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Pumped storage is an important green, low-carbon, and clean flexible regulating power source in the power system, which can provide regulation services for the power system, promote the construction of a new type of power system, and facilitate the green transformation of energy. To improve the efficiency and stability of the centrifugal pump turbine under multiple operating conditions, a surrogate model combining radial basis functions with a high-dimensional model is used for performance optimization. Taking the active guide vane of the centrifugal pump turbine as the research object, the airfoil profile is parameterized, and the surrogate model's independent variables and training range are determined. Combining programming and numerical simulation software, an efficiency prediction model for the centrifugal pump and water turbine based on guide vane airfoil control variables is constructed. The particle swarm algorithm is used to globally optimize the constructed model to obtain the optimal efficiency point and corresponding airfoil-related parameters. Finally, numerical simulation and experimental research methods are used to validate the predicted data. The results show that under the premise of ensuring grid performance and operational stability, the numerical simulation efficiency of the pump turbine under the optimization scheme is increased by 1.6 and 0.32%, respectively, compared to the numerical efficiency of the prototype guide vane. In the experimental case, the efficiency of the water turbine and pump is increased by 0.76 and 0.14%, respectively, compared to the prototype guide vane. [ABSTRACT FROM AUTHOR]

In addressing the complexity, limited information, and dynamic spatiotemporal characteristics encountered in predicting pellet strength with traditional methods, this study proposes a novel prediction model for the strength of fusible pellets, developed on a Particle Swarm Optimization Deep Fuzzy Neural Network (PSO-DFNN). Initially, the model is constructed by observing and extracting fractal features of the microstructure of pellet ore. Subsequently, the fuzzy system is utilized to partition the spatiotemporal data and generate multi-layer fuzzy rules, thus constructing a deep fuzzy neural network. Lastly, the Particle Swarm Optimization algorithm is employed to optimize the fuzzy membership rule weights, achieving precise prediction of pellet strength. The results indicate a Mean Absolute Error (MAE) of 3.7218 and a Symmetric Mean Absolute Percentage Error (SMAPE) of 3.72 % when predicting pellet strength during the pellet roasting drying stage. The PSO-DFNN model exhibits high prediction accuracy, meeting the needs for pellet strength prediction and providing a more reliable basis for decision-making in the production process. [ABSTRACT FROM AUTHOR]

This paper introduces the Particle Swarm Optimization (PSO) algorithm to enhance the Latin Hypercube Sampling (LHS) process. The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation (MCS) to LHS for probabilistic trend calculations. The PSO method optimizes sample distribution, enhances global search capabilities, and significantly boosts computational efficiency. To validate its effectiveness, the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power. The performance was then compared with Latin Hypercubic Important Sampling (LHIS), which integrates significant sampling with the Monte Carlo method. The comparison results indicate that the PSO-enhanced method significantly improves the uniformity and representativeness of the sampling. This enhancement leads to a reduction in data errors and an improvement in both computational accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mixed shock model is an explicit construction method of failure probability model based on component independent failure, system nonfatal shock, and fatal shock failure, which considers common cause failure (CCF) in redundant system. For aerospace systems, a modified mixed shock model is proposed, which considers several components may fail independently and simultaneously in operation. In order to solve the issue that the parameters of the mixed shock model cannot be solved directly based on the failure probability data, a parameter solving method based on particle swarm optimization (PSO) algorithm is proposed. Additionally, the relationship between the failure probability and the gradient of the parameter change is deduced, and the reduced‐order (RO) solution based on the gradient of the parameter change is proposed to improve the efficiency of the solution. A fitness function construction method based on the relative error of the solution probability and the true probability is proposed to improve the probability solution accuracy of multicomponent failure. The nonlinear inertia factor optimization method combined with fitness change is studied to improve the particle swarm dynamics. The accuracy of the results of different parameters solving sequence and different PSO methods are compared, and the effectiveness of the RO solution is verified. The results of the mixed shock model before and after modification are compared with the different CCF data, which verifies the effectiveness and wide applicability of the modified mixed shock model. The results show that the modified mixed shock model for CCF and its parameter solution method can significantly improve the probability solution accuracy of all components failure, and also provide a new theoretical basis and solution method for the quantitative analysis of multiredundant system failure. [ABSTRACT FROM AUTHOR]

