# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### Anomaly Detection  
Anomaly detection, also known as outlier detection, is a process that collects and observes data to find behaviors that deviate from the standard pattern. Due to the wide usage of smart meters, anomaly detection of smart meter data has attracted much attention from academia. Abnormal energy consumption in daily life happens very often, such as electricity leakage and theft. Therefore, anomaly detection of meter data can benefit society and the economy significantly.  
One of the detection methods is centralized detection. This method, which combines multiple techniques such as big data processes, analyzes the smart grid data uniformly. The infrastructure, such as smart meters, measures consumers' energy consumption and sends the measurements to the data center for analysis. For future processing, the centralized facilities execute the detection algorithms to analyze the large volume of data gathered from electricity smart meters and these algorithms have great performance in detecting anomalous power consumption. [PERSON] et al. (2013) focused on centralized anomaly detection in school electricity facilities. The authors investigated hybrid anomaly detection models and collected electricity consumption data for analysis. According to the evaluation, the system can effectively detect the anomaly electricity consumption in the school. [PERSON] et al. (2014) proposed a centralized anomaly detection method based on Long Short-Term Memory (LSTM) and Negative Selection. This method can be applied to anticipate the anomalies in electricity consumption. Meanwhile, this method can also predict the occurrence of anomalies and monitor power consumption in real-time.  
Another commonly used detection method is decentralized detection. The traditional centralized anomaly detection methods rely on installing many electricity meter devices in households. The power consumption data are measured and collected by these meters and transmitted to the centralized data center for future processing and analysis. In the recent smart grid architecture, a large scale of smart devices are distributed across various facilities over a large geographical area for monitoring power consumption. It is difficult for traditional centralized detection methods to handle such a large amount of data generated by smart meters. In this case, edge computing has become one of the solutions to reduce latency and provide better performance in data processing. [PERSON] et al. (2015) proposed an anomaly detection method with edge computing architecture. Instead of applying the anomaly detection model in the data center, the authors distributed the model on the edge node devices. Moreover, to reduce the delay, two-way communication is adopted in this method. The experiment results show that this method greatly reduces the communication delay and improves detection accuracy. [PERSON] et al. (2016) presented an edge computing-based electricity theft detection method. Electricity theft by low voltage (LV) users could cause power loss and electric shock. It is essential to monitor and identify anomalous users of electricity theft. However, smart meters of LV users occasionally fail to update the data to the centralized advanced metering infrastructure. In the proposed approach, the Granger causality test is applied in the distribution transformer unit with edge computing. Since the distribution transformer unit collects the data without distortion, the proposed method can prevent the distortion caused by updating failure. Furthermore, fog computing can be introduced as another decentralized paradigm for anomaly detection. [PERSON] et al. (2017) proposed a hierarchical fog computing architecture to develop anomaly detection machine learning models. The proposed anomaly detection consists of two parts: model training and anomaly detection. The evaluation results confirm the efficacy of the proposed architecture.