Stream Data Cleaning for Dynamic Line Rating Application

Abstract

The maximum current that an overhead transmission line can continuously carry depends on external weather conditions, most commonly obtained from real-time streaming weather sensors. The accuracy of the sensor data is very important in order to avoid problems such as overheating. Furthermore, faulty sensor readings may cause operators to limit or even stop the energy production from renewable sources in radial networks. This paper presents a method for detecting and replacing sequences of consecutive faulty data originating from streaming weather sensors. The method is based on a combination of (a) a set of constraints obtained from derivatives in consecutive data, and (b) association rules that are automatically generated from historical data. In smart grids, a large amount of historical data from different weather stations are available but rarely used. In this work, we show that mining and analyzing this historical data provides valuable information that can be used for detecting and replacing faulty sensor readings. We compare the result of the proposed method against the exponentially weighted moving average and vector autoregression models. Experiments on data sets with real and synthetic errors demonstrate the good performance of the proposed method for monitoring weather sensors.

An Association Rules-Based Method for Outliers Cleaning of Measurement Data in the Distribution Network

“…For outliers detection, with the rapid development of machine learning technology, many machine learning algorithms have been utilized to improve the accuracy in power systems. In literature (Nemati et al, 2018), a constraint and association rule-based current transmission capability forecasting method was proposed for outliers detection in substation metering equipment. However, this model is complex and computationally intensive, which is not suitable for the detection of bad data in a large number of transformer districts.…”

Impacts of Measurement Errors on Real-Time Thermal Rating Estimation for Overhead Lines

“…In addition to MEs of weather variables, the uncertainties of conductor properties including emissivity and absorptivity were taken into account by [20] where affine arithmetic was used to translate the ranges of all the considered sources of uncertainty into the lower and upper RTTR boundaries. Considering that the errors in weather readings will have a greater impact than those from measurement devices, a data-driven method was developed in [21] to detect and replace the faulty readings of weather sensors prior to the RTTR calculation. For the EWC-based RTTR techniques, Albizu et al [22] analysed influences of a typical 𝑇 𝑐 error of ±2℃ on RTTR estimation in different cases where the difference 𝑇 diff between 𝑇 𝑐 and air temperature 𝑇 𝑎 varies for a specific set of weather conditions, or a particular weather parameter varies given a 𝑇 diff of 20℃.…”