Hydrogen will play critical pole in industrial field, heating field and transportation field, which can achieve mutual conversion of different energies. Hydrogen load prediction demand is important for establishing unified energy system, a novel prediction model is established based on particle swarm algorithm (PSA) and grey Ridgelet neural network (GRNN) to improve medium and long term hydrogen load demand prediction accuracy. Firstly hydrogen load demand prediction model in unified energy system is established, which concludes hydrogen load demand prediction models in industrial field, heating field and transportation field, and then total hydrogen demand model is deduced. Secondly, model of GRNN is constructed based on grey system theory and Ridgelet neural network, analysis procedure of GRNN is established. Structure of GRNN is confirmed, and mathematical model is constructed. To enhance prediction effectiveness of GRNN, PSA is used to optimize parameters of GRNN. Finally hydrogen load demand data in a province is selected to carry out prediction simulation, results show that prediction error of proposed PSA-GRNN ranges from 1.88% to 3.02%, which is less than that of other three prediction models, and fit goodness of proposed PSA-GRNN ranges from 0.958 to 0.985, which is also less than that of other three prediction models. Therefore proposed PSA-GRNN has better prediction precision and efficiency, which can obtain better precision effect and applicability. Hydrogen load demand prediction results in heating field based on PSAGRNN are closer to real value than that based on other three prediction models, results show that proposed PSA-GRNN has better prediction accuracy that other three prediction models. [ABSTRACT FROM AUTHOR]

To improve the current source-grid, load-storage microgrid coordinated optimal scheduling method, which is not ideal in terms of efficiency and effectiveness, the study combines convolutional neural network, variational modal decomposition, and long and short-term memory neural network to realize the short-term prediction of microgrid electric load. Based on this, a mathematical model having source-grid, load-storage coordinated optimal scheduling and an improved particle swarm algorithm are proposed for it. Compared with the particle swarm backpropagation model, the proposed microgrid power load short-term prediction model reduces the average absolute percentage error and root mean square error by 0.38% and 39.5%, respectively. In addition, the economic cost of the proposed power grid coordination and optimization scheduling model based on improved particle swarm optimization algorithm (IPSO) is lower, at $3954.3, and the load fluctuation is less, at 56.6 W. This indicates that the model proposed by the research institute helps to achieve self-sufficiency of electricity within the microgrid and mutual assistance between microgrids, thereby tapping into scheduling potential, and also helps to achieve economic electricity scheduling strategies, avoiding unnecessary thermal power generation and carbon dioxide emissions, and improving reliability. Therefore, the scheme proposed in the study can effectively realize the coordinated and optimal dispatch of source-network load and storage beneficial to the power enterprises. [ABSTRACT FROM AUTHOR]

The quantitative determination of wool and cashmere mixed fiber is an indispensable quality control link in the textile industry, crucial for improving international trade status, ensuring product quality, and safeguarding consumer rights. Therefore, the goal of this study is to develop a reliable method for estimating fiber contents in wool–cashmere blends based on near-infrared (NIR) spectroscopy. A total of 210 mixed samples of 21 different proportions of cashmere and wool are prepared in the experiment, and data are collected in the NIR spectral band of 1,000–2,500 nm. Convolution Savitzky–Golay (S–G) combined with the second-order derivative is then used for spectral preprocessing. The variable iterative space shrinkage approach (VISSA) optimizes the characteristic wavelengths, and 339 wavelength points are selected. The prediction model of the least squares support vector machine (LSSVM) is established by particle swarm optimization (PSO), fast positioning, and analysis of key information related to the target in complex spectral data. Finally, the training set and the prediction set are divided according to the ratio of 8 : 2. Experiments show that in terms of modeling and prediction, the PSO-LSSVM model based on the wavelength selected by VISSA has a prediction determination coefficient R-squared of 0.9821, a prediction root mean square error of 1.1263, and an mean absolute error of 0.6527. The hybrid modeling method of VISSA, PSO, and LSSVM based on NIR spectroscopy (VISSA–PSO–LSSVM) can provide a more accurate and stable method for the non-destructive detection of cashmere and wool blended fiber content.

