Search Results (120)

To address the uncertainty of intermittent energy sources and enhance the economic efficiency and operational performance of microgrids, this paper proposes a novel three-layer coupled microgrid scheduling model based on the principles of model predictive control, optimized and solved using an improved dung beetle algorithm. Firstly, by comprehensively considering time-varying electricity prices and pollution protection costs, the model optimizes and mitigates the impact of uncertain factors in day-ahead scheduling, thereby constructing a new three-layer scheduling framework. Secondly, improvements to the traditional dung beetle algorithm, including population initialization, rolling behavior, and foraging behavior, are validated through simulations, demonstrating enhanced accuracy and convergence speed. Furthermore, the improved dung beetle algorithm is utilized to optimize the economic performance of the scheduling layer, determining optimal controls within the rolling control framework. Finally, through economic comparisons, rolling scheduling analysis, and control effectiveness experiments, this study demonstrates that the proposed model and algorithm significantly improve the environmental economics of microgrids while enhancing system controllability and stability. Full article

Adhesion behavior between wet rice leaves and metal surfaces exacerbates the difficulty in separating and removing grains in the cleaning device. Reducing the adhesion between the wet rice leaves and the cleaning device is an important factor in improving the harvesting performance of rice combine harvesters. This paper investigates the possibility of reducing the adhesion between them. By studying the liquid shape characteristics between the removed grains and the surface, it was found that the adhesion force between the leaf and the surface is greatest when additional pressure is present. Based on biomimetic principles and the convex hull structure of a dung beetle’s head, a convex hull structure for the metal surface was designed to balance the atmospheric pressure on both sides of the leaf in order to eliminate additional pressure. Using the liquid bridge model between a spherical and a flat surface, a liquid bridge model for the leaf and convex hull surface was established. By optimizing the minimum liquid bridge force, the convex hull radius and distance were determined to be 2.47 mm and 1.38 mm, respectively. Contact and collision experiments verified that the convex hull surface is more effective in reducing the adhesion of moist leaves, providing a reference for future research on the cleaning methods of moist rice grains. Full article

The Grey Wolf Optimizer (GWO) is a well-known metaheuristic algorithm that currently has an extremely wide range of applications. However, with the increasing demand for accuracy, its shortcomings of low exploratory and population diversity are increasingly exposed. A modified Grey Wolf Optimizer (M-GWO) is proposed to tackle these weaknesses of the GWO. The M-GWO introduces mutation operators and different location-update strategies, achieving a balance between exploration and development. The experiment validated the performance of the M-GWO using the CEC2017 benchmark function and compared the results with five other advanced metaheuristic algorithms: the Improved Grey Wolf Optimizer (IGWO), GWO, Whale Optimization Algorithm (WOA), Dung Beetle Optimizer (DBO), and Harris Hawks Optimization (HHO). The performance results indicate that the M-GWO has a better performance than competitor algorithms on all 29 functions in dimensions 30 and 50, except for function 26 in dimension 30 and function 28 in dimension 50. Compared with competitor algorithms, the proposed M-GWO is the most effective algorithm, with an overall effectiveness of 96.5%. In addition, in order to show the value of the M-GWO in the practical engineering field, the M-GWO is used to optimize the PI controller parameters of the current loop of the permanent magnet synchronous motor (PMSM) system. By designing a PI controller parameter optimization scheme based on the M-GWO, the fluctuation of the q-axis current and d-axis current of the motor is reduced. The designed scheme reduces the q-axis fluctuation to around −2~1 A and the d-axis current fluctuation to around −2~2 A. By comparing the current-tracking errors of the $q$-axis and $d$-axis under different algorithms, the validity of the optimized parameters of the M-GWO is proved. Full article

As one of the main faults of a disconnector, a mechanical fault is difficult to diagnose in time because of its weak self-evidence, its wide range of fault categories, and the difficulty in obtaining fault sample data. To address this issue, this study proposes a new fault diagnosis algorithm based on multivariate variational mode decomposition optimized by the improved dung beetle optimizer, and at the same time, an experimental platform for vibration signal acquisition was built to simulate three typical mechanical faults. First, the parameters of multivariate variational mode decomposition were optimized using an improved dung beetle optimizer, and the intrinsic mode function with a Pearson correlation coefficient higher than 0.1 was retained after the signal was decomposed. Then, the energy, entropy, and time–frequency domain eigenvalues of the selected intrinsic mode function were calculated to construct the feature matrix, and its dimensions were reduced to two dimensions. Finally, this matrix was input to convolutional neural network–bidirectional long short-term memory for fault classification. The verification of the experimental data shows that the proposed algorithm can successfully diagnose different mechanical faults of the disconnector, and the accuracy rate was 96.67%. The research content provides a new idea for the fault diagnosis of disconnectors. Full article

