Identifying the types and locations of faults is a necessary precondition for the restoration of the power system in a normal state, and preventing possible damage to the electricity network elements. The doctoral thesis analysed the possibility of determining the location and type of fault in the power system transmission network by using artificial neural networks. The doctoral thesis is focused on recognition of high impedance faults (HIFs) in the power system transmission network, where the measured electrical parameters have values close to the nominal values that are present in the normal operation. To solve the above problems approach using artificial neural networks has been chosen. The doctoral thesis used three-phase models of the electric power network, as a basis of fault recognition apart from normal operation condition in the case of non-linear loads, a method proposed combines the determination of electrical quantities waveform frequency spectrum and artificial neural networks. In order to achieve this fast Fourier transform FFT was used to obtain a harmonics spectrum of calculated electrical quantities that are then used as input data for learning and testing artificial neural networks. In the same way the use of the frequency spectrum allowed the identification of the location and type of short circuit on the transmission line. The overhead line is an integral part of the power system and its protection is important to ensure system stability and to reduce the damage to equipment that may occur due to short circuits on the overhead lines. Short-circuiting leads to mechanical and thermal stresses that are potentially harmful to high-voltage equipment. Overhead protection includes three main tasks: detecting, classifying and defining a location on overhead line. Fast detection of overhead line failure enables quick isolation of faulted overhead line from the system to protect it from further damaging effects. Properly determining the fault location is necessary to allow reparation of faulted overhead line in the shortest possible time and return it to normal operation, increasing availability and power supply. Accurate determination of the fault location, the amount of time required by the maintenance crew in search of a fault can be reduced to a minimum. However, fault identification is not always an easy task. If there is a fault, it is necessary to isolate it as quickly as possible to preserve the stability of the rest of the system. Overhead protection relays normally use voltages and currents as input signals to detect, classify and locate a fault in overhead lines. In the event of a fault, the relay will send a switch-off signal to switch off the overhead line, so the rest of the system can continue working in normal or near normal conditions. Different types of algorithms and methods for finding faults on overhead lines have been developed and proposed over the years and can be classified into the following groups: a) Calculation of the phase current and voltage phases from which the fault location can be determined by line impedance, b) Using differential overhead line equations, c) A method of traveling waves using data from one or both ends of the overhead line. Some protective relays may have difficulties detecting a fault due to the high impedance and the DC component of the short circuit current. The approach used by travelling waves has problems with the detection of faults that are very close to the substation and if the angle at the time of the fault falls close to zero (or equal to zero). One of the tools recently introduced in the power system is the Artificial Neural Network (ANN). ANN is a powerful pattern recognition, classification and generalization tool that is useful for energy system analysis because ANN can learn with the off-line data. The ANN also has excellent features such as resistance to interference, robustness and error insensitivity. This doctoral thesis describes the application of the ANN to detect faults in the transmission system network of the power system, the application of which is described in a large number of papers. The fault itself depends on the impedance of the surrounding network and the impedance of the fault itself, so protection relays are most often set for topology with the highest value of fault current. Advantages of ANN such as the ability of continuous learning, associative memory, nonlinear mapping and parallelism allow its use in a wide range of engineering research. HIF is generated when the phase line makes unwanted contact with low conductivity objects such as road surface, pavement, lawn or tree branches, limiting the fault current to very low values. These faults can not be easily detected by conventional overcurrent protection devices and often cause an electrical arc fault if there is no available return path for the current, leading to presence of high frequency components in fault current. Different ways of detecting HIF were previously suggested by other researchers. These methods based on algorithms used can be divided into time domain and frequency domain. In the time domain, the relay method with the ground ratio, proportional relay algorithm, smart relay, flicker failure and semi-peripheral asymmetry were suggested. In the frequency domain several articles are published based on Kalman's filtering, fracture theory and neural networks. Recently the proposed wavelet transformation has been used to achieve better results. The techniques for fault locating can be classified into two categories: 1) method of using data from one end of the transmission line and 2) method of using data from both ends of the transmission line. It is well known that the techniques based on measurement at both ends require communication links and synchronized sampling equipment. In one paper it was proposed to protect the system of discrimination against HIF and the normal operating system events in a distribution system based on wavelet packet transformation and ANN. However, much research has not been carried out on the differences between HIFs and nonlinear load in high-voltage transmission networks. Doctoral thesis consists of nine chapters. Following the introduction of the research objectives and the literature