Power systems and their primary components, mostly the transmission and distribution of overhead lines, substations, and other power facilities, are distributed in space and are exposed to various atmospheric and meteorological conditions. These conditions carry a certain level of risk for reliable electrical power delivery. Various atmospheric hazards endanger the operation of power systems, where the most significant are thunderstorms, wildfire events, and floods which can cause various ranges of disturbances, faults, and damages to the power grid, or even negatively affect the quality of life. By utilizing a weather monitoring and early warning system, it is possible to ensure a faster reaction against different weather-caused fault detections and elimination, to ensure a faster and more adequate preparation for fighting extreme weather events, while maintaining overhead line protection and fault elimination. Moreso, it is possible to bypass overhead lines that have the highest risk of unfavorable meteorological events and hazards, and reroute the energy, thus providing electricity to endangered areas in times of need while minimizing blackouts, and consequently, improving the quality of human life. This paper will present an analysis of the various risks of atmospheric phenomena, in the meteorological and climate context, and discuss various power system components, the power system control, operations, planning, and power quality. A concept with the main functionalities and data sources needed for the establishment of an early warning weather hazard system will be proposed. The proposed solution can be used as a utility function in power system control to mitigate risks to the power system due to atmospheric influences and ongoing climate change.

One of the most serious obstacles to massive deployment of electric vehicles is insufficient and cumbersome charging infrastructure. Both the number of charging points and their power capacity are often insufficient. On top of that, this new technology often suffers from many issues related to insufficient testing, immaturity and irregular handling. This paper summarizes the issues with the electric vehicle charging infrastructure and describes first-hand experiences with long-range electric vehicle trips originating from Zagreb, Croatia, during 2022. Issues with the charging points locations, power and availability are assessed. Based on these experiences, the paper provides some thoughts on the possible directions of the further development of the electric vehicle charging infrastructure in Croatia.

A lightning current measurement system for wind turbines was developed and tested in the High Voltage Laboratory at the Faculty of Electrical Engineering and Computing, University of Zagreb, to determine actual distributions of lightning current parameters in a particular wind turbine area. The output of the measurement system is a waveform of lightning current. The measurement system consists of two current sensors with different frequency and amplitude ranges and a real-time controller with two digitizers and a GPS module. The current sensors are Rogowski coils connected to integrators with coaxial cables. The prototype was installed on a wind turbine in a high winter lightning activity area. The current stage of development involves integrating the high-speed camera into the prototype measurement system to obtain the visual confirmation of the waveform measurements and help categorize lightning strikes into downward or upward strikes. The camera control application was developed in LabVIEW. The camera was set to trigger by the real-time controller. Also, a redundant trigger occurs when there is a change in motion in the camera frame in case the prototype measurement system fails to register a lightning strike and therefore fails to send a trigger signal to the camera. The camera control application was tested in the High Voltage Laboratory by recording a flashover in a spark gap and a flashover on the insulator.

The trend toward increasing integration of wind farms into the power system is a challenge for transmission and distribution system operators and electricity market operators. The variability of electricity generation from wind farms increases the requirements for the flexibility needed for the reliable and stable operation of the power system. Operating a power system with a high share of renewables requires advanced generation and consumption forecasting methods to ensure the reliable and economical operation of the system. Installed wind power capacities require advanced techniques to monitor and control such data-rich power systems. The rapid development of advanced artificial neural networks and data processing capabilities offers numerous potential applications. The effectiveness of advanced deep recurrent neural networks with long-term memory is constantly being demonstrated for learning complex temporal sequence-to-sequence dependencies. This paper presents the application of deep learning methods to wind power production forecasting. The models are trained using historical wind farm generation measurements and NWP weather forecasts for the areas of Croatian wind farms. Furthermore, a comparison of the accuracy of the proposed models with currently used forecasting tools is presented.