The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. In this dataset, we present different images and videos for computer vision-based research. The dataset comprises images and videos taken from different sources such as a Drone, a DSLR camera, and a mobile phone camera.

The datasets consist of operational data and detailed information of three inverter transformers in a 3.275 MW PV plant in the outskirt of Brisbane, Australia. The data includes load current, top-oil temperature, moisture in top oil, ambient temperature, solar irradiance and individual current harmonics (up to 31st order). The time interval of the data is either 1 minute or 3 seconds (dependent on the data type). The data can be used to study the ageing of inverter transformers in this PV plant. 

Over the past two decades,  various machine learning methods are proposed for the prediction of electricity prices.  Existing literature discusses in detail the strength and weaknesses of these methods.  This dataset focuses on the exploration of online personalized machine learning models for the prediction of monthly electricity bills in developing countries. For this purpose, we gathered the dataset of monthly electricity bills of 70 users for one year.

This dataset is in support of my 3 research papers 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part I', 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part II' and 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part III'.

Preprint : 

This dataset is in support of my research paper 'Comparative Non-Linear Flux Matrices & Thermal Losses in BLDC with Different Pole Pairs' .

Preprint : https://doi.org/10.36227/techrxiv.19687287.v1

Distribution 20kV Surge Arrester Historical Inspection Data from year 2007 to 2019 and from an European utility company   

20kV PILC Cable Historical Inspection Data from year 2007 to 2019 from an European utility company 

20kV XLPE Cable Historical Inspection Data from year 2010 to 2019 from an European utility company

Asset health index evalualtion results from an European utility company

This provides the code and data used in the paper "Optimal EV Scheduling in Residential Distribution Networks Considering Customer Charging Preferences" by Mailys Le Cam and Barry Hayes. 

Some material has been adapated from the OpenDSS help files: http://smartgrid.epri.com/SimulationTool.aspx Some data has been taken from the IEEE test feeders archive: http://sites.ieee.org/pes-testfeeders/