Transformers are critical assets for power system reliability, as they connect different voltage levels across generation, transmission, and distribution. Their failure can lead to significant impacts on multiple aspects. Given the aging transformer fleet, supply chain challenges, and constrained investment capacity, the adoption of risk-based strategies is essential to support long-term maintenance planning and investment. This paper proposes a multicriteria framework to assess the probability and impact of transformer failure, enabling a more comprehensive and data-driven risk evaluation. The method was applied to a sample fleet, enabling the identification and prioritization of the most critical units through a risk plot. The framework enhances asset management by identifying critical units within a transformer fleet, promoting efficiency, reliability, and long-term planning based on objective risk indicators.

Power transformers are critical assets for system reliability, as they connect different voltage levels across generation, transmission, and distribution. Their failure can lead to significant impact in multiple aspects such as financial, social, and regulatory. In recent years, rising costs and extended lead times for replacements have posed additional challenges for utilities, increasing the need to extend the service life of the existing fleet. Continuous asset monitoring has emerged as a key enabler of data-driven asset management. By integrating online and offline data along with a comprehensive risk assessment, utilities can better evaluate a transformer fleet, optimize maintenance strategies, and support strategic decisions regarding repair, replacement, or investment in monitoring technologies. This work discusses the parameters used for power transformer condition and risk assessment and presents a framework for a tool designed to support resource allocation and investment prioritization in power transformer fleet asset management.

In this article, the primary characteristics of a Genetic Algorithm-based optimization toolbox, referred to as GAtoolbox, are thoroughly presented. This toolbox was specifically developed to serve as an educational resource for undergraduate and postgraduate courses focused on optimization techniques. Recognizing the widespread use of Matlab in both academic and research institutions, the GAtoolbox was implemented within the Matlab scientific programming environment, ensuring accessibility and ease of integration for students and professionals alike. After a concise theoretical overview of Genetic Algorithms, providing the necessary background for understanding the principles and mechanics behind the toolbox, the general architecture and key components of the GAtoolbox are detailed, highlighting its functionality and versatility in solving a range of optimization problems. A practical implementation example is then presented, where a step-by-step guide is provided to demonstrate how to utilize the GAtoolbox to address a specific optimization challenge, showcasing its potential as a powerful teaching tool in optimization courses.