Adjustment of Model Parameters to Estimate Distribution Transformers Remaining Lifespan

Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.

Adaptive algorithm for solving different types and different precisions and nonlinear surveying and mapping parameters adjustment

Approximate linear methods and nonlinear methods were adopted usually for solving models of nonlinear surveying and mapping parameters adjustment. But, these iterative algorithms need to compare harsh initial value. A kind of new algorithm-adaptive algorithm based on analyzing the general methods was put forward. The new algorithm has quick rate of convergence and low dependence for initial value, so it can avoid calculating complex second derivative of the target function. The results indicate that its performance is better than those of the others.

Algorithm of neural-network about solving nonlinear least squares adjustment by parameters

Study on solving nonlinear least squares adjustment by parameters is one of the most important and new subjects in modern surveying and mapping field . Many researchers have done a lot of work and gained some solving methods. These methods mainly include iterative algorithms and direct algorithms mainly. The former searches some methods of rapid convergence based on which surveying adjustment is a kind of problem of nonlinear programming. Among them the iterative algorithms of the most in common use are the Gauss-Newton method, damped least quares, quasi-Newton method and some mutations etc. Although these methods improved the quantity of the observation results to a certain degree, and increased the accuracy of the adjustment results, what we want is whether the initial values of unknown parameters are close to their real values. Of course, the model of the latter has better degree in linearity, that is to say, they nearly have the meaning of deeper theories researches. This paper puts forward a kind of method of solving the problems of nonlinear least squares adjustment by parameters based on neural network theory, and studies its stability and convergency. The results of calculating of living example indicate the method acts well for solving parameters problems by nonlinear least squares adjustment without giving exact approximation of parameters.

CONTROLLING HYPERCHAOS IN PLANAR SYSTEMS BY ADJUSTING PARAMETERS

For the two_parameter family of planar mapping, a method to stabilize an unstable fixed point without stable manifold embedding in hyperchaos is introduced. It works by adjusting the two parameters in each iteration of the map. The explicit expressions for the parameter adjustments are derived, and strict proof of convergence for method is given.

On-line detecting of transformer winding deformation based on parameter identification of leakage inductance

Transformers are required to demonstrate the ability to withstand short circuit currents.Over currents caused by short circuit can give rise to windings deformation.In this paper,a novel method is proposed to monitor the state of transformer windings,which is achieved through on-line detecting the leakage inductance of the windings.Specifically,the mathematical model is established for online identifying the leakage inductance of the windings by applying least square algorithm(LSA) to the equivalent circuit equations.The effect of measurement and model inaccuracy on the identification error is analyzed,and the corrected model is also given to decrease these adverse effect on the results.Finally,dynamic test is carried out to verify our method.The test results clearly show that our method is very accurate even under the fluctuation of load or power factor.Therefore,our method can be effectively used to on-line detect the windings deformation.

Need for multiple biomarkers to adjust parameters of closed-loop deep brain stimulation for Parkinson's disease

Closed-loop deep brain stimulation（DBS）：DBS has been established as a surgical therapy for movement disorders and select neuropsychiatric disorders.Various efforts to improve the clinical outcomes of the procedure have been previously made.Several factors affect the DBS clinical outcomes such as lead position,programming technique,

Tuning method for governor control parameters of hydropower generator in isolated grid considering primary frequency performance and small-signal stability

This paper presents a method of tuning governor control parameters of an isolated hydropower generator considering the primary frequency performance and small-signal stability. First, generators that can be operated in isolated state are identified. Second, different schemes are proposed for generator mode switching from on-grid to off-grid state through comparison and mechanism analysis. Third, the time domain model and frequency domain model of the isolated generator governor are constructed to respectively estimate the primary frequency performance and small signal stability. Parameter sets that satisfy the primary frequency performance and small signal stability are acquired as optimal values of governor control parameters. Finally, the measurement-based parameters of the governor are identified and validated using simulations to demonstrate the feasibility and effectiveness of the method.

Simulation of γ spectrum-shifting based on the parameter adjustment of Gaussian function space

Based on the statistical characteristics of energy spectrum and the features of spectrum-shifting in spectrometry,the parameter adjustment method of Gaussian function space was applied in the simulation of spectrum-shifting.The transient characteristics of energy spectrum were described by the Gaussian function space,and then the Gaussian function space was transferred by parameter adjustment method.Furthermore,the spectrum-shifting in measurement of energy spectrum was simulated.The applied example shows that the parameters can be adjusted flexibly by this method to meet the various requirements in simulation of energy spectrum-shifting.This method was one parameterized simulation method with good performance for the practical application.