In addressing the complexity, limited information, and dynamic spatiotemporal characteristics encountered in predicting pellet strength with traditional methods, this study proposes a novel prediction model for the strength of fusible pellets, developed on a Particle Swarm Optimization Deep Fuzzy Neural Network (PSO-DFNN). Initially, the model is constructed by observing and extracting fractal features of the microstructure of pellet ore. Subsequently, the fuzzy system is utilized to partition the spatiotemporal data and generate multi-layer fuzzy rules, thus constructing a deep fuzzy neural network. Lastly, the Particle Swarm Optimization algorithm is employed to optimize the fuzzy membership rule weights, achieving precise prediction of pellet strength. The results indicate a Mean Absolute Error (MAE) of 3.7218 and a Symmetric Mean Absolute Percentage Error (SMAPE) of 3.72 % when predicting pellet strength during the pellet roasting drying stage. The PSO-DFNN model exhibits high prediction accuracy, meeting the needs for pellet strength prediction and providing a more reliable basis for decision-making in the production process.

In order to solve the problem of unsatisfactory control effect caused by uncertain parameters in the fuzzy skyhook control method, a fuzzy skyhook control strategy optimized by particle swarm strategy is proposed. The magnetorheological (MR) damper is put on the bench for mechanical characteristics experiment, and the forward model of MR damper is established. The adaptive network fuzzy inference system (ANFIS) system in simulation software is used to build its inverse model to verify the accuracy of the forward model of MR damper. A quarter vehicle suspension model is established, and a fuzzy skyhook control strategy based on particle swarm optimization is designed. Numerical simulations are carried out under the excitation of random road. The acceleration, suspension dynamic deflection and tire dynamic load are used to evaluate its performance. Compared with the passive control, the root mean square (RMS) values of the acceleration for the fuzzy skyhook control strategy are improved by 41.9% and 37.6%, the RMS values of the suspension dynamic deflection for the fuzzy skyhook control strategy are improved by 53.3% and 48.0%, and the RMS values of the tire dynamic load for the fuzzy skyhook control strategy are improved by 17.6% and 14.5% under the B-Class and C-Class road excitations. The simulations and experimental results verify the effectiveness of the controller. [ABSTRACT FROM AUTHOR]

【Objective】To improve the linear fit between the reflected spectral center wavelength and external environmental variables in Fiber Bragg Grating (FBG) sensing system, this paper proposes to use particle swarm optimization of Gaussian process regression model to the field of FBG stress sensing.【Methods】For the reflectance spectral characteristics of the FBG, the paper studies the impact of the linear fit in the spectral fitting of FBG sensing system. The particle swarm algorithm is used to search for the optimal hyperparameters in the Gaussian process regression model in order to enhance the predictive performance of the reflectance spectral wavelength of the center. A FBG stress sensing experimental platform was built, and the FBG was laid on the strength beam. Different weights were applied to one end of the equal strength beam to produce axial strain on the FBG, and the reflectance spectral data were collected by the spectrometer and analyzed by linear fitting with the studied model. The results obtained by the unoptimized Gaussian process regression model, the maximum value method, the Gaussian fitting method, and the center of mass method were used as the control group.【Results】The results show that under the conditions of erbium-doped fiber amplifier output power of 10 dBm, transmission fiber distance of 50 m, and the number of sampling points of the spectrometer of 501, the linear fit between the reflected spectral center wavelength and the mass of the weights is better than that of the control group. The linear fit of the studied model can reach up to 0.951 9, which is improved compared with that of the control group. Under the conditions of 501, 251, 167 and 126 spectral sampling points, the studied model can improve the linear fit of the system to 0.990 0, which is a maximum improvement of 0.258 7 compared with the maximum value method.【Conclusion】The analysis results show that the Gaussian process regression model optimized by the particle swarm is able to effectively improve the linear fit of the FBG stress sensing system.

