## 1 Introduction
### Literature Review  
One of the important issues in the grid modernization process is how different measurements can be incorporated correctly in a control center to perform SE calculations accurately during high-speed operations [19; 20]. Inconsistent reporting rates of measurement devices, as well as different types of measurement information with various precision levels, are two of the most critical problems for performing hybrid SE calculations from traditional and advanced measurement devices [21]. PMUs commonly provide synchrophasor measurements at a rate of 30 samples per second as compared to SCADA measurements, which update every 2-6 s [22; 23]. Hybrid SE methods have been well established in TNs to integrate and make use of the potential benefits of traditional and advanced measurements [24; 25]. Different hybrid transmission state estimation methods to combine traditional and advanced measurements are discussed in [26]. Hybrid methods will be helpful during the power grid modernization development while replacing all-traditional with all-advanced measurement devices.  
Machine learning techniques--including supervised, unsupervised, and reinforcement approaches [27; 28]--are applied widely in DNs for different purposes, such as generating pseudo-measurements [29; 30], SE calculation [31; 32], False Data Injection Attacks (FDIAs) detection [33; 34], reliability and resiliency analysis, etc. [35; 36]. In [37], a Deep Neural Network (DNN) approach was applied for topology and SE in DNs. Machine learning methods have also been developed to improve hybrid traditional and smart measurement data for DN modernization. In [38], different attributes of PMU and SCADA measurements are determined based on data accuracy, time scale, and refreshing frequency. Then, aspecific solution is created based on the differences between measurement properties. In [9], in order to include both SCADA and PMU measurements for DSSE calculation, the state estimator switched between Weighted Least Square (WLS) and Weighted Least Absolute Value (WLAV) calculations. In [39], a new hybrid SE method based on Particle Swarm Optimization (PSO) [40] and Artificial Neural Network (ANN) approaches was proposed to combine the different measurements. In [41], nonlinear static state estimation, linear static state estimation, and linear dynamic state estimation were applied to hybrid SCADA, PMU, and AMI measurements in ADNs. In [42], a new hybrid state estimator considering a distributed state estimation method was proposed to use SCADA and \(\mu\)PMU measurements in an unbalanced DN. In [43], improved robust and linear state estimators were applied to respond to different frequency levels on measurement data from a SCADA and PMU for medium voltage DNs. In [44], a hybrid simultaneous state estimator based on the WLS method was proposed to incorporate both SCADA and PMU measurements. In [45], a hybrid SE method based on WLAV estimator and Deep Neural Network (DNN) methods was proposed to manage the unsynchronized AMI and SCADA measurements and to monitor ADNs. In [46], SCADA, \(\mu\)PMU, and Smart Meter (SM) data were used for unbalanced DNs, where Kalman smoother and Expectation Maximization (EM) methods were used to manage the missing measurements and combine measurements in different time samples, respectively. In [47], a static state estimator was used to perform DSSE calculations using both SCADA and PMU measurements. When SCADA measurements were not available, a fast state reconstruction algorithm was applied using only PMU and pseudo-measurements. In [48], a Spatio-Temporal Estimation Generative Adversarial Network (ST-EGAN) was proposed to generate pseudo-measurements to perform DSSE calculations in high resolution when SCADA and PMU measurements were not available simultaneously. In [49], an improved sequential state estimation method was proposed to take advantage of AMI and \(\mu\)PMUs measurements to address the asynchronization issue for performing DSSE calculations.