PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.     

软土基坑工程的技术管理

通过建设单位在福建大剧院的基坑工程中总结出的加强技术管理、掌握方案优选的主动权、正确处理局部和整体的关系、主次问题的关系,并设立有效的应急预案等经验,说明建设单位技术管理在软土基坑工程中的重要作用。     

An Improved On-line Contingency Screening for Power System Transient Stability Assessment

This paper presents a contingency screening method and a framework for its on-line implementation. The proposed method carries out contingency screening and on-line stability assessment with respect to first-swing transient stability. For that purpose, it utilizes the single machine equivalent method and aims at improving the prior developed contingency screening approaches. In order to determine vulnerability of the system with respect to a particular contingency, only one time-domain simulation needs to be performed. An early stop criteria is proposed so that in a majority of the cases the simulation can be terminated after a few hundred milliseconds of simulated system response. The method’s outcome is an assessment of the system’s stability and a classification of each considered contingency. The contingencies are categorized by exploiting parameters of an equivalent one machine infinite bus system. A novel island detection approach, appropriate for an on-line application since it utilizes efficient algorithms from graph theory and enables stability assessment of individual islands, is also introduced. The New England and New York system as well as the large-scale model of the Continental-European interconnected system are used to test the proposed method with respect to assessment accuracy and computation time.     

考虑元件综合重要度的电网安全性风险评估方法

从系统学角度出发,综合考虑系统中元件的结构属性、状态属性及社会属性,提出一种考虑元件综合重要度的电网安全性风险评估方法。综合元件结构重要度因子模型与元件状态灵敏度因子模型构建元件综合重要度模型,该模型克服了传统风险评估方法中对于不同元件的重要度无法表征的不足;综合考虑电网结构与状态,建立表征电网潮流分布均衡性与负荷损失的严重度模型,完善安全性风险评估指标体系。仿真结果表明,相较于传统方法,该方法对电网N-1故障风险排序更为合理,对N-k故障序列分级风险辨识度更高。     

一种快速求解大规模安全约束最优潮流的多核并行方法

针对传统安全约束最优潮流计算方法占用内存大、计算时间长的问题,提出了一种多核并行安全约束最优潮流算法。由正常运行状态检验预想故障状态,选取少量严重故障逐步修正正常运行状态,直到所有状态都得到满足。多核并行技术用于实现算法从高到低层次的高效率整体并行,以提高计算速度。其中,采用有向无环图并行数值分解算法实现正常运行状态修正过程的细粒度并行,并根据算法特性,直接实现预想故障状态检验过程的粗粒度并行。某省-3301节点系统(预设693个故障)等3个系统的计算结果表明:随着系统规模的扩大,所提方法占用内存不足传统方法的1%,且计算速度可快于传统方法三个数量级以上。     

Classification and assessment of power system static security using decision tree and random forest classifiers

The power system static security classification and assessment is essential in order to identify the post‐contingency problems and take corrective measures and to protect the system from blackout. In this paper, application of two data mining classifiers have been proposed for the security classification and assessment of a multiclass security problem. To design the security problem, contingency analysis is carried out under N‐1 line outage, and static severity index (SSI) is computed, which is a function of the line overload and the voltage deviation using Newton–Raphson load flow method, considering the variable load and generating conditions. Corresponding to the computed values of SSI, the voltage, phase angle, Mega Volt Ampere line flow and so on, a 1 × 7 pattern vector is generated. The generated pattern vectors are used to design a multiclass security problem. The designed security pattern vectors are given as inputs to the decision tree (DT) and random forest (RF) model in order to classify the security status of the power system. The proposed classifiers are investigated on an IEEE 30‐bus test system. The classification accuracy of the DT and the RF are compared with state‐of‐the‐art classifier models, namely, multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM).The simulation results clearly indicate that the proposed DT and RF classifiers are more efficient, reliable, and out performs MLP, RBF, and SVM classifiers for the assessment of the security status of the power system. Hence, DT and RF classifiers are found to be suitable for online implementation. Copyright © 2015 John Wiley & Sons, Ltd.     

高速公路特长隧道应急救援预案

以五女峰隧道为例,在确定了特长隧道应急预案总原则的前提下,提出了五女峰特长隧道灾害事故预案的编制,分别介绍了火灾、交通堵塞、突发安全事故发生情况下的应急预案。     

A unified dynamic neural field model of goal directed eye movements

ABSTRACT

Primates heavily rely on their visual system, which exploits signals of graded precision based on the eccentricity of the target in the visual field. The interactions with the environment involve actively selecting and focusing on visual targets or regions of interest, instead of contemplating an omnidirectional visual flow. Eye-movements specifically allow foveating targets and track their motion. Once a target is brought within the central visual field, eye-movements are usually classified into catch-up saccades (jumping from one orientation or fixation to another) and smooth pursuit (continuously tracking a target with low velocity). Building on existing dynamic neural field equations, we introduce a novel model that incorporates internal projections to better estimate the current target location (associated to a peak of activity). Such estimate is then used to trigger an eye movement, leading to qualitatively different behaviours depending on the dynamics of the whole oculomotor system: (1) fixational eye-movements due to small variations in the weights of projections when the target is stationary, (2) interceptive and catch-up saccades when peaks build and relax on the neural field, (3) smooth pursuit when the peak stabilises near the centre of the field, the system reaching a fixed point attractor. Learning is nevertheless required for tracking a rapidly moving target, and the proposed model thus replicates recent results in the monkey, in which repeated exercise permits the maintenance of the target within in the central visual field at its current (here-and-now) location, despite the delays involved in transmitting retinal signals to the oculomotor neurons.     

A MULTIMODAL BIOMETRIC IDENTIFICATION SYSTEM USING COMPRESSED FINGER IMAGES

Finger image recognition remains one of the most prominent biometric identification methods. However, storage of finger databases needs allocation of huge secondary storage devices. In addition, there has been limited success in obtaining a satisfactory system due to the complexity of the problem. In this article, a low-cost, high-speed, multimodal biometric identification system trained with compressed finger images is presented with the objective to increase the overall matching confidence level. For this, three finger images of the left or right hand, or both, for one person are matched and the output decision is combined. A prototype optical-based finger-data acquisition system using the CCD (charge coupled device) digital still camera is adopted to capture a complete impression of finger area required for accurately identifying an individual. The acquired images then are compressed with a Coif5 wavelet packet-based scheme to increase the overall performance and eliminate bulk storage requirements. The finger image features are extracted with an adaptive neural network for the implementation of a three-finger multimodal system to achieve a peak identification rate of 100% (99.4% on average) in 0.15s for a database of 50 persons and 450 test images.     

IDENTIFYING SYSTEM FUNCTIONS

This article provides certain fairly rigorous, although still heuristic, methods for identifying the functional components of systems in such a way as to facilitate making relationships between the components and the overall system. Based on the definitions of function commonly used, making such a relationship is not always as easy as it could be made. Specific matters discussed herein include the following: functions and objectives-oriented thinking; logical versus physical thinking; functional outputs versus functional objectives; functional objectives versus system mission; directness of relationship to an objective; relationships between multiple functional objectives; distinguishing specific objectives within more general ones; the need to develop a consistent set of terms; etc.