The rapid growth in photovoltaic (PV) power plant installations has rendered traditional inspection methods inefficient, necessitating advanced approaches for fault detection and classification. This study introduces a novel hybrid metaheuristic method, the Dung Beetle Optimization Algorithm combined with Fick’s Law of Diffusion Algorithm (DBFLA), to address these challenges. The DBFLA enhances the performance of machine learning models, including artificial neural networks (ANNs), support vector machines (SVMs), and ensemble methods, by fine-tuning their parameters to improve fault detection rates. It effectively identifies critical faults such as module mismatches, open circuits, and short circuits. The research demonstrates that DBFLA significantly improves the performance of conventional machine learning techniques by forming a stacking classifier, achieving an individual meta-learner accuracy of approximately 98.75% on real PV datasets. This approach not only accommodates new operating modes and an expanded range of fault conditions but also enhances the reliability of fault detection schemes. The primary contribution of DBFLA lies in its ability to balance exploration and exploitation efficiently, resulting in superior classification accuracy compared to existing optimization techniques. By combining real and simulated datasets, the proposed hybrid method showcases its potential to substantially improve the precision and speed of PV fault detection models. Future work will focus on integrating these advanced models into real-time PV monitoring systems, aiming to reduce detection times and further enhance the reliability and operational efficiency of PV systems. Full article

The rapid expansion of unmanned aerial vehicle (UAV) applications in complex environments presents significant challenges in 3D path planning, particularly in overcoming the limitations of traditional methods for dynamic obstacle avoidance and computational efficiency. To address these challenges, this study introduces an adaptive whale optimization algorithm (DBO-AWOA), which incorporates chaotic mapping, nonlinear convergence factors, adaptive inertia mechanisms, and dung beetle optimizer-inspired reproductive behaviors. Specifically, the algorithm utilizes ICMIC chaotic mapping to enhance initial population diversity, a cosine-based nonlinear convergence factor to balance exploration and exploitation, and a hybrid strategy inspired by the dung beetle optimizer to mitigate stagnation in local optima. When evaluated on the CEC2017 benchmark suite, DBO-AWOA demonstrates superior convergence precision and robustness, achieving the lowest minimum and average values across 72% of test functions. In 3D path-planning simulations within mountainous environments, DBO-AWOA generates smoother, shorter, and safer trajectories compared to existing variants, with fitness values reduced by 5–25%. Although the algorithm demonstrates slight instability in highly dynamic hybrid functions, its overall performance marks an improvement in global optimization and UAV path planning. Full article

Accurate prediction of the State of Charge (SOC) of lithium-ion batteries is the foundation for the stable and efficient operation of battery management systems. This paper proposes a lithium-ion battery SOC estimation method based on the Dung Beetle Optimizer (DBO), optimizing the second-order Kalman filter algorithm (DBO-DKF). Leveraging the DBO’s fast convergence speed and strong global search capability, this method optimizes the Kalman filter algorithm in the parameter identification stage and the extended Kalman filter algorithm in the SOC estimation stage to address the issue of insufficient estimation accuracy caused by noise covariance matrices of input current and voltage measurements. Through the discharge of current tests under complex conditions, as well as comparing and analyzing credibility indicators such as MAE, RMSE, and MSE as measures of estimation accuracy, it can be verified that the proposed method effectively enhances SOC estimation accuracy. Full article

Aiming at the problem of the low collaborative efficiency of outsourced processing of wallboard parts of a coating machine under a network collaborative manufacturing mode, this paper proposes a wallboard manufacturing resource composition optimization method based on the Improved Polar Bear Optimization (IPBO) algorithm. The processing process of the wallboard is analyzed, and the process-level splitting of the wallboard manufacturing task is completed; the required manufacturing resource service portfolio is determined, and the resource evaluation indicator system for key performance indicators of wallboard manufacturing resources is established; non-cooperative game decision-making is used to construct a wallboard manufacturing resource composition optimization model from two aspects, namely, quality indicators and flexibility indicators; an adaptive vision and mutation strategy is introduced to carry out the Polar Bear Optimization (PBO) algorithm. Finally, the improved algorithm is used to solve the wallboard manufacturing resource composition optimization model. The experimental results show that the IPBO algorithm reduces the average convergence time by 6.51% and the optimal convergence time by 9.26% compared with the suboptimal Dung Beetle Optimization (DBO) algorithm, and 65%–72% of the test points of the IPBO algorithm are more in line with the preference criteria of the Pareto frontier. Meanwhile, it demonstrates both validity and superiority in solving the problem of expanding the size of wallboards for coating machines. Full article

