Abstract
Recent anticipated advancements in ad hoc Wireless Mesh Networks (WMN) have made them strong natural
candidates for Smart Grid’s Neighborhood 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 behavior 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.
candidates for Smart Grid’s Neighborhood 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 behavior 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.