Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart ...Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.展开更多
Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algori...Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.展开更多
针对浣熊优化算法(coati optimization algorithm,COA)全局搜索能力不足、易陷入局部最优和收敛速度慢的问题,提出一种基于非线性自适应的改进浣熊优化算法(improved coati optimization algorithm based on nonlinear adaptation,NACOA...针对浣熊优化算法(coati optimization algorithm,COA)全局搜索能力不足、易陷入局部最优和收敛速度慢的问题,提出一种基于非线性自适应的改进浣熊优化算法(improved coati optimization algorithm based on nonlinear adaptation,NACOA)。采用Logistic-Tent映射初始化浣熊种群,提升算法初始搜索空间覆盖度,生成更加分散且高质量的初始解;引入莱维飞行策略,利用其长跳跃特性,增强算法的全局搜索能力,有效避免算法陷入局部最优;利用非线性递减惯性权重提高种群的适应性与搜索效率,平衡全局搜索和局部搜索能力,并通过黄金正弦策略提高种群收敛精度。在基准测试函数上进行对比仿真试验,结果表明NACOA具有更好的收敛速度和寻优精度。将NACOA应用到工程问题设计中,证明了该算法的有效性和实用性。展开更多
针对蚁狮优化算法(Ant Lion Optimization,ALO)后期收敛速度较慢和易陷入局部最优等问题,本文提出基于对数惯性权重的改进蚁狮优化算法(Logarithmic inertia weight based Ant Lion Optimization,LALO)。LALO利用对数函数的特点,实现对...针对蚁狮优化算法(Ant Lion Optimization,ALO)后期收敛速度较慢和易陷入局部最优等问题,本文提出基于对数惯性权重的改进蚁狮优化算法(Logarithmic inertia weight based Ant Lion Optimization,LALO)。LALO利用对数函数的特点,实现对惯性权重的非线性调整,从而更好地平衡算法的全局勘探和局部开采能力。同时,在算法的位置更新中,通过引入对数惯性权重策略来优化蚁狮个体的位置更新过程,降低算法陷入局部收敛的可能性,进而加快收敛速度。本文使用3个经典的测试函数来测试LALO的寻优性能。与已有的群智能算法相比,LALO加快了算法的收敛速度,提高了收敛精度和稳定性。展开更多
基金Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1445)。
文摘Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.
基金supported by the Foundation of the Scientific and Technological Innovation Team of Colleges and Universities in Henan Province(Grant No.181RTSTHN009)the Foundation of the Key Laboratory of Water Environment Simulation and Treatment in Henan Province(Grant No.2017016).
文摘Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.
基金Acknowledgements： This work was supported by the Foundations of Post Doctor of China （No. 20060401001） and by the Science Research Projects of Ministry of Education of China （No. 06JA630056） and by the Natural Science Foundations of Ningxia （No. NZ0848）.
文摘针对浣熊优化算法(coati optimization algorithm,COA)全局搜索能力不足、易陷入局部最优和收敛速度慢的问题,提出一种基于非线性自适应的改进浣熊优化算法(improved coati optimization algorithm based on nonlinear adaptation,NACOA)。采用Logistic-Tent映射初始化浣熊种群,提升算法初始搜索空间覆盖度,生成更加分散且高质量的初始解;引入莱维飞行策略,利用其长跳跃特性,增强算法的全局搜索能力,有效避免算法陷入局部最优;利用非线性递减惯性权重提高种群的适应性与搜索效率,平衡全局搜索和局部搜索能力,并通过黄金正弦策略提高种群收敛精度。在基准测试函数上进行对比仿真试验,结果表明NACOA具有更好的收敛速度和寻优精度。将NACOA应用到工程问题设计中,证明了该算法的有效性和实用性。
文摘针对蚁狮优化算法(Ant Lion Optimization,ALO)后期收敛速度较慢和易陷入局部最优等问题,本文提出基于对数惯性权重的改进蚁狮优化算法(Logarithmic inertia weight based Ant Lion Optimization,LALO)。LALO利用对数函数的特点,实现对惯性权重的非线性调整,从而更好地平衡算法的全局勘探和局部开采能力。同时,在算法的位置更新中,通过引入对数惯性权重策略来优化蚁狮个体的位置更新过程,降低算法陷入局部收敛的可能性,进而加快收敛速度。本文使用3个经典的测试函数来测试LALO的寻优性能。与已有的群智能算法相比,LALO加快了算法的收敛速度,提高了收敛精度和稳定性。
文摘为了解决现有主汽温控制算法存在的控制稳定性欠佳等问题,提出一种基于改进粒子群优化(particle swarm optimization,PSO)算法的控制方案。通过锅炉蒸汽流量扰动分析确定主汽温核心控制量,构建改进PSO算法模型,并引入动态非线性参数赋值策略,将惯性权重和学习因子设为可变值并根据迭代次数优选最优解;同时,引入比例积分微分(proportional integral derivative,PID)控制器提升主、副参数协同控制效果,实现主汽温稳定控制。采用BoilerSim仿真软件进行仿真实验,并与其他控制算法进行对比。结果表明:所提方案的主汽温方差为0.125,二级减温器水量方差为0.223,均低于经典PSO控制算法(主汽温方差为0.557、二级减温器水量方差为0.882)、模糊自适应PID控制算法(主汽温方差为0.265、二级减温器水量方差为1.125)、神经元PID控制算法(主汽温方差为0.271、二级减温器水量方差为1.131)3种对比控制方案的对应指标,控制稳定性显著提高。所提控制方案通过提升控制精度、降低控制偏差展现出优异的控制性能,具备良好的适用性,能够为实际工程应用提供可靠的技术支撑。
