基因人工蜂鸟算法支持矢量机器用于及时检测电力盗窃
Emmanuel Gbafore1, Davies Rene Segera1, Cosmas Raymond Mutugi Kiruki1
1Department of Electrical and Information Engineering University of Nairobi, Nairobi 301971, Kenya.
TheScientificWorldJournal
|September 11, 2024
概括
这项研究介绍了一种混合基因的人工蜂鸟算法支持矢量机器分类器,用于有效检测电力盗窃. 这种新的方法在确定能源欺诈的准确性和可靠性方面明显优于现有的方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 电力盗窃给全球的公用事业公司带来了重大的财务和运营挑战.
- 开发可靠和准确的方法来检测电力盗窃对于收入保护和电网效率至关重要.
研究的目的:
- 引入一种新的混合元启发算法,即基因人工蜂鸟算法支持矢量机 (GAHA-SVM),用于增强电力盗窃检测.
- 为了优化支持向量机 (SVM) 分类器的超参数,使用拟议的GAHA算法.
- 评估GAHA-SVM分类器的性能与传统方法和其他元启发式方法相比.
主要方法:
- 通过将遗传算法运算符 (突变和交叉) 与人工蜂鸟算法的探索阶段集成,开发了一种混合GAHA算法.
- 使用GAHA算法优化SVM超参数,将电力消费者分类为欺诈性或非欺诈性.
- 该模型使用利比里亚电力公司7270个标记的历史电力消耗数据点进行训练和验证,数据分成80-10-10.
- 绩效使用六个关键指标进行评估,并与其他13个元启发式分类器和标准SVM进行比较.
主要成果:
- 在所有六个评估指标中,GAHA-SVM分类器实现了卓越的性能,包括0.9986的准确性,1的回忆率和0.9986.6的F1得分.
- 使用Wilcoxon等级和值测试的统计分析证实了GAHA-SVM在90%的比较中明显优于其竞争对手.
- 虽然该算法平均运行时间更长 (4,656秒),但在基准函数上表现出色,这表明它有很强的平衡勘探和开发的能力.
结论:
- 拟议的GAHA-SVM分类器是检测电力盗窃的高度有效和准确的工具,其性能优于现有的方法.
- 混合算法的优化SVM超参数和避免局部优化的能力使其成为对metaheuristic优化和电力系统安全性的宝贵贡献.
- 未来的研究可以探索进一步的改进,以解决时间复杂性,同时利用该模型的经过验证的检测能力.
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