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Hydrogen energy is playing an increasingly important role in integrated energy systems (IES) due to its multiple uses. Green hydrogen can be produced from electricity generated from renewable energy, reducing carbon emissions and environmental pollution. Previous work usually only considers electric vehicles (EVs) as flexible loads, and there are few studies on the production and application process of green hydrogen. In this paper, hydrogen electrolysis centers (HECs) and EVs are simultaneously schedulable loads, integrated into IES, and optimized. The distribution network model is established, and the optimization goal is to minimize network loss while ensuring the safe operation of the power grid. Through the power flow optimization method of mixed‐integer second‐order cone programming, the space optimization scheduling of EVs in the IES is carried out, and the optimal location of the HEC is reasonably planned and set. A simulation instance of IEEE 33 nodes is used to verify the proposed method. The final results show that this method can minimize network loss in the range of safe operation of the system for the spatial scheduling of electrolytic hydrogen centers and EVs. At the same time, it can also provide a reference for HEC addressing. [ABSTRACT FROM AUTHOR]

Due to the rapid development of today's industry, it not only greatly accelerates the consumption of energy, but also causes serious pollution to the environment. Therefore, reducing energy waste and recovering energy has become an important research direction today. This paper introduces the future development trend of renewable energy, discusses the progress of recovery of nonpolluting energy such as marine, bioenergy and thermal energy, and analyzes the efficiency and social benefits of various types of energy recovery. Energy recovery methods for natural gas pipelines are briefly analyzed, and current research advances in energy pressure recovery devices are provided, leading to a summary of methods to improve the efficiency of fluid energy recovery. Finally, it is concluded that although the current energy sources are diversified, the most valued energy sources at present and in the future should be solar energy and ocean, which are infinitely recyclable. This paper will benefit researchers to further understand and study the various aspects of energy recovery. [ABSTRACT FROM AUTHOR]

During the multistage fracturing of shale gas horizontal wells, casing damage frequently occurs, making it essential to evaluate casing stress and deformation. This paper establishes a mathematical model of thermo‐hydro‐mechanical coupling and a finite element model of casing‐cement sheath‐formation (CCF). This paper examines the effects of temperature, casing thickness, nonuniform in situ stress, mechanical properties of cement sheath, and treating pressure on the stress and deformation of CCF. Finally, the study investigates the variation of local in situ stress at the heel and casing stress from toe to heel. The results show that the low temperature of CCF, high elastic modulus of cement sheath, small casing thickness, high treating pressure, and large nonuniform in situ stress can exacerbate casing stress. After pumping off, the local in situ stress and its nonuniformity near heel gradually increased, and the effective stress of casing increases compared with the pumping on the process. As the fracturing stages increase, the effective stress of casing at heel increases, and the casing at heel preferentially damages. The results provide a theoretical basis and guidance for maintaining wellbore integrity and preventing casing damage in the multistage fracturing of shale gas horizontal wells. [ABSTRACT FROM AUTHOR]

The filler is the core component of the cooling tower, filler performance refers to both its thermal and flow resistance characteristics, which use empirical formulas of tower characteristic N, volumetric mass transfer coefficient βxv, and pressure drop ΔP obtained through experimentation under specific conditions. However, the performance equations for identical countercurrent fillers can vary at different heights or seawater concentrations. Linear interpolation is the conventional method for obtaining the filler performance under different conditions, but its uncertainty limits the application. This paper explores the use of the radial basis function artificial neural network (RBF ANN) to analyze filler performance based on existing performance equations. The data set is generated by the filler performance equations. The results demonstrate that RBF ANN has a preferable prediction effect with high correlation (the determination coefficient R2 > 0.99) and prediction accuracy (the proportion of relative error within 10% N10 > 90%). Furthermore, the predicted results are consistent with the experimental results of the filler performance. Therefore, RBF ANN can accurately predict filler performance at varying heights and seawater concentrations, making it universal and providing a basis for cooling tower design. [ABSTRACT FROM AUTHOR]

Since the "double carbon" target was proposed in 2020, emissions reduction has become the focus of the Chinese government's work. Industrial development is a crucial factor contributing to the increase in carbon emissions. Therefore, implementing carbon reduction in the industrial sector is an important part of China's goal of "double carbon." In this context, this paper uses a three‐stage data envelopment analysis model to calculate the green total factor productivity (GTFP) of 18 industry sectors in Shanxi Province. The results show that: (1) the external environment has influence on the GTFP of energy revolution reform pilot area industrial sectors, and the efficiency value of two industries is overestimated before excluding environmental factors; (2) the industry difference of GTFP is significant, among which pharmaceutical manufacturing industry has the greatest promoting effect on GTFP, and ferrous metal mining and dressing industry has the greatest inhibiting effect on GTFP; (3) there is a serious redundancy in R&D investment of energy revolution reform pilot area in China, and only six industries belong to zero redundancy. (4) To reflect the GTFP of various industrial sectors more clearly, this paper divides four industries: "Double‐high" industry, "High‐low" industry, "Low‐high" industry, and "Double‐low" industry. [ABSTRACT FROM AUTHOR]

A convenient and effective method based on vector analysis was proposed in this paper to quantitatively describe the pattern of the wind speed distribution curve. This method can accurately describe the steepness of the single‐peak curve by using two parameters of concentration degree (CD) and concentration period (CP). Through the analysis of wind speed data in China over the past 40 years, this paper found that regional wind resources with larger CD were poorer, while regional wind resources with larger CP were richer. In addition, there were obvious cubic relationships between the CP and the average wind power density. These two parameters could reflect the richness of wind energy and realize the comparisons of wind resources across time and scales. In comparison with conventional approaches, this method is simpler and avoids fitting process, which has broad promotion prospects in the field of power grids. [ABSTRACT FROM AUTHOR]

