Operation Of Power Plants Research Articles

Environmental Impact of Wind Farms

The aim of this article is to analyse the global environmental impact of wind farms, i.e., the effects on human health and the local ecosystem. Compared to conventional energy sources, wind turbines emit significantly fewer greenhouse gases, which helps to mitigate global warming. During the life cycle of a wind farm, 86% of CO2 emissions are generated by the extraction of raw materials and the manufacture of wind turbine components. The water consumption of wind farms is extremely low. In the operational phase, it is 4 L/MWh, and in the life cycle, one water footprint is only 670 L/MWh. However, wind farms occupy a relatively large total area of 0.345 ± 0.224 km2/MW of installed capacity on average. For this reason, wind farms will occupy more than 10% of the land area in some EU countries by 2030. The impact of wind farms on human health is mainly reflected in noise and shadow flicker, which can cause insomnia, headaches and various other problems. Ice flying off the rotor blades is not mentioned as a problem. On a positive note, the use of wind turbines instead of conventionally operated power plants helps to reduce the emission of particulate matter 2.5 microns or less in diameter (PM 2.5), which are a major problem for human health. In addition, the non-carcinogenic toxicity potential of wind turbines for humans over the entire life cycle is one of the lowest for energy plants. Wind farms can have a relatively large impact on the ecological system and biodiversity. The destruction of animal migration routes and habitats, the death of birds and bats in collisions with wind farms and the negative effects of wind farm noise on wildlife are examples of these impacts. The installation of a wind turbine at sea generates a lot of noise, which can have a significant impact on some marine animals. For this reason, planners should include noise mitigation measures when selecting the site for the future wind farm. The end of a wind turbine’s service life is not a major environmental issue. Most components of a wind turbine can be easily recycled and the biggest challenge is the rotor blades due to the composite materials used.

The development of international civil liability legal regime for nuclear damage and their inspiration for domestic law-making

As International Atomic Energy Agency has stated in its Handbook on Nuclear Law, “Even in situations for which the highest standard of safety has been achieved, the occurrence of nuclear accidents cannot be completely excluded.” Therefore, the international legal framework for nuclear damage compensation liability has been evolving since the establishment of Nuclear Energy Agency of Organization for Economic Co-operation and Development (OECD NEA) and International Atomic Energy Agency (IAEA). Over the years, various international treaties have been enacted to address the compensation of nuclear damage and to establish liability regimes for nuclear incidents. To date, these treaties have established a series of legal principles of nuclear damage liability, such as the sole liability principle, the strict liability principle, the financial guarantee principle etc., which have been developing since establishment. This paper offers an overview of the historical development of the principles of these international treaties for nuclear damage liability and thus draws upon both primary and secondary sources, including treaties, official documents, academic literature, and reports by international organizations. Including the legislation study methodology, comparative methodology is also adopted in this paper to analyze the changes and trend of these principles. The paper reveals that the Paris Convention, which was established in 1960, was the first attempt to establish a comprehensive legal regime for nuclear damage liability. Most of the principles of this Convention have been inherited by subsequent international treaties and domestic legislations. With the awareness of protecting public’s rights having been significantly strengthened, the range of compensation has been broader, the matters of immunity from liability for operators of nuclear power plants have been reduced, the limitation of the compensation amount has been higher etc. In conclusion, the international legal regime for nuclear damage liability has been showing a shift from protecting the development of the nuclear industry to a joint protection of both public health and rights and the nuclear industry, which should be paid attention to and deeply learnt by domestic legislators of all states for the establishment and perfection of their domestic legislation in this field.

