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1.
针对气电综合能源系统优化调度中未充分挖掘可调度因素、未充分考虑风电预测误差不确定性的现状,研究含风电的气电综合能源系统日前区间优化调度方法。首先,基于区间数学对风电出力的不确定性进行区间描述,综合考虑抽水蓄能、气电双向转换以及需求响应等资源和技术,并用区间数学表示优化目标和优化约束,构建含高比例风电的气电综合能源系统日前优化模型,综合优化系统的经济性、环保性。然后,基于区间序关系和可能度的方法将区间优化模型转化为确定性优化模型,继而采用Jaya算法进行求解,保证求解的高效性和解空间的可行性。最后,进行算例分析,仿真结果验证了综合利用抽水蓄能、气电双向转换以及需求响应等要素对系统进行区间优化,能取得优化效果上的优势,并能为系统调度人员提供风电不确定度对系统优化影响的信息。  相似文献   
2.
Brain tumor segmentation and classification is a crucial challenge in diagnosing, planning, and treating brain tumors. This article proposes an automatic method that categorizes the severity level of the tumors to render an effective diagnosis. The proposed fractional Jaya optimizer-deep convolutional neural network undergoes the severity classification based on the features obtained from the segments of the magnetic resonance imaging (MRI) images. The segments are obtained using the particle swarm optimization that ensures the optimal selection of the segments from the MRI image and yields the core tumor and the edema tumor regions. The experimentation using the BRATS database reveals that the proposed method acquired a maximal accuracy, specificity, and sensitivity of 0.9414, 0.9429, and 0.9708, respectively.  相似文献   
3.
The paper presents an effective two-stage approach based on modal strain energy change and Jaya algorithm for damage assessment in plate-like structures. In the first stage, a newly developed damage indicator, named as normalized modal strain energy-based damage index (nMSEBI), is proposed to help locate potential damage elements more effectively. After eliminating most of the healthy elements, the actual damaged sites and its extent in plate structure are determined in the second stage by minimizing an objective function, which is solved using Jaya algorithm. For finding a suitable objective function used in optimization process, two different objective functions are considered to examine their effects on the performance of the utilized optimization algorithm. The efficiency and accuracy of the proposed two-stage damage detection method are investigated by two numerical examples comprising a concrete plate and a four-layer (0°/90°/90°/0°) laminated composite plate with multiple damage locations. All of the obtained results indicate that even under measurement noise, the proposed method can identify the actual damage sites and estimate the extent of damage with high precision. In addition, the numerical results also show that the computational cost of the optimization process using the objective function based on modal flexibility change is much lower than that using the objective function based on mode shapes change.  相似文献   
4.
一种利用Java进行Web网络管理的技术策略的研究   总被引:1,自引:1,他引:0  
介绍了传统的网络管理模式和基于Web的3层网络管理模式的区别,讨论了如何利用Java技术实现Web网络管理的一种方法及其工作原理,举例说明了如何实现网络管理中的拓扑发现和报警推送的问题。  相似文献   
5.
To improve the driving performance of the electric vehicles, batteries or ultracapacitors (UCs) are frequently preferred in the energizing systems. In hybrid structures with multiple supply sources, an energy management system (EMS) is needed to improve the system efficiency, and to provide the optimum power sharing between a battery and a UC. The purpose of this study is to investigate the effectiveness of the Jaya optimization method for the urban use of the EMS of an ultralight electric vehicle powered by battery/UC. The performance of the proposed method is compared with dynamic programming (DP) that is one of the global optimization methods and particle swarm optimization (PSO) that is one of the other heuristic methods for real-time applications. The simulation results show that Jaya-EMS approached 3.1% to the DP, which yields the optimum result with respect to the total energy loss. In addition, the proposed method yields a loss of less than 1.9% from the PSO-EMS. If all the above situations are considered, the proposed EMS method has less lossy alternative solution for the real-time applications.  相似文献   
6.
曾一婕  王龙  黄超 《太阳能学报》2022,43(2):198-202
为提升太阳电池模型参数辨识的准确率,该文提出基于Jaya算法与蜻蜓算法相融合的辨识方法,运用Jaya算法进行初步全局搜索,并结合蜻蜓算法进行局部搜索最优解,使算法收敛精度得到有效提升。研究结果表明:运用Jaya-DA算法求得太阳电池模型的电流均方根误差为9.861×10-4,相较于单一使用Jaya算法、蜻蜓算法、人工蜂群算法、粒子群算法,该方法所得结果均方根误差更小,可更准确地确定太阳电池模型参数。  相似文献   
7.