Gas Insulated Switchgear (GIS) is a type of critical substation equipment in the power system, and its safe and stable operation is of great significance for ensuring the reliability of power system operation. To accurately identify partial discharge in GIS, this paper proposes an acoustic identification method based on improved mel frequency cepstral coefficients (MFCC) and dung beetle algorithm optimized random forest (DBO-RF) based on the ultrasonic detection method. Firstly, three types of typical GIS partial discharge defects, namely free metal particles, suspended potential, and surface discharge, were designed and constructed. Secondly, wavelet denoising was used to weaken the influence of noise on ultrasonic signals, and conventional, first-order, and second-order differential MFCC feature parameters were extracted, followed by principal component analysis for dimensionality reduction optimization. Finally, the feature parameters after dimensionality reduction optimization were input into the DBO-RF model for fault identification. The results show that this method can accurately identify partial discharge of typical GIS defects, with a recognition accuracy reaching 92.2%. The research results can provide a basis for GIS insulation fault detection and diagnosis. Full article

With the gradual cessation of budget quota standards and the emphasis on market-based pricing, accurately predicting project investments has become a critical issue in construction management. This study focuses on cost indicator prediction for irrigation and drainage projects to address the absence of cost standards for farmland water conservancy projects and achieve accurate and efficient investment prediction. Engineering characteristics affecting cost indicators were comprehensively analyzed, and principal component analysis (PCA) was employed to identify key influencing factors. A prediction model was proposed based on support vector regression (SVR) optimized using the dung beetle optimizer (DBO) algorithm. The DBO algorithm optimized SVR hyperparameters, resolving issues of poor generalization and long prediction times. Validation using 2024 farmland water conservancy project data from Liaoning Province showed that the PCA–DBO–SVR model achieved superior performance. For electromechanical well projects, the root mean square error (RMSE) was 1.116 million CNY, mean absolute error (MAE) was 0.910 million CNY, mean absolute percentage error (MAPE) was 3.261%, and R² reached 0.962. For drainage ditch projects, RMSE was 0.500 million CNY, MAE was 0.281 million CNY, MAPE was 3.732%, and R² reached 0.923. The PCA–DBO–SVR model outperformed BP, SVR, and PCA–SVR models in all evaluations, demonstrating higher prediction accuracy and better generalization capability. This study provides theoretical support for developing cost indicators for farmland water conservancy projects and offers valuable insights for dynamically adjusting national investment standards and improving construction fund management. Full article

This paper proposes an improved Jellyfish Search algorithm, namely TLDW-JS, for solving the problem of optimal path planning of multi-robot collaboration in the multi-tasking of complex vertical farming environments. Vertical farming is an efficient way to solve the global food problem, but how to deploy agricultural robots in the environment constitutes a great challenge, which involves energy consumption and task efficiency. The most important improvements introduced by the proposed TLDW-JS algorithm are as follows: the Tent Chaos used to generate a high-quality, diversified initial population, Lévy flight used in the improved JS to strengthen global exploration, and finally, the nonlinear dynamically weighted adjustment with logistic functions to balance exploration and exploitation. A Vertical Farming System Multi-Robot Collaborative Trajectory Planning (VFSMRCTP) model has been developed in accordance with the environmental constraints specific to vertical farms, the task constraints, and the constraints between agricultural robots. The VFSMRCTP model is solved using the TLDW-JS algorithm and a number of comparison algorithms in order to analyze the algorithm’s performance. Comparative experiments demonstrate that TLDW-JS outperforms classic optimization algorithms such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Dung Beetle Optimization (DBO), achieving superior path length optimization, reduced energy consumption, and improved convergence speed. The results indicate that TLDW-JS achieved a 34.3% reduction in average path length, obtained one of the top three optimal solutions in 74% of cases, and reached convergence within an average of 55.9 iterations. These results validate the efficiency of TLDW-JS in enhancing energy optimization and demonstrate its potential for enabling automated systems in vertical farming. Full article

To address the problem of UAV path planning in complex mountainous terrains, this paper comprehensively considers constraints such as natural mountain and obstacle collision threats, the shortest path, and flight altitude. We propose a more practical UAV path planning model that better reflects the actual UAV path planning situation in complex mountainous areas. In order to solve this model, this paper improves the traditional dung beetle optimization (DBO) algorithm and proposes an improved dung beetle optimization (IDBO) algorithm. The IDBO algorithm optimizes the population initialization method based on the concept of symmetry, ensuring that the population is more evenly distributed within the solution space. Additionally, the algorithm introduces a sine–cosine function-based movement strategy, inspired by the symmetry principle, to enhance the search efficiency of individual population members. Furthermore, a population evolution strategy is incorporated to prevent the algorithm from getting stuck in local optima. To demonstrate the algorithm’s performance, tests were conducted using 23 commonly used benchmark functions provided by the CEC 2005 competition and six commonly used engineering problem models provided by the CEC 2020 competition. The results indicate that IDBO significantly outperforms DBO in terms of convergence performance, effectively solving various engineering optimization problems. Finally, experimental tests under three different threat scenarios show that the proposed IDBO algorithm has scientific validity when applied to UAV path planning. This solution method effectively reduces UAV flight energy consumption costs and obstacle collision threats while improving the efficiency and accuracy of UAV path planning. Full article

