Power Plant Production Research Articles

Long-term PV system modelling and degradation using neural networks

The power production of photovoltaic plants can be affected throughout its operational lifetime by multiple losses and degradation mechanisms. Although long-term degradation has been widely studied, most methodologies assume a specific degradation behaviour and require detailed metadata. This paper presents a methodology for the calculation of long-term degradation of a photovoltaic plant based on neural networks. The goal of the neural network is to model the photovoltaic plant's power production as a function of environmental conditions and time elapsed since the plant started operating. A big advantage of this method with respect to others is that it is completely data-driven, requires no additional information, and makes no assumptions related to degradation behaviour. Results show that the model can derive a long-term degradation trend without overfitting to shorter-term effects or abrupt changes in year-to-year operation.

Graph classification based fault detection in nuclear power plants with graph formulation

In the routine production and operation of nuclear power plants (NPPs), safety management is extremely important, and consequently, a fault detection system yielding high accuracy could provide effective decision support and prevent the accidents to the maximum extent. Most of existing studies are based on the analysis of time-series data, while researches oriented to the implementation of graph data mining are barely seen. In this paper, we propose an improved fault detection method to address this task from the perspective of graph representation learning. By calculating the similarity between indicators, the multivariate temporal sequences of the running characteristics of NPPs have been formulated as a group of graphs with corresponding labels, on which the graph convolutional network (GCN) has been introduced to detect fault samples in the way of graph classification. Compared to conventional supervised learning classifiers, our proposed methodology performs more superior identification of major faults, and the sensitivity regarding significant parameter or settings in the model design has been analyzed to investigate their optimal arrangements. The findings in this article not only present the promising prospects of introducing graph learning techniques into safety management, also indicate that various kinds of digital signals could be reformulated into graph dataset based on which the original task could be executed more effectively in the way of graph representation learning.

In-situ heat losses measurements of parabolic trough receiver tubes based on infrared camera and artificial intelligence

Thermal problems occurring in glass cover, getters, vacuum, expansion bellows, and HTF lead to significant thermal losses due to convection-conduction, which accelerates the absorber degradation and implies replacing immediately these tubes. The replacement of the absorbers tubes is very expensive and interrupts the normal production of concentrated solar power plants. Therefore, the maintenance of the absorber tubes is one of the most critical challenges. In this paper, the Heat Loss Out System (Heat LOS) is proposed for regular evaluation and monitoring of absorbers tubes to maintain their efficiency and reduce operation and maintenance costs through immediate intervention. The proposed system is based on the intelligent processing of photos and videos in real time taken by a robust infrared camera with exact GPS coordinates and implemented on a vehicle. The Heat LOS evaluates in real-time, precisely, and rapidly, the temperature distribution of the absorbers tubes without contacting the surface or interrupting the normal production. First, an analytical study is performed to determine the appropriate speed of the vehicle, which can reach up to and the distance between the camera and the absorber tube that meet a high accuracy of measurement and do not disturb it which should not exceed 5 m. In addition, several absorbers tubes installed in the Green Energy Park (GEP, Morocco) research center are tested and evaluated using the Heat Loss Out system. The results obtained show a promising performance of the proposed system up to an accuracy of 93% based on deep learning with the CNN method, which will be valid when compared to manual analysis. This solution was assembled on a vehicle to rapidly evaluate several tubes and goes through many loops.

Coupling of the Simulation Planning and Power Flow Calculation Tools: Case Study of the Republic of Serbia