This paper investigates four energy utilization scenarios with or without home‐distributed photovoltaic generation (HDPG) to reduce the generation cost and pollutant emission of the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles (DEED‐PEV). The first scenario considers valley filling for the charging of PEVs. The second scenario combines valley filling and peak shaving for the charging and discharging of PEVs. The third scenario adds peak shaving of HDPG to the first scenario, followed by the peak shaving with the discharging of PEVs. The fourth scenario rearranges the distribution of photovoltaic (PV) power for the third scenario, and the PV power in the afternoon is stored by a photovoltaic energy storage system (PESS) and consumed in the evening. A universal procedure is designed for the valley filling and peak shaving of the four scenarios, which is beneficial for determining the filled and shaved loads with respect to certain time intervals. An NSGA‐II method based on a modified crossover and an elimination of individuals (NSGA‐II‐MCEI) is proposed for the multiobjective optimization of DEED‐PEV. The modified crossover can improve the convergence of NSGA‐II‐MCEI, and the elimination operator can maintain the evenness of the nondominated solutions. According to experimental results, scenario 4 achieves cost savings of 95386.62 $, 85636.87 $, and 6776.85 $, respectively, for Scenarios 1, 2, and 3, and it reaches emission reductions of 27617.64 kg, 17252.71 kg, and 220.98 kg, respectively, for scenarios 1, 2, and 3. Also, scenario 4 outperforms the other three scenarios for three weather conditions such as sunny day, cloudy day, and rainy day. [ABSTRACT FROM AUTHOR]

In recent years, energy sharing has attracted a lot of attention. However, the intermediate platforms in centralized energy‐sharing methods cause the rapid growth of communication complexity and the risk of privacy leakage. Unlike complex energy‐sharing market mechanisms, in this paper, a simple and efficient distributed energy sharing for microgrids (MGs) is proposed, where game theory is employed to form flexible prices for prosumers and only local information is used to improve the privacy protection of prosumers. First, prosumers located in different areas are characterized more precisely and a two‐tier carbon emission cost model is built. Next, a game‐theory‐based distributed energy‐sharing model is proposed, where a flexible pricing mechanism is developed to enable prosumers to independently reach price agreements and achieve supply–demand balance within MGs. In the process, optimization models for obtaining equilibrium are formed and only local information is needed. However, solving these optimization models is generally time‐consuming. So, a distributed optimization method based on weighted subgradients is proposed to accelerate the equilibrium‐finding process. Finally, four cases are designed, and simulation results demonstrate that the prosumers' costs of our method are reduced by 7.5%–22.5% compared to the costs obtained by feed‐in tariff. Moreover, in the case of solving the distributed trading model for an MG at a 24‐h time scale, the iteration numbers of our method are only 38.9% and 49.3% of the two traditional solving methods. [ABSTRACT FROM AUTHOR]

Due to its high energy storage density, high instantaneous power, quick charging and discharging speeds, and high energy conversion efficiency, flywheel energy storage technology has emerged as a new player in the field of novel energy storage. With the wide application of flywheel energy storage system (FESS) in power systems, especially under changing grid conditions, the low‐voltage ride‐through (LVRT) problem has become an important challenge limiting their performance. In this paper, we propose a machine‐grid side coordinated control strategy based on model predictive current control (MPCC) for the insufficient LVRT capability of traditional FESS during grid faults. Excellent dynamic properties are demonstrated by the technique, which allows the grid‐side converter output current to swiftly follow the reference current instruction. The FESS's LVRT capability is increased when the grid‐side converter uses the MPCC current inner loop rather than the proportional–integral current inner loop during grid voltage dips. This greatly increases the reactive power response speed and efficiently supports the quick recovery of grid voltage. According to simulation verification carried out by Matlab/Simulink, the suggested control approach can assure the long‐term dependable operation of the FESS during voltage dips. This study can also be used as a reference for improving the FESS's LVRT capabilities in the future. [ABSTRACT FROM AUTHOR]

Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells. [ABSTRACT FROM AUTHOR]

As the penetration of distributed generation (DG) systems in the grid is increasing, the challenge of combining large numbers of DGs in the power systems has to be carefully clarified and managed. The control strategy and management concept of the interconnected systems should be flexible and reliable to handle the various types of DGs. This can be suitably met by microgrids. This paper introduces the microgrid structure and elements and states the main objectives that should be achieved by the microgrid controllers and each DG controller in both operation modes (grid‐connected and island mode). It also presents the challenges of having multiple DG units in a microgrid in terms of accurate power control/sharing, voltage and frequency regulation, power management between DGs, different renewable energy sources integration and deployment, seamless mode transfer, and the modeling issues. The centralized and decentralized control techniques as potential solutions have been discussed and compared by highlighting the advantages and disadvantages of each. Furthermore, the recent control techniques for drooped alternating current microgrids and the main proposed solutions and contributions in the literature have been exposed to finally overcome the droop control limitations and obtain a flexible and smart distributed power system. [ABSTRACT FROM AUTHOR]

In this paper, we selected representative electric buses (EBs) and diesel buses as research cases, aiming to explore a localized method suitable for various vehicle types that can be implemented in any region to measure the carbon emissions of urban buses throughout the lifecycle. We take into account the influence of seasonal factors over the lifecycle. We fully consider the production, assembly, and recycling stages of vehicle systems. We construct a model for estimating CO2 emissions during the fuel cycle. We analyze and compare the impacts of the optimized power grid structure on the lifecycle CO2 emissions of the two types of buses. The results indicate that seasonal factors affect both bus types' energy consumption, with EBs being more sensitive. Carbon emissions primarily stem from manufacturing the body, chassis, and notably, the lithium‐ion batteries, with the latter having a pronounced impact on EBs. A decrease in coal power generation from 71% to 21% in the grid reduced EB fuel‐cycle emissions by 70.7%. Although EBs are sensitive to seasonal factors, they still have the greatest potential for carbon reduction, depending on the proportion of clean energy in the power grid and the production processes of vehicle components. [ABSTRACT FROM AUTHOR]

Reservoir stimulation using hydraulic fracturing technology is one of the most common stimulation measures to develop tight oil and gas reservoirs, and the conductivity of fractures after fracturing is the key to the stimulation effect of oil fields. In this paper, the laboratory simulation system of fracture conductivity is used to study the long‐term fracture conductivity under the displacement of formation water and fracturing fluid. The experimental results show that the fracture conductivity decreases with the increase of displacement time under the displacement of formation water and fracturing fluid, and the decrease of fracture conductivity under the displacement of fracturing fluid with time is greater than that of formation water displacement because of the plugging effect of fracturing fluid residue on pore channels. The use of formation water and fracturing fluid circulation displacement can delay the decline rate of conductivity with time. The conductivity increases with the increase of sand concentration, proppant particle size, and elastic modulus, and decreases with the increase of closure pressure. In addition, by studying the influence of elastic modulus on fracture width, it is found that the smaller the elastic modulus is, the more serious the embedding phenomenon of proppant is, and the smaller the fracture width is. [ABSTRACT FROM AUTHOR]

