Study of Distributed Generations in Voltage Sag Mitigation Using Unbalanced Load Flow

Authors U. Sur, D. Chakraborty , D. Nath

Future Institute of Engineering and Management, Kolkata 700150 West Bengal, India

Issue Volume 14, Year 2022, Number 3
Dates Received 29 March 2022; revised manuscript received 22 June 2022; published online 30 June 2022
Citation U. Sur, D. Chakraborty, D. Nath, J. Nano- Electron. Phys. 14 No 3, 03016 (2022)
PACS Number(s) 02.50.Ng
Keywords Distribution load flow (DLF), Distributed generations (DGs), Voltage sag, Radial distribution network.

In this paper, a new three-phase unbalanced distribution load flow (DLF) technique is proposed to study the impact of distributed generations (DGs) installed with the distribution networks in mitigating the voltage sag. For analysis purpose, DGs are modeled as variable reactive power and constant power factor source with both wind and solar powered DGs in the case study. The proposed technique is based on the application of classical set theory, where different impedance matrices were formed for the test case with the help of set theory. It enables the method to be more flexible and robust for handling in any situation. Within the defined PSIM matrix, DG modeling is incorporated for the impact study of DGs. Effect of DGs in mitigating voltage sag is mathematically explained with the help of phase diagram. The proposed technique is tested on the standard IEEE 13 bus radial distribution networks with programming done on the MATLAB platform. Different other test networks can be used for analytical purpose. To justify the claims, different cases are studied, such as load increment, an increase in the maximum power limit of DGs. Also, a comparison is drawn between the proposed DLF technique and the traditional backward forward sweep method, where the efficacy of the proposed technique is proved over the traditional method. In overall analysis of the proposed method, it shows better results compared to the traditional backward forward sweep method in terms of execution time and number of iterations.

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