This study conducts a comparative assessment to optimize the design of a Hybrid Renewable Energy System (HRES) consisting of PV panels, wind turbines (WTs), and hydrogen storage (PV/WT/FC). The study focuses on Dakhla...
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This study conducts a comparative assessment to optimize the design of a Hybrid Renewable Energy System (HRES) consisting of PV panels, wind turbines (WTs), and hydrogen storage (PV/WT/FC). The study focuses on Dakhla City, leveraging its favorable weather conditions to evaluate the potential of renewable energy sources. A novel approach, Selective Ensemble marinepredators (SEMPA), is introduced. SEMPA utilizes a selective ensemble learning strategy to enhance the performance of the marine predators algorithm (MPA) and determine the optimal system size with the lowest Total Net Present Cost (TNPC) and improved reliability. To benchmark SEMPA's performance, we compare it against established methods, including MPA, Particle Swarm Optimization (PSO), and Harmony Search (HS). Results demonstrate that the integrated PV/WT/FC system provides a reliable and cost-effective energy solution for the southwest region of Morocco, particularly in Dakhla. SEMPA outperforms HS, PSO, and traditional MPA, achieving stable convergence after 30 iterations. The system exhibits a superior Cost of Energy (COE) at 0.3127 $/kWh, a TNPC of 0.9689 M$, and a low Loss of Power Supply Probability (LPSP) at 0.04747.
Bridge bearings play significant roles in the mechanical responses of bridges and foundations and impact the operation of bridges. This paper presents an adaptive bearing with adjustable height and develops an approac...
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Bridge bearings play significant roles in the mechanical responses of bridges and foundations and impact the operation of bridges. This paper presents an adaptive bearing with adjustable height and develops an approach to control bearings toward smart bridges based on Physics-Informed Neural Network (PINN). The approach integrates the mechanical governing equation, which describes the relationship between bridge responses and bearing heights, with data-driven neural networks, enabling efficient prediction of bearing reaction forces and effective optimization of bearing heights for controlling the reaction forces. The effectiveness of the approach is evaluated by examining various types of bridges. The results showed that the proposed approach outperformed 20 machine learning models. The case study showed that the approach effectively limited the force adjustment error to 18 % while reducing both vehicle-bridge response and displacement on bearing top plate. This research will enhance bridge controllability, thereby improving bridge operation.
The combined heat and power economic dispatch (CHPED) problem is a non-convex multivariate global optimization problem. The objective of the problem is to reduce total production costs while imposing a variety of cons...
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The combined heat and power economic dispatch (CHPED) problem is a non-convex multivariate global optimization problem. The objective of the problem is to reduce total production costs while imposing a variety of constraints and meeting the demand for power and heat. Three recently presented metaheuristic approaches, Slime Mould algorithm (SMA), COOT algorithm and marine predators algorithm (MPA), are applied for solving CHPED problem. Studies dealing with the CHPED problem in the literature often do not consider valve points effect, prohibited operation zones for power-only units, feasible region constraints of combined heat and power units, all at once. Furthermore, power losses are neglected especially in large-scale problems. In this study, the CHPED problem is solved by considering all operational constraints including active power transmission losses. Three separate case studies with dimensions of 11 units, 48 units, and 96 units were used in the tests under various limitations. The experimental results revealed that MPA outperformed not only SMA, and COOT but also the algorithms proposed previously in the literature.
Fault diagnosis of rolling bearings is essential for the safe operation of rotating machinery. However, in the production process, the rolling bearings have a complex working environment embedded with weak fault signa...
