Fuzzy Ant Colony Optimization Technique for Predefined Performance of Distribution Systems Considering DGs and Shunt Capacitors

2019 North American Power Symposium (NAPS) Pub Date : 2019-10-01 DOI:10.1109/NAPS46351.2019.9000250

Preetham Goli, Suresh Makkena, S. Gampa, D. Das

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Abstract

This paper presents a Fuzzy Ant Colony Optimization (ACO) based approach for optimum allocation and sizing of Distributed Generation units (DGs) and Shunt Capacitors (SCs) in order to improve the predefined performance of the distribution system. The DG units are considered to be operating at lagging power factor and capable of supplying both real and reactive power. The penalty functions are developed for total DG penetration limit, total reactive power injection limit, real power loss reduction target and voltage profile improvement. The fuzzy multi-objective function is formulated by developing the fuzzy membership functions for each penalty function considered for performance improvement. Ant colony optimization technique is used for obtaining the optimum size of DG units and shunt capacitors in order to achieve the predefined performance. The simulation results are demonstrated for a 51 node distribution system to show the effectiveness of the proposed methodology compared to conventional multi-objective function based approach.

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考虑dg和并联电容的配电系统预定义性能模糊蚁群优化技术

本文提出了一种基于模糊蚁群算法的分布式发电机组和并联电容器的优化配置和优化尺寸的方法，以提高配电系统的预定性能。DG机组被认为在滞后功率因数下运行，能够同时提供实功率和无功功率。建立了总DG渗透极限、总无功注入极限、实际功率损耗降低目标和改善电压剖面的惩罚函数。通过建立各惩罚函数的模糊隶属度函数来实现性能改进的模糊多目标函数。采用蚁群优化技术求解DG单元和并联电容器的最优尺寸，以达到预先设定的性能。仿真结果表明，与传统的基于多目标函数的方法相比，该方法是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 North American Power Symposium (NAPS)

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