Power Engineering Applications Research Articles

Boron Nitride Modified Superhydrophobic Anti‐Icing Coating for Electrical Equipment Protection

AbstractAnti‐icing of superhydrophobic materials has attracted a lot of attention. The potential for applications in power electronics, transportation, engineering and other fields is enormous. However, in practical applications, the anti‐icing performance parameters of superhydrophobic materials are still far from meeting the engineering requirements. It is a feasible solution to modify superhydrophobic materials to assist surface deicing by combining the vibration and heat generated by equipment in actual engineering. In this study, conventional superhydrophobic materials are modified by h‐BN. The electrical properties are improved to meet the engineering requirements. At the same time, it reduces the ice adhesion at the interface and improves the thermal conductivity of the material. The modified material has the possibility of assisting in de‐icing in engineering applications. A feasible solution is found for the engineering interface ice‐covering problem.

Interaction of Titanium Atoms with the Surface of Perfect and Defective Carbon Nanotubes

The dispersion of metal atoms over the surface of 1D and 2D carbon systems is the most affordable way to control their properties, which are attractive for many applications in electronics, power engineering, and catalysis. In this work, the features of the interaction of titanium atoms with the surface of carbon nanotubes, caused by various structural defects on these surfaces, were studied by first-principles computer simulation based on the density functional theory. Nanotubes (7, 7) and (11, 0) with similar diameters (≈1 nm) but different types of conductivity, metallic and semiconductor, respectively, were chosen for the study. Three types of defects were studied: a single vacancy, a double vacancy, and a topological defect. Two possible orientations of each type of defect relative to the tube axis were considered. We mainly used the basis of atomic-like orbitals (the SIESTA package) and in some test calculations also the basis of plane waves (the VASP package). Computational experiments have shown that the binding energy of Ti atoms with a defect-free nanotube is always lower than with defective ones, regardless of the used approximation for the exchange-correlation functional (LDA or GGA). The binding energies predicted in the LDA approximation are noticeably higher than in the GGA approximation (up to ~15% for the (7, 7) tube and up to ~50% for the (11, 0) tube). The strongest coupling occurs when the titanium atom is adsorbed on a nanotube with a single vacancy. The resulting configuration can be considered as a defect in the substitution of one carbon by a titanium atom.

Solving differential‐algebraic equations in power system dynamic analysis with quantum computing

AbstractPower system dynamics are generally modeled by high dimensional nonlinear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system nonlinearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.

Combining genetic algorithm and deep learning to optimize a chemical kinetic mechanism of ammonia under high pressure

Ammonia is a potential zero-carbon fuel, but the difficulties of experimenting at high pressure limit its development of application in power engineering. In this work, deep learning and genetic algorithm are combined to optimize the chemical reaction kinetics mechanism of ammonia under high pressure. The result shows that deep learning model is able to regress experimental data of ignition delay time in the range of less than 0.2% |Elog| (logarithmic absolute error). At the same time, the trained model can be applied to the trend prediction of ignition delay time at high pressure, enriching combustion data of ammonia fuel that is not available from experiments. Optimization of PLOG reactions with a large population size was performed by strengthen elitist genetic algorithm, and the prediction accuracy of ammonia chemical reaction kinetics mechanism was improved by about 5% finally. The application value of artificial intelligence in combustion science is demonstrated.

Optimal sensor placement for permanent magnet synchronous motor condition monitoring using a digital twin-assisted fault diagnosis approach

Efficient health monitoring for identifying and quantifying damages can substantially improve the performance and structural integrity of engineered systems. Specifically, new advances in sensing technologies have pushed the research of large sensor networks to monitor complex mechanical structures. Given the need for health state monitoring, designing an optimal sensor framework with accurate detectability of failure modes has great significance. However, there is often little to no experimental data available for newly proposed mechanical systems; so a digital-twin method would make fault detection feasible for this applications. In this paper, a data-driven reliability-based design optimization (RBDO) approach is employed for sensor placement and fault detection of a permanent magnet synchronous motor (PMSM), which is a relatively new system for high power engineering applications. This system suffers from inter-turn and inter-phase short-winding faults, which can cause catastrophic failure of the whole structure. For PMSMs, current sensing and magnetic field sensing can be utilized for the detection of faults, but actual sensor placement has not been considered in recent literature. In this study, the first step is to create an FEA model of the PMSM for the simulation of faults, which serves as the digital twin. Next, a data-driven approach is implemented for sensor placement and classification of faults. The proposed method utilizes distance clustering for identification of various failure modes, which is suitable for many applications due to its high accuracy and computational efficiency. In addition, a genetic algorithm is implemented to determine the minimum number and optimal placement of sensors. This framework simultaneously searches for the optimal placement of sensors while training the classifier for detectability of system health states. Ultimately, the proposed methodology shows convergence to a solution with high accuracy for detection of faults, and is demonstrated on the novel system of a PMSM with magnetic field sensors.