Planning of the national energy system by looking at alternative investment decisions through annual simulations (using the EnergyPlan tool) due to its (combinatorial) complexity leaves little room for explicitness regarding the network's impact on planning decisions and therefore there is a need to look at the power flows in the network. The investment decisions that are made remain with a degree of vagueness in the technical aspect, which can be removed with this connection. The CASE tool is used for numerous system calculations of power flows, shortcircuit currents, series faults, stability to small disturbances and transient stability. Calculations based on the calculation of power flows are also implemented in the program: network reduction, security (N-1 and N-X), NTC and OTDF/PTDF. The coupling of the CASE program and EnergyPlan was achieved through the output text file of EnergyPlan, which contains the total production of power plants by type, the total consumption of the system divided into several subtypes, export and import from the regulatory area. CASE loads the output file of EnergyPlan and distributes the total production by power plant types to each individual power plant in the system according to predefined distribution coefficients, while the total consumption is distributed proportionally to the existing loads by network nodes. Thus, a system model for power flow calculation is defined for each hour of the year. Calculation results for each hour are stored in the working memory of the CASE program and can be sorted by the most loaded element of the system, the largest exchange of the observed control area, losses in the control area, production by a certain type of power plant, consumption, etc. In this way, it is possible to efficiently analyse the operation of the system according to different criteria. First of all, the goal of this work is to couple the two software tools, which has not been realized until now, as well as the presentation of some of the most significant results selected from among the numerous options of state sorting. The result of the work is the integration of software tools that serve the benefit of planning activities in terms of their technical feasibility, that is, to examine more precisely whether a prospective scenario is realistic from the aspect of power flows in transmission grid.

Phase Synchronization for Helix Enhanced Wake Mixing in Downstream Wind Turbines

Wind farm controllers such as the Helix approach have shown potential in increasing plant power production through wake mixing. The concept suggests that actuating the upstream turbines’ blade pitching with a specific periodic signal can induce a helix-shaped wake, thereby alleviating wind velocity deficit on downstream turbines. Wake mixing initiation by downstream turbines may also be shown advantageous for power production; however, little to no attention has been given to such an approach. Similar wake mixing is expected to be achievable at lower control costs if the downstream turbine can benefit from the periodic component already present in the wake of the upstream turbine. Such a hypothesis is studied in this work by designing a minimal control scheme where the wake acting on the downstream turbine is simulated by a periodic input disturbance. A Kalman filter is proposed for incoming input disturbance phase estimation using SCADA data. The reconstructed phase information allows synchronization of the downstream control action with the periodic input disturbance by means of a phase synchronization wake mixing controller. The periodic component was estimated with a minimal root-mean-square error and the resulting control action was in phase with the input disturbance and demonstrated satisfactory performance even with a small phase perturbation. Future work will include applications in a high-fidelity wind turbine model and wind tunnel studies.

Developing a Neural Network Based Fault Prediction Tool for a Solar Power Plant in Uganda

Solar photovoltaic (PV) systems are one of the fastest growing renewable energy technologies and plenty of research has been and continues to be carried out in this domain. Maximization of solar PV power plant production, efficiency and return on investment can only be achieved by having adequate and effective maintenance systems in place. Of the various maintenance schemes, predictive maintenance is popular for its effectiveness and minimization of resource wastage. Maintenance activities are scheduled based on the real time condition of the system with priority being given to the system components with the highest likelihood of failure. A good predictive maintenance system is based on the premise of being able to anticipate faults before they occur. In this study therefore, a fault prediction tool for a solar plant in Uganda is proposed. The hybrid tool is developed using both feed forward and long short term memory neural networks for power prediction, in conjunction with a mean chart statistical process control tool for final fault prediction. Results from the study demonstrate that the feed forward and long short term memory neural network modules of the proposed tool attain mean absolute errors of 4.2% and 6.9% respectively for power production predictions. The fault prediction capability of the tool is tested under both normal and abnormal operating conditions. Results show that the tool satisfactorily discriminates against the fault and non-fault conditions thereby achieving successful solar PV system fault prediction.

