Optimal control of water distribution systems by network flow theory

This paper presents a modeling technique and an optimal control scheme for water distribution networks. To overcome the large scale and nonlinearity of the network, a network aggregation method and a two-level control scheme are developed. The first level of the scheme decides operating points using a nonlinear optimization method, where the pressure/flow equations are solved using a high-speed technique derived from network flow theory. The second level is a feedback control around the operating points, which absorbs estimate error and small variations in consumption. The scheme has been implemented on a minicomputer system and is presently in operation.     

Operational predictive optimal control of Barcelona water transport network

This paper describes the application of model-based predictive control (MPC) techniques to the supervisory flow management in large-scale drinking water networks including a telemetry/telecontrol system. MPC is used to generate flow control strategies (set-points for the regulatory controllers) from the sources to the consumer areas to meet future demands, optimizing performance indexes associated to operational goals such as economic cost, safety storage volumes in the network and smoothness of the flow control actions. The designed management strategies are applied to a model of a real case study: the drinking water transport network of Barcelona (Spain).     

电力网络监控仪中数据采集模块的设计

讨论了一种电力网络监控仪的数据采集模块的设计方法。结合锁相环同步技术改进数据采集模块,加入锁相环频率跟踪电路,实现同步等间隔采样,减小了非同步采样引入的误差;针对快速傅里叶变换(FFT)算法的栅栏效应和频谱侧漏提出了加窗插值FFT算法,并把算法移植到ARM内核的微控制器LPC2138中,精确实现了电网各次谐波幅值频率和总谐波畸变率的计算。     

A silicon-glass-based microfabricated wide range thermal distribution gas flow meter

Recently, there has been high demand on miniaturizations of bio-instruments and wide range gas flux measurement in the field of chemistry and mechanics. This paper presents the design, fabrication, and characterization of a silicon-glass-based thermal distribution gas flow meter (20 mm × 10 mm × 1.6 mm) with a wide detection range. To facilitate the fabrication and maintain the stability of the sensor, a platinum (Pt) thin film was adopted as the heater and thermometers. Both the thermal property and temperature sensitivity of Pt thin film were characterized. SiO₂ passivation layers were deposited on top of the Pt film to prevent thermal and electrical shift of sensitive elements. Three pairs of thermometers were constructed beside the heater. Sensitivity and gas flux range of the gas flow meter can be increased by alternate use of these three sensor pairs. We also introduced a specific hardware control circuit system for real-time gas flux monitoring through the connection with a computer interface. The proposed gas flow sensor device was capable of measuring gas flux within the range of 0.8-2800 ml/min, thus demonstrating the potential for a wide range of applications.     

Training with synthesised data for disaggregated event classification at the water meter

Activity recognition in monitored environments where the occupants are elderly or disabled is currently a popular research topic, with current systems implementing ubiquitous sensing or video surveillance techniques. Using disaggregated data from smart meters could be a viable alternative to what is often perceived as intrusive recognition technology. Disaggregation methods have proven to perform exceptionally well when trained with large quantities of data, but gathering and labelling this data is, in itself, an intrusive process that requires significant effort and could compromise the practicality of such promising systems. Here we show that by synthesising labelled training data, using a domain specific algorithm, an innovative water meter disaggregation system that uses Artificial Neural Networks (ANN), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) classifiers can be trained in minutes rather than hours. We show that by artificially synthesising labelled data accuracies of 83%, 79% and 85% with the SVM, ANN and KNN classifiers, respectively can be achieved. Though these values are marginally lower than 89%, 83% and 89% achieved with no synthesis, the measure of accuracy masks the underlying imbalance of representative classes in the data set.     

考虑数据时变性的主动配电网动态网络重构

目前的配电网网架重构主要考虑停电发生时刻单场景下的电网信息,但实际上配电网网架重构贯穿于整个故障修复时期, 仅考虑停电发生时刻的电网信息可能造成后续时段电网过载,因此本文提出计及数据时变性的动态网络重构方案。本文以分布式电源大规模接入下的主动配电网为研究对象,首先基于贪婪法思想提出单一场景下的网络重构方法, 然后利用负荷及各类分布式电源的预测信息,以故障排除周期内能量损失最少为目标建立基于连续场景考虑数据时变性的动态网络重构模型。同时本文引入滚动优化的思想, 对动态网络重构方案进行纠偏,以适应分布式电源的随机波动。最后, 通过仿真分析, 论证了分布式电源对优化系统恢复方案的作用,说明了所提模型和方法的有效性。     

配电网中大数据的挖掘应用

为了适应日益增长的电力需求,解决配电网点多面广、设备多、管理难度大、配电网信息实时性低等问题,本文系统梳理了国际、国内配电网发展现状,以及数据挖掘与分析处理技术在配电网的应用研究情况,将大数据挖掘应用引入配电网日常管理,利用大数据分析,针对电网运行和设备检测或监测数据、电力企业营销数据、交易电价、售电量、用电客户等方面的数据,结合地域配电网网架结构,对配电网存在的薄弱点,尤其在迎峰度夏和春节保供电等用电高峰期,通过事前提前研判、事中实时指挥、事后深入分析,提前发现未来病态设备,推进配电网设备在线化、透明化、智能化,将配电网由“修得快”向“不停电”转变,提升优质服务水平,并希望能对今后相关研究时间工作提供一定的参考价值。     

A methodology and a software tool for sensor data validation/reconstruction: Application to the Catalonia regional water network

In this paper, a sensor data validation/reconstruction methodology applicable to water networks and its implementation by means of a software tool are presented. The aim is to guarantee that the sensor data are reliable and complete in case that sensor faults occur. The availability of such dataset is of paramount importance in order to successfully use the sensor data for further tasks e.g. water billing, network efficiency assessment, leak localization and real-time operational control. The methodology presented here is based on a sequence of tests and on the combined use of spatial models (SM) and time series models (TSM) applied to the sensors used for real-time monitoring and control of the water network. Spatial models take advantage of the physical relations between different system variables (e.g. flow and level sensors in hydraulic systems) while time series models take advantage of the temporal redundancy of the measured variables (here by means of a Holt–Winters (HW) time series model). First, the data validation approach, based on several tests of different complexity, is described to detect potential invalid or missing data. Then, the reconstruction process is based on a set of spatial and time series models used to reconstruct the missing/invalid data with the model estimation providing the best fit. A software tool implementing the proposed data validation and reconstruction methodology is also described. Finally, results obtained applying the proposed methodology to a real case study based on the Catalonia regional water network is used to illustrate its performance.     