A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting.

Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes can dramatically affect wind power system performance and predictability. Researchers and practitioners are creating more advanced wind power forecasting algorithms that combine more parameters and data sources. Advanced numerical weather prediction models, machine learning techniques, and real-time meteorological sensor and satellite data are used. This paper proposes a Recurrent Neural Network (RNN) forecasting model incorporating a Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm to predict wind power data patterns. The performance of this model is compared with several other popular models, including BER, Jaya Algorithm (JAYA), Fire Hawk Optimizer (FHO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO)-based models. The evaluation is done using various metrics such as relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), mean bias error (MBE), Pearson's correlation coefficient (r), coefficient of determination (R2), and determination agreement (WI). According to the evaluation metrics and analysis presented in the study, the proposed RNN-DFBER-based model outperforms the other models considered. This suggests that the RNN model, combined with the DFBER algorithm, predicts wind power data patterns more effectively than the alternative models. To support the findings, visualizations are provided to demonstrate the effectiveness of the RNN-DFBER model. Additionally, statistical analyses, such as the ANOVA test and the Wilcoxon Signed-Rank test, are conducted to assess the significance and reliability of the results.

Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond

More than 3.5 billion people live in rural areas, where water and water energy resources play an important role in ensuring sustainable and productive rural economies. This article reviews and critically analyses the recent advances in the field of analogue and reservoir computing that have been driven by the unique physical properties and energy of water waves. It also demonstrates that analogue and physical reservoir computing, taken as an independent research field, holds the potential to bring artificial intelligence closer to people living outside large cities, thus enabling them to enjoy the benefits of novel technologies that are already in place in large cities but are not readily available or suitable for regional communities. In particular, although the physical reservoir computing systems discussed in the main text are universal in terms of processing input data and making forecasts, they can be used to design and optimise power grid networks and forecast energy consumption, both at local and global scales. Thus, this review article will be of interest to a broad readership interested in novel concepts of artificial intelligence and machine learning and their innovative practical applications in diverse areas of science and technology.

A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering

Swarm intelligence algorithms are metaheuristics inspired by the collective behavior of species such as birds, fish, bees, and ants. They are used in many optimization problems due to their simplicity, flexibility, and scalability. These algorithms get the desired convergence during the search by balancing the exploration and exploitation processes. These metaheuristics have applications in various domains such as global optimization, bioinformatics, power engineering, networking, machine learning, image processing, and environmental applications. This paper presents a systematic literature review (SLR) on applications of four swarm intelligence algorithms i.e., grey wolf optimization (GWO), whale optimization algorithms (WOA), Harris hawks optimizer (HHO), and moth-flame optimizer (MFO) in the field of software engineering. It presents an in-depth study of these metaheuristics’ adoption in the field of software engineering. This SLR is mainly comprised of three phases such as planning, conducting, and reporting. This study covers all related studies published from 2014 up to 2022. The study shows that applications of the selected metaheuristics have been utilized in various fields of software engineering especially software testing, software defect prediction, and software reliability. The study also points out some of the areas where applications of these swarm intelligence algorithms can be utilized. This study may act as a guideline for researchers in improving the current state-of-the-art on generally adopting these metaheuristics in software engineering.

A novel U-Net based data-driven vanadium redox flow battery modelling approach

This research proposes a highly accurate data-driven vanadium redox flow battery (VRB) modelling approach for power engineering applications. The proposed approach addresses the common problem of excessive model dependency in the existing electrochemical principle or equivalent circuit based VRB modelling methods. Furthermore, an experimentally trained U-Net is applied to directly learn the behavioral relationship between VRB current, flow rate, state-of-charge, and voltage with excellent accuracy, avoiding the usage of model parameters that are subject to variations. Once trained, the U-Net based neural network becomes mathematically very simple and thus, can be easily implemented in simulation studies. This contributes to substantially simplify the analysis of electrical systems with VRB. The validity of the proposed approach is verified experimentally.

