With the growing requirements to keep the security of supply higher than ever the room for failures is getting smaller in today's power systems, while the increased integration of distributed renewable energy sources is additionally complicating fault detection. By using big data that is collected in modern power systems, artificial intelligence algorithms can significantly improve the capabilities of traditional protection schemes. However, the choice of the artificial intelligence algorithm can significantly impact the scheme accuracy. This paper analyses a novel approach for power system fault detection and classification by using automated machine learning procedure that iterates over different data transformations, machine learning algorithms, and hyperparameters to select the best model. By simulating and testing tens of thousands of fault scenarios on a realistic test system, the suggested approach resulted with robustness and high accuracy.

Despite the rapid improvements in the field of microgrid protection, it continues to be one of the most important challenges faced by the distribution system operators. With the introduction of this new operation concept, the existing protection devices are not able to successfully identify, classify and localize different types of faults that occur in the microgrids due to their dynamic behaviour, especially in the islanded mode of operation. This paper presents a methodology that provides the station protection functionalities that include detection and classification of faults, isolation of the faulty feeder and fault location estimation. The proposed method is based on discrete wavelet transform and artificial neural networks. The test system based on the real data, completely developed in MATLAB Simulink, is used to demonstrate the accuracy of all functionalities of the station protection algorithm that can be easily applied in microgrids. The presented results demonstrated the method accuracy and showed that it can be used as an upgrade of the existing protection equipment for the future implementation of the advanced microgrid station protection system.

This paper presents a method for distributed generation (DG) allocation in low voltage distribution network based on the total annual energy loss reduction and Artificial Neural Network (ANN). The proposed method is applied to the PV solar based DG allocation problem in the low voltage distribution network using realistic network data and measurements. This research is motivated by numerous realistic issues faced by the Distribution System Operator in the area of DG planning. The main objective of this work is to develop, test and validate a robust method for DG allocation which can be used in practical problems without the need for extensive system modelling and load flow analysis. The results confirm the importance of appropriate DG planning and show that the proposed method can be used as a promising tool for efficient and effective DG allocation in low voltage distribution network.

Fault location estimation is a very important question in electric power system, in order to isolate the fault as soon as possible, and to recover the system with minimal interruptions. In that way, electric equipment is less stressed, and buyers more satisfied. Electric power lines are exposed to environment and probability of line failure is generally higher than other system element failure. Current electric power systems are equipped with high sampling rate power quality meters that are installed in the places of common coupling with distribution systems or high voltage consumers. Data obtained by these power quality meters, especially the voltage and current harmonics present a valuable information about system behavior, even in the faulty conditions. In this paper fault location and fault resistance is estimated by using a combination of artificial neural networks and voltage and current harmonics measured by power quality meters installed only in important system busbars. Results obtained from the real 110 kV transmission system show that a proposed algorithm can be used successfully in fault location and fault resistance estimation in one part of the electric power system. This paper makes a contribution to the existing body of knowledge by developing and testing a new method whose application represents a natural and a feasible upgrade using the existing measurement and communication equipment.

The modern power system operation is faced with numerous challenges related to the power quality improvements such as identification and classification of power distribution network (PDN) faults. The recent advances in the area of signal processing allow the development of new algorithms and methods which can be used for fault identification and classification in PDN. This study presents a comparison of two approaches for identification and classification of high-impedance faults (HIFs) in medium-voltage PDN. The first approach is based on the voltage phase difference algorithm, whereas the second approach is based on the combination of discrete wavelet transform and artificial neural networks algorithm. The proposed algorithms are tested on models of a real distribution network, which represents a typical PDN currently used in Bosnia and Herzegovina. It was demonstrated that the proposed methods are capable to accurately detect and classify HIF in PDN. This study makes a contribution to the existing body of knowledge by developing, testing and comparing two methods for HIF classification and identification, whose application represents an improvement when compared with the capability of the existing protection devices.

Identification and classification of high-impedance faults (HIFs) in electric-power distribution systems (EPDSs) represent some of the most significant challenges faced by the distribution system operators (DSOs). The recent advances in signal processing and changes in the EPDS regulatory framework have prompted acceleration in the development of advanced methods used for fault identification and classification in EPDS. The paper presents a method for identification and classification of HIFs in medium-voltage (MV) EPDSs, based on the Discrete Wavelet Transform and Artificial Neural Networks. The method was tested on generated signals based on a real EPDS and it was demonstrated that it is capable to accurately detect and classify HIFs in EPDS. The paper contributes to the existing research by developing and testing, on a real EPDS, a HIF-identification and classification method which offers a better performance compared to the currently installed protection devices.