The effort of this article is to solve one of the main problems in the field of banking, which is closely related to the field of information technology. The combination of the management discussion of this topic with the field of information technology will be one of the important topics in the field of information technology management. The main goal of this article is the clustering of bank customers.At first, all customer characteristics were extracted from the bank's database, which was randomly extracted for 900,000 customers, which will be provided as input to the proposed method of this article. All the characteristics of these customers were extracted and 10 characteristics (except four characteristics of the LRFM method) were listed using the opinions of experts. The proposed method should be able to choose among these 10 features for clustering customers, which results in more resolution in clustering. Due to the high number of cases of this problem, it is not possible to do it manually, and the proposed method tries to provide a separate model for clustering for the customers of each bank by examining different cases. Also, the problem of choosing the right value for the number of clusters in the K-means method is solved by the method proposed in this article. The results show that it is better than the basic RFM and LRFM methods.Keywords: relationship management with bank customers, clustering, RFM model, LRFM model, particle swarm algorithm, K-means method.

Abstract This study presents a comprehensive investigation into the optimization of PID control parameters for marine dual-fuel engines using an improved particle swarm algorithm. Through the development of a Matlab/Simulink simulation model, the thermodynamic behavior of the engine and the functionality of its control system are analyzed. The PID control parameters for air–fuel ratio control and mode switching control systems are fine-tuned utilizing the improved particle swarm algorithm (PSO). Simulation results demonstrate that the proposed improved PID-PSO approach outperforms traditional PID and traditional PSO-PID control methods in terms of reduced overshoot, minimized steady-state error, faster response times, and improved stability across various operating conditions and response modes. In comparison to traditional PID and PSO-PID controllers, the improved PSO-PID controller reduces the response time by 0.47 s and 0.21 s, the maximum overshoot by 98.43% and 96.05%, and decreases the absolute errors by 87.42% and 90.55%, respectively, in air–fuel ratio control using the step response method. The study's findings offer valuable insights into enhancing the performance and efficiency of marine dual-fuel engines through advanced control strategies.

In the context of the coordinated development of new energy sources and distribution networks, a hybrid energy storage twolayer optimal configuration model was proposed for the optimal configuration of distributed energy storage connected to distribution networks. The upper layer optimization determined the energy storage access location and capacity, and divided the power by Fourier transform, using super capacitor and battery to bear the power of different frequency parts respectively. The lower layer optimization was designed with the objective function of maximizing the benefits from low storage and high discharge operations. It was optimized using a combination of the particle swarm algorithm and the Pareto file. The model's rationality and effectiveness were confirmed through simulation experiments conducted on the IEEE33 nodes network. The results show that the model can achieve multi-objective comprehensive optimization, including reducing network losses, optimizing power index and reducing investment costs of energy storage equipment, which provides an effective solution for the optimal configuration of distributed energy storage connected to distribution networks. [ABSTRACT FROM AUTHOR]

