Substation

<p>This dataset consists in an ANAREDE equivalent grid for the Bongi Substation, located in in the Brazilian city of Recife. It contains the configurations for both the year of 2023 and the forecasts for the year 2027. The file for the year 2027 takes into account that the reconfiguration will occur in this substation as suggested by the Brazilian institutions responsible for the operation and planning of the electrical system. The demand of energy was based on the research done by the brazilian institution of Empresa de Pesquisa Energética.

This dataset is in support of my research paper - Short Circuit Analysis of 666 Wh Li-Ion NMC

 Faults and datasets can be copied to submit in fire cause investigation reports or thesis. The simulation is run for 20 hours (72000 seconds) of simulation time for each fault of 100 faults. 

PrePrint : (Make sure you have read Caution.)

This dataset is in support of my 2 research papers - 'Short Circuit Analysis of 72Ah Li-Ion BMC - Part I' and 'Short Circuit Analysis of 72Ah Li-Ion BMC - Part II'.

 Faults and datasets can be copied to submit in fire cause investigation reports or thesis. 

This dataset is a collection of data of battery and BMC  faults. 

PrePrint : (Make sure you have read Caution.)

This dataset contains daily maximum load data with the average demand, customer count and PV capacity at two substations Arkana and Muchea, Western Australia used in the accepted IEEE Transactions on Power Systemspaper titled “The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand” by Li and Jones (2019).  The dataset spans from 01/01/2008 to 30/06/2022, part history (01/01/2008 to 16/09/2018) and part forecast (17/09/2018 to 30/06/2022).  The dataset is beneficial to various research such as long-term load forecast.

This dataset contains daily maximum load data with the average demand, customer count and PV capacity at two substations Arkana and Muchea, Western Australia used in the accepted IEEE Transactions on Power Systemspaper titled “The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand” by Li and Jones (2019).  The dataset spans from 01/01/2008 to 30/06/2022, part history (01/01/2008 to 16/09/2018) and part forecast (17/09/2018 to 30/06/2022).  The dataset is beneficial to various research such as long-term load forecast.

This dataset contains daily maximum load data with the average demand, customer count and PV capacity at two substations Arkana and Muchea, Western Australia used in the accepted IEEE Transactions on Power Systemspaper titled “The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand” by Li and Jones (2019).  The dataset spans from 01/01/2008 to 30/06/2022, part history (01/01/2008 to 16/09/2018) and part forecast (17/09/2018 to 30/06/2022).  The dataset is beneficial to various research such as long-term load forecast.