns and their diagnostic value. Generators with brushless excitation systems exhibit dominant harmonics at 150 Hz (3rd), 250 Hz (5th), and 400 Hz (8th), whereas static-excited units show a 150 Hz (3rd) and 450 Hz (9th) pattern. These findings confirm that excitation architecture, rather than OEM design, governs the shaft voltage harmonic “fingerprint.” The persistent 150 Hz component across all machines serves as a stable indicator of generator condition. The results provide a practical reference for establishing harmonic-based baselines to enhance early fault detection and predictive-maintenance strategies in power station generators. This work contributes new comparative insights linking excitation topology to harmonic behavior, enabling improved condition monitoring across diverse generator fleets. This study establishes harmonic profiles defined as the amplitude, frequency, and relative proportion of key harmonic components in the shaft voltage spectrum obtained via FFT analysis to serve as spectral fingerprints representing the generator’s health condition.

This study introduces a novel dual-model deep learning framework based on Bidirectional Gated Recurrent Units (Bi-GRUs) with the Attention Mechanism to predict intermediate-pressure (IP) turbine casing temperatures in a 370 MW coal-fired power plant under varying operational regimes, including startup, shutdown, and load-following conditions. Accurate temperature prediction is critical, as thermal gradients induce significant stresses in the turbine casing, potentially causing fatigue crack initiation. To mitigate sensor failures, which lead to costly downtime in power generation systems, the proposed soft sensor leverages an extensive dataset collected over one year from Unit 4 of the Opole Power Plant. The dataset is partitioned into shutdown and active regimes to capture distinct thermal dynamics, enhancing model adaptability. The framework employs advanced preprocessing techniques and state detection heuristics to improve prediction robustness. Experimental results show that the dual-model approach outperforms traditional machine learning models (Random Forest Regressor, XGBoost) and single-model deep learning baselines (LSTM, Single Attentive Bi-GRU), achieving a mean squared error (MSE) of 2.97 °C and a mean absolute error (MAE) of 1.07 °C on the test set, while also maintaining low prediction latency suitable for real-time applications. This superior performance stems from a tailored architecture, optimized via Hyperband tuning and a strategic focus on distinct operational regimes. This work advances soft sensing in power systems and provides a practical, real-time solution for stress monitoring and control, particularly as coal plants in Poland face increased cycling demands due to the growth of renewable energy sources, rising from 7% in 2010 to 25% by 2025. The approach holds potential for broader application in industrial settings requiring robust temperature prediction under variable conditions.

With the increasing penetration of renewable energy, the frequency regulation burden on thermal power units is growing significantly. Among them, combined cycle gas turbine (CCGT) units are playing an increasingly important role in grid ancillary services due to their high efficiency and low emissions. This paper investigates coordinated control strategies to improve the auxiliary frequency regulation capability of CCGTs, addressing the limitations of traditional control approaches where gas turbines dominate while steam turbines respond passively. A decentralized model predictive control (MPC) strategy based on rate-limited signal decomposition is proposed to improve auxiliary frequency regulation. First, a dynamic model of the F-class CCGT systems oriented towards control is established. Then, predictive controllers are designed separately for the top and bottom cycles, with control accuracy improved through a fuzzy prediction model, Kalman filtering and state augmentation. Furthermore, a multi-scale decomposition method for AGC (Automatic Generation Control) signals is developed, separating the signals into load-following and high-frequency components, which are allocated to the gas and steam turbines respectively for coordinated response. Comparative simulations with a conventional MPC strategy demonstrate that the proposed method significantly improves power tracking speed, stability, and overshoot control, with the IAE (Integral of Absolute Error) index reduced by 83.7%, showing strong potential for practical engineering applications.

The article presents an analysis of the influence of ejector characteristics and their number on the vacuum in the steam turbine condenser. An analysis of industrial test results for 12 ejector groups of various steam turbine units (STUs) was conducted. Based on this analysis, a new quantity — the ejector group quality indicator K eg — was introduced, which depends on the volumetric and mass flow rates of the ejector group, as well as the amount of air leakage into the vacuum system of the condensing unit. A linear relationship was identified between the ejector group quality indicator K eg and the relative pressure increase in the condenser δ P . The coefficients of the approximating linear equation relating the ejector group quality indicator K eg to the relative pressure increase in the condenser δ P were calculated. It was established that at values of K eg ≈ 2.8 or higher, the pressure in the condenser does not depend on the number of operating ejectors. The ejector group quality indicator K eg was calculated for 33 different operating ejector groups of STUs with factory characteristics under conditions of normal and threefold excess air leakage. It was found that under normal air leakage conditions, about 94% of ejector groups have a K eg value greater than 2.8, indicating a high performance margin. It was shown that when air leakage exceeds the normal value by three times, about 78% of the considered STUs fall into the region with K eg below 2.8. A method is proposed for assessing the technical condition of an ejector without experimental determination of its characteristics on dry air, using the equations of the approximating linear equation relating the ejector group quality indicator K eg to the relative pressure increase in the condenser δ P .

The paper discusses a methodology for analyzing, predicting the condition, and identifying latent defects in steam turbines using parametric diagnostics based on data measured during the operation of a turbine at a thermal power plant. The article presents a diagnostic analysis of a T-110/120-130 turbine. The assessment and prediction of the T‑110/120-130 turbine's condition are based on the results of analyzing measured parameters and calculating a technical condition index using artificial intelligence methods and neural network algorithms. The main emphasis is placed on the principles of building parametric diagnostic systems for auxiliary work as assistants at power stations. The proposed methodology allows for the development of a diagnostic system for the T-110/120-130 turbine with the capability to detect and identify latent defects and predict the turbine's condition over specific time intervals.