To enhance low-carbon economies within Park Integrated Energy Systems (PIES) while addressing the variability of wind power generation, an innovative optimization scheduling strategy is proposed, incorporating a reward-and-punishment ladder carbon trading mechanism. This method effectively mitigates the unpredictability of wind power output and integrates Power-to-Gas (P2G), Carbon Capture and Storage (CCS), and Combined Heat and Power (CHP) systems. This study develops a CHP model that combines P2G and CCS, focusing on electric-heat coupling characteristics and establishing constraints on P2G capacity, thereby significantly enhancing electric energy flexibility and reducing carbon emissions. The carbon allowance trading strategy is refined through the integration of reward and punishment coefficients, yielding a more effective trading model. To accurately capture wind power uncertainty, the research employs kernel density estimation and Copula theory to create a representative sequence of daily wind and photovoltaic power scenarios. The Dung Beetle Optimization (DBO) algorithm, augmented by Non-Dominated Sorting (NSDBO), is utilized to solve the resulting multi-objective model. Simulation results indicate that the proposed strategy increases the utilization rates of renewable energy in PIES by 28.86% and 19.85%, while achieving a reduction in total carbon emissions by 77.65% and a decrease in overall costs by 36.91%. Full article

Wind power, as a pivotal renewable energy source, is anticipated to play a critical role in ensuring the reliability, security, and stability of the global energy supply system. Accurate prediction of wind power density (WPD) holds significant practical importance for wind farms, grid operators, and the entire wind power industry, as it facilitates informed decision-making, optimized resource allocation, and enhanced system performance. This paper proposes a novel WPD forecasting model based on RF-DBO-VMD feature selection and BiGRU optimized by an attention mechanism. The proposed model consists of three main stages. First, critical physical features relevant to WPD are identified using random forest (RF), effectively eliminating data redundancy and enhancing prediction efficiency. Second, the variational mode decomposition (VMD) parameters are optimized via the dung beetle optimizer (DBO) algorithm to extract independent intrinsic mode functions (IMFs), which, alongside the original data, serve as temporal feature inputs. Finally, an attention mechanism is employed to identify important information from the outputs of the BiGRU model, and the Grid Search (GS) method is used to optimize the BiGRU-Attention model, yielding optimal predictions. The experimental results demonstrate the model’s high predictive accuracy, evidenced by an R² value of 0.9754. Notably, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) are substantially minimized compared to alternative models. These results highlight the model’s potential to provide effective forecasting insights for future applications, such as energy trading and power system management, which will be further explored in real-world scenarios. Full article

To comprehensively address the interests of both the supply and demand sides within a microgrid, a two-layer optimal scheduling model incorporating demand response was formulated. The upper tier aims to optimize the load profile, focusing on maximizing electricity consumption satisfaction and minimizing user electricity costs. Meanwhile, the lower tier targets the optimization of output from each controllable generation unit, with the goal of reducing operational costs. Given the nonlinear and multi-constrained nature of this model, an improved nutcracker optimization algorithm (INOA) is proposed. This enhancement introduces chaotic sequences into the original nutcracker optimization algorithm (NOA) for population initialization, employs a hybrid butterfly optimization algorithm to enhance the algorithm’s local search capabilities, and integrates dynamic selection adaptive T-distribution for updating individual positions. The solution tests involving INOA, NOA, dung beetle optimizer (DOB), particle swarm optimization (PSO), grey wolf optimization (GWO), and sparrow search algorithm (SSA) were conducted using the CEC2022 intelligent algorithm test suite. Analysis reveals that INOA exhibits superior comprehensive optimization performance compared to other algorithms, validating the effectiveness of the improvements introduced in this paper. Ultimately, a simulation analysis of the microgrid was performed, demonstrating that, despite a 3.58% reduction in user satisfaction, participation in demand response led to a 25.16% decrease in electricity costs and a 5.92% reduction in microgrid operational costs. These findings substantiate the model’s capability to effectively balance the economic interests of both the supply and demand sides within the microgrid. Full article