Adaptive Forecasting of Nuclear Power Plant Operational Status Under Sensor Concept Drift Using a Bridging Distribution Adaptive Network

A large number of sensors are required to collect information during the operation of nuclear power plants to ensure their absolutely safe operation. However, because of the unique nature of nuclear reactions, the physical environment of nuclear power production is prone to changes, leading to concept drift in the data collected by the sensors. Concept drift describes the phenomenon of sample distribution changing over time, which typically negatively impacts the model’s training and inference processes. We found that nongradual distribution changes could be guided by generating transitional intermediary distributions within the distribution, thereby achieving a gradual change process. Based on this, we designed a bridging distribution adaptive network (BDAN), which consisted of identical-depth TDoA (time difference of arrival) homomorphic backbone neural networks on both sides with a latent adaptive bridging module in the middle. By calculating the distribution differences over multiple timesteps, a series of bridge distributions were generated to guide the gradients in the latent space, updating the parameters of the latent adaptive guiding module in a directional manner and enabling the model to perceive nongradual distribution changes in the time domain. Experimental results showed that the BDAN outperformed the previous state-of-the-art benchmark methods by 5.6% in terms of mean squared error in the nuclear power data prediction task under concept drift, achieving the best fault prediction performance.

Matching Analysis of Technical Parameters and Safety Standards for Nuclear Replacement of Coal-Fired Units

In the context of the growing share of renewable energy and the impending decommission of a large number of coal-fired units, nuclear energy is the only green energy that can replace coal power for a stable, clean, efficient, and large-scale power supply. This article compares the differences between coal power and nuclear power in terms of thermal system, thermal cycle, turbine parameters, and safety. It discusses the possibility of replacing the boiler of a coal power plant with nuclear power, that is, replacing the boiler of a coal power plant with a nuclear reactor for generation/heating/cogeneration. For coal-fired units with similar capacity that do not use a reheat cycle (at or below high pressure) and nuclear power units (such as a high-temperature gas-cooled reactor), as well as coal-fired units with a reheat cycle (ultra-high pressure and above) and nuclear power units (such as pressurized water reactors), there are great differences in steam parameters. In terms of steam turbines, the size of nuclear power units is relatively larger, requiring additional dehumidification measures. In addition, the safety factors and management methods considered in the site selection, construction, and operation of nuclear power plants are more stringent and complex, and comprehensive analysis and evaluation are needed in aspects such as waste treatment and accident emergency response. Except for the relevant provisions of the American Society of Mechanical Engineers code for pressure vessels, nuclear power units are not compatible with coal-fired units in terms of safety standards. Therefore, considering comprehensively, it is believed that the scheme of nuclear power replacing coal-fired units for power generation/heating/cogeneration program is not feasible at present.

Protection of Nuclear Power Plants from Fraudulent Items

At present a lot of problems with fraudulent items occurred. They disrupt the safety of industry, power engineering and other objects that are important for the life of human society. The article gives infor-mation on causes of occurrence of fraudulent items in nuclear power plants and shows that protection against fraudulent items needs to perform during the whole supply chain of critical items. In detail, it concentrates to the quality of awarding terms of references, which nuclear power plant operator prepares when it gives order to commercial supplier and of process of take-over control of the deliver commodity. Due to national security, the safety of nuclear facilities is also supervised by national nuclear regulatory body. During our research we processed checklists by which national nuclear regulatory body can reveal risks in awarding the contracts to commercial suppliers and in take-over control, and ensures improve-ment of these processes.

Investigation of Abnormal Level Control Oscillations in a BWR Feedwater System

In the long-term operation of nuclear power plants, the aging of systems, structures, and components can lead to maintenance issues that must be dealt with to maintain cost-effective plant operations. One common issue affecting the currently operated boiling water reactors is the onset of unexpected level oscillations in feedwater heaters. This phenomenon can cause excessive cycling of drain valves and lead to premature failures. In this work, we develop a dynamic model of a set of feedwater heaters to determine the root cause of oscillations observed in an operating plant. Simulation results of various transient scenarios were used to investigate the effects of the controller parameters, boundary conditions, and possible valve and instrument issues. The analysis led to the conclusion that the most likely causes of the observed self-sustained oscillations in the system are the nonlinear behaviors of the drain valve and the level transmitter induced by degraded equipment condition. A partial plug of the pressure line used for level sensing in the system can account for a significant deadtime in the level transmitter, a nonlinear effect shown to induce self-sustained oscillatory behaviors.

