Robust Data Driven Analysis for Electricity Theft Attack-Resilient Power Grid
Robust Data Driven Analysis for Electricity Theft Attack-Resilient Power Grid
复制标题
DOI：
10.1109/tpwrs.2022.3162391
复制
发表时间：
2023-01
影响因子：
6.6
通讯作者：
I. Khan;Nadeem Javaid;James Taylor;Xiandong Ma
中科院分区：
文献类型：
--
作者：
I. Khan;Nadeem Javaid;James Taylor;Xiandong Ma
电力盗窃检测（ETD）的作用对于保持智能电网的成本效益至关重要。然而，现有的ETD方法不能有效地处理现在可用的大量数据，受到缺失值，高方差和非线性等问题的限制。还需要一个综合的基础设施，以同步不同的程序，在电力盗窃分类。为了帮助解决这些问题，提出了一种新的ETD框架，结合了三个不同的模块。第一个模块处理缺失值、离群值和非标准化的电力消耗数据。第二个模块采用了新提出的混合类平衡方法来处理高度不平衡的数据集。第三个模块利用改进的人工神经网络（iANN）为基础的分类引擎，准确和有效地预测电力盗窃案件。我们提出了三种独特的机制，包括超参数调整，正则化和跳过连接，以提高标准人工神经网络的性能，使用智能电表（SM）数据处理更复杂的分类任务。此外，各种结构的人工神经网络进行了研究，以提高最终分类的泛化能力和函数拟合能力。来自真实世界能源使用数据集的数值结果证实，与现有的机器学习和深度学习方法相比，所提出的ETD模型具有上级性能，并且可以有效地应用于工业应用。
The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing ETD methods cannot efficiently handle the sheer volume of data now available, being limited by issues such as missing values, high variance and non-linearity. An integrated infrastructure is also required for synchronizing diverse procedures in electricity theft classification. To help address such problems, a novel ETD framework is proposed that combines three distinct modules. The first module handles missing values, outliers, and unstandardised electricity consumption data. The second module employs a newly proposed hybrid class balancing approach to deal with highly imbalanced datasets. The third module utilises an improved artificial neural network (iANN) based classification engine, to predict electricity theft cases accurately and efficiently. We propose three distinctive mechanisms, including hyper-parameters tuning, regularization and skip connections, to improve the performance of standard ANN to handle more complex classification tasks using smart meter (SM) data. Furthermore, various structures of iANN are investigated to improve the generalization and function fitting capabilities of the final classification. Numerical results from real-world energy usage datasets confirm that the proposed ETD model has superior performance compared to existing machine learning and deep learning methods, and can effectively be applied to industrial applications.