一种使用主动功耗损失灵敏度指数和修改的狮优化算法的综合方法,用于DG在辐射发电网中的放置
P Rajakumar1, P M Balasubramaniam2, Mohammed Hasan Aldulaimi3
1Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India.
Scientific reports
|March 27, 2025
概括
本研究介绍了一种使用分析和元启发算法的综合方法,以优化分布式发电 (DG) 在电网中的放置和大小. 该方法有效地减少了功率损失和电压偏差,优于其他优化技术.
科学领域:
- 电气工程 电气工程
- 电力系统工程 电力系统工程
- 优化技术 优化技术
背景情况:
- 配电电力网 (DPN) 面临着由于高需求而造成的显著功率损失和电压偏差.
- 超出极限的电压偏差可能导致DPN中的电压不稳定问题.
- 尽量减少电力损失对于电网的经济运行至关重要.
研究的目的:
- 提出一种综合优化方法,尽量减少DPN的功耗损失和电压偏差.
- 为了优化多个分布式发电 (DG) 单元的放置和尺寸.
- 通过战略 DG 整合,提高 DPN 的效率和稳定性.
主要方法:
- 一种综合方法,结合了分析方法 (主动功率损失灵敏度指数) 和元启发算法 (修改狮优化).
- 修改后的狮优化 (MALO) 算法结合了莱维的飞行,以改进探索.
- 优化多个光伏 (PV) 和风力轮机 (WT) 单元的多目标功能.
主要成果:
- 在 IEEE 69 总线,85 总线和118 总线辐射 DPN 具有 PV 和 WT 分配的总活性功率损耗显著减少.
- 在DG集成后,在测试的DPN中大幅减少电压偏差.
- 拟议的APLS-MALO综合方法与ALO,BAT和ABC算法相比显示出更高的性能.
结论:
- 综合APLS-MALO方法有效地减少了DPN中的功率损失和电压偏差.
- 通过Lévy航班增强的MALO算法为DG的位置和大小提供了近乎最佳的解决方案.
- 该研究通过统计指标和比较分析验证了拟议方法的优越性.
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