Seismic fragility analysis of nuclear containment structure with Bayesian cloud analysis framework

Nuclear containment structure plays a critical role in safe operation of nuclear power plants. Seismic safety of nuclear power plant structures has become a hot issue in nuclear engineering community following Fukushima nuclear accident. Seismic fragility analysis, as a key element in probabilistic safety assessment of nuclear power plants, is widely used in nuclear power plant structures. This study presents an innovative method for seismic fragility analysis of engineering structures. The proposed methodology combines conventional Cloud analysis method and Bayesian framework. The accuracy and efficiency of the proposed methodology were demonstrated by a single-degree-of-freedom system, heavy computational efforts but high accuracy seismic fragility analysis method was adopted for comparison. Seismic fragility analysis of a typical prestressed concrete nuclear containment structure was analyzed using the proposed method. Results indicated that method proposed in this study can improve the accuracy of seismic fragility analysis. Furthermore, with the enhance of performance level, the increasing ratios for median seismic capacity of the nuclear containment structure was decreasing.

Research on reactor power prediction of nuclear power plant based on multivariate optimization GRU model

In the operation of nuclear power plants, the accurate prediction of power change trends is crucial for ensuring safety and stability. In this work, a ML-GRU-RS method, based on model-agnostic meta-learning (MAML), gate recurrent unit (GRU), and random search optimization, is proposed for the long-term prediction of key parameters of nuclear power plants. This method combines the fast adaptability of MAML, the time series data processing capability of GRU, and the optimization efficiency of random search to achieve high-precision predictions under varying power conditions. The results demonstrate that this method can effectively predict the future trends of key parameters in nuclear power plants. The ability of operators to anticipate these trends has been significantly enhanced, contributing to the overall safety of the nuclear power plants.

Технико-экономические показатели энергокомплекса при использовании ветроэнергетических установок для покрытия собственных нужд ТЭЦ

In accordance with the Russia Energy Strategy until 2035, within the framework of the implementation of energy-saving technologies in the power-generating sector, it is planned to use hybrid energy, which is one of the ways of transition from conventional generation to generation with a high share of renewable energy sources. However, to implement such complexes, it is necessary to conduct research that considers the natural and climatic characteristics of the region, its resource capabilities, as well as the mutual influence of conventional and renewable energy sources in the structure of generating equipment capacities. The wind potential at the construction site has been analyzed to assess the feasibility of incorporating wind turbines. Based on the technical characteristics of wind turbines and meteorological conditions, the model of wind turbines to be installed has been selected. Technical and economic indicators of the energy complex operation, providing for the use of wind power plants to partially cover the house load of CHPs have been obtained. The indicators are determined on an annual, monthly basis using the physical method of attributing the total fuel costs for heat and electricity output in case of their combined production. Operation of wind power plants as a part of the energy complex with CHP in winter has a positive effect on the process of thermal efficiency of combined heat and power generation, and in summer, on the contrary, leads to deterioration of the efficiency indicators. Relatively small fuel savings per year when using wind power plants as a part of the energy complex with CHP does not allow to consider the project cost-effective. The obtained results should be considered when making system-wide decisions on the development of renewable energy sources in parallel operation with fossil fuel power plants.

Estimation of the Put Option Value for Solar Power Plant Operators Participating in the Renewable Portfolio Standard (RPS) Spot Market

Estimation of the Put Option Value for Solar Power Plant Operators Participating in the Renewable Portfolio Standard (RPS) Spot Market

Hydro-thermal Power Reserve Allocation Under Outage Uncertainty

In recent decades, the energy sector, namely energy markets have proved to be changing considerably. Most of these changes result in increasing volatility related to global political and environmental factors. This volatility affects all entities operating in the power sector. This paper focuses on the power plant operator’s perspective. First, the global context of the power sector is discussed and the power plant operator’s role is reviewed. Further, the problem of power reserve is discussed revealing the motivations of the presented work. The central contribution of this work lies in the formulation and development of a stochastic model of power reserve coupled with a mixed-integer programming scheduling model. In the results section, we show that the developed model allocates power reserve in an optimal way respecting the technical and economic parameters of the power plants as well as handling the stochastic nature of the power reserve allocation problem by minimizing the mean value of the sum of penalty for not delivering contracted power and lost opportunity.