Today, multilevel inverters are widely utilized in various applications. Reducing the number of components in the design of these converters is one of the crucial goals so that they have less volume and cost. This paper presents a single‐source structure for a multilevel switched‐capacitor inverter with voltage boost capability. The proposed structure can generate 25 sinusoidal‐like step voltage levels with a six times amplitude increase compared to the input. This converter uses only one direct current (DC) power source, 14 switches, and four capacitors. The nearest level control switching method has been used to control the converter, create voltage levels, and select the switching modes. A comprehensive comparison has been made with other recently presented structures to demonstrate the efficiency of the proposed structure. The advantages of the proposed structure include using only one DC voltage source, the ability to increase the output voltage six times compared to the input, the use of fewer power electronics components compared to the number of voltage levels, proper efficiency, self‐balancing of capacitors, and as a result, the lower cost. The performance accuracy of the proposed converter has been simulated in the MATLAB Simulink environment and then evaluated by a laboratory prototype. [ABSTRACT FROM AUTHOR]

Researchers and engineers are actively working on enhancing the efficiency of heat exchangers in engineering applications by developing novel designs, exploring new materials, and utilizing nanofluids. Three kinds of nanofluids with varying concentrations are investigated in this paper. The objective is to assess the performance of N‐shaped double‐pipe heat exchanger used in thermoelectric power plants. The performance has been evaluated using COMSOL Multiphysics software. The findings show that higher nanofluid concentrations resulted in elevated heat transfer coefficients and improved efficiency of N‐shape double pipe heat exchanger. The analysis revealed that a mere 1% rise in the volume fraction of nanofluids enhanced the efficiency of the heat exchanger on average 23% when compared to the base fluid (water). In comparison to the N‐shape double pipe (Inconel 625) heat exchangers, the N‐shape double pipe (copper) heat exchangers appear to be more efficient. The introduction of nanoparticles has a notable impact on the heat transfer coefficients. Specifically, within an N‐shaped double pipe (copper) heat exchanger, the inclusion of a 1% volume fraction results in a 2.09% enhancement in the heat transfer coefficient for Al2O3/water, a 1.3% improvement for Fe3O4/water, and a 1.15% increase for TiO2/water. It also exposed that adding 1% Al2O3/water led to a significant 0.623% increase in effectiveness, while TiO2/water showed a 0.259% rise, and Fe3O4/water exhibited a 0.375% improvement. Moreover, increasing the Reynolds number enhances the Nusselt number for Al2O3/water and Fe3O4/water nanofluids by 55.22%, and for TiO2/water by 54.60% at a 6% volume concentration, leading to additional increases in exchanger efficiency. Therefore, the augmentation in nanofluid concentration leads to a reduction in the temperature pinch points both at the intake and outflow. This observation suggests that nanofluids exhibit a superior ability compared to conventional fluids when it comes to effectively lowering temperatures. [ABSTRACT FROM AUTHOR]

The increasing trend of energy generation and management systems towards decentralized structures such as using renewable energy resources makes it necessary to use digital and smart platforms for exchanging information and even conducting financial transactions in a decentralized manner, known as the peer‐to‐peer model. The decentralized transaction verification of cryptocurrencies makes it possible to use these encrypted currencies and decentralized blockchain networks in energy management systems and carry out financial transactions related to carbon trading. Carbon and other greenhouse gas (GHG) emission trading systems reduce the competitiveness of fossil fuel projects in the market and accelerate investment in low‐carbon energy sources such as wind and photovoltaic power generation units. This market mechanism allows large entities such as countries and companies that emit GHGs into the atmosphere to buy and sell these gases. This paper reviews the blockchain solutions developed for carbon markets. Studies related to the design of smart contracts in the platform of blockchain are investigated. Special cryptocurrencies that are used in the field of green energy transactions and carbon trading are introduced. In addition, the application of artificial intelligence and game theory in energy trading is stated. The study of different blockchain frameworks for carbon trading shows that the use of decentralized platforms in carbon trading can have a significant impact on the trend towards low‐carbon measures and achieving the goals of the Kyoto Treaty, increasing the value of green cryptocurrencies and the volume of transactions. These technologies offer a promising avenue for creating a more decentralized, efficient, and environmentally conscious energy ecosystem. [ABSTRACT FROM AUTHOR]

Industry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings. [ABSTRACT FROM AUTHOR]

Aiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness. [ABSTRACT FROM AUTHOR]

From core observations of shales of the Niutitang formation in the northern part of Guizhou Province, China, calcite was often found to act as a natural fracture filler and affect the extension of fractures in hydraulic fracturing. Therefore, it is crucial to understand the tensile mechanical behavior of shales by calcite veins. In this paper, by computed tomography scanning of shales containing calcite veins of different dip angles from the Niutitang formation, a three‐dimensional numerical model reflecting the internal fine structure of the shale was constructed. Direct tensile numerical simulations were carried out to investigate the effect of calcite veins at different angles on the fine‐scale damage process and mechanical properties of shale. The experimental results show that the tensile capacity of the shale increases with the increase in calcite veins. Depending on the dip angle of the calcite vein, the damage pattern of the shale is divided into three types of damage: pull‐off damage along the calcite vein, jagged damage, and horizontal damage perpendicular to the loading method. At high dip angles, the shale damage is more intense and the fracture network more complex. The temporal and spatial characteristics of the acoustic emissions provide a good indication of the microscopic behavior of the shale specimens during the damage process. The box dimension method was used to calculate the fractal dimension of the shale specimens at the final damage, and it was found that the damage was more intense and the fracture network was more complex at high dip angles, and there was an angular threshold. [ABSTRACT FROM AUTHOR]