Techno-Economic Analysis of Electricity Generation by Photovoltaic Power Plants Equipped with Trackers in Iran

The use of solar trackers can help increase the production and time period of electricity generation in photovoltaic power plants. Different types of trackers in terms of rotation mechanisms and sun tracking systems have been used in these types of power plants in recent years. In this article, a comparison has made between the electricity produced of fixed and tracking structures in a number of power plants which are located in different cities of Iran but in similar geographical locations. Following this, software modeling is used to evaluate various sun tracking scenarios in the design of sample power plants. Finally, a techno-economic analysis has been made to evaluate the decision-making regarding the construction of such power plants in Iran using this technology. Using east-west detectors has had a positive effect on increasing the production of power plants, especially in summer. Due to the higher initial costs of using this technology in the power plants, as well as the higher maintenance costs, the economization of the power plants’ business plan is extremely dependent on other economic conditions governing the project. Using trackers alone cannot lead to a better situation in a project’s lifetime. From the national point of view, if domestic companies can produce east-west single-axis tracking technology at an acceptable cost and provide related services in the long term, it would be beneficial for the power grid. One of the attractive proposals for the power plants under construction will be the use of this technology, because of additional electricity production in peak hours in summer. Annual experimental data from different tracking/fixed PV power plant have been used for the first time in this techno-economic investigations, they are validated with the simulation process, and the results have been predicted for a wide range of tracking scenarios. Techno-economic analyses of this type of power plant are an essential need for policy makers and investors in Iran’s energy market.

Razvoj metodologije za utvrđivanje optimalne snage fotonaponske elektrane postavljene na nagnutom terenu

Models for the calculation of the production of large photovoltaic (PV) power plants with different types of modules (monofacial or bifacial) have been developed in detail in the literature. In addition, detailed models have been developed for PV power plants that have fixed modules as well as PV power plants with tracking the azimuth angle of the Sun and/or the tilt angle of the PV module. On the other hand, due to the increasing installation of PV power plant, their construction is often imposed on the field with complex geometry, so it is interesting to develop models that can optimize the spatial layout and geometric elements of panels on sloping surface in relation on horizontal. Namely, during various analyses in the literature, production models for PV power plants that are planned on sloping terrains have not been developed. Accordingly, in this paper, a procedure was developed for the calculation of the production of PV power plants that are placed on terrain that is not flat, but oriented towards the arbitrary side of the World and with an arbitrary tilt angle.

Controlling and Monitoring of Boiler Parametrs Using Scada

The control and monitoring of heater operation are covered in this paper. A boiler is a large component in an enterprise that heats water and produces steam. Steam can be utilised to run turbines or engines. In this project, the boiler's functioning is controlled by a PIC microcontroller, which regulates the boiler's temperature and pressure. This is due to the fact that PICs are highly adaptable, low-cost, space-efficient, and reduce complexity. The data from the microcontroller is received by SCADA and shown on the screen. The suggested boiler automation ensures that the boiler system, and thus the power production plant, runs safely, reliably, and continuously. Boilers are used in many industrial and commercial buildings to create hot water or steam for space or process heating. Boiler setup is made using LPG cylinder and heater is placed in the cylinder so that it acts as boiler. Pressure and temperature sensor is used to measure the both pressure and temperature in the boiler. To control temperature, pump is placed and when the temperature goes beyond the certain level pump automatically turns ON and the feedwater is supplied. When the pressure goes beyond the limit solenoid valve turns ON and steam released through the solenoid valve. LCD is placed to monitor the values from outside. This page discusses the many type of boilers are used to heat buildings, as well as fundamental boiler controls and factors that influence energy efficiency.

Diphenyl-diphenyl oxide eutectic mixture for high temperature waste-heat valorization by a partially evaporated cycle cascade

Organic Rankine Cycle power production plants are very promising heat-to-power systems. High-temperature waste-heat valorization presents a great potential for the development of this technology. However, the operating limit of usual organic working fluids is close to 300 °C. To increase this limit, Diphenyl-Diphenyl Oxide mixtures could be used as high-temperature working fluids thanks to their thermal stability limit of 400 °C. This work presents an original cascade of a Diphenyl-Diphenyl Oxide Partially Evaporated Cycle and an Organic Rankine Cycle. This solution can help in increasing the power output of an ORC system used for high temperature sensible waste-heat valorization. The paper presents first an original state of the art of Diphenyl Oxide used in Rankin Cycles. Then, a single stage Diphenyl-Diphenyl Oxide Partially Evaporated Cycle is explored to analyze its thermodynamic particularities before the presentation of the cascade system optimization. Besides, a comparison to a simple Organic Rankine Cycle system is done to assess the performance improvement achieved by the proposed cascade. This is done for various bottom cycle fluids and hot source temperatures. The power production can be increased by 4%–17% for source temperatures ranging between 400 °C and 500 °C. The proposed Diphenyl-Diphenyl Oxide cycle could be used as a plug-in system in heat recovery and transport oil loops.

