Electrical Engineering (EE) has substantial potential to advance Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 11 (Sustainable Cities and Communities). Yet, limited evidence exists on how EE students perceive the alignment of their academic training with the SDG agenda, especially in the Global South. This study provides a mixed-methods, datadriven assessment of SDG awareness, curricular integration, and perceived professional relevance among EE students (n = 51) at a private university in the Philippines. The methodology integrates quantitative survey analytics using weighted scoring, descriptive statistics, and visualizations with qualitative thematic coding of open-ended responses. Results reveal a triangular gap: awareness is moderate across most goals, curricular integration is uneven and biased toward energy and infrastructure themes, while perceived professional relevance is consistently high. Students highlight contributions to renewable systems, grid modernization, and sustainable infrastructure, while also identifying gaps in equity, biodiversity, and circular economy integration. Qualitative responses reinforce these findings, pointing to isolated project-based engagement but limited systemic curricular embedding. The study demonstrates how data-driven mixedmethods analysis can inform curriculum design, highlighting both technical strengths and social blind spots, and provides baseline evidence for aligning EE education with global sustainability imperatives.

Extreme weather, temperature fluctuations, and long-term changes in demand patterns are just a few of the previously unheard-of stresses that climate change brings to power systems. These occurrences jeopardize grid dependability, put traditional security measures to the test, and reveal weaknesses in operational procedures and infrastructure. This study examines the various ways that climate change affects the protection and dependability of power systems, highlighting the necessity of adaptive engineering techniques. Dynamic line rating (DLR), climate-integrated load forecasting, and adaptive protection schemes backed by machine learning and wide-area monitoring are important tactics. The review highlights important research gaps in probabilistic coordination, climate downscaling, and sensor trust while synthesizing recent developments. This work advances the development of climate-resilient power systems by coordinating technical innovation with resilience objectives.

Insulation coordination and surge protection devices (SPDs) are fundamental to the safety and reliability of modern power systems, especially in renewable energyintegrated grids. These systems protect critical electrical infrastructure from transient overvoltages, ensuring stable and sustainable operation. However, traditional methods, such as simulation-based analyses and manual fault detection, face challenges in scalability, adaptability, and efficiency, particularly in dynamic energy environments. Advancements in artificial intelligence (AI) and Internet of Things (IoT) technologies have introduced transformative capabilities for insulation coordination and SPDs. AI techniques, such as machine learning and neural networks, enable precise fault prediction, real-time monitoring, and adaptive control, significantly enhancing grid reliability. IoT-enabled SPDs further improve operational efficiency through predictive maintenance and continuous performance monitoring, aligning with sustainable energy goals. These innovations also address the needs of resilient infrastructure development, smart grid implementation, and urban sustainability. This paper explores the evolution of these systems, emphasizing the shift from traditional to AI-driven and hybrid approaches. By integrating advanced technologies, power systems can achieve enhanced reliability, efficiency, and resilience.

Power system protection is essential for maintaining the reliability and stability of electrical grids, ensuring continuous service, and preventing catastrophic failures. As power systems evolve to incorporate renewable energy and increasingly complex configurations, the role of Artificial Intelligence (AI) in enhancing protection mechanisms has become indispensable. This paper reviews the integration of AI in power system protection, highlighting its potential to improve fault detection, adaptive protection strategies, predictive maintenance, and real-time monitoring. AI techniques, including machine learning, deep learning, and expert systems, offer significant advancements in overcoming the limitations of traditional protection schemes. Furthermore, the integration of AI contributes to the development of resilient and sustainable infrastructure, supports innovation in intelligent urban systems, and enhances the reliability of modern power grids. Despite its promising potential, challenges such as data scarcity, model scalability, and real-time processing need to be addressed for effective implementation. This review synthesizes the current literature on AI applications in power system protection, comparing them with conventional methods, and provides information on future research directions and practical applications to improve energy reliability, sustainable urban development, and industrial innovation.

Determination of relay pairs from breakpoints in a given network is essential for maintaining the current protection system. Pairs of relays as primary or backup maintain the operation of the protection scheme within its zone of protection in tandem. All calculations and assumptions that are made in protection systems are based on breakpoints. It is inadequately documented that machine learning can be used to determine breakpoint sets and relay pairs. This paper presents the implementation of supervised decision tree machine learning approach for determining directional overcurrent relay breakpoint set in 3-bus networks. Using the one-hot encoding method, 45 input features are extracted from a matrix derived from 3-bus, 5-line network data. Bayesian optimization is used to further optimize the hyperparameters of each model for each of the break point set outputs. Tree diagrams are also provided here to assist in the interpretation of the decision rule resulting from the regression analysis. Experiment tests indicated that the proposed method shows promising results in determining breakpoint set in terms of RMSE.