In this paper, a multigeneration cycle of electricity, cooling, and Bitcoin whose energy source is geothermal, has been subjected to energy, exergy, and economic analyses. The cycle under consideration includes the steam cycle (upstream cycle), the carbon dioxide cycle (downstream cycle), and the liquid–gas line to absorb the heat dissipated by the carbon dioxide cycle. In this cycle, the steam cycle condenser acts as the carbon dioxide cycle evaporator. Part of the electricity generated by this cycle is used to generate Bitcoins. Energy and exergy efficiencies at baseline (excluding Bitcoin production) are 45.8% and 38.1%, respectively. In this cycle, if more power is spent on producing Bitcoin as a product, the energy and exergy efficiencies of the cycle are reduced. Because Bitcoin itself is not valuable in terms of energy and exergy. Considering the average price of Bitcoin during the years 2015–2022 and if 100% of the electricity generated by the system is spent on Bitcoin production, the payback period in 2018, 2021, and 2022 when the price of Bitcoin is equal to $13,412.4, $21,398.8, and $47,743.0, respectively, are less than the baseline. Therefore, the production of Bitcoin with a variety of renewable energies can be considered as a solution. Of course, it should be noted that large changes in the price of Bitcoin can affect the issue of economic benefit. [ABSTRACT FROM AUTHOR]

To accurately calculate the carbon emission of integrated energy system (IES), and fully explore the potential for load side on carbon emission reduction, this paper proposes a method of guiding load to participate in demand response based on node carbon emission intensity, and constructs a bi‐layer low‐carbon scheduling model for source‐load collaborative optimization. First, the carbon flow calculation model of IES in the total process is established, such as source, network, load, and storage. It can depict the carbon emission characteristics of energy conversion equipment and energy storage devices. The principle of proportional sharing is used to track carbon emission flows, the changes in carbon emission intensity at each node is perceived from a spatiotemporal perspective. Second, carbon flow analysis is incorporated into the load demand response mechanism, and a carbon emission model for load aggregator (LA) after demand response is established based on the node carbon emission intensity. Third, a bi‐layer low‐carbon scheduling model is constructed, which considers the source‐load collaborative optimization. The upper layer is the optimal economic dispatch of IES operators, while the lower layer is the optimal economic dispatch of LAs. Finally, the effectiveness of the proposed method is verified using the system as an example, such as improved 14‐node power grid, 6‐node heating network, and 6‐node nature gas network. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of feature extraction and fault identification in fault diagnosis of wind power converters, a novel method for open circuit fault diagnosis of wind power converters based on variational mode decomposition (VMD) energy entropy (EE) and time domain feature analysis (TDFA) is proposed. Primarily, the three‐phase output current at the grid side of the wind power converter is collected as the original signal, and the VMD is used to decompose the original signal into a series of intrinsic mode functions (IMF). To reduce noise interference as much as possible, the Pearson correlation coefficient between each mode component and the original signal under different fault states is analyzed, and the IMF component containing the major failure features is selected to calculate the energy entropy of each component; afterward, according to the Pearson correlation coefficient results, the modal components of the first layer are selected for time domain feature analysis; finally, the feature matrix that combines energy entropy and time domain feature analysis is inputted into the long short‐term memory neural network for training and fault identification. The simulation and experimental results show that the open circuit fault diagnosis method proposed in this paper has high accuracy and robustness. [ABSTRACT FROM AUTHOR]

This work addresses the ongoing challenge of optimizing sand control screens, attributed to a limited understanding of screen blocking. A novel one‐way computational fluid dynamics and discrete element method (CFD–DEM) coupling technique is presented to analyze the structural parameters of unconsolidated prepacked gravel screens (PPGS). CFD–DEM involves systematically investigating the width of the punched slot on the outer protective screen, the size of the gravel, and the efficiency of gravel packing to improve the antiblocking capability of unconsolidated PPGS. Results from numerical simulations reveal that under conditions of effective sand control, the amount of sand entering the gravel layer to the bridge (referred to as the sand pass rate) is positively correlated with the antilocking ability (referred to as oil productivity index) for unconsolidated PPGS. The efficiency of gravel packing has the most substantial effect on the antilocking performance of unconsolidated PPGS. The data suggest that appropriately reducing the efficiency of gravel packing can enhance the antilocking capability of unconsolidated PPGS. To design an unconsolidated PPGS with enhanced antilocking capability, the efficiency of gravel packing be determined first. Then, the gravel size is designed based on this efficiency, and the width relative to the gravel size is decided. This contemporary design principle diverges considerably from previous design principles for gravel‐packed sand control screens. Supporting validation experiments agree with simulation outcomes, suggesting the established one‐way CFD–DEM coupling method in this paper is suitable for analyzing the plugging mechanism of unconsolidated PPGS. Thus, the CFD–DEM coupling method improves the design of such screens for improved antiblocking performance. [ABSTRACT FROM AUTHOR]

Considering the dynamic behavior of doubly‐fed induction generators (DFIGs) under the influence of random factors, this paper not only analyzes the phenomenon of stochastic instability and bifurcation of a DFIG dynamic variable in its random space when they are affected by environmental noise, but also proposes a method based on the Tkagi–Sugneo (T–S) fuzzy control strategy to control its stochastic bifurcation. First, a four‐dimensional stochastic dynamic DFIG model is established by using multiplicative white noise to simulate the influence of environmental noise on electrical variables, and stochastic central manifold theory is used to reduce the dimensionality of a planar model in the bifurcation neighborhood. Then, the stochastic stability of the model is investigated based on singular boundary theory, while the steady‐state probability density of the stochastic DFIG is determined using the Fokker–Plank–Kolmogorov equation to obtain the location and probability density of its stochastic P‐bifurcation. Finally, the influence of stochastic bifurcation behavior is eliminated by H∞‐fuzzy output feedback control. The numerical simulation results indicate that the location and probability of stochastic bifurcation in a DFIG will vary with the change in noise intensity, and the bifurcation parameter values and stochastic stability domain are obtained. The harm caused by random factors can be solved based on H∞‐fuzzy output feedback, which provides a theoretical basis for the stable operation of the DFIG system. [ABSTRACT FROM AUTHOR]

