Adaptive Setting Method Research Articles

Adaptive setting of background cross sections for generation of effective multi-group cross sections in FRENDY nuclear data processing code

ABSTRACT An adaptive setting method of background cross sections is implemented to FRENDY/MG, which is a multi-group neutron cross section generation module, for accurate interpolation of self-shielding factors with a minimum number of background cross sections. Since the dependence of self-shielding factors on background cross section is significantly different among energy group, reaction type, and nuclide, appropriate setting of background cross sections usually requires considerable works. In the present adaptive setting method, the range of background cross section is initially divided into 10 equal intervals and unnecessary background cross section points are eliminated. Then interpolation accuracy at each interval is tested. If the interpolation accuracy in an interval is not sufficient, the interval is successively halved until sufficient interpolation accuracy is obtained. For accurate interpolation of self-shielding factor or reaction rates, the monotone cubic interpolation is used. Verification calculations are carried out for all isotopes in JENDL-4.0. Calculation results indicate that the present method provides an appropriate set of background cross sections while satisfying input error tolerance for self-shielding factors or reaction rates. Typical numbers of background cross sections are from 10 to 25 when the monotone cubic interpolation and error tolerance of 0.01 for self-shielding factors are used.

A practical building energy consumption anomaly detection method based on parameter adaptive setting DBSCAN

In order to realize the Building Energy Consumption Anomaly Detection (BECAD) for the green building assessment, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is adopted for data clustering. To deal with the parameter setting difficulty of the DBSCAN, a practical parameter adaptive setting method is proposed. The presented method determines values of the DBSCAN parameters, MinPts and ε, according to four distribution characteristics (average data distance, data local densities, cosine similarity, and equivalent space radius) of data, and does not need prior knowledge of the datasets. Furthermore, parameter values determined by the proposed method can improve the clustering effect of the DBSCAN on datasets with various data densities. After testing the proposed method with open datasets, DBSCAN with the parameter adaptive setting method is applied to the BECAD. Experiment results show that identified building energy utilization patterns and abnormal buildings are reasonable and the results can offer the management departments a clear understanding of building energy consumption patterns, as well as decision supports to make subsequent improvement measures.

Adaptive Zone-1 Setting Following Structural and Operational Changes in Power System

Change in the network structure or operating condition in a power system affects the impedance as calculated by the distance relay during fault. Such changes may lead to malfunction of the distance relay at times. In this paper, an adaptive distance relay setting method using local data is proposed to prevent zone-1 malfunction following structural and operational changes. The performance of the proposed adaptive setting method is tested on a 39-bus New England system and a generic 12-bus power system using PSCAD/EMTDC simulation data, and a comparative assessment is also provided.

一种新的谱聚类算法及其仿真

针对传统谱聚类基于欧氏距离度量样本之间的相似性，不能反映样本的概率分布特性，特别是具有多峰分布的样本聚类，欧氏距离具有较大的偏差，另外在传统谱聚类中广泛使用的k-means算法由于初始中心的随机性设置，经常会产生不稳定的聚类结果。本文针对相似性度量问题提出了一种能够反映样本概率分布特性的相似性度量方法，并在此基础上对谱聚类中的k-means算法进行了改进，给出了一种对初始中心进行自适应设置的方法。实验结果表明，该算法相比传统的谱聚类在人工数据集与UCI真实数据集上具有更好的聚类效果。 The similarity between the samples based on Euclidean distance measurement can not reflect the probability distribution characteristics of the samples, especially the sample clustering with multi-peak distribution, and the Euclidean distance has a large deviation. In addition, the k-means algorithm widely used in the class often produces unstable clustering results due to the randomness of the initial center. In this paper, a similarity measure method which can reflect the probability distribution of the sample is proposed for the similarity measure. On this basis, the k-means algorithm in spectral clustering is improved, and a method is proposed for the initial center adaptive setting method. The experimental results show that the proposed algorithm has better clustering effect than the traditional spectral clustering in the artificial data set and the UCI real data set.

Adaptive setting of distance relay for MOV-protected series compensated line considering wind power

Series compensated lines are protected from overvoltage by metal-oxide-varistors (MOVs) connected in parallel with the capacitor bank. The nonlinear characteristics of MOV devices add complexity to fault analysis and distance protection operation. During faults, the impedance of the line is modified by an equivalent impedance of the parallel MOV/capacitor circuit, which affects the distance protection. The intermittent wind generation introduces additional complexity to the system performance and distance protection. Wind variation affects the fault current level and equivalent MOV/capacitor impedance during a fault, and hence the distance relay operation. This paper proposes an adaptive setting method for the mho relay distance protection of series compensated line considering effects of wind power variation and MOV operation. The distributed parameters of a transmission line are taken into account to avoid overreaching and underreaching of distance relays. A test system is simulated, and results demonstrate the ability of the proposed method to resetting the distance relays to adapt to various system conditions, including three wind generation and different compensation levels.

