## 4 The Application of Edge Computing System
### Fault Location  
A system developed using EC stores transition amplitude ratios at edge nodes and utilizes a radial basis function (RBF) neural network to accurately detect faults in networks with hybrid feeders [174]. Transients are sampled at measurement nodes and transmitted to the edge nodes, which identify the faulty branch and perform the detection process. To address overload issues caused by centralized management in large-scale digital distribution networks, ref. [175] proposes a power grid mapping-based EC structure that improves network efficiency by optimizing the use of centralized management resources. In [212], an innovative line selection and fault detection method for distribution networks with cloud-edge-terminal hierarchical architecture is proposed. When a fault occurs, the traveling wavefront arrival times are transmitted via optical fiber to the edge computing gateway. Here, large volumes of fault data are filtered, and invalid and noisy information is eliminated. The time characteristic matrices generated from the valid data are transferred to the cloud layer, where line selection is performed. Then, fault localization is handed over to the edge layer, and fault localization is performed. The result is transmitted to the terminal layer to trigger the relevant circuit breaker. Finally, the correct fault information is transmitted to the cloud and presented to the user in a pop-up window format. Similarly, ref. [169] proposes an EC-based fault detection method to promptly identify faults in distribution networks and improve electricity supply reliability. The EC-supported fault monitoring and control framework is depicted in Figure 15. In [213; 214; 215; 216], a hierarchical fault monitoring system for large and complex distribution networks is developed, offering low latency and localized data processing with EC. This system aims to overcome thelimitations of traditional methods, particularly in detecting and processing small current faults, such as single-phase ground (SLG) faults.  
Table 8 summarizes the power system application of EC.  
\begin{table}
\begin{tabular}{c c c} \hline
**Ref. No** & **Scenarios** & **Key Technologies** \\ \hline
[152] & Data Transmission & Centralized Software Defined Network (SDN) and EC \\
[153] & Data Transmission & TATSR \\
[154] & Data Transmission & Optimal Power Allocation Strategy with Transmission Power Constraints \\
[155] & Data Transmission & Information/Digital Technologies and Artificial Intelligence \\
[156] & Data Transmission & ANN and VVC and EC \\
[157] & Data Transmission & VVC and Reactive Power Control \\
[158] & Data Transmission & Voltage Stability \\
[159] & Data Transmission & Zigbee Wireless Communication and EC \\
[161] & Monitoring and Control & Transient Electromechanical Simulation \\
[162] & Monitoring and Control & Reactive Power Condition Online Monitoring and EC \\
[163] & Monitoring and Control & A priori Frequent Item Set Algorithm and EC \\
[165] & Monitoring and Control & PD-IoT System \\
[217] & Monitoring and Control & A GA Based on Predator Search Strategy \\
[172] & Microgrid System & Model-Based Deep Reinforcement Learning (MDRL) and EC \\
[173] & Microgrid System & MADRL \\
[174] & Microgrid System, Fault Location & Radial Basis Neural Network Function \\
[175] & Microgrid System, Fault Location & DRL and EC \\
[176] & Microgrid System & ML Algorithm and EC \\
[178] & Microgrid System, Smart & Hierarchical Decision-Making Strategy Based on Prediction Strategy and Task Grading (HDTG) and Real-Time Electricity Price Forecast \\
[179] & Microgrid System & Edge-AI Algorithm \\
[180] & Microgrid System & Neural-Network-Based Identification Scheme \\
[183] & Smart Metering System & NILM \\
[185] & Smart Metering System & 6 LoWPAN Protocol and EC \\ \hline \end{tabular}
\end{table}
Table 8: The survey on the integration of edge computing in applications.  
Figure 15: Proposed EC-supported fault monitoring and control framework [212].