On-line Chatter Detection Using an Improved Support Vector Machine

On-line chatter detection can avoid unstable cutting through monitoring the machining process.In order to identify chatter in a timely manner,an improved Support Vector Machine(SVM)is developed in this paper,based on extracted features.In the SVM model,the penalty factor(e)and the core parameter(g)have important influence on the classification,more than from Kernel Functions(KFs).Hence,first the classification results are conducted using different KFs.Then two methods are presented for exploring the best parameters.The chatter identification results show that the Genetic Algorithm(GA)approach is more suitable for deciding the parameters than the Grid Explore(GE)approach.

Sensitivity analysis of relevant parameters in complicated mine ventilation network by numerical test

Depending on the numerical test approach on a computer, the relationships among relevant parameters, eg branch number, node number, mesh number, computation accuracy, preliminary value of airflow rate, iteration number, computation time and convergence in a mine ventilation network analysis, were investigated based on 5 mine ventilation systems. The results show that a higher computation accuracy greatly influences the iteration number. When the accuracy reaches 10-6m3·s-1 for solving a complicated mine ventilation network, the running time is too long though a high-speed computer is used. The preliminary value of airflow rate in the range of 1100m3·s-1 has little effects the iteration number. The structure of network also has some effect on the iteration number.

基于自适应非奇异终端滑模自抗扰的ROV控制

ROV浮游工作时由于存在系统模型不确定性和环境干扰导致跟踪效果不佳的问题,因此,提出一种参数变化的非奇异终端滑模自抗扰控制方法;首先,建立ROV的动力学模型;其次,利用非奇异终端滑模控制器代替了传统线性自抗扰的线性控制器,提高了系统的控制性能和抗干扰能力,考虑到引入参数过多问题利用梯度下降的RBF神经网络调整趋近率系数,通过李雅普诺夫定理证明了该系统的稳定性;最后,通过轨迹跟踪仿真实验与线性自抗扰对比,提高了系统响应过程的快速性和平稳性。

活动断裂带大调整量无砟轨道设计与试验研究

研究目的:为满足跨活动断裂带线路铺设无砟轨道需求,提升该地区无砟轨道的线路平顺性与舒适性,提出一种大调整量活动承轨台式无砟轨道。采用ANSYS有限元软件建立整体和轨枕局部有限元模型,分析活动承轨台式无砟轨道的受力特性,在此基础上进行大调整量无砟轨道结构参数优化研究以及疲劳试验研究,分析结构刚度、位移和应变变化情况。研究结论:(1)活动承轨台式无砟轨道在列车荷载与温度荷载叠加作用下,最大拉应力出现在轨枕位置,为3.47 MPa,各部件应力均满足强度要求且在轨枕尺寸和材料优化后进一步降低;(2)疲劳试验中随着疲劳加载次数增加,结构横、纵向刚度在疲劳前后变化分别为3.4%和24.2%,结构应变小幅增加,未见塑性变形与明显损伤;(3)仿真与试验结果可为活动断裂带地区无砟轨道设计提供参考和借鉴。

PSO算法下GP-周期随机共振的微弱OFDM信号检测

为了解决传统随机共振系统在微弱正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)信号解调应用中存在的人工选择参数困难、解调效果差的问题,提出一种基于粒子群优化(Particle Swarm Optimization,PWO)算法的GP(Gaussian Potential)-周期随机共振系统的微弱OFDM信号检测方法。该方法利用GP-周期随机系统对OFDM信号经正交变换后的两路高频信号分别进行随机共振,同时运用阻尼参数与信号频率之间的关系对两路信号进行降频处理,使得上下两路信号都能够发生随机共振;然后使用PWO算法对随机共振的系统参数进行优化处理,以使得上下两路信号达到最优的共振效果;最后,将上下两路共振之后的信号合成完成OFDM信号解调,得到最终的数字序列。本文探讨了优化前后GP-周期随机共振系统的共振效果,研究了优化前后GP-周期随机共振诱导下的OFDM系统星座图的聚集程度以及误码率情况,对比分析了经典双稳随机共振、经典三稳随机共振和所提模型对OFDM信号检测的影响。仿真结果表明:相比常用的传统随机共振模型,该方法用于OFDM信号检测的星座图中信号点的聚集程度更高,同时OFDM系统误码率在同样的信噪比下降低50%左右。