Local online adaptive technique for optimal coordination of overcurrent relays within high voltage substations

Summary The optimal coordination of overcurrent relays is an important task in the field of power system protection. Traditionally, this problem is solved with the assumption of the grid being in maximum or minimum loading and dominant topology, that is, the topology in which the grid operates most of the time. However, the grid condition changes during the time because of many reasons. In this paper, by sampling voltage and load current phasors at the high-voltage (HV) bus of a substation, an online method, based on an evolutionary algorithm, is introduced to estimate the grid's Thevenin equivalent viewed from the HV bus. Then, the Thevenin equivalent together with the connection status and short-circuit model of local power transformers and local distributed generations are used to estimate three-phase short-circuit currents in the existing condition of the grid. Finally, these fault currents are used for adaptive optimal coordination of overcurrent relays of the HV substation, such that the optimal coordination of the relays is preserved in all the grid conditions. Case studies, performed on the overcurrent protection of a typical 230/20 kV substation connected to a 9-bus grid, prove desired performance of the proposed online adaptive setting method. Furthermore, this method needs no telecommunications and represents a step toward self-adjusting overcurrent relays. Copyright © 2016 John Wiley & Sons, Ltd.

Differential Equation Based Adaptive Distance Relaying Setting Method for Series Compensated Line

The distance relays of transmission lines find limitations in the presence of series compensated capacitor. One of them is the distance relay setting issue, in which either over-reach problem or under-reach problem may occur. To deal with this issue, a differential equation based adaptive distance relaying setting method for series compensated line is proposed. As for it do not need to identify whether SC/MOV is in fault loop, not need to know the parameters and the operating states of SC/MOV/GAP, and furthermore, it can be realized without the iterative calculation, it is very suitable to be used for the distance relaying adaptive setting of series compensated line. Simple simulation results certificate the proposed adaptive setting method.

Spiking cortical model for multifocus image fusion

Spiking Cortical Model (SCM) is derived from primate visual cortex. It has a high sensitivity for low intensities of stimulus, but low sensitivity for high intensities, and is suitable for image processing. This paper adopts an improved SCM for multifocus image fusion. Firstly we analyze and compare various image clarity measures, and then we propose a new SCM fusion method based on a composite image clarity criterion which synthesizes virtues of two classic clarity criteria. As to the iteration number of SCM model for image processing, we introduce time matrix as an adaptive setting method instead of using fixed constant, which can automatically and adaptively calculate iteration number for each image accurately. Besides, we optimize pulsing output matrix of source image according to natural optical focus principle before forming and outputting the final fused image. In order to verify the effectiveness of the proposed method, we compare it with other ten methods under four fusion effect evaluation indices. The experimental results show that the proposed approach can obtain better fusion results than others, and is an effective multifocus image fusion method.

Recursive identification for Hammerstein–Wiener systems with dead-zone input nonlinearity

A new recursive algorithm is proposed for the identification of a special form of Hammerstein–Wiener system with dead-zone nonlinearity input block. The direct motivation of this work is to implement on-line control strategies on this kind of system to produce adaptive control algorithms. With the parameterization model of the Hammerstein–Wiener system, a special form of model estimation error is defined; and then its approximate formula is given for the following derivation. Based on these, a recursive identification algorithm is established that aims at minimizing the sum of the squared parameter estimation errors. The conditions of uniform convergence are obtained from the property analysis of the proposed algorithm and an adaptive setting method for a weighted factor in the algorithm is given, which enhances the convergence of the proposed algorithm. This algorithm can also be used for the identification of the Hammerstein systems with dead-zone nonlinearity input block. Three simulation examples show the validity of this algorithm.

SAVQ: stabilized adaptive virtual queue management algorithm

We observe that the desired utilization parameter /spl gamma/ in AVQ algorithm has an influence on the dynamics of queue and link utilization. It is difficult to achieve a fast system response and high link utilization simultaneously using a constant value /spl gamma/. An adaptive setting method for /spl gamma/ is proposed according to the instantaneous queue size and the given reference queue value. This new algorithm, called stabilized AVQ (SAVQ), stabilizes the dynamics of queue maintaining a high link utilization.

Asymptotically Optimal RLS Adaptive Identification Algorithms

A major, and yet unresolved, problem has been the choice of the gain adjusting parameter in some parameter estimation algorithms. This paper presents an adaptive setting method of the forgetting factor for estimating time-varying parameters when the Recursive Least Squares (RLS) algorithm is used. The method is to choose the forgetting factor λ so as to minimize the performance index defined by E {e2t (λ)} where et (λ) is the prediction error based on some λ. This is concretely done by solving the minimization problem of a certain object function with respect to λ. It is shown that this object function is unimodal with respect to λ and thus the minimization can be attained by using the stochastic Newton method. As a result, the proposed method becomes asymptotically optimal. The resultant algorithm can be executed by linking the minimization routine with RLS.Numerical examples indicate acceptable performance.

An Adaptive Setting Method of Step-Size Parameter in NLMS Algorithms

A major, and yet unresolved, problem has been the choice of the step-size in some parameter tracking algorithms. This paper presents an adaptive setting method of step-size parameter for tracking time-varying parameters when normalized least mean square (NLMS) algorithm is used. The usual method suggested by Benveniste et al is to adjust the step-size so as to minimize the performance measure defined by the mean squares of prediction error. The weak point of this method is that the performance measure converges only on a local minimum.The main object of this paper is to give a solution for this problem. The solution obtained is that the performance measure converges on the global minimum through the minimization of another performance measure. As a result, the proposed algorithm becomes asymptotically optimal.Numerical examples indicate acceptable performance of the proposed method.