Research on Fault Prediction of Power Devices in Rod Control Power Cabinets Based on BiTCN-Attention Transfer Learning Model

The Insulated Gate Bipolar Transistor (IGBT) is the key power device in the rod control power cabinet of nuclear power plants; its reliable operation is of great significance for ensuring the safe and economical operation of the nuclear power plants. Therefore, it is necessary to conduct fault prediction research on IGBT to achieve better condition-based maintenance and improve its operational reliability. However, power cabinets often operate under multiple, complex working conditions, so predicting IGBT faults from single working condition data usually has limitations and low accuracy. Its failure probability has an important relationship with the actual operating conditions of the cabinet. In order to improve the reliability and maintainability of the control power cabinet in nuclear power plants, this paper takes IGBTs in the rod control power cabinet as the object and makes full use of the data of IGBTs under multiple working conditions to carry out research on the cross-condition fault prediction of IGBTs under multiple-source working conditions. A transfer learning (TL) model based on a bidirectional time convolutional network (BiTCN) combined with attention was proposed to solve the problem of low accuracy of cross-operating fault prediction in a multi-source domain. Firstly, an IGBT fault simulation model was built to collect the life cycle state data of the module under different working conditions. Then, after pre-processing such as removing outliers, kernel principal component analysis (KPCA) was used to integrate all source domain data, obtain source domain characterization data, and train the BiTCN-attention model. Finally, the BiTCN-attention model trained in the source domain was transferred, and the model was fine-tuned according to the target domain data. Simulation results show that the accuracy of the proposed BiTCN-attention transfer learning prediction method can reach more than 99%, which is significantly better than that of the recurrent neural network transfer learning (RNN-TL) model, long short-term memory network transfer learning (LSTM-TL) model, gated cyclic unit transfer learning (GRU-TL) model, and time convolutional network transfer learning (TCN-TL) model. This method can not only reduce the inconsistency of fault characteristic values caused by changes in working conditions but also accurately predict the degradation trend when only early fault data are available, providing an effective solution for IGBT fault prediction across working conditions in multi-source domains.

Generación renovable eólica y fotovoltaica en Ecuador: Una revisión sistemática de literatura

INTRODUCTION. Photovoltaic and wind renewable generation has grown exponentially in recent years worldwide, in Ecuador this technology has not been the exception, as several photovoltaic and wind power plants have been installed, increasing the generation of renewable energy. OBJECTIVE. This article presents a systematic review of the literature on operational photovoltaic and wind power plants in Ecuador, offering a perspective on the renewable sources connected to the national energy system. METHOD. Through a bibliographic search in the Scopus database, 8 publications were obtained that met the eligibility criteria, in addition to reports issued by government entities, to ensure that the information and technical aspects are accurate. RESULTS. The documentary analysis allows us to know the current status of photovoltaic and wind power plants, identifying their installed capacity, technology used, and operational characteristics. DISCUSSION AND CONCLUSIONS. The information collected provides a solid basis for understanding the current status and development prospects of photovoltaic and wind power generation in Ecuador. Despite certain limitations related to the availability of data in open sources, it was possible to obtain significant information on the main renewable plants in the country. These results serve as a basis for future research aimed at the expansion and optimization of renewable energies in the national context..

Effect of Constant Cyclic Stress Coupling on the Fatigue Behavior of 304LN Stainless Steel.