Peristaltic mechanism in a micro wavy channel

In this article the peristaltic transport of Williamson fluid in a wavy microchannel has been discussed. Williamson fluid has number of applications in petroleum industry and power engineering. In order to determine the energy distribution, viscous dissipation is reckoned. Here we have considerd the wavelength of the wall motion much larger than the channel width to validate the theory of lubrication. For simplification of Poisson Boltzmann equation, we have applied the Debye-Hunkel linearization which is valid for small surface potential situations with zeta wall potential ≤ 25 mV. In the cases of large surface potentials, it has been found that, in comparison with the exact solution of the Poisson-Boltzmann equation, the linear solution predicts slightly lower values of the potential in the region near the wall. After a small distance from the wall, the difference between the linear solution and the exact solution diminishes. We have considered the Williamson fluid in the absence of body force. However, an external electric field has been employed. The solution of axial velocity, flow rate, pressure rise, heat transfer, mass concentration and stream functions subjected to physical partial slip boundary conditions are calculated. The effects of appropriate parameters like Debye length, Helmholtz-velocity which characterize the EDL phenomenon and external electric field are also examined. The results reveal that the peristaltic pumping varies by applying external electric field. The resulting non-linear problem has been solved analytically through the perturbation technique to analyze the distribution and change in velocity, temperature, pressure, concentration and volumetric flow rate. Results have been found through MATHEMATICA and the effects of important parameters have been discussed graphically.

A novel vanadium redox flow battery modelling method using honey badger optimization assisted CNN-BiLSTM

This research proposes a highly accurate data-driven vanadium redox flow battery (VRB) modelling approach for power engineering applications. The proposed approach addresses the common problem of excessive model dependency in the existing electrochemical principle or equivalent circuit based VRB modelling methods. Furthermore, a honey badger algorithm optimized CNN-BiLSTM is applied to directly learn the behavioural relationship between VRB current, flow rate, state-of-charge, and voltage with excellent accuracy, avoiding the usage of model parameters that are subject to variations. Besides, an outstanding modelling accuracy is obtained under variable current and flow rate. Once trained, the honey badger algorithm optimized CNN-BiLSTM neural network becomes mathematically very simple and thus, can be easily implemented in simulation studies. This contributes to substantially simplify the analysis of electrical systems with VRB. The validity of the proposed approach is verified experimentally.

Approximations for generalized unsplittable flow on paths with application to power systems optimization

The Unsplittable Flow on a Path (UFP) problem has garnered considerable attention as a challenging combinatorial optimization problem with notable practical implications. Steered by its pivotal applications in power engineering, the present work formulates a novel generalization of UFP, wherein demands and capacities in the input instance are monotone step functions over the set of edges. As an initial step towards tackling this generalization, we draw on and extend ideas from prior research to devise a quasi-polynomial time approximation scheme (QPTAS) under the premise that the demands and capacities lie in a quasi-polynomial range. Second, retaining the same assumption, an efficient logarithmic approximation is introduced for the single-source variant of the problem. Finally, we round up the contributions by designing a (kind of) black-box reduction that, under some mild conditions, allows to translate LP-based approximation algorithms for the studied problem into their counterparts for the Alternating Current Optimal Power Flow (AC OPF) problem -- a fundamental workflow in operation and control of power systems.

A novel aging characterization method for silicone rubber based on terahertz absorption spectroscopy

To address the challenge of the lack of aging characterization methods for outdoor silicone rubber-based insulation materials in power engineering applications, this paper proposes a novel high precision non-destructive characterization method for aging of organic materials based on the vibration characteristics of silicone rubber materials in the terahertz frequency band. In order to analyze and verify the method proposed in this paper, the sources of vibration peaks in the THz band absorption spectrum of aged silicone rubber are discussed by accelerating test, microscopic characterization, and molecular simulation. Finally, the characteristic peak frequency and quantitative calculation method of aged silicone rubber are obtained. Compared to other aging assays, this method overcomes the limitations of the existing THz assay which requires testing in a nitrogen atmosphere, and has the advantage of not damaging the sample.

