An Adaptive Overcurrent Protection Method for Distribution Networks Based on Dynamic Multi-Objective Optimization Algorithm

With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stab...

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Published in:Algorithms Vol. 18; no. 8; p. 472
Main Authors: Xu, Biao, Ouyang, Fan, Li, Yangyang, Yu, Kun, Ao, Fei, Li, Hui, Tan, Liming
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2025
Subjects:
adaptive current protection
Algorithms
Alternative energy sources
Communication
distribution network
Elitism
Energy distribution
Energy resources
Energy storage
Genetic algorithms
Mathematical optimization
Methods
multi-objective optimization
Multiple objective analysis
Networks
NSGA-II algorithm
Optimization algorithms
Overcurrent
Pareto optimization
Pareto optimum
Real time
Redundancy
Renewable energy
renewable energy-integrated distribution network
Renewable resources
Wind power
ISSN:1999-4893, 1999-4893
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Abstract With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This paper proposes an adaptive overcurrent protection method based on an improved NSGA-II algorithm. By dynamically detecting renewable power fluctuations and generating adaptive solutions, the method enables the online optimization of protection parameters, effectively reducing misoperation rates, shortening operation times, and significantly improving the reliability and resilience of distribution networks. Using the rate of renewable power variation as the core criterion, renewable power changes are categorized into abrupt and gradual scenarios. Depending on the scenario, either a random solution injection strategy (DNSGA-II-A) or a Gaussian mutation strategy (DNSGA-II-B) is dynamically applied to adjust overcurrent protection settings and time delays, ensuring real-time alignment with grid conditions. Hard constraints such as sensitivity, selectivity, and misoperation rate are embedded to guarantee compliance with relay protection standards. Additionally, the convergence of the Pareto front change rate serves as the termination condition, reducing computational redundancy and avoiding local optima. Simulation tests on a 10 kV distribution network integrated with a wind farm validate the effectiveness of the proposed method.
AbstractList With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This paper proposes an adaptive overcurrent protection method based on an improved NSGA-II algorithm. By dynamically detecting renewable power fluctuations and generating adaptive solutions, the method enables the online optimization of protection parameters, effectively reducing misoperation rates, shortening operation times, and significantly improving the reliability and resilience of distribution networks. Using the rate of renewable power variation as the core criterion, renewable power changes are categorized into abrupt and gradual scenarios. Depending on the scenario, either a random solution injection strategy (DNSGA-II-A) or a Gaussian mutation strategy (DNSGA-II-B) is dynamically applied to adjust overcurrent protection settings and time delays, ensuring real-time alignment with grid conditions. Hard constraints such as sensitivity, selectivity, and misoperation rate are embedded to guarantee compliance with relay protection standards. Additionally, the convergence of the Pareto front change rate serves as the termination condition, reducing computational redundancy and avoiding local optima. Simulation tests on a 10 kV distribution network integrated with a wind farm validate the effectiveness of the proposed method.
Audience Academic
Author Ouyang, Fan
Ao, Fei
Li, Yangyang
Yu, Kun
Li, Hui
Tan, Liming
Xu, Biao
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SubjectTerms adaptive current protection
Algorithms
Alternative energy sources
Communication
distribution network
Elitism
Energy distribution
Energy resources
Energy storage
Genetic algorithms
Mathematical optimization
Methods
multi-objective optimization
Multiple objective analysis
Networks
NSGA-II algorithm
Optimization algorithms
Overcurrent
Pareto optimization
Pareto optimum
Real time
Redundancy
Renewable energy
renewable energy-integrated distribution network
Renewable resources
Wind power
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