Well-to-Wheel Analysis of Electric and Hydrogen Light Rail

The application of renewable energy technologies to rail transit should be evaluated on a comprehensive energy pathway efficiency basis to ensure that the renewable energy technology is truly beneficial. One such method is the well-to-wheel analysis method, which combines the energy efficiencies of each component of the energy pathway into a single energy efficiency value. The focus of this paper is on well-to-wheel analysis of electric and hydrogen light rail. The inefficiencies of the hydrogen train’s power plant and hydrogen production process are apparent in the hydrogen train’s well-to-wheel efficiency value of 16.6–19.6%. The electric train, due to improved pathway efficiencies, uses substantially less feedstock energy with a well-to-wheel efficiency value of 25.3%. While this result is specific to Charlotte, North Carolina, the electric train efficiency is influenced by the main source of electricity production—it is 24.6% in Cleveland, Ohio (coal heavy) and 50.3% in Portland, Oregon (hydroelectric heavy).

The water hammer in the long-distance steam supply pipeline: a computational fluid dynamics simulation

In this paper, a computational fluid dynamics (CFD) simulation based on FLUENT software was performed to analyze the flow and heat transfer of the accident pipeline network in the water hammer burst accident of a thermal power plant in Jiangsu Province, China. Furthermore, the CFD simulation is also performed on the modified pipeline network to verify the effect of pipe network modification. The simulation results from the original accident pipeline show that in the accident pipeline, the steam velocity can reach up to 50 m/s due to the aggregation of liquid water. And when the high-velocity steam strikes the pipe elbow, it will lead to a sharp pressure change and cause the burst accident finally. Then it can be concluded that in the modified pipeline network, the distribution of temperature, pressure, and velocity at the pipe burst place is more uniform and reasonable. In addition, the simulation is also used to analyze the variable working conditions, which ensures that the new design of the pipe network can be used in more complex conditions. The paper is expected to guide the design of other similar pipelines and ensure the safe production and operation of thermal power plants.

Analysis of Gas Turbine Power Plant Production and Fuel Consumption

The main problem of gas turbine generating units operating in the simple cycle system is their relatively low thermal efficiency, which leads to large thermal energy losses. The current research aims to study the economic analysis of Kirkuk gas power plant. Field data of gas turbine unit for the year of 2019 were used. The results of the study showed that the cost of exported energy is higher than the cost of energy produced. The results showed a 23% increase in the cost of energy produced in summer compared to winter. A decrease in actual production compared to the planned production is observed by 12.8%, where the highest actual production for the month of March was 162,108 MW, while the planned was 186,000 MW. Also, the month of July witnessed a very low fuel consumption rate for natural gas compared to the rest of the months, where the average fuel consumption was 0.273 m3/kW, while the month of January has a high fuel consumption rate for natural gas compared to the rest of the year with average fuel consumption of 0.312 m3/kW. The rate of increase in fuel consumption between July and January was 14.2%, and the results also showed that the ratio of production to design capacity for January was the highest and amounted to 87%, and the highest efficiency reached 33%, while July was the lowest production rate in terms of design capacity, reaching 53% and efficiency of 29.8%.

Hydroxy-Functionalized Hypercrosslinked Polymers (HCPs) as Dual Phase Radioactive Iodine Scavengers: Synergy of Porosity and Functionality.