Cancer epidemiological situation near nuclear facilities

Relevance. Development of nuclear technologies and their wide application in medicine, power engineering and industry is always associated with health risks both for employees of nuclear facilities and for the population living in the vicinity of such facilities. Adequate assessment of such risks is possible only on the basis of the results of health monitoring of the personnel of potentially hazardous nuclear facilities and the population living in the vicinity of such facilities. One of the topical directions of research within the framework of such monitoring is assessment of specific morbidity and mortality from malignant neoplasms (MN) (C00-C97 according to ICD-10). Intention. To analyze the main medico-statistical indicators of MN-related morbidity and mortality over time among the personnel of nuclear industry and nuclear power plants and the population living near such facilities, as well as the general population of Russia in 2012-2018. Methodology. We analyzed basic medical and statistical indicators of the morbidity among the personnel of potentially hazardous nuclear facilities and the population with MN living in the vicinity of such facilities; the data was obtained from the Federal Center for Information Technologies of Extreme Problems of the Federal Medical and Biological Agency (FMBA) of Russia. The results were compared with MN incidence in Russia using data from P.A. Herzen Moscow Cancer Research Institute, a branch of the National Medical Research Center for Radiology of the Russian Ministry of Health. Results and Discussion. There is an increase in MN primary incidence rate in the medical institutions of the FMBA and the Ministry of Health of Russia. The congruence of the trends is strongly positive and statistically significant (r = 0.932; p < 0.001), which may indicate a unidirectional influence of factors contributing to MN development in patients of FMBA and Russian Ministry of Health institutions. The average annual rates of primary morbidity (354.2 8.9) per 100,000 people, mortality (158.5 ± 4.2) per 100,000 people and one-year mortality (19.6 ± 0.5) % of patients registered in FMBA medical organizations were statistically significantly lower than those in Russia - (398.0 ± 8.5) per 100,000 population, (200.5 ± 0.6) per 100,000 population and (24.0 ± 0.6) %, respectively. The trend of MN-related mortality rate in the Russian population approached a straight horizontal line, i.e. showed a tendency to stability, in patients of FMBA indicators increased. There was a decrease in one-year MN-related mortality rate and an increase in 5-year survival rate in the departments under consideration. Conclusion. The results of the study may become the basis for developing medical and social rehabilitation measures for employees of the enterprises serviced by FMBA medical organizations, as well as for attached contingents. Continuous monitoring and analysis of oncological morbidity on a personal level using registry technologies are necessary in the areas with potentially hazardous nuclear facilities.

Research on influencing factors of cable traction force in horizontal directional drilling

Horizontal directional drilling and pipe pulling technology is playing an increasingly important role in power engineering applications because of its low social and environmental impact and high construction speed. However, due to the more complex urban underground pipeline environment and the uncertainty in the process of cable pulling and laying, in order to ensure the safe laying of cables and later operation, so the cable traction force verification in the process of pulling is particularly important. Relying on specific engineering examples and relevant regulations and specifications, the cable traction force formula in the process of pulling the tube is studied, modified and given its arc-making section equivalent model, the selection principle of the discriminative cable traction method, and the influencing factors affecting the safety coefficient of cable traction force are studied.

A highly sensitive magnetic field sensor based on FBG and magnetostrictive composite with oriented magnetic domains

In this paper, an optical fiber sensor for magnetic field measurement is proposed using a fiber Bragg grating (FBG) and a magnetostrictive composite. The FBG coated by the composite is used as the magnetic field transducer. The composite consists of the Terfenol-D particles and a matrix of epoxy resin. An external magnetic field is employed to orient the magnetic domains in the composite. An FBG encapsulation structure is designed to enhance sensor sensitivity. The results of the calibration test demonstrate that the sensor has good repeatability and a high sensitivity of 9.83 pm/mT, which is about 3 ∼ 4 times higher than other sensors based on the similar principle in the literature. The dynamic test shows a fast response of the sensor to the dynamic magnetic field. This optical fiber sensor has the instinct advantage of immunity to electromagnetic interference and possesses good potential in power engineering applications.

STUDY OF PHASE TRANSFORMATIONS IN FERROELECTRICS BASED ON CALCIUM TITANATE.