Objectives: Flash floods are natural disasters caused by sudden rise in water levels in mountainous rivers, which are characterized by instantaneity and great destructiveness. In recent years, the frequent occurrence of flash floods in Longnan city, Gansu province, has posed a serious threat to the safety of local people's lives and property, thus it is urgent to carry out a risk assessment of flash floods in this region. Methods: This study takes Longnan city as the study area, and utilizes the MaxEnt model combining with the particle swarm algorithm to evaluate the vulnerability of study area based on 834 flash flood hazard points investigated and 32 disaster-causing factors. It also predicts the spatial pattern changes and potential mass migration trends of the future flash flood vulnerability areas based on three periods of climate data from the current period (2021— 2040) and the future period (2041— 2060, 2061— 2080, 2081— 2100). Results and Conclusions: The area under receiver operating characteristic curve of the results of the study in each period is above 0.85, which indicates that the precision of the results of the method is good. The main cause factors in this study area are driest month precipitation, monthly mean diurnal temperature dif‐ ference, coefficient of variation of precipitation, warmest month maximum temperature, land use, distance from the river, soil texture, profile curvature, elevation, and topographic relief. The flash flood-prone areas in the study area varies in different periods, but are mainly distributed in Wudu District, Wen County and Tanchang County, and the simulation results for the three future periods (2041—2060, 2061—2080, 2081— 2100) reflected a decreasing trend compared with the current period (2021—2040). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, there is a coverage blind zone for very high frequency (VHF) communication stations in Tibet area in 7 000 m and above, but there is no research and corresponding solution for VHF communication signal blindness replenishment in China. In order to solve the above problems, a supplementary blind deployment mathematical model was proposed, which takes the actual terrain of Tibet as the optimal search object, and the single and double coverage rate of the airway communication signal within the jurisdiction of Lhasa control area as the index. Then, on the basis of not changing the number and location of the original VHF communication stations in Tibet, simulated annealing particle swarm optimization algorithm was used to find an optimal station with minimum frequency and minimum number of stations to study the coverage of VHF communication signals on a certain uncovered route. The simulation results show that the algorithm not only realizes the goal of using the minimum frequency and the minimum number of stations to solve the coverage blind area of the route communication signal, but also overcomes the shortcoming of the particle swarm optimization algorithm which is easy to fall into the local optimal solution in the optimization process, and also proves the correctness of the proposed mathematical model of the complement blind deployment and the efficiency of the improved particle swarm optimization algorithm. The algorithm and model can provide theoretical and technical support for route network planning, station deployment optimization, and finally solve the problem of spectrum scarcity through this method. [ABSTRACT FROM AUTHOR]

The main objective of this research is to improve the reliability of the electrical system under study by reducing both the frequency of outages (SAIFI) and the downtime of electrical service (SAIDI). These problems are largely affected by adverse weather conditions, vegetation growth, and bird contact. To carry out the analysis, the 5011 urban feeder of Canton La Troncal was selected, since it presents critical values in terms of reliability compared to other feeders. The proposed methodology involves a mathematical model of heuristic optimization based on the Particle Swarm Optimization (PSO) algorithm. Two scenarios are defined for the modeling: the first focuses on relocating the existing recloser and the second on analyzing the impact of adding additional protection equipment. These scenarios are validated and demonstrate their effectiveness, achieving an improvement of 29.05% in the first case and 70.93% in the second case within a real conventional distribution system. [ABSTRACT FROM AUTHOR]

This study analyzes the key processes of hydrometallurgical technology, constructs an optimization con- trol model, and improves the particle swarm algorithm using adaptive inertia weight and simulated annealing operators to achieve optimization control of hydrometallurgical technology. Simulation test results show that in the test on the A wind farm optimization dataset, the AIWSAO - PSO algorithm stabilizes after 225 iterations with a fitness value of 0. 165. At 100 iterations, the algorithm's root mean square error, mean absolute error, and relative standard deviation (RSD) are 0.008 0, 0.004 5, and 0.971%, respectively. In the optimization control model for hydrometallurgical technology, the obtained comprehensive benefit value is 1.9 x 105 yuan/h, with an absolute error of about 0. 1 x 104 yuan/h from the target expected value. This achieves the optimization control of the hydrometallurgical process and provides technical support for similar optimization control applications. [ABSTRACT FROM AUTHOR]

It is very important to detect fault and extract fault features of mechanical systems at an early stage, because the above two steps promise normal operation of mechanical systems. However, they are also very challenging. In this context, this article has put forward improved particle swarm optimization-based adaptive multiresolution dynamic mode decomposition of rolling bearing (IPSO-AMDMD). Multiresolution dynamic mode decomposition (MRDMD) is used to decompose signals of rolling bearing at the early stage, multiscale fuzzy entropy (MFE) is employed to divide low-rank components and sparse components. In order to make up for the shortcomings of the above two methods, namely truncated rank of MRDMD and inaccurate selection in threshold of MFE, this paper has proposed a new fitness function, which is called synthetic envelope kurtosis characteristic energy difference ratio, and adopted the improved particle swarm optimization algorithm (IPSO) to select the optimal parameters adaptively. With these two steps, signals can be decomposed perfectly. Finally, reconstructed signals, which are obtained through the combination of signals from each layer according to a certain weight, go through DMD again, thus getting the final recovered signal. Through simulation experiment and in-field experiment, it has proved that IPSO-AMDMD is viable and sound in accurately extracting features from fault signals. [ABSTRACT FROM AUTHOR]

