## 3 Latest Applications in Distribution Systems
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Topic** & **Algorithm** & **Comments and Keynotes for Selected Papers** \\ \hline \multirow{6}{*}{Power Flow} & \multirow{3}{*}{GL [29; 30; 31; 32; 33]} & [29; 30]: GNN deals with varying network topology and outperforms FCNN and CNN models [33]: GNN is equipped with ARMA layers to prevent oversmoothing \\ \cline{2-3}  & \multirow{3}{*}{Attention [31; 32]} & [31]: Inductive learning is used to enhance computation efficiency [32]: Solar and wind power generations are accounted for probabilistic power flow. \\ \cline{2-3}  & \multirow{3}{*}{PCNN [27; 28] DRL [34; 35]} & [28]: Network topology as physics knowledge [34]: Adapt to topology changes and DER uncertainty \\ \hline \multirow{6}{*}{GL [36; 37; 38; 39]} & [36]: Two-layer framework of GCN and GRU [37]: Simultaneously captures spatiotemporal correlation of data [38]: Pseudo-measurements are generated via GNN [39]: Low pass property of voltage phasors are utilized to reconstruct network \\ \cline{2-3}  & \multirow{3}{*}{TL [40; 41; 42]} & [40]: A temporal convolutional network is developed to learn the dynamic features of individual appliance load [42]: Privacy-preserving TL for non-intrusive load monitoring \\ \cline{2-3}  & \multirow{3}{*}{PCNN [43; 44; 45]} & [43]: PMU allocation are optimized and embedded into NN design [44]: Jacobian matrix as physics knowledge to construct the loss function [45]: Encoder–decoder architecture for evaluating physical consistency with measurement equations \\ \hline \multirow{6}{*}{Volt-Var Control} & \multirow{3}{*}{DRL [46; 47; 48; 49; 50; 51]} & [51]: Achieve near constraint satisfaction in time-varying conditions via a safe DRL algorithm [52]: An adversarial SAC algorithm enhances robustness against modeling errors of distribution networks [46; 48; 49; 52; 54]: Multi-agent DRL; physics-based shielding mechanism in PV/battery energy storage systems [48] \\ \cline{2-3}  & \multirow{3}{*}{GL [49]} & [49]: Privacy is preserved by training agents locally via GCNs \\ \cline{2-3}  & \multirow{3}{*}{Attention [54]} & [54]: Attention is used to automatically assign the learning weights for cooperative learning \\ \hline \multirow{6}{*}{Topology Identification} & \multirow{3}{*}{GL [55; 56; 57; 58]} & [55]: Graph learning algorithm with backtracking [56]: Power flow equation is used to estimate missing node measurements [57]: Topology is estimated from a constructed topology graph bank \\ \cline{2-3}  & \multirow{3}{*}{Attention [59; 60]} & [59]: Separate convolution layers for voltage and power measurements [60]: Meta-paths are constructed via graph transformer network \\ \hline \multirow{6}{*}{Parameter Calibration} & \multirow{3}{*}{GL [61]} & [61]: Effects of temperature and humidity on line impedances are accounted for \\ \cline{1-1} \cline{3-3}  & \multirow{3}{*}{PCNN [62; 63]} & [62]: The backward/forward sweep distribution flow formulation is used to construct the graph learning [63]: Kron reduction is implemented to reduce computational complexity \\ \cline{1-1} \cline{3-3}  & & [61]: The backward/forward sweep distribution flow formulation is used to construct the graph learning [63]: Kron reduction is implemented to reduce computational complexity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The technical summary classifies the AI application in distribution systems into various learning-enabled techniques.