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Fault diagnosis of rolling bearings is essential for the safe operation of rotating machinery. However, in the production process, the rolling bearings have a complex working environment embedded with weak fault signals and a large number of interfering signals, which is a considerable challenge to automatically and accurately detect bearing fault type from the actual vibration signal. Therefore, a novel fault diagnosis scheme is proposed based on singular spectrum decomposition (SSD) and an optimized stochastic configuration network (SCN). Firstly, SSD is used to pre-process the original rolling bearings vibration signal to obtain several singular spectral components (SSCs) and the practical component is selected according to the maximum correlation coefficient for signal reconstruction. Furthermore, time domain and power spectrum entropy (PSE) features of the reconstructed signal are extracted to obtain a fault information -rich feature sets. In addition, the parameters of the SCN are optimized by marine predators algorithm (MPA) to enhance the learning ability and generalization performance of the SCN. Finally, the feature sets are input into MPA-SCN to achieve fault classification. Experimental results exhibit that the proposed method has higher accuracy in rolling bearings fault diagnosis compared with other methods, which provides a high -efficiency solution for rolling bearings fault diagnosis.
In smart grid and smart building environments, accurate forecasting of load demand in residential buildings is of critical importance. This helps to enhance the stability of the power system, facilitate the integratio...
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In smart grid and smart building environments, accurate forecasting of load demand in residential buildings is of critical importance. This helps to enhance the stability of the power system, facilitate the integration of distributed renewable energy sources, and develop efficient demand response strategies. In view of this, this paper proposes a day-ahead power load regulation assessment model based on the maximum information coefficient (MIC) combined with an improved marine predators algorithm (IMPA) to optimize the extreme learning machine (ELM). The daily lagged load is used to construct the initial feature set, and the MIC is used for feature selection to filter out the top five features with the largest values. The MPA improvement strategy includes self-mapping to generate chaotic sequence initialization and boundary mutation operations. The experimental results show that the proposed model has the optimal performance in evaluating the load regulation amount compared to other models. Taking Mean Absolute Error (MAE) as an example, compared with ELM and MPA-ELM, IMPA-ELM improved by 13.85% and 3.04%, respectively.
Suspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications in building materials, textiles, and cooling systems. This stud...
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Suspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications in building materials, textiles, and cooling systems. This study focuses on accurately predicting the dynamic viscosity, a critical thermophysical property, of suspensions containing MPCMs and MXene particles using Gaussian process regression (GPR). Twelve hyperparameters (HPs) of GPR are analyzed separately and classified into three groups based on their importance. Three metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and marine predators algorithm (MPA), are employed to optimize HPs. Optimizing the four most significant hyperparameters (covariance function, basis function, standardization, and sigma) within the first group using any of the three metaheuristic algorithms resulted in excellent outcomes. All algorithms achieved a reasonable R-value (0.9983), demonstrating their effectiveness in this context. The second group explored the impact of including additional, moderate-significant HPs, such as the fit method, predict method and optimizer. While the resulting models showed some improvement over the first group, the PSO-based model within this group exhibited the most noteworthy enhancement, achieving a higher R-value (0.99834). Finally, the third group was analyzed to examine the potential interactions between all twelve HPs. This comprehensive approach, employing the GA, yielded an optimized GPR model with the highest level of target compliance, reflected by an impressive R-value of 0.999224. The developed models are a cost-effective and efficient solution to reduce laboratory costs for various systems, from TES to thermal management.
Until recently the conventional PI and PID controllers have been widely used in the load frequency control (LFC) problem. However, in today's modern power system those controllers cannot cope effectively with the ...
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ISBN:
(纸本)9781450385169
Until recently the conventional PI and PID controllers have been widely used in the load frequency control (LFC) problem. However, in today's modern power system those controllers cannot cope effectively with the increasing complexity of the power system resulting in system performance degradation. To tackle this problem, this paper proposes the application of two distributed order PID (DOPID) controllers which cover the whole range of integrals and derivatives of order -1 to order 1. Those controllers comprise with numerous fractional integrators and differentiators. The gains of the proposed controllers are optimized using the newly introduced marine predators algorithm (MPA). The analysis of the controllers is conducted in a realistic two area interconnected power system having diverse sources of power generation including communication delay, HVDC link and a geothermal power plant (GTPP) in both areas. A performance evaluation is conducted among various controllers like PIDN and FOPID with the proposed controllers and it is found that the DOPID controllers exhibit the best dynamic performance. Moreover, the transient response of the system is tested under the presence of wind power generation (WTG).
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