Large-scale nuclear power plant production of iodine radionuclides (129 I, 131 I) pose huge threat in the events of nuclear disaster. Effective removal of radioiodine from nuclear waste is one of the most critical challenge because of the drawbacks of state-of-the-art adsorbents such as high cost, low uptake capacity and non-recyclability. Herein, two hydroxy-functionalized (-OH) hypercrosslinked polymers (HCPs), namely HCP-91 and HCP-92, have been synthesized and employed towards capture of iodine. High chemical stability along with synergistic harmony of high porosity and functionality of these materials makes them suitable candidates for capture of iodine from both vapor phase and water medium. Moreover, both the HCPs showed superior iodine removal performance from water in terms of fast kinetics and high removal efficiency (2.9 g g-1 and 2.49 g g-1 for HCP-91 and HCP-92 respectively). The role of functionality (-OH groups) and porosity has been established with the help of HCP-91, HCP-92 and non-functionalized biphenyl HCP for the efficient capture of I3 - ions from water. In addition, both HCPs exhibited excellent selectivity and recyclability towards triiodide ions, rendering the potential of these materials towards real-time applications. Lastly, Density functional theoretical studies revealed key insights and corroborate well with the experimental findings.

Experimental Study on the Effect of Soiling at Various Tilt Angles of Photovoltaic Modules

Abstract: Power generation from a solar photovoltaic system is one of the glowing research fields these days, even governments are also planning toward installation and production of power generation from renewable energy sources because in the future its feasibility and crisis of conventional energy sources will increase. Further government liberalization and technical developments vitalize the use of renewable sources for electricity generation in terms of solar power. In any power production plant, improvement in the efficiency of the plant is a big and important issue. After installing the solar power plant, it is very urgent to get maximum performance from them. With all other factors, the tilt angle and azimuth angle affect the efficiency of the plant. Thus, it is very important to orient the solar modules at optimum tilt angle and azimuth angle for any given location because they are most efficient when they are perpendicular to the sun's rays. A comparison study was executed to achieve the power variation and dust deposition with different tilt angle in natural environment of Ajmer. Powers generated by solar cells over study period are in range of 5.10W and 3.0W. It is evident that Solar module tilted at 260 generate higher power than Solar module tilted at 00 tilted module, followed by Solar module tilted at 450

Interdisciplinary fracture network characterization in the crystalline basement: a case study from the Southern Odenwald, SW Germany

Abstract. The crystalline basement is considered a ubiquitous and almost inexhaustible source of geothermal energy in the Upper Rhine Graben (URG) and other regions worldwide. The hydraulic properties of the basement, which are one of the key factors in the productivity of geothermal power plants, are primarily controlled by hydraulically active faults and fractures. While the most accurate in situ information about the general fracture network is obtained from image logs of deep boreholes, such data are generally sparse and costly and thus often not openly accessible. To circumvent this problem, an outcrop analogue study was conducted with interdisciplinary geoscientific methods in the Tromm Granite, located in the southern Odenwald at the northeastern margin of the URG. Using light detection and ranging (lidar) scanning, the key characteristics of the fracture network were extracted in a total of five outcrops; these were additionally complemented by lineament analysis of two different digital elevation models (DEMs). Based on this, discrete fracture network (DFN) models were developed to calculate equivalent permeability tensors under assumed reservoir conditions. The influences of different parameters, such as fracture orientation, density, aperture and mineralization, were investigated. In addition, extensive gravity and radon measurements were carried out in the study area, allowing fault zones with naturally increased porosity and permeability to be mapped. Gravity anomalies served as input data for a stochastic density inversion, through which areas of potentially increased open porosity were identified. A laterally heterogeneous fracture network characterizes the Tromm Granite, with the highest natural permeabilities expected at the pluton margin, due to the influence of large shear and fault zones.

Productivity assessment of power generation in Kenya: What are the impacts?

