Improved Fine Particles Monitoring in Smart Cities by Means of Advanced Data Concentrator

Traffic reduction and air-quality improvement are among the main goals of several projects worldwide. This article presents a fine particle monitoring based on heterogeneous air quality mobile sensors and an advanced data concentrator (AdDC), so that the level of pollution in the urban area, where few accurate fixed measurement stations are present, can be assessed with better accuracy. Some urban buses are used to carry low-cost sensors, thus implementing a mobile sensor network and increasing the time and space resolution of air quality information. The data obtained by these low-cost sensors are significantly affected by uncertainties, also due to atmospheric factors, such as humidity. The proposed AdDC processes all the obtained measurements and exploits the information obtained by the accurate fixed stations to improve the accuracy of the low-cost mobile sensors. In particular, a new compensation methodology, specifically targeted to the fine particles monitoring, is proposed. The monitoring of relative humidity is added, with the relevant on-the-fly calibration, so that the measured values can be used to correct the effects of humidity on PM2.5 sensors. The validity of the proposed system is proven by means of simulations performed on an appropriate set up

EXPLORING SIMILARITIES AND VARIATIONS OF HUMAN MOBILITY PATTERNS IN THE CITY OF LONDON

The availability of new spatial data represents an unprecedented opportunity to better understand and plan cities. In particular, extensive data sets of human mobility data supply new information that can empower urbanism research to unveil how people use and visit urban places over time, overcoming traditional limitations related to the lack of large, detailed data sets. In this work, we explore patterns of similarities and spatial differences in human mobility flows in London, analysing their temporal variations in relation to the liveliness measured in a number of places. Using data sourced from the Oyster smart card and Twitter, we perform a time-series cluster analysis, exploring the similarity of temporal trends amongst places assigned to each cluster. Results suggest that differences in patterns appear to be related to the central and peripheral location of places, which present two or more temporal trends over the week. The type of transport network connecting the places (Tube, Railways, etc.) also appears to be a factor in determining significant differences. This work contributes to current urbanism research investigating the daily rhythms in cities. It also explores how to use mobility data to classify places according to their temporal features, with the aim of enhancing conventional analysis tools and integrating them with new quantitative information and methods

Forecasting-Aided Monitoring for the Distribution System State Estimation

In this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. High-quality pseudomeasurements are produced by a neural model fed with both exogenous and historical load information and applied in a realistic measurement scenario. Aggregated active and reactive powers of small or medium enterprises and residential loads are simultaneously predicted by a one-step ahead forecast. The correlation between the forecasted real and reactive power errors is duly kept into account in the definition of the estimator together with the uncertainty of the overall measurement chain. The beneficial effects of the ANN-based pseudomeasurements on the quality of the state estimation are demonstrated by simulations carried out on a small medium-voltage distribution grid

A Practical Solution for Locating the Source of Voltage Dips in HV/MV Interconnected Grids

Monitoring the technical performance of a power system is significantly enhanced when distributed instrumentation produces coherent field data, i.e., synchronized by GPS timestamping. In this paper a practical methodology is presented to improve the localisation of the source of a voltage dip on power grids. The proposed solution makes use of synchronised dip data provided by power quality meters. Field data reporting events occurred in an HV/MV interconnected system in South Africa are used to validate the results obtained by the improved method and compare with results of two alternative methods

Compressive Sensing-Based Harmonic Sources Identification in Smart Grids

Identifying the prevailing polluting sources would help the distribution system operators in acting directly on the cause of the problem, thus reducing the corresponding negative effects. Due to the limited availability of specific measurement devices, ad hoc methodologies must be considered. In this regard, compressive sensing (CS)-based solutions are perfect candidates. This mathematical technique allows recovering sparse signals when a limited number of measurements are available, thus overcoming the lack of power quality meters. In this article, a new formulation of the ell _{1} -minimization algorithm for CS problems, with quadratic constraint, has been designed and investigated in the framework of the identification of the main polluting sources in smart grids. A novel whitening transformation is proposed for this context. This specific transformation allows the energy of the measurement errors to be appropriately estimated, and thus, better identification results are obtained. The validity of the proposal is proven by means of several simulations and tests performed on two distribution networks for which suitable measurement systems are considered along with a realistic quantification of the uncertainty sources