The regulation of voltage in transmission networks is becoming increasingly complex due to the dynamic behavior of modern power systems and the growing penetration of renewable generation. This study presents a comparative analysis of three artificial intelligence approaches—Deep Q-Learning (DQL), Genetic Algorithms (GAs), and Particle Swarm Optimization (PSO)—for training agents capable of performing autonomous voltage control. A unified neural architecture was implemented and tested on the IEEE 30-bus system, where the agent was tasked with adjusting reactive power set points and transformer tap positions to maintain voltages within secure operating limits under a range of load conditions and contingencies. The experiments were carried out using the GridCal simulation environment, and performance was assessed through multiple indicators, including convergence rate, action efficiency, and cumulative reward. Quantitative results demonstrate that PSO achieved 3% higher cumulative rewards compared to GA and 5% higher than DQL, while requiring 8% fewer actions to stabilize the system. GA showed intermediate performance with 6% faster initial convergence than DQL but 4% more variable results than PSO. DQL demonstrated consistent learning progression throughout training, though it required approximately 12% more episodes to achieve similar performance levels. The quasi-dynamic validation confirmed PSO’s advantages over conventional AVR-based strategies, achieving voltage stabilization approximately 15% faster. These findings underscore the potential of neuroevolutionary algorithms as competitive alternatives for advanced voltage regulation in smart grids and point to promising research avenues such as topology optimization, hybrid metaheuristics, and federated learning for scalable deployment in distributed power systems.

Data preprocessing is the initial and utmost critical phase of wind resource assessment and wind power curve performance analysis. Without ensuring high quality and site representative data availability, an evaluation for the wind resource potential of a wind farm site means investment decision making involving a great deal of uncertainty. The current practice in the field is to use fix rules and via data scanning by manual labouring of data field experts. Although rule-based (if-else, interval-based) prefiltering applications can be found in some commercial software, and these rules are also limited for addressing the needed preprocessing requirements fully. Therefore, this process is time consuming and causes inefficient resource allocation considering the manual scanning process performed by data scientists & experts. In this study, reviewed data by experts are used as data with correct labels, and then efficient classifiers are generated by applying machine learning algorithms to unfiltered and filtered data. It is reported that tree-based classifiers are performing better.

Corrective congestion management in a transmission network with embedded HVDC requires coordinating continuous set-points and discrete topological actions under the full nonlinear AC power flow constraints, a mixed-integer nonlinear problem for which no polynomial-time solution is known. This paper formulates the task as a Markov decision process and solves it with a hybrid Soft Actor-Critic agent that handles both action types under one maximum-entropy objective: a squashed-Gaussian head for redispatch and the VSC-HVDC set-point, and two categorical heads trained through the Gumbel-Softmax Straight-Through estimator for generator and line disconnection. Each action subspace carries its own entropy temperature rather than a shared coefficient, and the behaviour of the three is measured. The state is 906-dimensional and covers branch loadings and reactive flows, bus voltages and angles, and the HVDC set-point. On the IEEE 118-bus system extended with a VSC-HVDClink,theagentclears98.6%ofa500-scenariointact-gridbatch and 97.4% of a 500-scenario batch that includes up to three simultaneous branch outages, in a mean of 1.5 and 1.7 corrective steps respectively, with an independent AC verification power flow confirming the resulting states. Inference requires a single forward pass, which makes the policy usable inside an operational decision cycle.

This study evaluates the impact of measurement campaign duration on wind resource characterization using three MCP (Measure–Correlate–Predict) models: Total Least Squares (TLS), Multiple Linear Regression (LR), and Quantile Gradient Boosting (GB). The analysis is based on data from 30 meteorological masts (nine primary and twenty-one secondary masts) installed worldwide across different terrains, with up to twenty-seven months of concurrent wind measurements between primary and secondary masts. Fixed campaign durations of 3, 4, 5, 6, 9, and 12 months were simulated using moving intervals to quantify the effect of measurement length on mean wind speed estimation. This working framework also serves to represent conditions typical of campaigns where LIDAR systems are used to complement meteorological mast deployments, as LIDAR units generally operate for shorter periods due to frequent relocation as part of broader measurement strategies. Wind speed estimation was assessed through metrics such as Mean Absolute Error (MAE), relative uncertainty, and monthly uncertainty reduction, taking into account terrain complexity and correlation coefficient ($R^2$) between masts. Results indicate that extending the measurement period improves the accuracy and consistency of wind speed estimates, with significant reductions in uncertainty observed after six months. Across sites, the average monthly uncertainty reduction ranges from 0.13% to 0.41% of the mean wind speed per additional month of measurements, depending on terrain complexity and inter-mast correlation. Linear models (TLS and LR) consistently show better performance in terms of error and uncertainty reduction compared to GB. Based on an extensive and diverse MCP dataset covering multiple terrains and locations, this study provides empirically derived monthly uncertainty-reduction benchmarks for campaign-length optimisation under different site conditions, contributing to more reliable wind resource assessments and, consequently, energy yield estimates.