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Copyright of Business Intelligence Management Studies is the property of Allameh Tabatabai University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chemical Engineering (China) / Huaxue Gongcheng is the property of Hualu Engineering Science & Technology Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the urbanization rate in China has continued to increase, the highway congestion problem has become more severe, significantly reducing the efficiency of traffic operations. To accurately predict highway short-term traffic flow and effectively solve congestion issues, in this paper, the basic equations of fluid mechanics are described, variable coefficient differential Euler equations are introduced into a grey model and a high-order variable coefficient grey prediction model is constructed based on the principle of grey differential information. The model is solved using mathematical methods such as recursive sequences and mathematical transformations, and the time response function of the model is obtained. The order of derivatives can be used to effectively simulate fluctuations in traffic flow data; therefore, to improve the accuracy of the new model, the particle swarm optimization algorithm is used to optimize the order of the new model, leading to refined modelling steps. Finally, the new model is applied to a case study of traffic flow on highways in Canada, and its efficacy is assessed from three distinct viewpoints. The findings demonstrate that the new model can stably predict traffic flow under different prediction methods, and the performance of the new model under different traffic flow conditions is verified using four different periods of traffic flow data. The findings indicate that the simulation and prediction results of the new model are superior to those of six other grey models. The new model can be used to effectively determine the fluctuation patterns of highway traffic flow data and yields good stability and prediction accuracy. [ABSTRACT FROM AUTHOR]

To address the problem of stochastic delay in network control systems, this paper introduces the idea of crossover and variation in genetic algorithm based on the PSO algorithm, and uses a linear decreasing and asynchronous time-varying improvement strategy for inertia weights and The improved PSO algorithm with better performance is proposed, and the BP neural network is optimized with this algorithm to construct an improved PSO-BP neural network delay prediction model; then the MATLAB TrueTime2.0 toolbox is used to build a simulation platform and combine the obtained historical delay sampling data to improve the PSO-BP neural network delay prediction model and the PSO-BP neural network delay prediction model. BP delay prediction model and PSO-BP,BP model for performance comparison test. The experiments show that the proposed model has higher prediction accuracy and smaller error, which can better solve the stochastic delay prediction problem of network control system. [ABSTRACT FROM AUTHOR]

The Huanglong Scenic and Historic Interest Area in China, a UNESCO World Heritage Site, is famous for its large-scale, diverse, intricately structured, and brightly colored surface travertine landscapes. However, severe degradation of the Huanglong travertine formations, such as blackening and algae erosion, has occurred in recent years, necessitating monitoring and identification. We collected hyperspectral reflectance data of the travertine formations in different states and bare ground using a ground-based hyperspectral radiometer (PSR-2500) from ASD company. After conducting a correlation analysis between the hyperspectral reflectance data and the travertine formations, we identified healthy travertine formations, blackened travertine formations, travertine formations affected by algae erosion, and bare ground. The Siamese network method was employed to generate data labels, and the spectral features of the travertine formations were extracted by combining the sensitive bands with pre-processed and reduced data. The PSO-BPNN classifier was developed by optimizing the back propagation neural network (BPNN) using the particle swarm optimization algorithm (PSO). To verify the effectiveness of PSOBPNN in accurately distinguishing different states of travertine formations, we compared its performance with that of BPNN using three performance indices. Finally, the proposed method was applied to the real-world hyperspectral image data collected by the Micro-Hyperspectral imaging instrument to classify the travertine formations in different states and bare ground. The test set demonstrated good overall performance, with an average overall accuracy (OA) of 0.93, F1-score of 0.92, and Kappa coefficient of 0.97. [ABSTRACT FROM AUTHOR]

