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This research project aims to improve a power plant maintenance program by using the theory of RAM (Reliability Availability and Maintainability). A North Bangkok Combined Cycle Power Plant, especially a steam turbines power plant, is selected to conduct research from 2021 to 2022. The steps of this research can be separated into 2 phases as follows; the first phase involves root cause analysis from failure notifications stored in the CMMS-KKS code database and risk prioritization. Unit C10 and the compressed air unit system in steam turbines operation are the critical systems subsequently, the RAM approach is applied to improve a preventive maintenance program of unit C10 by estimating an operation time before failure, system availability, and the ability to repair. RAM information is brought to reschedule a PM program, for example, MTTF, MTTR , failure rate, and repair rate, etc. It is found that the percentage of unit C10 system reliability, R(t) , and availability, A(t) , are higher than 90%. Furthermore, the trade-off between the cost of maintenance and failure for unit C10 is decided by running unit C10 at a percentage of reliability of 88% which can schedule a maintenance interval every 300 hours

Conventional exergy analysis of thermal systems does not fully reveal the inter-component interactions that result in sub-optimal performances nor the full potentials for improving performance. The advanced exergy analysis overcomes these limitations by determining avoidable and unavoidable, endogenous and exogenous exergy destructions in energy system components. This paper reports the advanced exergy analysis of a steam power plant. Exogenous exergy destruction rates were determined using the recent decomposition method, while a second-level splitting of component exergy destruction rates was implemented. More of the exergy destruction rates (83%) were also found to be endogenous, indicating a limited contribution of inter-component interactions to reduced component performances. The contributions of the avoidable endogenous and avoidable exogenous exergy destruction rates to overall plant exergy destructions were 17.1% and 3.6%, respectively. The modified exergy efficiencies in the boiler section were, respectively, 81.8% and 79.57% for the superheater and the reheater. These were obtained by realistically excluding the avoidable exergy destruction portions and were significantly higher than the respective conventional exergy efficiencies (41.08% and 44.86%). The resulting ranking of the plant components (based on the advanced exergy performances) prioritizes the boiler section the most for improvement, while the least prioritized component is the deaerator.

The growing demand for reliable and sustainable energy sources, coupled with concerns over greenhouse gas emissions and fossil fuel depletion, necessitates the development of hybrid renewable energy systems that can ensure energy security, improve efficiency, and reduce environmental impact. This study addresses the need for integrated renewable solutions by investigating the energy performance and economic feasibility of a hybrid system that combines photovoltaic (PV) panels, parabolic trough collectors (PTC), and a lab-scale biomass-fired steam power plant. The primary objective is to optimize system performance while minimizing fuel consumption and operational costs. The proposed system includes a PTC unit, a 4.6 kW PV array, a 6.4 kW biomass-powered DC generator, three 3 kWh batteries, and a 3 kW converter. Energy assessment was conducted through experimental measurements supported by simulation and optimization using HOMER and PVsyst software. Results show that the integration of PV panels reduced biomass fuel consumption by approximately 70%, leading to a 50% reduction in operational costs over a 10-year period. The system achieved a favorable payback period of just 2.2 years. These findings highlight the viability of hybrid PV–PTC–biomass systems as a sustainable and cost-effective solution for clean energy generation in decentralized or off-grid applications.

Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model’s effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models.

Matters concerned with the tenacity of thermal power plants still remain of issue for the power industry of Russia. In view of power capacities concentrated within the boundaries of a single power plant, various off-design situations (accidents) occur in it, and the likelihood of such situations to occur increases significantly since the tendency toward aging of the existing equipment is still continuing. The situation in which the Surgut GRES-2 thermal power plant’s generating steam power equipment become shut down completely (so-called blackout) considered in the article is one of such contingencies. To cope with system accidents of this sort, relevant possibilities and means must be available. The Surgut GRES-2 power plant consists of two sages: six 800-MW seam power units (SPUs) and two 400-MW combined cycle units (CCUs). The article presents solutions to the problem stated above through interaction of the equipment of the power plant’s two stages. Five possible schemes for starting the SPUs from zero when there is loss of auxiliary steam are considered. The organizational and technical measures necessary for implementing these options are developed. The minimal requirements for an external steam source (flowrate, pressure, and temperature) are determined theoretically and confirmed by tests. Calculations of the CCU’s heat-recovery steam generator (HRSG) are carried out, which confirmed its ability to behave as the external steam source. For two promising options, schemes for the necessary modification of the power units are developed, and an aggregative comparative assessment of the costs for implementing them is carried out.

