为科学评估水电站与抽水蓄能联合运行的综合性能,提出一种基于层次分析法(analytic hierarchy process,AHP)与逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)相结合的多指标综合评价模...为科学评估水电站与抽水蓄能联合运行的综合性能,提出一种基于层次分析法(analytic hierarchy process,AHP)与逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)相结合的多指标综合评价模型。首先,从经济性、技术性、环境效益、水情及政策5个维度出发,构建包含发电效率、调峰能力、碳排放强度、来水量变化等11项指标的综合评价体系。其次,采用AHP确定各指标权重以体现不同维度的差异性影响,并通过TOPSIS计算各运行方案与理想解的贴近度,实现联合运行模式的优劣排序。最后,以某区域水电站与抽水蓄能联合工程为实例进行验证分析。结果表明:相较于传统单一评价方法,AHP-TOPSIS模型能够有效兼顾主客观因素,量化评价结果,其中调峰能力与动态投资回收期对综合性能影响显著;同时,联合运行方案中储能容量配置与调度策略的协同优化可提升系统综合效益15%以上。研究结果为多能互补系统中水电-抽蓄联合运行的方案优选与决策制定提供了理论依据,对推动清洁能源高效利用具有实际意义。展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
文摘为科学评估水电站与抽水蓄能联合运行的综合性能,提出一种基于层次分析法(analytic hierarchy process,AHP)与逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)相结合的多指标综合评价模型。首先,从经济性、技术性、环境效益、水情及政策5个维度出发,构建包含发电效率、调峰能力、碳排放强度、来水量变化等11项指标的综合评价体系。其次,采用AHP确定各指标权重以体现不同维度的差异性影响,并通过TOPSIS计算各运行方案与理想解的贴近度,实现联合运行模式的优劣排序。最后,以某区域水电站与抽水蓄能联合工程为实例进行验证分析。结果表明:相较于传统单一评价方法,AHP-TOPSIS模型能够有效兼顾主客观因素,量化评价结果,其中调峰能力与动态投资回收期对综合性能影响显著;同时,联合运行方案中储能容量配置与调度策略的协同优化可提升系统综合效益15%以上。研究结果为多能互补系统中水电-抽蓄联合运行的方案优选与决策制定提供了理论依据,对推动清洁能源高效利用具有实际意义。
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