孟加拉湾波浪数值模型计算初始参数调整研究

在对孟加拉湾海域进行波浪建模的过程中,SWAN模型初始参数调整的研究显得尤为重要。鉴于该区域波浪特性的复杂性和多变性,利用波浪浮标数据,对SWAN模型在各种初始参数配置下的模拟结果进行了深入评估。研究结果表明,在某些特定条件下,仅仅提升计算分辨率并不能显著增强模型的模拟精度;因此,选择合适的分辨率对于确保计算效率与模拟精度之间的平衡显得至关重要。特别是在对BD08、BD11和BD14三个浮标的数据进行分析时发现,当模型设置为10 min时间步长和最大迭代次数为1时,波高模拟展现出了高度稳定性和准确性。因此,建议在模拟过程中采用较短的时间步长并减少迭代次数,尤其是在水深变化剧烈和近岸地区,以提高模型的模拟精度和效率。对SWAN模型初始参数进行细致的调整,对于提高模型在不同地区的模拟精度和效率是至关重要的。

仿生隔振器设计方法研究进展

基于仿生学的思想,通过模仿生物系统的结构几何构成特性,创新优化隔振模型是当今隔振领域的热门问题.本文基于仿生灵感来源的不同,对现有的仿生隔振器进行了系统的分类,并对不同设计原理的仿生隔振器恢复力本构与隔振性能做了详细的阐述和对比,旨在通过隔振器隔振频带的横向对比厘清几何构型与可调参数的影响,以此厘清隔振器几何设计和隔振性能的关系.并且,本文展望了未来仿生隔振器的发展前景和研究方向.

高速列车座椅调节参数对舒适性的影响研究

为研究高速列车座椅参数对不同身高百分位人群的舒适性影响程度,文中对第5百分位、第50百分位及第95百分位人群在不同调节参数下开展主观舒适性和体压分布的试验研究。针对座椅调节参数对舒适性的影响,选择座面高度、座面深度、座面倾角和靠背倾角4个参数作为因素,设计4因素3水平的正交试验。采用极差法和灰色关联度分析法对正交试验数据进行分析,得到了座椅调节参数对3类百分位人群主观舒适性的影响规律及座椅参数的优化组合,同时得到了体压分布指标与主观舒适性的关联程度。研究发现:座椅调节参数对不同身高百分位人群的主观舒适性影响不同;第5百分位人群舒适性受座椅的靠背倾角和座面高度影响较大;第50百分位和第95百分位人群受靠背倾角、座面倾角影响较大;体压分布指标与不同人群主观舒适性的关联程度不同。研究结果可为高速列车座椅的参数优化和舒适性提升提供参考依据。

基于Innovus混合放置的布局规划方法优化

随着集成电路后端设计中宏单元数量增多,传统布局规划方法效率低且耗时,而自动布局规划的混合放置(MP)技术存在物理规则违例数量多、电压降大和功耗高等问题。针对传统方式和MP方式的不足,提出了一种优化的MP布局规划方法,通过控制宏单元通道空间和标准单元密度大小、固定边界宏单元位置及脚本修复TSMC芯片集成检查(TCIC)违例的方法解决MP技术存在的问题。研究结果表明,优化的MP方式保留了MP技术的性能、功耗和面积(PPA)优势,且相比于传统方式布线长度优化了28%,时序违例优化了65%,功耗优化了609%。该方案可为多宏单元大规模设计的布局规划提供参考。

基于带阈值的BPE-dropout多任务学习的端到端语音识别

针对语音识别任务中出现的未登录词问题,提出一种带阈值的BPE-dropout多任务学习语音识别方法.该方法采用带随机性的字节对编码算法,在形成子词时引入带字数阈值的策略,将子词作为建模单元,编码器部分采用Conformer结构,与链接时序分类和注意力机制相结合.为进一步提升模型性能,引入动态参数对损失函数进行动态调节,并同时进行多任务训练和解码.实验结果表明,该方法采用子词作为建模单元可有效解决未登录词问题,在多任务学习框架下进一步提升了模型的识别性能.在公开数据集THCHS30和ST-CMDS上,该模型实现了超过95%的识别准确率.