Power quality (PQ) problems, including voltage sag, flicker, and harmonics, are the main concerns for the grid operator. Among these disturbances, voltage sag, which affects the sensitive loads in the interconnected system, is a crucial problem in the transmission and distribution systems. The determination of the voltage sag relative location as a downstream (DS) and upstream (US) is an important issue that should be considered when mitigating the sag problem. Therefore, this paper proposes a novel approach to determine the voltage sag relative location based on voltage sag event records of the power quality monitoring system (PQMS) in the real distribution system. By this method, the relative location of voltage sag is defined by Gaussian naive Bayes (Gaussian NB) and K-nearest neighbors (K-NN) algorithms. The proposed methods are compared with support vector machine (SVM) and artificial neural network (ANN). The results indicate that K-NN and Gaussian NB algorithms define the relative location of a voltage sag with 98.75% and 97.34% accuracy, respectively.

Using a hybrid renewable energy source with an energy storage system, this paper proposed a novel multi-stage non-isolated three-port converter with a 5H inverter to feed a residential load varying from 50 Watts to 3500 Watts. The proposed three-port converter operates in grid-tied and standalone power modes. A novel demand-side management algorithm, which covers eight operation modes, is described. Additionally, a complete control system is discussed. The proposed control system controls the PV maximum power point and battery charging/discharging, and regulates the 400 V DC bus voltage, the load voltage in standalone mode, and the grid-tied injected current. The proposed converter and the control system are validated through simulation for all power modes. The simulation results reveal that the proposed system is viable for Building-Integrated PV Systems.

One of the common power quality (PQ) problems in transmission and distribution systems is the voltage sag that affects the sensitive loads. Losses and problems caused by the voltage sag in the power system can be reduced by correctly determining the relative location of the voltage sag. This paper proposes a novel algorithm to classify voltage sag relative location and fault type, which is the main root cause of voltage sag, based on the actual voltage and current data before and during the voltage sag. The performance of the algorithm is investigated by performing a numerical simulation utilizing MATLAB/Simulink. Moreover, the proposed algorithm is integrated into the power quality monitoring system (PQMS) of the real distribution system and tested. The results show that the performance of the proposed method is satisfactory.

Accurate and real-time estimation of power factor (PF) and phase angle (PA) is critical for tracking energy efficiency and monitoring grid stability. This study proposes a deep neural network (DNN)-based approach for PF and PA estimation using voltage and current signals in real-time applications. Unlike conventional methods that rely on time-series analysis, the proposed method performs power factor estimation using convolutional neural networks (CNNs) for efficient feature extraction by converting one-dimensional electrical signals into two-dimensional image representations. The experimental validation demonstrates that the developed model achieves high accuracy across various load conditions, including resistive, capacitive, and inductive loads. The results show that the DNN-based approach provides fast and precise estimations, making it a viable alternative to expensive equipment, such as power analyzers. Results show that the proposed CNN-based model achieved an R² value of 0.9683 for PA estimation and 0.8842 for PF estimation, with root mean square error (RMSE) values as low as 6.71 and 0.11, respectively. The system successfully predicted PF and PA with less than 5% error during the experimental test phases. Future works will focus on extending the model’s capabilities to handle more complex power quality (PQ) disturbances and integrating it into proposed energy monitoring system.