Thermal energy is one of the most widely encountered energy forms in our daily life. To ensure efficient utilization and conversion of this energy, the balance between supply and demand needs to be maintained. For this purpose, thermal energy storage is required. There are various thermal energy storage systems available; one of the most basic is sensible thermal energy storage which includes rock thermal energy storage (RTES). This rock‐based energy storage has recently gained significant attention due to its capability to hold large amounts of thermal energy, relatively simple storage mechanism and low cost of storage medium. Accordingly, numerous studies have been conducted to elucidate the basic flow and heat transfer mechanism and to evaluate the performance of this energy storage. The major technical challenges hindering the wide adoption of this technology are the enormous pressure drop across the storage and nonoptimal heat transfer from the heat transfer fluid to the storage medium and vice versa. These issues will directly and indirectly affect the overall cost (capital, operational, and maintenance costs) of the system. To eliminate this issue and assist further development of this technology, it is crucial to compile and extract important findings from these previous studies, identify the challenge and research gap, and draw guidelines for the upcoming research and development. At the moment, this kind of compilation does not exist. Hence, this paper is prepared with the primary objective to comprehensively review the current technology and development of RTES and to propose a potential way forward based on the pain point identified. Discussion on the nontechnical aspect such as policy and regulations as well as community awareness will also be outlined and discussed. [ABSTRACT FROM AUTHOR]

The battery energy storage system (BESS) is considered as an effective way to solve the lack of power and frequency fluctuation caused by the uncertainty and the imbalance of renewable energy. Based on these, this paper proposes a mixed control strategy for the BESS. First, this paper divides the demand for frequency modulation, peak regulation, and state of charge (SOC) of the battery into different zones. Then the Kuramoto model modulates the frequency, and the self‐recovery strategy is used to optimize the SOC. Meanwhile, the proposed mixed control strategy is used to integrate frequency control, demand control, and SOC optimization. Finally, the simulation results verify the rationality of the strategy. [ABSTRACT FROM AUTHOR]

This paper proposes an improved deep learning‐based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series‐based environmental inputs. Generally, artificial neural network‐based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time‐based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer‐based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological‐year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non‐zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours; resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real‐time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions. [ABSTRACT FROM AUTHOR]

Based on the energy flow analysis method, this paper establishes the relationship between the heat accumulator and the thermal system from the perspective of heat transfer. Different types of heat accumulators are converted into different equivalent models based on energy flow, the existing sneak circuit analysis methods based on energy flow are optimized, and the starting point and end point of path search are redefined. The energy flow‐based correlation method proposed in this paper is applied to a system with two different types of heat accumulators. The results show that the sneak circuit analysis method based on energy flow proposed in this paper can search for more paths and discover new sneak circuits on the basis of the original method. Therefore, the new method is feasible and effective. [ABSTRACT FROM AUTHOR]

Commercial buildings are consuming an increasing amount of energy, and accurate load demand forecasting is critical for the reliable operation of power systems and the efficient use of resources. Therefore, in this paper, a short‐term commercial load forecasting model based on tunicate swarm algorithm (TSA) combined with an extreme learning machine (ELM) under peak‐valley features is proposed as a research case for a shopping mall in Romania. This paper's overall structure is divided into two steps. In the first step, the 24‐h day is divided into six periods by analyzing the daily load characteristics of the training set, and the peak and valley loads are obtained. The ELM optimized by TSA (TSA‐ELM) algorithm is then used to forecast the peak and valley values of the test set one day ahead. In the second step, the actual load, peak, and valley for the previous week of historical load are chosen using the maximum information coefficient (MIC). Following that, the MIC ≥ 0.8 features are added to the TSA‐ELM to achieve short‐term commercial electricity load forecasting. The results show that the PV (Peak & Valley)‐TSA‐ELM model proposed in this paper has higher prediction accuracy compared with other models. Taking ELM as an example, compared with the traditional ELM, the mean absolute error, root mean square error, and mean absolute percentage error of PV‐TSA‐ELM are reduced by 20.59%, 20.13%, and 19.19% on average in the three commercial data sets. The proposed model is validated with an industrial data set, and ideal results are obtained, which verifies the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours. [ABSTRACT FROM AUTHOR]

The capacity of lithium batteries varies under different temperature conditions. However, many studies still neglect the influence of temperature on battery capacity. Besides, some studies only considered the combination of the heat and electricity of the battery monomer and failed to study the performance of the battery pack in groups. This paper investigates a new method for dynamic situations that takes the influence of temperature on battery capacity into consideration. After acquiring the parameter through experiment, we can substitute the performance parameters into the thermoelectric coupling model and use MATLAB to realize the iterative calculation of thermoelectric coupling. By combining the grouping mode with the model, we can simulate the thermodynamic and electrical properties of the battery cell in the pack. The novelty is evident because the paper focused on the battery pack instead of a single cell and the lumped parameter thermal model is adopted. Besides, the method proposed in this paper considered the influence of temperature on battery capacity. The program of calculation process can be solidified on the vehicle system. After the simulation, we can get the available capacity of the battery pack at different initial temperatures, with different grouping modes and different inconsistency of parameters. The results predicted by MATLAB model are compared with the results of MATLAB‐FLUENT thermoelectric coupling simulation. The result reflects that the prediction by the MATLAB model about the available capacity, temperature, and residual electricity is of good precision. [ABSTRACT FROM AUTHOR]

Condensers are often required in power generation systems to condense the exhaust steam generated at the end of the turbine. This paper will investigate the enhanced condensation process of spent steam in a parallel flow type. The operating parameters of steam temperature (Tcond), steam flow rate (Gin), cooling water temperature (Tin), and cooling water flow rate (Lw) will affect the condensation effect by changing the gas–liquid arrangement in the tower. In this experiment, an orthogonal test was designed in the Raschig ring to investigate the significance of these four operating parameters on the experimental results. Different types of packings have different condensation effects due to different accumulation methods. The same type of packing has a different surface area, resulting in different contact areas between the gas and liquid phases during condensation. In this experiment, four different packings will be investigated to find their condensation performance. This paper uses the size of subcooling (ΔT) as an indicator to evaluate the effectiveness of condensation, and the values of condensation rate (R), number of liquid phase heat transfer units (NTUL), and total volume heat transfer coefficient (Kv) are used as a reference for the effectiveness of condensation. The results show that the overall condensation effect of regular packing is better than that of random packing, The average condensation rate of regular packing is 95%, the average condensation rate of random packing is 90%, the subcooling of regular packing is about three to five smaller than that of random packing, the heat transfer coefficient of regular packing is about 1.5 times of that of random packing. And by fitting the Kv data for different packings, an empirical formula was obtained that can be used to predict the Kv size of other packings. [ABSTRACT FROM AUTHOR]