Random noise attenuation is a fundamental task in seismic data processing aimed at improving the signal‐to‐noise ratio of seismic data, thereby improving the efficiency and accuracy of subsequent seismic data processing and interpretation. To this end, model‐based and data‐driven seismic data denoising methods have been widely applied, including f–x deconvolution, K‐singular value decomposition, feed‐forward denoising convolutional neural network and U‐Net (an improved fully convolutional neural network structure), which have received widespread attention for their effectiveness in attenuating random noise. However, they often struggle with low‐signal‐to‐noise ratio data and complex noise environments, leading to poor performance and leakage of effective signals. To address these issues, we propose a novel approach for random noise attenuation. This approach employs a multi‐model stacking structure, where the parameters governing this structure are optimized using a particle swarm optimizer. In the model‐based denoising method, we choose the f–x deconvolution method, whereas in the data‐driven denoising method, we choose K‐singular value decomposition for shallow learning and U‐Net for deep learning as components of the multi‐model stacking structure. The optimal parameters for the multi‐model stacking structure are obtained using a particle swarm optimizer, guided by the proposed novel hybrid fitness function incorporating weighted signal‐to‐noise ratio, structural similarity and correlation parameters. Finally, the effectiveness of the proposed method is verified with three synthetic and two real seismic datasets. The results demonstrate that the proposed method is effective in attenuating random noise and outperforms the benchmark methods in denoising both synthetic and real seismic data. [ABSTRACT FROM AUTHOR]

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Reducing thermal unit operating costs and emissions is the goal of the multi-objective issue known as multi-area economic/emission dispatch (MAEED) in smart grids. Using renewable energy (RE) have significantly lowered greenhouse gas emissions and ensured the sustainability of the environment. With regard to constraints such as prohibited operating zones (POZs), valve point effect (VPE), transmission losses in the network, ramp restrictions, tie-line capacity, this study aims to minimize operating costs and emission objectives by solving the multi-area dynamic economic/emission dispatch (MADEED) problem in the presence of RE units and energy storage (ES) systems. The conventional economic dispatch (ED) optimization approach has the following shortcomings: It is only designed to solve the single-objective optimization problem with a cost objective, in addition, it also does not have high calculation accuracy and speed. Therefore, to address this multi-objective MADEED problem with non-linear constraints, this paper introduces hybrid particle swarm optimization (PSO)-whale optimization algorithms (WOA). The reason for combining two algorithms is to use the advantages of both algorithms in solving the desired optimization problem. The introduced method is tested in two separate scenarios on a test network of 10 generators. Using the suggested hybrid methodology in this study, the MADED and MADEED problems are resolved and contrasted with other evolutionary techniques, such as original WOA, and PSO methods. Examining the results of the proposed method shows the efficiency and better performance of the proposed method compared to other methods. Finally, the results obtained by simulations indicate that integrating the necessary system restrictions gives the system legitimacy and produces dependable output. With regard to the results obtained from the introduced approach, the value of the overall cost function has clearly decreased by about 3 % compared to other methods.

The advent of multi-Microgrid (MG) energy systems necessitates the optimization of management strategies to curtail operational costs. This paper introduces an innovative MG energy management strategy that integrates Chaotic Local Search (CLS) with Particle Swarm Optimization (PSO) to fulfill this requirement. Our approach leverages PSO for extensive global exploration and subsequently employs CLS to refine local searches, thereby ensuring the attainment of optimal global outcomes. To further enhance performance, we have crafted a PSO algorithmic framework underpinned by chaotic local search principles, aimed at circumventing regions of local optima. The study presents a comprehensive MG energy system model that encompasses a photovoltaic generation unit, battery energy storage, and a micro gas turbine. The experimental data corroborates that our proposed algorithm secures optimal solutions within a range of 48.2–51.7, outperforming others in achieving these optimal resolutions. When juxtaposed with Scenario 1, there is a significant reduction in both operational and primary energy conversion costs by 24.22 % and 31.39 %, respectively. In comparison to Scenario 2, these figures are reduced by an additional 3.08 % and 6.05 %, respectively. The research findings underscore the strategy's exceptional performance in optimization tasks, as illustrated by the simulation outcomes. The methodology's application to a micro-energy network substantiates its practical relevance. Collectively, this research offers a holistic solution for the optimization of MG energy systems, effectively merging theoretical progress with tangible practical applications.

