A proposal of a fault location technique for power distribution systems considering distributed generation (DG) and uncertainties associated to the fault resistance, load magnitude and model, fault type and faulted node is presented in this study. The approach presented here uses only single end measurements at the main power substation and at the DG source. To demonstrate the good performance of the proposed approach, tests on the 24.9 kV IEEE 34 nodes test feeder, which includes three‐phase and single‐phase laterals, are performed. According to the results, a high performance is obtained in the fault distance estimation, considering the shunt faults and the uncertainties in the proposed test scenarios. Finally, it is demonstrated that the proposed approach helps to maintain high continuity indexes by speeding up the fault location task in modern power distribution utilities.

Protection schemes are essential in active distribution networks and microgrids’ reliable, efficient, and flexible operation. However, the protection of these networks presents significant challenges due to operational changes, such as variations in topology, distributed energy resources connection/disconnection, and microgrid operating modes, among others. This paper proposes an adaptive protection scheme based on overcurrent devices with several setting groups based on artificial intelligence algorithms. The developed strategy is composed of two stages. In the off-line stage, a clustering technique is employed to group the active distribution network operating scenarios exhibiting similarities. The optimal settings for the protection devices are determined for each set of scenarios. On the other hand, in the on-line stage, the protection strategy’s implementation and operation, considering the active distribution network’s existing communication system, are defined. Furthermore, the approach formulates the overcurrent relay coordination as a mixed-integer non-linear optimization problem, and as a result, the optimal setting of the overcurrent protection devices is obtained. It aims to minimize the operating time, considering the transformers’ thermal limits, fuse operating curves, and overcurrent relay settings. The solution is determined by using an Augmented Lagrangian genetic algorithm. The presented protection scheme is validated on the modified IEEE 34 node test feeder, considering the main operating scenarios of the active distribution networks, such as topology changes, distributed energy resource connection/disconnection, and microgrid operating modes (on-grid and off-grid). The results obtained and its easy implementation indicates the high potential for real-life applications.

The electric power markets have imposed new power service quality that make fault location in power distribution systems a mandatory issue. In this paper, a fault location approach based on the current waveforms measured at the power substation, the knowledge of protective device settings and ANFIS nets is presented. As a result, the approach may locate the faulted zone by using current descriptors. As a result, the ANFIS nets have proved the capability to locate the fault in a specific zone of the power distribution system. Result shows validation errors lower than 1% to locate faulted zones

Electric microgrids require accurate dynamic models for operation, control, stability, and protection studies, then adequate load modeling plays an important role. This paper presents a two-stage adaptive approach to improve the generalization capability of load models obtained with the measurement-based modeling. The load model and their respective parameters are obtained through machine learning tools like decision trees (DTs) and optimization algorithms as ant colony (ACO). In the off-line stage of the proposed approach, several parameterized load models are optimally obtained using a database of microgrid disturbances. Then, the best model to represent each disturbance is defined using a similarity criterion. This model and the disturbance characteristics are integrated into a DT (classifier), while the characteristics and the model parameters are related in a second DT (predictor). These DTs are used in an on-line stage to swiftly determine the adequate parameterized load model in the case of a new disturbance in the microgrid. The approach's performance is compared with the conventional measurement-based load modeling in a modified CIGRE benchmark low voltage microgrid. The results evidence the advantages of the proposed adaptive approach for dynamic load modeling.