In the circumstances of "Carbon Peak," it is imminent to implement green construction technology in the construction industry as it is a major source of CO2 emissions. Nevertheless, little study has been done on the impact of self‐face awareness (SA) on green building project performance (GP). On the basis of a large‐scale questionnaire survey in China and an in‐depth analysis of the four categories (cognition, behavior, satisfaction, and performance), this paper has added an SA dimension, and proposed a new impacting path. Then, based on the improved conceptual model for measuring the impact of green construction behavior on GP, this paper has conducted empirical tests by using a large sample of questionnaire data and partial least square‐structural equation modeling method. The results show that: (1) green construction behavior has a positive impact on GP, and the impact of perceived behavioral control of green construction technology is most significant for promoting green construction in the industry, and SA has a significant positive effect on green construction in the industry; (2) environmental perception and perceived behavioral control for green construction technology play a mediating role; and (3) significant differences indeed exist in the multigroup analysis, and the biggest difference is working experiences in the engineering field. Finally, this paper has proposed corresponding management implications based on the above findings. The main contribution of this study is that this study has developed a new dimension (SA) to the model considering Chinese national and cultural differences, in an attempt to reveal the impact of green construction technology on GP in the Chinese context. In addition, the promotion of green construction (PGC) in the construction industry was also added based on the sustainability perspective, which is the first attempt to explore the role of the PGC. [ABSTRACT FROM AUTHOR]

Accurately predicting the capacity of lithium battery is conducive to improving its safety. Affected by complex internal electrochemical reaction and external use conditions, the prediction accuracy is difficult to guarantee; at the same time, the existing prediction methods are unexplainable, resulting in the inability to trace the prediction process. Therefore, a capacity prediction model based on belief rule base with interpretability and interval optimization strategy is proposed in this paper. First, the reasoning process is designed according to interpretability modeling criteria. Second, to achieve a balance of accuracy and interpretability, based on the whale optimization algorithm, a model parameter optimization method using interval optimization strategy is proposed. Finally, through a case study, the model's effectiveness is verified. Comparison with other models shows that the proposed model has certain advantages in accuracy and interpretability. [ABSTRACT FROM AUTHOR]

In this paper, a novel model with a parallel structure is proposed to predict NOx emissions from coal‐fired boilers by using historical operational data, coal properties, and convolutional neural networks. The model inputs are processed and passed into three parallel subnetworks with well‐designed building blocks. The features learned by the three subnetworks are fused and used to predict NOx emissions from a 330‐MW pulverized coal‐fired utility boiler. A comprehensive comparison of different prediction models based on deep learning algorithms shows that the prediction model proposed in this paper outperforms other prediction models in terms of root mean square error criteria. The results show that the parallel structure is key to obtaining accurate predictions while reducing model complexity. This suggests that the model's performance can be improved by designing the model architecture. [ABSTRACT FROM AUTHOR]

Film forming amines (FFA) are corrosion inhibitors added to power plant water. The major concern associated with their application is the thermal stability in the high temperature power plant water medium, along with the risk of decomposition into low molecular weight organic acids that can cause corrosive damages in the water/steam cycle. However, there is still a lack of sufficient data on the thermal stability of FFA corrosion inhibitors. This paper presents a comprehensive critical review and state‐of‐the‐art assessment of the results obtained from studying the thermolysis of FFA corrosion inhibitors in power plant water/steam cycle conditions, highlighting the relevance for practical application and research needs. Temperature, exposure time, initial concentration, and alkalizing agents were identified as key factors influencing the thermal stability of FFA in high temperature power plant water. Organic acids are found in concentrations harmless to metal tubes. Advanced scientific background information and additional research are required on this topic. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cells (PEMFC) have been noticed by researchers due to their high efficiency, low pollution, and high‐power density in distributed generation systems. In this paper, an LCL‐type grid‐tied PEMFC fuel cell power conditioning system is evaluated in a harmonics‐polluted low‐voltage grid. The LCL‐filters can lead to resonance and instability despite their capability to attenuate harmonics. In this research, a transformer has been used to connect the fuel cell inverter to the grid. The grid‐side inductor of LCL‐filter is realized by the leakage inductance of the transformer. In addition, for more effective resonance damping and attenuation of current ripples caused by the grid voltage harmonics, a capacitor voltage comprehensive feedback control has been designed and investigated. The comprehensive feedback control of the capacitor voltage contains proportional, first and second‐order derivative terms. In the proposed control scheme, the capacitor‐current‐feedback is opposed by the capacitor voltage derivative term due to reverse loop gain, which leads to deleting both of these loop gains. As a result, there is no need to utilize a current sensor in this control method. Consequently, the proportional and second‐order derivative terms of the capacitor voltage attenuate the LCL‐filter resonance. A low‐pass filter is also considered in the second‐order derivative loop in the controllable frequency range to ensure system stability. The simulation results of the PEMFC power conditioning system in different conditions confirm the proper attenuation of LCL resonance of grid‐tied inverter, high‐quality current injection to the harmonics polluted grid, the suitable stability, and the appropriate dynamic response for the proposed system. Under the proposed control scheme, the fuel cell power conditioning system demonstrates satisfactory stability. Even when reducing the LCL filter values by 5%–20%, the system maintains its stability effectively. Moreover, the THD of the injected current into the grid, employing the proposed control strategy, has been successfully reduced to an impressive value of 1.97% in a weak and harmonical grid. [ABSTRACT FROM AUTHOR]