Kenya is classified among the fastest developing economies in Sub Saharan Africa. To sustain this growth, energy is seen as a key ‘enabler’ to oil its new long-term development blueprint, Vision 2030, which intends to transform the economy into a middle income country status and a newly industrialized economy. Yet, the recent energy generation capacity is faced with high growing demand, unreliability, and high cost. Applying Sequential Malmquist Index (SMI), the study examines dynamics of productivity of Kenya's hydro and thermal power plants over the 2013–2020 period. We then employ a Tobit regression method to analyze the influence of environmental factors on changes in the productivity index. The empirical findings show that regress in hydro productivity is mainly caused by the technical efficiency change index (TECI). Besides, productivity for thermal plants improved due to the technology change index (TCI). The SMI provides an appropriate estimate for the share of TCI than the conventional Malmquist index for both plants. The estimates from the regression technique highlight that the life of plants, size, rainfall variation influence hydro SMI changes, and fuel type, life, size also affect thermal SMI changes. Targeted policy implications are provided for future development and operations of power plants.

Context-informed conditional anomaly detection approach for wave power plants: The case of air turbines

The reliability and energy production of wave power plants (WPPs) depend on sea-state conditions, operation efficiency and degradation of its constituent assets. Air turbines are key assets for the efficient and reliable operation of WPPs and ensuring their correct operation leads to enhance the efficiency of WPPs. However, the lack of run-to-failure data and scarce fault records hampers the development of predictive condition monitoring solutions. In this context, focusing on unsupervised health monitoring methods, this paper presents an air turbine conditional anomaly detection (CAD) approach with a practical case study tested and validated on the Mutriku wave power plant. In contrast to anomaly detection models, which model the health-state without taking into account the influence of the operating context, the proposed CAD approach learns the expected air turbine operation conditioned on specific sea-states information modelled through wave energy flux concepts. This is achieved through an ensemble of Gaussian Mixture models and the expectation–maximization algorithm. Results show that, the integration of sea-states in the anomaly detection learning process improves the discrimination capability of the CAD model compared with the anomaly detection model without sea-state information, reducing false positive events and improving the accuracy of the CAD model.

Techno–economic–environmental comparison of floating photovoltaic plant with conventional solar photovoltaic plant in northern Iran

Abstract Photovoltaic (PV) systems can be used to generate electricity due to the potential for solar energy in Iran. Applying floating photovoltaic (FPV) systems is a new approach to utilizing PV systems in water. Most of Iran’s energy consumption is supplied from fossil fuels, especially oil and gas. In recent years, Iran has faced environmental problems and air pollution. Electricity generation using fossil fuels has led to increased environmental pollution. Accordingly, PV systems can be used to generate electricity due to the potential for solar energy in Iran. The interest in predicting the energy production of PV power plants has increased in recent years. In this regard, the techno–economic–environmental study of constructing PV power plants is a basic process to encourage people to use solar energy. A techno–economic–environmental feasibility study has been performed to construct a 5-kW FPV and ground PV (GPV) power plant in a northern city of Iran. Also, the FPV system is compared with the ground PV system using MATLAB® Simulink and RETScreen® software. In this study, the effects of wind and water temperature have been considered. Also, a sensitivity analysis was performed due to the uncertainty in climatic conditions and the amount of PV energy generation. The simulation results show that due to the cooling effect for panels in the FPV system, the production capacity and panels’ efficiency are respectively 19.47% and 27.98% higher than the those of the GPV system. In addition, the FPV system was found to have a 16.96% increase in the annual performance ratio. Overall, using the FPV system reduces the equity payback to 6.3 years (a 22.2% reduction compared to the GPV power plant).

Calculation and enhancement of fatigue life by ε-N approach and corrosion fatigue in steam turbine rotor

Power plants production’s growth capacity has led to increased concerns about efficiency and reliability of steam turbines. Its components are prone to cracking and failure caused by creep and fatigue. The present work used Ansys® and nCode GlyphWorks® package to predict tensions and deformations. The methodology used allows calculate the level of structural damage caused to each work cycle. Analysis of the application of a chemical process on the rotor to improve failure resistance influenced the component life, avoiding corrosion fatigue. To create residual stresses on the surface of material was used Shot peening process. Thermal analysis results show that the rotor supports approximately 3.18 × 104 cycles, obtaining a maximum error of 12.5% compared to experimental reference. The shot peening process increased the rotor life by about 3000 h (10.5%) and changes in the heat treatment microstructure increased by 9.6% in number of cycles.