## 1 Introduction  
The energy sector is undergoing a profound change aimed at drastically reducing CO\({}_{2}\) emissions. Changes are concentrated on research into new methods of energy generation, modernisation, and digitisation of conventional supply networks, as well as concentrated on the development of systems that enable consumer participation. In this transition to a distributed generation system, digitalisation at scale plays a key role. This transformation will allow for the intelligent optimisation of energy production; the rapid detection of energy supply problems; and the reduction of losses with permanent perimeter management and operation. Edge-Cloud Computing [1] techniques combined with fibre-optic communications are commonly used for the instantaneous detection of disturbances, ensuring early responses in restoring power supplies. This paper proposes a hybrid Edge-Cloud Computing framework that satisfies the delay requirements associated with time-critical applications based primarily on phasor measurement unit (PMU) measurements, establishing a framework that allows for the minimisation of delays associated with the transfer of information. In this proposal, the capabilities of the PMU are extended to the synchronisation and simultaneous capture of other magnitudes measured inside the plants and not only in the power distribution lines. PMUs play an essential role in this process. More specifically, micro-phasor measurement units (\(\upmu\)PMU) [2] allow for an analysis with a much higher time resolution, revealing voltage fluctuations in great detail. In addition, they allow the transmission of up to 120 phasors per second. A paper published in 2018 [2] explained the importance of maintaining a balance between the temporal resolution of the measurements and the volume of data that can be stored to detect and analyse the propagation of transients in the distribution network; however, it did not include a technological solution. It is necessary to apply Edge-Cloud computing techniques to detect events with Edge-Signal processing and to store only the required information when each event occurs, as proposed in a publication in 2021 [1]. Keeping a history with such a high resolution is a problem because of the need to manage massive databases. In this context, there is a need for technologies that allow for a digitisation of energy based on quality standards considering the need for very high-quality synchronisation. The various monitoring systems for the management of energy generation and consumption in a given geographical area must be able to detect the origin of a disturbance, analyse its propagation, and determine the areas with supplies that may be affected [3,4]. The aim is to ensure accurate control of the bi-directional flow of energy by allowing different generation systems to cooperate and compensate for imbalances due to intermittent generation or fluctuating demand. These two publications highlight the importance of the instantaneous detection of disturbances in the grid as well as the need to know the origin of these disturbances. The current scenario of the insertion of renewable resources into the electricity grid, such as through photovoltaics (PV) plants, can cause electricity disturbances. Extending the capabilities of a \(\upmu\)PMU to coordinate the capture of events inside the plant would facilitate the detection of the origin of each disturbance. IEEE bus models contribute to the study of load flows [5] and the Time-Sensitive Networking (TSN) standard allows for the synchronisation of the capture of critical events with equipment connected to a conventional Ethernet infrastructure. In this context, this research is justified to establish a reference framework of such high quality that allows it to work simultaneously with models of the distribution network and with real data from various installations [6]. The aim is to facilitate fault diagnosis and to analyse the propagation of faults in distribution networks.