The optimization of overcurrent relays’ operation is a topic associated with protection coordination of distribution networks. Usually, this refers to medium-voltage networks, since they are protected by numerical relay devices, as opposed to low-voltage networks, where utility operators allocate fuses. Correct setting of relays and optimal coordination is becoming a serious challenge to Distribution Network Operators around the world, since their networks’ passive operation has been greatly altered in the past two decades. Distributed generation units, a growing liberalized electricity market and more stringent legislation for distribution network planning and operation by state regulatory bodies have all indirectly affected the evolving of protection philosophy for distribution networks. In this paper the traditional optimization problem of overcurrent relay operation will be addressed and critically examined from both a theoretical and practical point of view. Optimization function, constraints and relay parameters will all be observed and compared with solutions used in distribution networks, and their modifications and improvements will be proposed and elaborated in detail.

The goal of this review paper will be to address complex optimization functions in the area of overcurrent relay optimization, to inspect and valorize their objectives and to critically examine their application in real distribution networks. Special emphasis will be put on observing the optimization of discrimination time between primary and backup relay pairs, and methods which prevent the latter becoming penalty functions will be shown. Furthermore, the impact of distributed generation units on the overcurrent relay optimization problem will be elaborated in detail. Protection settings of all the existing and new relays must be thoroughly inspected before the connection of new production units, because contributing fault currents may have a significant effect on the solution of the overcurrent optimization problem. This is because the relays being used are of an inverse-type. Next, meshed network operation will be critically assessed in comparison with the actual practice of radial operation, and some topologies will be highlighted for future research, while others will be discarded. The distribution network in mesh operation implicitly suggests using numerical relays on both sides of protected elements (usually lines), so their total number will be much higher than in radial operation. Finally, a relatively new concept of adaptive distribution network protection will be reviewed, and its potential application to the overcurrent relay optimization problem will be examined. The purpose of this review paper will be to lay a groundwork for future research papers by inspecting the overall development of the overcurrent relay optimization problem for contemporary and future active distribution networks.

Network reconfiguration enables the restoration of power supply in interrupted areas after faults or planned maintenance events. However, changes in network topology can often lead to alteration in the direction and magnitude of fault currents, undermining the coordination of overcurrent relays. In response to this challenge, adaptive protection systems have been developed that utilise system-wide communication to recalibrate relays dynamically. However, implementing system-wide communication comes with significant economic burdens, data integrity concerns and infrastructure limitations, especially in underdeveloped communities. In this study, the analysis of the overcurrent protection strategy based on thermal stress curves is conducted, to validate the appropriateness of the relay settings for a reconfigurable distribution network. The methodology was demonstrated on a test distribution system utilising directional overcurrent relays. The results demonstrate that the proposed protection strategy offers a simple and cost-effective means to enhance the effectiveness of power system protection within a reconfigurable grid. Consequently, this method presents an alternative to the concept of adaptive protection.

The global energy landscape is increasingly shaped by renewable energy sources, particularly photovoltaic systems, which are influenced by economic, environmental and geopolitical factors. Technological advances and political incentives have reduced the cost of photovoltaic systems and driven their integration into the energy grid. This trend has been accelerated by energy crises and geopolitical events, underlining the importance of energy security and diversification of energy sources. However, the inherent variability of solar energy, which depends on climatic and natural cycles, poses a major challenge for the management of energy production. In this paper, short-term forecasting models for photovoltaic power plants are developed and applied using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) system. These models improve grid management by predicting photovoltaic output more accurately, enabling better integration and reducing dependence on balancing power plants, thereby optimising cost efficiency and reliability. A novel approach for fine-tuning the models at different geographical locations is presented, where the pre-trained models are adapted to local datasets to maintain forecast accuracy despite varying climatic conditions. The evaluation results show that the fine-tuned CNN-LSTM model achieves a mean absolute error (MAE) of 242.46 W, a root mean square error (RMSE) of 406.63 W and an R2 value of 0.78, indicating a significant improvement in forecast accuracy over the baseline model. This robust framework demonstrates the practical applicability of the model for real-world photovoltaic power generation forecasting, highlighting the importance of fine-tuning to local conditions.