With the increasing scale of distribution network at distribution time, its complexity grows geometrically, and its fault diagnosis becomes more and more difficult. Aiming at the slow convergence and low accuracy of traditional backpropagation neural network in dealing with single-phase ground faults, the study proposes a backpropagation neural network based on improved particle swarm optimization. The model optimizes the weights and acceleration constants of the particle swarm algorithm by introducing dynamic coefficients to enhance its global and local optimization seeking ability. It is also applied in optimizing the parameters of backpropagation neural network and constructing the routing model and ranging model for fault diagnosis about distributed energy distribution network. The simulation results revealed that the maximum absolute error of the improved method is 0.08. While the maximum absolute errors of the traditional backpropagation neural network and the particle swarm optimized backpropagation neural network were 0.65 and 0.10, respectively. The fluctuation of the relative errors of the research method was small under different ranges of measurements. At 8.0 km, the minimum relative error was 0.39% and the maximum relative error was 2.81%. The results show that the improved method proposed in the study significantly improves the accuracy and stability of fault diagnosis and localization in distribution networks and is applicable to complex distribution network environments. The method has high training efficiency and fault detection capability and provides an effective tool for distribution network fault management.

The optimal scheduling method of the power grid considering the node coupling degree and the duality of mutual inverse mapping is studied to effectively establish the random dynamic scene of the power grid and improve its optimal scheduling effect. Considering the duality of the mutual inverse mapping, a random dynamic scene of the power grid is generated. The optimization model of the grid random dynamic scene partition was established considering the coupling degree of the nodes in the region. The anti-prey particle swarm optimization algorithm was used to solve the optimization model, and the scene partition results were obtained. To construct an optimization scheduling model for the regional power grid with the objective functions of minimizing energy abandonment rate and grid loss and the constraints of power flow, output, and climbing power. A predator-prey particle swarm optimization algorithm is used to solve the optimization scheduling model, and the minimum energy abandonment rate, minimum grid loss, and corresponding optimal scheduling strategies are obtained. The experimental results show that this method can effectively generate random dynamic scenes of a power grid and has a better scene partitioning effect. Under different working conditions, this method can achieve optimal dispatching of the power grid and reduce the power grid energy abandonment rate and network loss.

Aiming at the problem that traditional fixed-weight multi-objective reactive power optimization is unable to make the most suitable control decisions for real-time working conditions when dealing with the complex and changing working conditions of new power systems, an adaptive multi-objective reactive power optimization control strategy is proposed, which takes the weighted minimum of the deviation of the system active network loss and the voltage of the grid connection points as the objective function, and the weighting coefficients of the objective function are adaptively adjusted according to the deviation of the voltage of the grid connection points. The strategy takes the minimization of active network loss and the deviation of grid voltage as the objective function. Firstly, the relationship between voltage fluctuation at the grid-connected points of offshore wind farms and the active and reactive power outputs is analyzed to establish the corresponding reactive power allocation model, and the corresponding reactive power control model is established with respect to the input and output characteristics of the wind turbine and the static var generator (SVG). In addition, considering the power constraints and safe operation constraints of offshore operation, the variable inertia weight particle swarm optimization algorithm is used to solve the reactive power control strategy. Finally, the offshore wind farm model is built in MATLAB for simulation verification, and the simulation example shows that, compared with the traditional fixed-weight multi-objective reactive power optimization, the adaptive multi-objective reactive power optimization control strategy can quickly adjust the priority of each optimization objective according to the real-time working conditions of the grid, which can achieve the coordinated optimization of the active network loss and grid-connected point voltage.