The importance of flexible alternating current transmission system (FACTS) devices is increasing day by day as they provide compensation for power systems. The installation of such devices solves many issues regarding the stability and power transferability of power systems. However, for optimizing the performance of FACTS devices, the location and sizes of FACTS devices must be selected very carefully. On the other side, the utilization of optimization techniques is increasing daily, as it is capable of solving nonlinear, random problems by its stochastic nature. As a result, it became a reliable tactic for solving stochastic problems almost in every aspect of our lives. Therefore, it is adopted for solving power system problems, like, FACTS devices allocation, distributed generators allocation, determining component's parameters, and more. In this paper, the optimal size and location of several FACTS devices are selected to achieve the following single objective functions separately: fuel cost minimization and power losses minimization, then combining the mentioned objective function into one multiobjective function for gross cost minimization. The power system optimized is the Institute of Electrical and Electronics Engineers 30‐bus standard system, and the FACTS devices installed are static VAR compensator, thyristor control series compensator, and thyristor‐controlled phase shifter, while the optimization is performed using different conventional and recent optimization algorithms, including a newly developed algorithm, typically Leader Walrus Optimization Algorithm "LWaOA," making a comparison among such algorithms to investigate the algorithm that possesses the best performance. The results of the optimization processes show the superiority of LWaOA. [ABSTRACT FROM AUTHOR]

In this paper, a new control strategy, along with an innovative switching pattern, is proposed for the transfer of charge from one cell to another, whether they are adjacent or nonadjacent, or even to multiple cells simultaneously. Therefore, this strategy improves the efficiency of state of charge balancing, which is critical for lithium polymer (LiPo) batteries. Unlike in previous studies, all these functions are realized with a simple structure that employs standard DC–DC buck‐boost converters, without any additional complexities. By implementing the proposed control strategy, the effects of inaccuracies in the gain of current sensors, as well as multiplicative measurement noises, are reduced. The proposed method is applied to a set of five series LiPo cells, and a comprehensive investigation is conducted to assess both its balancing algorithm and operation. The simulation results illustrate the effective balancing performance and the achievement of the desired charge transfer through the proposed approach. An experimental setup, which includes five LiPo cells with a capacity of 1530 milliampere‐hours (mAh), is used to verify the proposed method. The outcomes demonstrate that the balancing of all cells is accurately performed under different initial conditions, within an acceptable time frame. [ABSTRACT FROM AUTHOR]

The reversible pump‐turbine plays an important role in hydropower stations, but pressure pulsation during their operation affects their performance and lifespan. Accurate prediction of pressure pulsation signals can provide an important basis for energy planning and stable operation of pumped storage units, thereby promoting sustainable development of the environment. This study introduces an optimization method that combines long short‐term memory (LSTM) and variable mode decomposition (VMD) to enhance the prediction accuracy of pressure pulsation signals. First, by decomposing the pressure pulsation signal into multiple relatively stable subsequence components using VMD, the characteristics of the original signal become more distinct. Subsequently, individual LSTM‐based time series prediction models were constructed for each modal function, and the hyperparameters related to subsequence were optimized using the sparrow search algorithm. To validate the efficacy of the proposed approach, this paper conducted experiments using pressure pulsation signals of a pump‐turbine obtained through numerical simulation. The experimental data was divided into training and testing sets, with the former used to train the LSTM model and the latter used for validation. The experimental results show that the optimized VMD with an optimized LSTM method can effectively improve the prediction accuracy of pressure pulsation signals in reversible pump‐turbine. [ABSTRACT FROM AUTHOR]

Leakage is one of the most serious challenges for the safe production of high‐pressure gas wells for its high risks, including abnormal annular pressure, natural gas accumulation, and environment pollution, but available methods can hardly accurately measure the leakage type and depth, which are the key parameters for the rigless leakage repair and risk assessment. Therefore, this paper proposes a method to measure the leakage based on the characteristics, which combines qualitative and quantitative measurement together. Qualitative measurement considers the annular pressure, tubing pressure, liquid level, cement quality, and workover history. Quantitative measurement is determined by noise logging, electromagnetic logging, pressure logging, and temperature logging. The logging should be optimized according to the qualitative measurement. The method was successfully applied in high‐pressure gas well belonging to Tarim Oilfield. Two potential leakage types are provided based on the annular pressure, liquid level, cement quality, and workover history, including tubing leakage and linger hanger leakage. Based on the potential leakage types, the pressure difference, logging devices string, stopping length, and time are optimized to make the engineering logging reliable. Through measurement, two leakage points are found in tubing string. One is tubing body crack at the depth of 2724 m and the other is tubing thread leakage at the depth of 5211.7 m, which well matches the production data. [ABSTRACT FROM AUTHOR]

This paper introduces a Modified Reptile Search Algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs‐based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness‐distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard Reptile Search Algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112‐bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case. [ABSTRACT FROM AUTHOR]

Wind turbine bearings play a crucial role in ensuring the safe and efficient operation of wind turbines. Accurate estimation of the remaining useful life (RUL) of bearings can significantly reduce operating and maintenance costs. In this paper, we propose three advanced data‐driven models to predict the RUL of high‐speed shaft bearings in wind turbines. These models combine the sparrow search algorithm (SSA) with three different regression models, namely support vector machine, random forest (RF) regression and Gaussian process regression. The models are based on features extracted from the vibration signal analysis, and the features are selected based on their monotonicity to evaluate bearing degradation. To optimize the performance of the regression models, all model parameters are tuned using the SSA algorithm. The proposed models are validated using vibration data collected from a real 2 MW commercial wind turbine. Our results demonstrate that the proposed models are effective in predicting the RUL of wind turbine bearings, and the SSA algorithm improves the accuracy of the predictions. [ABSTRACT FROM AUTHOR]

The main bearing assembly structure is one of the most important main load‐bearing structures of a diesel engine. The working loads of this structure increases dramatically with the increase of the power density of diesel engines, resulting in the problem of unsatisfactory reliability coordination design in restricted design space, while the mechanisms for coordinated design are still at the blank stage. Aiming at the problem, a multilevel multiobjective coordination matching design technique is innovatively proposed in this paper. This technique is characterized by a multilevel multiobjective coordination evaluation system for the assembly structure and its components based on the improved analytic hierarchy process. The finite element model and mathematical model of the main bearing assembly structure are established to realize the joint of finite element technology and optimization technology for coordination matching design. By carrying out verification experiments, the strength coordination, deformation coordination and contact strength coordination of the assembly structure increase by 13.91%, 14.96%, and 2.63%, respectively, after matching design, while the mass coordination remains almost constant meeting the lightweight design requirements. The overall coordination of the main bearing assembly structure is improved by 11.10%, achieving the goal of matching design of the main bearing assembly structure. The results show that the coordination evaluation system can quantitatively characterize the coordination relationship of the assembly structure and the multi‐reliability of components, and it is a feasible coordination evaluation method. The demonstrated coordination evaluation system and coordination matching design modeling approach provide important theoretical guidance for the matching design of complex assembly structures. [ABSTRACT FROM AUTHOR]

