The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACT...The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.展开更多
The interest in nano-engineered cements basically brought by the demands on increasing the performance of concrete structures in challenging applications,such as extreme thermal,electrical,and electromagnetic environm...The interest in nano-engineered cements basically brought by the demands on increasing the performance of concrete structures in challenging applications,such as extreme thermal,electrical,and electromagnetic environments.Traditional cementitious materials lack technological capabilities regarding thermal conductivity,electrical resistivity,mechanical strength,and electromagnetic shielding.Such limitations prevent their application in highperformance and multifunctional concrete structures,which are increasingly required in modern construction.The highdimensional complexity of multivariable optimization of multiple properties makes most of the current approaches unsuitable,and so requires new ways to predict,model,and optimize the performance of such advanced materials.In the present contribution,we introduce a holistic approach to the optimization of nano-engineered cements composites incorporating epoxy resin,nano titanium dioxide,carbon nanotubes,and portland cement.Advanced techniques of machine learning used include random forest regressor for multi-output property prediction and eXtreme gradient boosting,an implementation of the gradient-boosting algorithm that proved particularly useful in multi-objective optimization,for electrical codes’thermal and mechanical properties.Bayesian optimization is also employed to decrease the experimental trials and fine-tune the processing parameters;high-dimensional input space is reduced using principal component analysis to attain optimal model performance.Graph neural networks are utilized for modeling structureproperty relations,and Gaussian processes serve as a surrogate model mimicking the outcome of finite element analysis simulation effectively reducing delays at the computational level.The model yields noteworthy improvements:resistivity decreases by 30%–40%,thermal conductivity increases by 25%–30%,and tensile strength increases by 15%–20%.These enhancements make nano-engineered cements composites highly promising for multifunctional applications such as vibration absorption and electromagnetic shielding,which presents the need for smart,high-performance concrete structures for advanced applications in construction.展开更多
文摘The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.
文摘The interest in nano-engineered cements basically brought by the demands on increasing the performance of concrete structures in challenging applications,such as extreme thermal,electrical,and electromagnetic environments.Traditional cementitious materials lack technological capabilities regarding thermal conductivity,electrical resistivity,mechanical strength,and electromagnetic shielding.Such limitations prevent their application in highperformance and multifunctional concrete structures,which are increasingly required in modern construction.The highdimensional complexity of multivariable optimization of multiple properties makes most of the current approaches unsuitable,and so requires new ways to predict,model,and optimize the performance of such advanced materials.In the present contribution,we introduce a holistic approach to the optimization of nano-engineered cements composites incorporating epoxy resin,nano titanium dioxide,carbon nanotubes,and portland cement.Advanced techniques of machine learning used include random forest regressor for multi-output property prediction and eXtreme gradient boosting,an implementation of the gradient-boosting algorithm that proved particularly useful in multi-objective optimization,for electrical codes’thermal and mechanical properties.Bayesian optimization is also employed to decrease the experimental trials and fine-tune the processing parameters;high-dimensional input space is reduced using principal component analysis to attain optimal model performance.Graph neural networks are utilized for modeling structureproperty relations,and Gaussian processes serve as a surrogate model mimicking the outcome of finite element analysis simulation effectively reducing delays at the computational level.The model yields noteworthy improvements:resistivity decreases by 30%–40%,thermal conductivity increases by 25%–30%,and tensile strength increases by 15%–20%.These enhancements make nano-engineered cements composites highly promising for multifunctional applications such as vibration absorption and electromagnetic shielding,which presents the need for smart,high-performance concrete structures for advanced applications in construction.
