Reverse Power Flow Analysis in Distribution Network

Turiman, Muhammad Syahir; Sarmin, Mohd Khairun Nizam Bin Mohd

doi:10.1109/icpea51500.2021.9417756

Cited by 7 publications

(1 citation statement)

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“…Up until recently, power flew from higher voltage towards the loads which are connected on the low voltage (LV). Now, in instances when the generation from plants that are connected on the LV side is higher than the load itself, the power will flow upstream [1,2]. There are several reasons for the popularization of distributed generation (DG).…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network-Based Autonomous Voltage Control for Power Distribution Networks with DGs and EVs

Musiqi,

Kastrati,

Bosisio

et al. 2023

Applied Sciences

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This paper makes use of machine learning as a tool for voltage regulation in distribution networks that contain electric vehicles and a large production from distributed generation. The methods of voltage regulation considered in this study are electronic on-load tap changers and line voltage regulators. The analyzed study-case represents a real-life feeder which operates at 10 kV. It has 9 photovoltaic systems with various peak installed powers, 2 electric vehicle charging stations, and 41 secondary substations, each with an equivalent load. Measurement data of loads and irradiation data of photovoltaic systems were collected hourly for two years. Those data are used as inputs in the feeder’s model in DigSilent PowerFactory where Quasi-Dynamic simulations are run. That will provide the correct tap positions as outputs. These inputs and outputs will then serve to train a Deep Neural Network which later will be used to predict the correct tap positions on input data it has not seen before. Results show that ML in general and DNN specifically show usefulness and robustness in predicting correct tap positions with very small computational requirements.

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Section: Introductionmentioning

confidence: 99%