Anomaly detection methods are part of the systems where rare events may endanger an operation's profitability, safety, and environmental aspects. Although many state-of-the-art anomaly detection methods were developed to date, their deployment is limited to the operation conditions present during the model training. Online anomaly detection brings the capability to adapt to data drifts and change points that may not be represented during model development resulting in prolonged service life. This paper proposes an online anomaly detection algorithm for existing real-time infrastructures where low-latency detection is required and novel patterns in data occur unpredictably. The online inverse cumulative distribution-based approach is introduced to eliminate common problems of offline anomaly detectors, meanwhile providing dynamic process limits to normal operation. The benefit of the proposed method is the ease of use, fast computation, and deployability as shown in two case studies of real microgrid operation data.
翻译:异常探测方法是稀有事件可能危及行动利润、安全和环境的系统的一部分,虽然迄今为止已经开发了许多最先进的异常探测方法,但其部署仅限于示范培训期间的运行条件; 在线异常探测使适应数据漂移和变化点的能力能够适应模型开发期间可能无法反映的、造成长期使用寿命的数据开发过程中的数据漂移和变化点; 本文建议对现有实时基础设施采用网上异常探测算法,在这种基础设施中,需要低纬度探测,数据的新模式难以预测; 采用网上反向累积分布法,以消除离线异常探测器的共同问题,同时为正常运行提供动态流程限制; 拟议方法的好处是便于使用、快速计算和可部署,如对实际微电网操作数据的两项案例研究所示。