In order to enhance the reliability of power systems, a continuous monitoring of the network to detect and clear disturbances is crucial. Fast detection and isolation of disturbances can prevent equipment damage, downtime and other adverse effects associated with their occurrence. The focus of this project is on the detection and classification of two non-linear, complex and severe disturbances: ferroresonance and arcing faults. These disturbances are detected and classified by continuous signal processing of the three-phase voltage and current signals. The models of these disturbances are simulated in EMTP and the three-phase voltage and current are extracted. The extracted data are pre-processed using the discrete wavelet transform (DWT) to extract fault signatures and features used in classifying the disturbances. A decision tree classifier is trained with the extracted features and it is able to detect and classify a disturbance as either ferroresonance or arcing faults using an adaptive time based on the disturbance class. The computational burden in the detection and classification process is reduced by using the superimposed component of the voltage and current to detect transient inceptions prior to classification. Adaptive dead time is adopted to classify the sustained period of the ferroresonance signals and to detect the extinction time of the secondary arc. The results show that the proposed methodology can detect the different ferroresonance modes and arcing faults with 99.8 % accuracy within 100 ms and classify the sustained modes afterwards.

Testing of protection schemes is important before it can be used for an actual topology. Nowadays, DC protection has not readily been designed and implemented in power systems. Therefore, due to high requirements on DC fault interruption, the designing and testing the corresponding DC protection with the use of a DC circuit breaker (DCCB) becomes a challenge. In this thesis, a four-terminal meshed VSC-MMC HVDC system is developed in RTDS environment. Diverse fault tests, concerning different fault types, fault locations and fault impedances are conducted in a remote testing way. The MATLAB-based testing controller by a remote computer will command the local computer and the RTDS-modelled system through the communication network, in order to automatically run all test scenarios and do data processing. The related testing results demonstrate the DC fault dynamics in an effective way in order to test DC protection. In order to do this a refined DCCB model has been developed. 

This thesis investigates the performance of distance protection function of actual relay placed on high voltage 400 kV transmission line in a system with high photovoltaic (PV) sources penetration. The benchmark model of system with radial connection of PV plant and conventional power plant was designed in the platform of RSCAD software. By hardware-in-the-loop (HiL) test setup two commercial relays were connected to the Real-Time Digital Simulator (RTDS) and linked to two virtual breakers in transmission line ends modelled in RSCAD. Special logic for correct signal processing of analogical inputs and binary outputs of relays was designed and employed in RSCAD. Several test scenarios were recreated and analysed. Distance protection function was tested with radial connection of synchronous generator to check response of benchmark system and validate correct settings of the relays. Afterwards synchronous generator was substituted by generic PV farm model. Impact of different fault locations, types of fault, power generation level and fault impedance values were studied on a basis of the benchmark model. The goal of this research was to determine the extreme scenarios in the power system when real relay would fail to detect the fault conditions. Failures of distance protection function operation were detected and explained from mathematical side. On the basis of this research the author revealed the cases of relay maloperation and suggested possible ways of solution to this problem by the set of recommendations.