Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/169611
Title: Pareto front analysis method for optimization of PV inverter based volt/var control considering inverter lifetime
Authors: Chai, Qingmian
Zhang, Cuo
Xu, Yan
Dong, Zhao Yang
Zhang, Rui
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2023
Source: Chai, Q., Zhang, C., Xu, Y., Dong, Z. Y. & Zhang, R. (2023). Pareto front analysis method for optimization of PV inverter based volt/var control considering inverter lifetime. CSEE Journal of Power and Energy Systems, 9(1), 111-121. https://dx.doi.org/10.17775/CSEEJPES.2022.00840
Project: NTU Grant 021542-00001 
Journal: CSEE Journal of Power and Energy Systems 
Abstract: Photovoltaic (PV) inverter-based volt/var control (VVC) is highly promising to tackle the emerging voltage regulation challenges brought by increasing PV penetration. However, PV inverter operational reliability has arisen as a critical concern for practical VVC implementation. This paper proposes a new PV inverter based VVC optimization model and a Pareto front analysis method for maintaining a satisfactory inverter lifetime. First, reliability of the vulnerable DC-link capacitor inside a PV inverter is analyzed, and long-term VVC impact on inverter operational reliability is identified. Second, a multi-objective PV inverter based VVC optimization model is proposed for minimizing both inverter apparent power output and network power loss with a weighting factor. Third, a Pareto front analysis method is developed to visualize the impact of the weighting factor on VVC performance and inverter reliability, thus determining the effective weighting factor to reduce network power loss with expected inverter lifetime. Effectiveness of the proposed VVC optimization model and Pareto front analysis method are verified in a case study.
URI: https://hdl.handle.net/10356/169611
ISSN: 2096-0042
DOI: 10.17775/CSEEJPES.2022.00840
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 CSEE. Published by IEEE. This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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