The stress state of the primary circuit main pipeline is very complex mixed constant stress and periodic cyclic stress during the operation of nuclear power plants, which often affects the failure behavior of the stainless steel (SS) pipe. The fatigue behavior of 304LN SS under mixed constant stress and cyclic stress was investigated, and it was found that the coupling effect of constant stress and cyclic stress has a significant collaborative acceleration on the fatigue behavior. The research results showed that the cracks in the 304LN SS did not propagate even after 78 days under both constant load conditions of 5 KN and 7.5 KN, while the crack growth rate (CGR) of the specimen increased by about an order of magnitude when coupled with a cyclic fatigue load. The fatigue life of 304LN SS was the longest under 200 °C high-temperature water, and its life was roughly the same under 250 °C and 300 °C water. In a 300 °C high-temperature water environment, the fatigue life under the coupling of constant stress and cyclic fatigue stress is significantly lower than that under symmetric cyclic stress alone. There is a synergistic acceleration effect on the fatigue life of 304LN SS when constant stress is coupled with periodic cyclic stress, which is attributed to the combined effect of a corrosion environment and mechanical factors.

Improved Early Detection of Tube Leaks Faults in Pulverised Coal-fired Boiler Using Deep Feed Forward Neural Network

Boiler tube leaks significantly reduce the operational availability of power units, yet their early detection and prediction have not been fully realised in the industry. This paper introduces a novel approach employing deep feedforward neural networks for early detection of boiler tube leaks in pulverised coal-fired boilers. Early detection enhances repair planning, minimising downtime and production losses. It also improves monitoring and control of boiler tube failures, thereby optimising power plant operations and revenue. Diverse deep neural network models were developed and rigorously tested by leveraging 9 years of operational data (2012–2020). Exhaustive hyper-parameter optimisation, involving seven parameters, substantially improved predictive accuracy. By achieving training and testing accuracies of 82.8% to 99.3%, the study assessed their ability to detect boiler tube leaks over the same 9-year span, providing insights into leak detection capabilities. The models notably predicted all 12 identified tube leak events, averaging a 14-day lead time before boiler shutdown. In addition to leak prediction, a leak detection matrix was devised to analyse residual behaviour and reduce the likelihood of false alarms. However, the models’ predictive performance was observed to be limited to the following year, with satisfactory results for 2021 only. Incorporating the 2021 data into retraining significantly improved the predictions for 2022. The study concludes that while the models demonstrate robust short-term prediction capabilities, they require continuous retraining to maintain accuracy and relevance. This ongoing refinement is essential for keeping the models up-to-date and reliable in predicting future boiler tube leaks.

Modeling of temperature deformations on the Dnister HPP dam (Ukraine)

Abstract The extended operation of Ukrainian hydroelectric power plants has caused spatial deformations and aging of hydrostructures. This can lead to man-made disasters, especially after explosions resulting from missile attacks. As a result, various defects can appear in the concrete structures of the dam. Continuous structural monitoring of hydropower plants is essential to prevent man-made accidents. The research paper also demonstrates that changes in water temperature can affect the dam’s vertical and horizontal displacements. A model was developed and analysed to determine the spatial deformations of the Dnister HPP based on water temperature and distance from the dam’s edge. It was determined that horizontal displacement rates range from −2.2 to 2.7 mm/month, and vertical displacement rates range from −2 to 1.3 mm/month. They are also seasonal in nature. The studies conducted enable the prediction and identification of seasonal spatial deformations of the Dnister HPP dam. The research also helps to find their variations due to water temperature at the definite depth. If the temperature deformation model deviates from the measurement results, the structure in those areas should be more thoroughly inspected and analysed. This may indicate the structure’s weakening due to natural aging processes or construction-related deficiencies. Deformations in the dam’s structure can lead to cracks, compromising its stability and watertightness. Dams can vary in shape, design features, depth, volume, and water temperature. So, it is important to customize the displacement model for each dam. Any deviation from the specified model may suggest defects due to abnormal temperature distribution and spatial displacements.

