# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 8 Conclusions  
The objective of the SE is to give accurate information of the state of a power system. With the proper information obtained from the SE, the system operator can operate and control the power system securely. The distribution system is an example of a section of the power system where real-time measurement is unfortunately lacking. The integration of DC microgrids in this system and the need for system control that comes with it intensify the urgency for a more accurate SE. In this paper, a new method for a DC microgrid's SE is proposed. The state estimator is based on the deep learning method, which is the LSTM network. With LSTM as the deep learning method, the dependence on the error variance of measurement types for weight selection, such as in conventional WLS SE, was reduced, since the deep learning method can optimize its own weight. Furthermore, a specifically  
\begin{table}
\begin{tabular}{c|c c} \hline \hline \multirow{2}{*}{**State Estimator**} & \multicolumn{2}{c}{**Computation Time**} \\ \cline{2-3}  & **Training (s)** & **Testing (s)** \\ \hline Conventional WLS & - & 14.109 \\ MLP-based & 15.944 & 0.029 \\ LSTM-based & 42.586 & 0.169 \\ Modified & 51.092 & 0.157 \\ LSTM-based & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computation times of state estimators.  
modified WLS loss function was integrated into the training process to assign certain weights for each measurement, which were used as inputs, based on their respective error variance. This novel method ensures more consistent accuracy in the estimation of all the bus voltages, irrespective of their distance to the voltage measurement point. The performance of the modified LSTM-based SE method and other previously investigated methods discussed in this paper was further validated by comparing the state estimators' error value. Out of all state estimators, the modified LSTM-based state estimator is shown to have the lowest error value for all error types. Aside from increased accuracy, when compared to the conventional WLS state estimator, the deep-learning-based state estimators also give significantly faster computational time in the online testing stage after intensively training the network model in the offline training stage. Therefore, it can be concluded that with the adoption of the modified LSTM-based state estimator as the deep learning method, the estimation of the DC microgrid's state can be performed more efficiently and with more accuracy compared to its predecessor.  
[PERSON] surveyed the research background, designed the codes for proposed method, and performed the simulations and analysis. [PERSON] reviewed the methodology and simulation results. [PERSON] supervised and supported the study. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Human Resources of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) Grant (no. 20174030201840), funded by the Korea Government Ministry of Trade, Industry and Energy, and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1A6A1A03032119).  
The authors declare no conflict of interest.