低功耗无线水表数据采集通信终端的设计和研究

针对工业地下水管道难以抄收实际用水量导致无数据参考依据、无节制的滥用水资源这种现象,研发了一种可以埋在地下的水表数据采集通信终端。此终端通过无线射频技术将水表抄收的数据发送出去,实现了抄收用水量。由于终端设备深埋在地下,为了降低终端节点功耗和提高抗干扰性,采用了低功耗的Atmega128L单片机和CC1020无线收发芯片。实验数据分析表明,该设计的功耗和稳定性都能够达到在现实中应用的要求。     

电磁流量计信号调理和激磁方法研究

电磁流量计在信号调理过程中会出现信号失真、干扰信号被放大的问题,并且仪表测量误差也会随运行时间的增加而变大.为了解决这些问题,本文提出了基波平均值的信号调理方法和间歇激磁方法,检定结果表明了该种方法的有效性.     

网络流量数据管理系统的开发与应用

本文从功能、数据结构等方面介绍了网络流量管理系统的设计。详细描述了其程序实现。最后，给出了两个网络流量数据分析的应用实例。     

V型锥流量计的原理及应用

传统的流量计主要是差压式流量计,距今已有百年的历史。事实上,其中有很多弊端难以在文丘里管为代表的第一代流量计中解决,无法满足现代工业的需求。而且经实验证明,文丘里管流量计有诸如测量精度低、压差阻力大等缺点[1]。如果我们将V型锥流量计大量的使用到工业现场中,将会大大改善流量计量精度,以达到现代工业现场所要求的范围。同时呼吁尽快制定V-Cone流量计的国家标准。     

基于数据集约束的并行数据流建模与描述研究

为了简化带有数据集约束的抽象网格工作流语言(AGWL)的工作流建立过程,提出了一种基于扩展面向对象Petri网(DOPN)建模并行数据流的方法.研究了AGWL并行数据流的特点,在此基础上对面向对象Petri网进行扩展,设计了基于扩展面向对象Petri网描述的并行数据流模型,之后定义一套从DOPN到AGWL的映射规则.最后通过一个应用实例对一个带有数据集约束的数据流建立DOPN描述,并生成AGWL数据流代码.该方法简化了AGWL工作流建立过程,并保留了流程验证和仿真的扩展性.     

一种面向可用性的P2P数据分发方法

对等网络由高度动态的节点构成,节点间会话时间极端异构。对等节点频繁加入和退出导致对等网络维护开销增大,数据可用性降低。面向可用性的对等网络数据分发方法,针对热点数据和节点可用性的分析,通过增加系统中数据的副本数量,提高数据的可用性,同时减小消息扩散的范围,提高系统的效率,克服会话时间异构性。实验结果表明,该方法可以有效提高数据可用性,降低系统的维护开销。     

A network flow approach to reconstruction of the left ventricle from two projections

A new method for binary reconstruction of the left ventricle from two orthogonal projections is presented. A priori knowledge has to be incorporated to reduce the ambiguity of the problem. A minimum cost capacitated network flow algorithm is discussed, which yields the optimal solution with respect to the priori information. It is shown that this method can also be used in the presence of observation noise. The method is demonstrated by reconstructing several cross sections of a dog's left ventricle.     

The p-median problem in a changing network: the case of Barcelona

In this paper a p-median-like model is formulated to address the issue of locating new facilities when there is uncertainty in demand, travel times or distance. Given several possible scenarios, the planner would like to choose a set of locations that will perform as well as possible over all future scenarios. This paper presents a discrete location model formulation to address this p-median problem under uncertainty. The model is applied to the location of fire stations in Barcelona.     

Approximate reconstruction of PET data with a self-organizing neural network.

Self-organization was observed using the algorithm of Kohonen with an original "distance" adapted to stimuli resulting from coincident detections of electron-positron annihilation photon pairs. This has led to a method for approximate reconstruction of two-dimensional positron emission tomography (2-D PET) images that is totally independent of the number of detectors. To obtain meaningful information about the distribution of the radioactive tracer, a toroidal architecture must be used for the network.     

Constrained state estimation with applications in water distribution network monitoring

This paper investigates the effects of imposing bounds on the measurements used in weighted least-squares (WLS) state estimation. The active limits for such bounds are derived and algorithms based on linear and quadratic programming kernels are presented. Using the lower limit for the bounds, the constrained WLS scheme becomes an adaptive maximally constrained scheme: M-WLS. For some networks, the poor prior knowledge of the global error characteristic results in some measurements having less influence than would be expected from the local error characteristics of their transducers. By using M-WLS estimation, the influence of such measurements on state estimation may be improved. Analysis of the adaptive bounding of the scheme can also lead to identification of critical measurement discrepancies. For the purpose of illustration, results are presented using simulated measurements; the head measurements (pressures) are consistent with nominal demands (nodal flows) and the demand measurements are generated by superimposing random errors of 2.5ls^-1 rms on the nominal demands.