The governor valve actuator (GVA), as the actuating mechanism of the steam turbine governing system, directly impacts production safety and economic efficiency. Its highly coupled nature leads to high-dimensional operational data, complex fault modes, and inherent opacity in diagnostic algorithms, posing significant challenges to the real-time performance, reliability, and generalizability of fault diagnosis and early warning tasks. To address these challenges in complex multi-sensor networks, this paper proposes a post-hoc and pseudo-post-hoc predictive maintenance (PPPM) framework leveraging advanced machine learning and SHapley Additive exPlanations, an XAI technology. The PPPM optimizes fault diagnosis and early warning models and provides interpretable attribution analysis to guide predictive maintenance workflows. Experimental results on the GVA fault testing platform prove the effectiveness of the proposed method. For the fault diagnosis and localization task, taking the random forest model as an example, PPPM achieves the optimization of 50% of the measurement points of the sensor network and the attribution analysis of fault localization, which improves the real-time, generality and reliability of the diagnosis model. For the warning task, PPPM carries out sensor network optimization and attribution analysis to improve the pseudo-supervised warning model through the pseudo-supervised learning method. Taking isolated forests as an example, the optimized model improves the W-F1 score by 5.997% and the AUC by 6.942%.

Abstract: Power plants form the backbone of modern infrastructure by providing the electricity essential for residential, industrial, and commercial applications. Their primary function is to convert various forms of energy—whether fossil fuels, nuclear energy, or renewable sources—into electrical energy that can be distributed through the power grid. Power plants can be broadly categorized based on the type of energy source they utilize, including: 1. Fossil fuel-based power plants (natural gas, coal, diesel ,e.g.,) 2. Renewable energy power plants (solar, wind, hydroelectric, e.g.,) 3. Nuclear power plants 4. Combined Cycle Power Plants (CCPP), which integrate gas and steam turbines to enhance efficiency A key challenge facing conventional power plants is achieving a balance between thermal efficiency, environmental sustainability, and economic viability. As global energy demand continues to grow, there is an increasing emphasis on improving the performance of power generation systems through advanced technologies, emissions reduction, and integration of cleaner energy sources. Thus, conducting technical evaluations and performance analyses of power plants is critical for identifying optimal configurations, minimizing fuel consumption, reducing greenhouse gas emissions, and supporting the transition toward sustainable energy systems. Keywords: Gas Power Plant, Simulation, Dwsim, Electric Power, Thermal Efficiency. 1.INTRODUCTION The AL-Mansuriya Gas Power Plant, located in Diyala Governorate – Iraq, is one of the key facilities contributing to the national electric grid. It relies primarily on natural gas as its fuel source. The plant comprises four gas turbine units, each with a design capacity of 181 MW, while the actual output per unit is 151 MW. Accordingly, the total design capacity of the plant is 724 MW, and the current total actual output is 604 MW, Indicating a performance gap that can be improved. At present, the plant operates under a simple gas turbine cycle, a system known for its reliability .But considered low in thermal efficiency compared to modern technologies (IEA, 2021, p. 112) [1].. Due to increasing energy demand, rising fuel costs, and the global trend toward improving energy efficiency and reducing emissions, it has become essential to upgrade the plant. One of the most effective solutions is to convert the system to a combined cycle configuration, by integrating a steam cycle that utilizes the exhaust heat from the gas turbine to generate additional electricity without extra fuel (Horlock, 2003, pp. 45–50; Kehlhofer et al., 2009, pp. 88–92) [2]. As part of this study, four different models have been designed to convert the AL-Mansuriya Gas Power Plant into a combined cycle facility. These models aim to evaluate and analyze the influence of key variables such as steam pressure, inlet temperature, fuel mass flow rate, steam mass flow rate, and pressure ratio (rp) on overall efficiency and power output. The models have been assessed using dynamic simulation tools and thermodynamic analysis to identify the optimal design in terms of performance, efficiency, and sustainability. It is expected that this conversion could increase the plant’s overall thermal efficiency to approximately 50–60%, reduce fuel consumption, and lower carbon emissions (Breeze, 2014, p. 136) [3]. This aligns with the strategic plans of Iraq’s Ministry of Electricity to modernize the energy infrastructure and achieve sustainability in the power sector (Ministry of Electricity – Iraq, 2020, p. 21) [4].Iraq, despite being rich in oil and gas, suffers from chronic electricity shortages. The national grid is comprised of three key segments: generation, transmission, and distribution, all of which face structural, administrative, and technical obstacles. Reports from the (Ministry of Electricity, 2022) [5] . reveal that although installed capacity should theoretically meet demand, actual generation often falls short—especially in the summer months when demand exceeds 34,000 MW, yet production hovers around 20,000 MW (IEA, 2021, p. 10) [6] .As part of this study, four different models have been designed to convert the AL-Mansuriya Gas Power Plant into a combined cycle facility. These models aim to evaluate and analyze the influence of key variables

Flow characteristics and backpressure optimization of low-pressure stages in steam turbines under low flow rate conditions

Efficiency enhancement of heat supply steam turbines