Wide area monitoring and control of modern power network demand real-time estimation of state variables from sensor measurements. Maintaining a high degree of reliability and accuracy in the state estimation process is important in avoiding any disruption in the electricity market operation. The market operation in power networks aims at providing a win-win situation for both the utility and consumer. The exposure and vulnerability of cyber components in smart grids allow for manipulating the electricity market by falsifying the state variables. The attacker can cause intentional profit/loss to the utility/consumer by misdirecting the estimated states through the injection of false data into the sensor information. Hence, maintaining integrity in the market operation demands a mechanism for detecting false data injection attack (FDIA). This paper proposes a classification-based approach for detecting FDIAs aiming at electricity market disruption. For any variation in the predicted and real-time nodal electricity price, the proposed decision tree (DT) based ensemble classifier is executed using state information to identify the prevailing scenario as a contingency or FDIA. The effectiveness of the proposed scheme has been extensively validated for various contingency and FDIA scenarios in IEEE 14 bus, 39 bus, and 57 bus test power systems.

现代电力网络的广域监测和控制需要从传感器测量中实时估计状态变量。在状态估计过程中保持高度的可靠性和准确性对于避免电力市场运行中的任何中断非常重要。电力网络的市场运作旨在为公用事业和消费者提供双赢的局面。智能电网中网络组件的暴露和脆弱性允许通过伪造状态变量来操纵电力市场。攻击者可以通过将虚假数据注入传感器信息来误导估计的状态，从而对公用事业/消费者造成故意的利润/损失。因此，保持市场运作的完整性需要一种检测虚假数据注入攻击（FDIA）的机制。本文提出了一种基于分类的方法，用于检测针对电力市场中断的 FDIA。对于预测和实时节点电价的任何变化，建议的基于决策树 (DT) 的集成分类器使用状态信息执行，以将流行场景识别为应急或 FDIA。所提出方案的有效性已在 IEEE 14 总线、39 总线和 57 总线测试电源系统中的各种应急和 FDIA 场景中得到广泛验证。