Optimal Dispatching Strategy for Textile-Based Virtual Power Plants Participating in GridLoad Interactions Driven by Energy Price

The electricity consumption of the textile industry accounts for 2.12% of the total electricity consumption in society, making it one of the high-energy-consuming industries in China. The textile industry requires the use of a large amount of industrial steam at various temperatures during production processes, making its dispatch and operation more complex compared to conventional electricity–heat integrated energy systems. As an important demand-side management platform connecting the grid with distributed resources, a virtual power plant can aggregate textile industry users through an operator, regulating their energy consumption behavior and enhancing demand-side management efficiency. To effectively address the challenges in load regulation for textile industry users, this paper proposes a coordinated optimization dispatching method for electricity–steam virtual-based power plants focused on textile industrial parks. On one hand, targeting the impact of different energy prices on the energy usage behavior of textile industry users, an optimization dispatching model is established where the upper level consists of virtual power plant operators setting energy prices, and the lower level involves multiple textile industry users adjusting their purchase and sale strategies and changing their own energy usage behaviors accordingly. On the other hand, taking into account the energy consumption characteristics of steam, it is possible to optimize the production and storage behaviors of textile industry users during off-peak electricity periods in the power market. Through this electricity–steam optimization dispatching model, the virtual power plant operator’s revenue is maximized while the operating costs for textile industry users are minimized. Case study analyses demonstrate that this strategy can effectively enhance the overall economic benefits of the virtual power plant.

Remaining useful life prediction of nuclear reactor control rod drive mechanism based on dynamic temporal convolutional network

The control rod drive mechanism (CRDM) is a critical equipment of the nuclear reactor, and the prediction of its remaining useful life (RUL) is important for the efficient maintenance and ensuring the safe, reliable operation of nuclear power plants. In this paper, a novel framework for the RUL prediction of CRDM is proposed, which is a dynamic temporal convolution network (DTCN) based on dynamic activation function and attention mechanism. Firstly, the temporal convolution network (TCN) is used as the backbone of the prediction model, to extract the temporal dependence of the input data. Next, the dynamic activation function DReLU is integrated into the TCN, which can dynamically activate features and capture variable degradation information. Then, introducing the attention mechanism improves the influence of important high-level features extracted by the network on RUL prediction, thereby improving the efficiency of feature extraction in the network. Finally, the DTCN outputs the predicted RUL by performing non-linear mapping on the extracted features. The CRDM accelerated life test platform is established and a series of experiments are conducted using the collected CRDM full-life vibration dataset. The results demonstrated the performance and advantages of the proposed method.

Study on the Economic Operation of a 1000 MWe Coal-Fired Power Plant with CO2 Capture

The flexible operation of carbon capture units is crucial for the economic performance of coal-fired power plants equipped with CO2 capture systems. This paper aims to investigate the impact of electricity, CO2, and fuel prices on the economic operation of such plants. A novel economic optimization model is proposed, integrating a static model of the carbon capture system with a particle swarm optimization algorithm. A new concept, the CO2 boundary price, is introduced as a key metric for determining the operating conditions of CO2 capture units. The CO2 boundary price rises when the power load decreases due to the decline in power generation efficiency, and it also increases with rising fuel prices, as the cost of steam for CO2 capture increases. Additionally, when the objective is to meet power load demand, CO2 prices have a great influence on the operation of CO2 capture units, assuming fixed coal and electricity prices. However, when the primary goal is to maximize plant profitability, the system’s operational conditions are strongly influenced by the relative prices of electricity and CO2. The proposed optimization model and the uncovered price-effect mechanisms provide valuable insights into the economic operation of carbon capture power plants.

Use of hybrid resin to reduce silica from spent fuel pool borated water of power plant at pilot scale

Removal of silica from the borated water of primary systems of the nuclear power plant remains a long-term challenge for the industry. The same was tried to solve by membrane technology, however, membrane technology has its issues like generations of additional radioactive wastes. To address this issue a specialized hybrid resin for selective silica removal from borated water was prepared and tested for its efficiency at a pilot scale on an inventory taken from the spent fuel pool of an operational nuclear power plant having 2110 ppm boron and 400 ppb silica. The developed hybrid resin technique offers a simple and easy procedure to overcome several problems encountered in other methods of silica removal such as additional equipment, additional waste handling and maintenance. The validation shows that the application of the developed technique has a silica removal efficiency of 38% without affecting boron concentration.