The integration of numerous monitoring points poses a significant challenge to the efficient modeling of dam displacement behavior, and multi-point synchronous prediction is an effective solution. However, traditional approaches usually construct site-specific data-driven models for each monitoring point individually, which focus on single-target regression and discard the underlying spatial correlation among different displacement monitoring points. This study therefore proposes a multi-input multi-output (MIMO) machine learning (ML) paradigm based on support vector machine (SVM) for synchronous modeling and prediction of multi-point displacements from various dam blocks. In this method, a novel multi-output data-driven model, termed as multi-target SVM (MSVM), is formulated through a deep hybridization of classical SVM architecture and multi-target regression. During the initialization of MSVM, the intercorrelation of multiple target variables is fully exploited by decomposing and regulating the weight vectors. The proposed MSVM is designed to capture the complex MIMO mapping from influential factors to multi-block displacements, while taking into account the correlation between multi-block displacement outputs. Additionally, in order to avoid obtaining the unreliable prediction results due to the empirical selection of parameters, an efficient optimization strategy based on the parallel multi-population Jaya (PMP-Jaya) algorithm is used to adaptively tune the hyperparameters involved in MSVM, which contains no algorithm-specific parameters and is easy to implement. The effectiveness of the proposed model is verified using monitoring data collected from a real concrete gravity dam, where its performance is compared with conventional single-target SVM (SSVM)-based models and state-of-the-art ML-based models. The results indicate that our proposed MSVM is much more promising than the SSVM-based models because only one prediction model is required, rather than constructing multiple site-specific SSVM-based models for different dam blocks. Moreover, MSVM can achieve better performance than other ML-based models in most cases, which provides an innovative modeling tool for dam multi-block behavior monitoring.  相似文献   
8.
为了提高高拱坝物理力学参数反演的精度及效率,将Jaya智能优化方法与高斯过程机器学习理论引入大坝安全监控领域,提出了基于Jaya-高斯过程回归代理模型的拱坝参数反演分析方法。采用高斯过程回归代理模型代替传统的有限元计算,并利用3种智能优化算法进行参数寻优。结果表明:Jaya算法相比于PSO算法、GWO算法,不仅反演精度高、收敛速度快,且具有很好的稳定性;所提出反分析策略在反演用时方面比直接调用有限元计算的反分析方法节省80%以上。本文方法不仅能够满足计算精度要求,且大大缩减了计算时间,为高拱坝物理力学参数反演分析提供了一种高效的方法。  相似文献   
9.
As an extension of the classical job shop scheduling problem, flexible job shop scheduling problem (FJSP) is considered as a challenge in manufacturing systems for its complexity and flexibility. Meta-heuristic algorithms are shown effective in solving FJSP. However, the multiple critical paths issue, which has not been formally discussed in the existing literature, is discovered to be a primary obstacle for further optimization by meta-heuristics. In this paper, a hybrid Jaya algorithm integrated with Tabu search is proposed to solve FJSP for makespan minimization. Two Jaya operators are designed to improve solutions under a two-vector encoding scheme. During the local search phase, three approaches are proposed to deal with multiple critical paths and have been evaluated by experimental study and qualitative analyses. An incremental parameter setting strategy and a makespan estimation method are employed to speed up the searching process. The proposed algorithm is compared with several state-of-the-art algorithms on three well-known FJSP benchmark sets. Extensive experimental results suggest its superiority in both optimality and stability. Additionally, a real world scheduling problem, including six instances with different scales, is applied to further prove its ability in handling large-scale scheduling problems.  相似文献   
10.
准确的预测风速对于风电场的安全运行和高效发电具有重要意义。 针对已有文献在风速预测问题中采用的单一分解 策略存在固有缺陷、优化预测模型效果不稳定等问题,提出了一种融合两阶段分解与 iJaya-ELM 的混合预测模型。 首先,对原 始风速序列进行 ICEEMDAN 分解,得到 12 个分量后基于排列熵熵值重构为高频项、中频项与低频项;随后对高频项进行奇异 谱分解滤去序列噪声;提出一种改进的 Jaya 算法 iJaya,利用 iJaya 算法获取极限学习机 ELM 的最优连接权值与阈值,最后将各 个分量的预测结果线性集成得到最终结果。 以我国甘肃地区风电场风速数据进行模型验证,并利用新疆地区数据集测试其鲁 棒性与通用性。 实验结果表明,iJaya 算法具有较强的寻优精度与稳定性,两阶段分解能够深度挖掘风速序列的特征;该混合模 型能够有效提升风速预测精度,平均绝对误差与均方误差分别为 0. 067 9 和 0. 134 5。  相似文献   
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