In this work, a hybrid system consisting of a parabolic trough collector and a steam power plant is proposed. The effect of utilizing the parabolic trough collector on improving the performance of the plant and reducing fuel consumption has been studied experimentally. This study was implemented on a lab scale hybrid energy system consisting of a parabolic trough collector unit incorporated into a biomass-oil shale fired steam power plant during startup conditions. To determine the performance of this lab-scale hybrid system, the efficiency of the parabolic trough collector standalone system has been measured and the flow rate of the system has been tuned to 0.31 L/min to obtain an efficiency of 10.2%. The biomass-oil shale fired power plant worked with superheated steam at 377 °C temperature and 0.6 MPa pressure. The thermal efficiency of the power plant was 12.6% with net output power of 6.3 kW without using the parabolic trough collector unit. It was found that the performance of the hybrid system has shown better efficiency than the standalone biomass fired power plant with the same fuel mixture ratio and steam flowrate. The fuel mixture consumed in the hybrid system decreased by 62.0% at starting up condition. This result may be extended to steady-state operating conditions by increasing the number of parabolic trough collector units utilized. Furthermore, the overall thermal efficiency of the hybrid parabolic trough collector power plant system may reach 33.3% during steady-state operation if 48 parabolic trough collector similar units were used. These parabolic trough collector units should be arranged in three parallel rows, each row of 16 units in series.

Ensuring operational reliability and efficiency in steam power plants requires advanced and generalized fault detection methodologies capable of addressing diverse fault scenarios in boiler and turbine systems. This study presents an autonomous fault detection framework that integrates deep feature extraction through Convolutional Autoencoders (CAEs) with the ensemble machine learning technique, Extreme Gradient Boosting (XGBoost). CAEs autonomously extract meaningful and nonlinear features from raw sensor data, eliminating the need for manual feature engineering. Principal Component Analysis (PCA) is employed for dimensionality reduction, enhancing computational efficiency while retaining critical fault-related information. The refined features are then classified using XGBoost, a robust ensemble learning algorithm, ensuring accurate fault detection. The proposed model is validated through real-world case studies on boiler waterwall tube leakage and motor-driven oil pump failure in steam turbines. Results demonstrate the framework’s ability to generalize across diverse fault types, detect anomalies at an early stage, and minimize operational downtime. This study highlights the transformative potential of combining deep feature extraction and ensemble machine learning for scalable, reliable, and efficient fault detection in power plant operations.

Heuristic optimization is used to find sustainable cogeneration steam power plants with steam reheat and supercritical main steam parameters. Design solutions are analyzed for steam consumer (SC) pressures of 3.6 and 40 bar and a heat flow rate of 40% of the fuel heat flow rate. The objective functions consisted in simultaneous maximization of global and exergetic efficiencies, power-to-heat ratio in full cogeneration mode, and specific investment minimization. For 3.6 bar, the indicators improve with the increase in the ratio between reheating and main steam pressure. The increase in SC pressure worsens the performance indicators. For an SC steam pressure of 40 bar and 9 feed water preheaters, the ratio between reheating and main steam pressure should be over 0.186 for maximum exergetic efficiency and between 0.10 and 0.16 for maximizing both global efficiency and power-to-heat ratio in full cogeneration mode. The average global efficiency for an SC requiring steam at 3.6 bar is 4.4 percentage points higher than in the case with 40 bar, the average specific investment being 10% lower. The Pareto solutions found in this study are useful in the design of sustainable cogeneration supercritical power plants.

The feed water system is one of the most important components of steam power plants because it defines the system’s performance. Quick turnaround time is an important factor in operations that should be achieved to avoid causing interruptions. Maintenance techniques should, therefore, be effectively used to prevent the failure modes that lead to system failure. Specifically, this research aims to implement the Reliability-Centered Maintenance (RCM) 3 on the feed water system to improve its maintenance. The study assesses the important failure modes from failure history and develops new maintenance tasks to address these issues. The following have been integrated upon the application of RCM-3: 15 activities are planned by condition, eight are restored, and two are discarded. These improvements minimize the probability of system failures and, at the same time, improve the reliability factor. This research offers useful knowledge and efficient ways to enhance the maintenance approaches in steam power plants, thus ensuring more reliable operations.

The marine steam power system includes a large amount of thermal equipment; meanwhile, the marine environment is harsh and the working conditions change frequently. Operation management involves many disciplines, such as heat, machinery, control, electricity, etc. It is a complex multi-discipline physical system with typical nonlinear, multi-parameter, strong coupling characteristics. In order to realize the health management of a marine steam power system, based on digital twin technology combined with the Modelica language, modular modeling, etc., this paper conducts in-depth research on the multi-domain modeling of the marine steam power system, characteristic analysis of variable working conditions, fault simulation, etc. The analysis results show that the dynamic response trend of the model is consistent with the actual operation, the error of the main steam flow at 1800 s is the largest and is −4.9%, and the error of the main steam flow, steam turbine output power, cooling water outlet temperature and other key parameters is within ±5%. Virtual reality mapping between the digital model and the physical equipment is realized, which lays a foundation for mastering the dynamic characteristics of the marine steam power system.