In power systems, maintaining transient stability is crucial to avoid unanticipated blackouts. The role of Transient Stability Assessment (TSA) is vital for quickly identifying and promptly addressing instabilities. TSA facilitates rapid reactions to serious fault conditions. This paper pioneers the integrated comparison of two distinct methodologies-Maximal Lyapunov Exponent (MLE) methods and Convolutional Neural Networks (CNN)-in a single unified framework for transient stability assessment in power systems, uniquely evaluating their accuracy and reliability for TSA. The CNN-based method uses measured time series data from voltage magnitude, phase angle, and frequency measurements at generator buses, while the MLE approach utilizes both phase angles and frequency of generator buses. This paper provides a qualitative and quantitative comparison of the performance and accuracy of MLE and CNN. This research utilizes the same case studies conducted on the Nordic32 system for both MLE and CNN to ensure robust, unbiased comparisons and promote interdisciplinary research, aligning with current trends in AI integration in power systems.

In this paper, a classifier based early warning system is designed, trained and tested based on time-series of Phasor Measurement Unit (PMU) measurements at all buses in a power system. The classifier is based on a novel combination of Graph Attention Networks and Long Short-Term memories, and is trained to label power system data in the form of captured windows of PMU measurements. These labels are then used to provide early warning for transient instability. The classifier is trained and tested data from simulations of the Nordic44 test system, and includes extensive topological variations under two different load levels. It is found that accurate early warnings can be provided, but the quality of prediction is highly dependent on specific power system characteristics, such as how quickly the power system responds to transient disturbances.

To increase the utilisation rate of the power system and accelerate electrification while providing a high degree of security and reliability, System Integrity Protection Schemes (SIPS) are of great importance. SIPS functions are automatic remedial actions, detecting abnormal conditions or contingencies in the system and taking control action to mitigate these conditions. Design, implementation, maintenance and coordination of SIPS are all important aspects for desired operation. However, different actors have chosen different approaches to using SIPS for capacity enhancement, and there are discrepancies in how capacity is valued in relation to for example complexity, reliability and risk. Additionally, definitions often vary between countries. This paper reports on a joint survey and interview study on SIPS with stakeholders and experts in the Nordic countries - including TSOs, DSOs and industry. Combined with a literature review, a comparison and analysis of how SIPS are used in the Nordics is performed, particularly in relation to ENTSO-E capacity allocation.

In this paper, we present a method for real-time transient stability prediction based on convolutional neural networks (CNN) using a novel CNN architecture compared to previous works on the topic. The method is based on monitoring voltage phasor and frequency measurements at the generator terminal buses, which are presented to the neural network in the form of three channel RGB images, taken as a sliding window. The sliding window consists of the most current set of measurements, as well as the four most recent historical measurements for a total of five time steps. When deployed, the neural network is continuously fed real-time measurements, thereby functioning as a real-time stability monitoring system. The neural network is trained and tested on simulated data of the Nordic32 system using five-fold cross-validation. The trained classifier is able to accurately predict instability in all but one case from the test set. In the successfully identified unstable cases, instability was predicted five cycles after fault clearance.

Distribution level outages generally affect fewer customers than regional or transmission level outages. However, as global temperatures continue to rise, the radial topology and overhead lines typical at this level make it particularly vulnerable to High Impact Low Probability weather events. A Machine Learning model is therefore proposed that uses Multinomial Logistic Regression (MLR) to predict the likelihood of an outage given the weather conditions and the composition of the Distribution System Operator (DSO). The model is tuned by using a traditional binary classification problem as ground truth, but is evaluated based on its probability distributions near outage events. Results show a greater classification confidence for true outages than false outages as well as a probability distribution that is skewed towards actual outage events.