Rozas Valderrama, Rodrigo

Partial discharge (PD) monitoring is one of the most used tools for diagnosing the condition of electrical equipment and machines that operate normally at high voltage levels. Ideally, PD identification can be easily done if there is a single source acting over the electrical asset during the measurement. However, in industrial environments, it is common to find the presence of multiple sources acting simultaneously, which hinders the identification process, due to sources of greater amplitude hiding the presence of other types of sources of lesser amplitude that could eventually be much more harmful to the insulation system. In this sense, the separation of PD through the use of clustering techniques allows individual source recognition once they have been clearly separated. This article describes the main clustering techniques that have been used over time to separate PD sources and electrical noise. The results obtained by the different authors in the utilization of each technique demonstrates good performance in terms of separation.

The increasing uptake of converter-interfaced generation (CIG) is changing power system dynamics, rendering them extremely dependent on fast and complex control systems. Regularly assessing the stability of these systems across a wide range of operating conditions is thus a critical task for ensuring secure operation. However, the simultaneous simulation of both fast and slow (electromechanical) phenomena, along with an increased number of critical operating conditions, pushes traditional dynamic security assessments (DSA) to their limits. While DSA has served its purpose well, it will not be tenable in future electricity systems with thousands of power electronic devices at different voltage levels on the grid. Therefore, reducing both human and computational efforts required for stability studies is more critical than ever. In response to these challenges, several advanced simulation techniques leveraging artificial intelligence (AI) have been proposed in recent years. AI techniques can handle the increased uncertainty and complexity of power systems by capturing the non-linear relationships between the system’s operational conditions and their stability without solving the set of algebraic-differential equations that model the system. Once these relationships are established, system stability can be promptly and accurately evaluated for a wide range of scenarios. While hundreds of research articles confirm that AI techniques are paving the way for fast stability assessments, many questions and issues must still be addressed, especially regarding the pertinence of studying specific types of stability with the existing AI-based methods and their application in real-world scenarios. In this context, this article presents a comprehensive review of AI-based techniques for stability assessments in power systems. Different AI technical implementations, such as learning algorithms and the generation and treatment of input data, are widely discussed and contextualized. Their practical applications, considering the type of stability, system under study, and type of applications, are also addressed. We review the ongoing research efforts and the AI-based techniques put forward thus far for DSA, contextualizing and interrelating them. We also discuss the advantages, limitations, challenges, and future trends of AI techniques for stability studies.