One of the major challenges facing fault diagnosis tools is their exposure to noise. The presence of noise may cause false alarms or the inability to detect a progressive fault in the early stages of its occurrence. Continuing previous efforts to address such a problem, in this paper, a noise‐robust diagnosis system for an industrial gas turbine is presented. The proposed structure employs a set of deep residual compensation extreme learning machines (DRCELMs). In this model, an optimal number of compensating blocks are trained to recover some of the lost useful information in the face of noise. Training and testing data required to develop the fault diagnosis model are generated by a performance model of the studied gas turbine. The t‐distributed stochastic neighbor embedding algorithm is employed for visualizing the gas path faults. Furthermore, the performance of the DRCELM is evaluated by comparing it with six other diagnosis models. The results indicate higher robustness of the DRCELM compared to other fault diagnosis systems. The proposed model presents a classification accuracy of >97% in noisy data and an accuracy of >98% in noise‐free data and combined data, while the average of fault positive rate and fault negative rate in noisy data is less than 2.5%. [ABSTRACT FROM AUTHOR]

The direct current (DC)‐link voltage control of the flywheel energy storage system plays an important role in realizing high‐quality grid connection. With the traditional proportional‐integral control, the DC‐link voltage cannot track its reference value quickly and smoothly when the flywheel energy storage system switches from the charging stage to other working stages. Therefore, a DC‐link voltage control strategy for the flywheel energy storage system based on active disturbance rejection control is proposed in this paper to deal with this issue. The DC‐link voltage and its differential value are considered as the state variables in this strategy. The internal and external disturbances, such as load power, switching loss, and parameter uncertainty, are regarded as an expanded state. By inputting the voltage error and the observed disturbance into the nonlinear state feedback control law, the rapidity and anti‐interference of the DC‐link voltage control are ensured under different States of Charge of the flywheel energy storage system. Then, the coefficient freezing method is used to analyze the effects of the disturbance observation bandwidth by nonlinear gain and pole position changes. The DC‐link voltage can track the reference value over a wider frequency range of disturbances. By configuring the appropriate observer structure parameters, the disturbance observation bandwidth under the nonlinear function gain variation is always higher than the expected bandwidth. The effectiveness of the proposed control strategy is verified by simulation results at last. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of protecting electrical networks in the presence of distribution generators (DGs). The use of DGs affects fault currents, leading to miscoordination between protection relays and causing constraints on network reliability. To tackle this issue, the authors propose an adaptive protection scheme (APS) based on a modified pelican optimization algorithm (MPOA) and active database management system (ADBMS). The APS coordinates directional overcurrent relays and distance relays, while the MPOA simulates a pelican mating strategy and includes a modified internal equation. The proposed APS is further upgraded with ADBMS to save system resources by storing relay settings in the database and calling them when the state of DGs changes without running optimization algorithms. The effectiveness of the proposed APS is validated on the Institute of Electrical and Electronics Engineers (IEEE) eight‐bus test system and the IEEE 14‐bus distribution network. Results indicate that the APS can effectively protect electrical networks in the presence of DGs, while the ADBMS upgrade saves system resources. [ABSTRACT FROM AUTHOR]

As the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making. [ABSTRACT FROM AUTHOR]

Changing the shape of the blade trailing edge profile of a centrifugal pump is one of the effective means to improve the internal unsteady flow and pressure pulsation of a centrifugal pump. In this paper, the effect of four shapes of the blade trailing edge profile on the internal nonconstant pressure pulsation and internal flow of a multistage centrifugal pump is investigated by numerical simulation. The four types of blade trailing edges include prototype impeller (original trailing edge), pressure surface cut (ellipse on pressure side [EPS]), suction surface cut (ellipse on suction side [ESS]), and two‐side cut (ellipse on both sides). The results show that reasonable cutting of the trailing edge of the blade can reduce the pressure pulsation of the centrifugal pump. By comparing the pressure pulsation at fBPF, we found that the blade trailing edge profile can change the pressure pulsation. When the speed decreases, the periodicity of pressure pulsation becomes worse and the amplitude of pressure pulsation at fBPF decreases. The static pressure distribution at the trailing edge of the blade shows that EPS and ESS can effectively reduce the area of the high‐pressure gradient region at the trailing edge of the blade, and the dynamic–static interference effect is weakened, which leads to a lower pressure pulsation amplitude. The distribution of turbulent energy in different blade trailing edge centrifugal pumps corresponds to the distribution of static pressure in different blade trailing edge centrifugal pumps, further demonstrating the reliability of the static pressure distribution. The results of this study will further provide theoretical support for the optimization of the blade trailing edge profile. [ABSTRACT FROM AUTHOR]

This paper reviews state‐of‐the‐art numerical tools for the operation and maintenance (O&M) of offshore wind farms, focusing on decision support models for maintenance scheduling and the consideration of human and environmental uncertainty. In this review, various factors that can influence the successful conduct of maintenance operations will be examined and special attention will be paid to the most significant ones. Data‐driven technologies for improved offshore asset management are also examined and the most used data‐driven methods for modeling and optimizing turbine operation and maintenance are presented. A focus will be placed on the choice of maintenance strategy, which is the basis for the planning of operations and thus the optimization problem discussed. As offshore maintenance is a complex operation whose efficiency and safety depend on human and environmental factors, special attention will be paid to the planning strategy that minimizes the risks involved while maximizing efficiency by considering these factors. The choice of planning technique for turbine maintenance and better consideration of uncertainties are crucial areas of improvement as they can lead to better overall efficiency, higher profit margins, better safety, and improved sustainability of offshore wind farms. The paper covers the application of digital technologies for offshore wind O&M planning and the associated challenges. The paper also highlights the various environmental and human factors to be considered for the operation and maintenance of wind turbines. [ABSTRACT FROM AUTHOR]