review, in the second chapter, a transmission network model was developed, using three-phase models, used to perform simulations necessary for further calculations. The third chapter briefly described types of short circuit in power system using three-phase models of network elements. The fourth chapter describes the working principle of artificial neural networks and their possible application in power system analyses. The fifth chapter describes the Fourier transformation process and its basic features, and its application in the analysis of periodic functions. The sixth chapter describes a method that uses ANN for the fault type of detection and its verification. The seventh chapter describes a method that, using ANN, calculates the location of a fault on overhead line and its verification. The eighth chapter describes a method that performs the recognition of HIF from a normal nonlinear load and its verification through ANN. Concluding considerations are presented in Chapter 9. Short circuits in the transmission network pose a danger both to the equipment and to the staff that can be found near the fault. High fault current cause dynamic forces that can cause mechanical damage to equipment and thermal damage caused by Jule's heat. A special problem is the HIF, where in cases when fault currents are not high enough to make the overcurrent protection work properly. Such failures are most often caused by a low conductive element such as a branch, a ground conductor drop or an electric arc. Unlike low-impedance faults, HIFs pose a major risk to electrical power equipment and are accompanied by an electric arc at the site of failure. The doctoral thesis proposed a methodology for detecting the type and location of low-impedance failt and HIF on the overhead line and the distinction of HIF from a normal non-linear load operation. The proposed methodology uses the ANN to solve the aforementioned problems by using voltage and current waveforms at the end of the overhead line for input quantities and switching from the time domain to the frequency domain. Calculation of voltage and current harmonics make input sets for ANN to provide satisfactory results. The tested network is three-phase modelled according to the actual part of the transmission system. The calculations were carried out in such a way as to take into account variable load flow, fault locations, fault inception and impedance at the fault location. In the reviewed literature, cases with less variable parameters were processed. The first part of the method deals with identifying the type of fault on overhead line. All types of short circuit are made except for a three-phase short circuit without a simultaneous earth connection since the possibility of such failure is negligible. Since detection of the type of fault is a classification problem, ANN has been used to identify patterns. ANN provided the best results in the case of network with 100 neurons in the hidden layer, and the lowest accuracy of the phase B to phase C with ground fault classification, whose classification accuracy was 93.52%, which was expected to overlap part of HIF's performance with the diode rectifier locomotive drive in normal drive condition. Verification of this method was performed on the example of a single-phase short circuit in the real network whose voltage and current parameters are translated from the actual network to the model. ANN successfully recognised the entry as a phase B with ground fault. In this section, the first scientific contribution of the ANN model to detect a failure has been realised. The second part of the method deals with identifying the location of the failure on overhead line. To solve this problem, ANN is used that uses feedforward network with backpropagation algorithm and the least squares error function. The best results were achieved by ANN with 10 neurons in the hidden layer where the average error was 1.13%, while the maximum error value was 2.48% with respect to the total length of the overhead line. Verification was also carried out on a real short circuit whose parameters were translated to the model while at the same time knowing the location of the failure detected by the fault locator in the relay. For ANN with 10 neurons in the hidden layer, the error was 1.23%, which is a satisfactory level for team teams on the field who must repair the failure in the shortest possible time. The third part of the method deals with the discovery of HIF from a nonlinear load. In this paper, nonlinear load was simulated by electric railway substation. The diode and thyristor locomotives are also modelled in detail. This is achieved by increasing the security of determining the type of fault, since the HIF is undetected by overcurrent protection and the location of the fault places it close to electric railway substation. And in this case, the ANN was used for pattern recognition. In this case, ANN had a 100% success in not only detecting electric railway substation load from fault but also discerning non-linear loads due to its specificity in waveforms. The verification was carried out on the example of the waveform of the diode locomotive load in the real network and its waveform was transmitted to the model. ANN correctly classified this form as a diode locomotive load. This part of the method has accomplished another scientific contribution that defines the differentiation of the high-impedance fault from the normal load state of the transmission network with a large share of unbalanced and non-linear loads. Throughout all three parts of the method in the final sub-headings, as a third scientific contribution, a model was evaluated to determine the type and location of the fault based on verified computational models and measurements. A set of proposed methods ensures increased accuracy and reliability of existing protective devices as well as additional support in the analysis after the protection process. The accuracy of determining the location of the overhead line fault can be considered satisfactory, although the amount of error depends largely on the amount of different topological network states. By selecting just realistic possible overhead line conditions the fault location accuracy would be further enhanced.