Recent anticipated advancements in ad hoc Wireless Mesh Networks (WMN) have made them strong natural candidates for Smart Grid’s Neighbourhood Area Network (NAN) and the ongoing work on Advanced Metering Infrastructure (AMI). Fault detection in these types of energy systems has recently shown lots of interest in the data science community, where anomalous behaviour from energy platforms is identified. This paper develops a new framework based on privacy reinforcement learning to accurately identify anomalous patterns in a distributed and heterogeneous energy environment. The local outlier factor is first performed to derive the local simple anomalous patterns in each site of the distributed energy platform. A reinforcement privacy learning is then established using blockchain technology to merge the local anomalous patterns into global complex anomalous patterns. Besides, different optimization strategies are suggested to improve the whole outlier detection process. To demonstrate the applicability of the proposed framework, intensive experiments have been carried out on well-known CASAS (Center of Advanced Studies in Adaptive Systems) platform. Our results show that our proposed framework outperforms the baseline fault detection solutions.

Ad hoc 无线网状网络 (WMN) 的近期预期进步使其成为智能电网邻域网 (NAN) 和正在进行的高级计量基础设施 (AMI) 工作的有力候选者。最近，数据科学界对这些类型的能源系统中的故障检测表现出了极大的兴趣，在那里可以识别能源平台的异常行为。本文开发了一种基于隐私强化学习的新框架，以准确识别分布式异构能源环境中的异常模式。首先执行局部异常值因子以导出分布式能源平台每个站点的局部简单异常模式。然后使用区块链技术建立强化隐私学习，将局部异常模式合并为全局复杂异常模式。此外，还提出了不同的优化策略来改进整个异常值检测过程。为了证明所提出框架的适用性，在著名的 CASAS（自适应系统高级研究中心）平台上进行了大量实验。我们的结果表明，我们提出的框架优于基线故障检测解决方案。