The aim of this work is to study phase transformations kinetics in ferroelectrics based on calcium titanate. The relevance of this study is in the assessment of new methods for obtaining complex phase composition ferroelectrics, which have the potential for application in microelectronics, photocatalysis, and power engineering. The methods of scanning electron microscopy and X-ray diffraction were used as the main methods of analysis. Analysis of morphological features made it possible to establish the kinetics of changes not only in grain sizes, but also in their geometry. During the studies of phase transformations, the following dependence of the TiO2 – anatase/CaTi2O4 → TiO2– anatase /CaTi2O4/CaTiO3 → CaTiO3/TiO2 – rutile type was established depending on the annealing temperature. At the same time, at a temperature of 1000°C, a stable structure of ceramic with a perovskite-like structure of the CaTiO3 type and a high structural ordering degree (more than 92%) is formed.

Integral Equations Related to Volterra Series and Inverse Problems: Elements of Theory and Applications in Heat Power Engineering

The paper considers two types of Volterra integral equations of the first kind, arising in the study of inverse problems of the dynamics of controlled heat power systems. The main focus of the work is aimed at studying the specifics of the classes of Volterra equations of the first kind that arise when describing nonlinear dynamics using the apparatus of Volterra integro-power series. The subject area of the research is represented by a simulation model of a heat exchange unit element, which describes the change in enthalpy with arbitrary changes in fluid flow and heat supply. The numerical results of solving the problem of identification of transient characteristics are presented. They illustrate the fundamental importance of practical recommendations based on sufficient conditions for the solvability of linear multidimensional Volterra equations of the first kind. A new class of nonlinear systems of integro-algebraic equations of the first kind, related to the problem of automatic control of technical objects with vector inputs and outputs, is distinguished. For such systems, sufficient conditions are given for the existence of a unique, sufficiently smooth solution. A review of the literature on these problem types is given.

$G$-deviations of polygons and their applications in Electric Power Engineering

For any metric space $X$ endowed with the action of a group $G$, and two $n$-gons $\vec x=(x_1,\dots,x_n)\in X^n$ and $\vec y=(y_1,\dots,y_n)\in X^n$ in $X$, we introduce the $G$-deviation $d(G\vec x,\vec y\,)$ of $\vec x$ from $\vec y$ as the distance in $X^n$ from $\vec y$ to the $G$-orbit $G\vec x$ of $\vec x$ in the $n$-th power $X^n$ of $X$. For some groups $G$ of affine transformations of the complex plane, we deduce simple-to-apply formulas for calculating the $G$-deviation between $n$-gons on the complex plane. We apply these formulas for defining new measures of asymmetry of triangles. These new measures can be applied in Electric Power Engineering for evaluating the quality of 3-phase electric power. One of such measures, namely the affine deviation, is espressible via the unbalance degree, which is a standard characteristic of quality of three-phase electric power.

Development of an algorithm of implementation of programs of repair of steam boilers and turbines of TPPs based on technical condition of power equipment

Deterioration and aging of the technical fleet of thermal power facilities lead to an unpredictable shutdowns of power equipment. Therefore, it is necessary to create a special approach in maintenance and repair programs, taking into account the possibility of predicting the moment of onset of the defect, its development, as well as the time of possible equipment failure. The equipment maintenance system used at the enterprises is based on the collection of retrospective data on defects and failures on the main and auxiliary equipment of the TPP and summarizing statistics on identical or similar equipment samples. Analysis of domestic and foreign methods of maintenance and organization of repair, as well as possibility of their application in modern power engineering is given. In order to create an efficient production asset management system, which addresses the problem of finding a balance between the potential risk of losses associated with both the operation of equipment and the cost of correcting defects, new class systems are now used in the software market, which carry out equipment maintenance based on the forecast. In order to optimize the equipment maintenance system and ensure uninterrupted and reliable operation of the equipment at minimum operating costs, as well as to reduce equipment downtime, unscheduled and emergency operations, it is advisable to use a modern approach to manage both reliability and risk, as well as the cost of asset ownership. This will enable to control the economic efficiency of the use of production assets. The necessity of creation of an algorithm of implementation of repair programs of power equipment base on technical condition for its use in digital power systems is shown. An algorithm is proposed for implementing the repair program of power units of electric power plants, including steam boilers and turbines of thermal power plants, differing by taking into account the technical condition of power equipment, which allows recognizing the defect that has appeared, determining the cause of its occurrence, its evolution and the duration of possible equipment failure. In the developed repair maintenance algorithm, it is proposed to make a transition from statistical empirical assessments of the technical condition of the equipment to objective estimates obtained on the basis of automated technical diagnostics systems and predictive analysis of situations.