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.

Research on the adjustment model of ventilation characteristic parameters based on integrated method of circuit and path

Ventilation characteristic parameters are the base of ventilation network solution; however, they are apt to be affected by operating errors, reading errors, airflow stability, and other factors, and it is difficult to obtain accurate results. In order to check the ventilation characteristic parameters of mines more accurately, the integrated method of circuit and path is adopted to overcome the drawbacks caused by the traditional path method or circuit method in the digital debugging process of ventilation system, which can improve the large local error or the inconsistency between the airflow direction and the actual situation caused by inaccuracy of the ventilation characteristic parameters or checking in the ventilation network solution. The results show that this method can effectively reduce the local error and prevent the pseudo-airflow reversal phenomenon; in addition, the solution results are consistent with the actual situation of mines, and the effect is obvious.

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,

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 10-6m3·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 1100m3·s-1 has little effects the iteration number. The structure of network also has some effect on the iteration number.

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.

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。

基于模态分解与SRU网络的时间序列预测

时间序列预测在工业、农业、金融及军事等领域中具有重要的应用价值。为了进一步提高预测的可靠性和准确性,构建一种基于模态分解与SRU网络的杂交预测模型。首先,针对模态个数难以确定的问题,构建基于平均样本熵来确定模态个数的自适应变分模态分解(AVMD)模型,以减少不同频率上的混叠及降低随机噪声的干扰。通过在Adam算法中引入了随机调整参数,来提高SRU网络的训练速度及增强网络跳出局部最优解的能力。最后,发展一种基于AVMD与SRU网络的杂交模型。为评估提出的预测模型的可靠性和准确性,将之与一些最新预测方法做比较。电力负荷序列的实验结果表明,所提出的杂交预测模型具有较高的准确性和可靠性。

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。

基于机器学习的盾构姿态预测模型与控制方法研究

为避免盾构轴线偏离引发衬砌管片错台、开裂等质量与安全问题,提出一种基于机器学习算法的盾构姿态智能预测模型与控制方法。以盾构掘进施工的实测数据为驱动,通过贝叶斯优化(BO)与支持向量回归(SVR)构建盾构姿态预测模型,挖掘施工参数-地层信息-盾构姿态三者间的非线性关系。结合模拟退火算法(SA)形成可控施工参数动态调整的盾构姿态控制方法,并将其应用于福州滨海快线南—三区间隧道的工程实践。主要结论如下:1)经数据预处理、特征筛选及BO超参数优化,基于SVR的盾构姿态预测模型具备优异的预测性能和泛化能力;2)结合SA算法进行可控施工参数调整时,需设置合理的优化规则,以确保所推荐的可控施工参数具备可操作性;3)将姿态控制方法应用于南—三区间后续掘进施工以辅助纠偏,盾尾垂直偏差在10环掘进过程中由45 mm减至18 mm,实现了连续稳定纠偏。

燃烧优化调整在智慧燃烧系统建模中的应用与研究

智慧燃烧系统的构建是建设智慧电厂必不可少的一部分,对保证发电效率具有重要的作用。介绍了府谷电厂智慧燃烧系统,并给出了智慧燃烧优化及控制技术路线。为获得氧量、燃尽风门开度、给煤机偏置等参数对机组运行的影响,进行了相关燃烧参数调整试验。建立了智慧燃烧系统动态模型,并应用于电力燃烧系统。

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。

现代控制理论教学中仿真软件的应用研究

文章主要探讨在现代控制理论教学中应用仿真软件的重要性和应用方式。首先,介绍了现代控制理论的基本概念和原理,以及其在工程领域中的应用。其次,探讨了传统教学方法存在的局限性,如存在理论与实践脱节、实验设备成本高等问题。然后,分析了仿真软件在现代控制理论教学中的优势,如能够提供真实的工程场景、灵活性高、成本低等。最后,详细阐述了仿真软件在现代控制理论教学中的应用,包括仿真实验的设计与实施、参数调节与优化、系统性能分析等方面。文章旨在提高学生的实践能力和理论应用水平,为他们今后的工程实践奠定坚实的基础。

基于GWO-XGBoost的工业污水水质关键数据预测算法

为解决XGBoost模型参数调整复杂和预测水质数据准确率低的问题,设计了一种基于灰狼算法(GWO)优化XGBoost的预测算法。首先利用灰狼优化算法全局寻参收敛能力强的优点,使用GWO优化XGBoost的超参数(最大生成树数目、学习率以及树的最大深度)进行优化,获取XGBoost的最佳预测表现。其次通过对水质关键数据的预处理提高数据的可靠性,使用多种算法进行对比分析实验。结果表明:与LSTM、未经GWO优化参数的XGBoost相比,采用灰狼算法优化后的XGBoost模型具有较好的非线性预测能力,模型的决定因数R^(2)达到0.85以上。