Effective reduction of power losses in radial distribution systems can be achieved through strategic network reconfiguration—disconnecting existing branches and connecting alternative ones—and through optimal siting of distributed generation (DG) units, in which load characteristics play a decisive role. Since electrical loads vary with voltage levels and differ by consumer category, they can be represented by a combination of fixed-impedance, power, and current components. Identifying the proportion of these elements for each load category enables the formulation of more adaptive and realistic models for feeder reconfiguration and DG allocation. In this context, the current work introduces a mathematical framework that explicitly links load composition with consumer categories and reformulates a nonlinear polynomial load model into a tractable quadratic form suitable for linear solvers. The developed framework is evaluated on standard 16- and 118-bus distribution systems. For the 16-bus network, the flexible formulation achieved power losses of 66.4–67.08 kW, which closely match the exact nonlinear model results (66.9–67.04 kW) while reducing computation time from about 2000–2230 s to nearly 1.2 s. For the 118-bus system, losses of 410.05–470.03 kW were obtained, compared with 409.38–471.10 kW for the exact model, with processing time reduced from approximately 58 000–157 000 s to 26–68 s. According to the results, the suggested framework preserves high solution accuracy while providing orders-of-magnitude improvements in computational efficiency. The developed approach, therefore, offers a practical and flexible tool for real-time distribution network reconfiguration and DG planning under voltage-dependent and category-based load modeling.
Meisam Mahdavi, Abdullah G. Alharbi, A. BaQais et al.· IEEE Canadian Journal of Ele...· 0 citations
The increasing demand for reliability in wind turbine systems makes early bearing fault detection under variable-speed conditions a persistent challenge. This paper proposes a hybrid methodology for classifying degradation stages and estimating a relative RUL-related degradation indicator for bearings by integrating synthetic data modeling, feature selection, and a combined unsupervised–supervised learning approach. Synthetic vibration signals are generated through logistic-curve interpolation with pink noise, enabling controlled degradation simulation. Features from time, frequency, and time–frequency domains were ranked using Mutual Information, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was employed to identify progressive wear stages. Cluster centers serve as anchors for mapping degradation into RUL percentages, while classification ensures stage consistency. Experimental results demonstrate six well-defined clusters for inner-race faults (Silhouette 0.5190), three moderate clusters for outer-race faults (0.2339), and overlapping patterns for rolling element faults (–0.1527), with zero RUL deviation in the best case. The proposed framework combines real and synthetic data to enhance generalization while reducing computational cost, offering a reliable and scalable solution for predictive maintenance and assessment of degradation progression in wind turbine bearings.
Gustavo Gomes Do Valle, Benjamin Soudhan, Meisam Mahdavi et al.· IEEE Access· 0 citations
Active distribution networks (ADNs) constitute a vital component of modern power systems, integrating multiple distributed generation (DG) units. Although DG integration improves efficiency by decreasing power losses, its deployment is frequently limited by technical constraints and financial considerations. Traditionally, network reconfiguration has been applied to loss optimization in distribution grids; however, in radial networks, its effectiveness is restricted because of the limited flexibility of power flow paths. Consequently, the combined application of DG placement and reconfiguration offers a more effective strategy for reducing active losses in contemporary distribution systems. During such optimization, it is crucial to include uncertainties in load and renewable generation to ensure realistic and stable results. In this context, the present study introduces a robust optimization framework designed to maintain reliable performance despite variations in generation and consumption. The proposed method ensures that optimal DG allocation and network configuration remain stable even under moderate fluctuations in system conditions. To evaluate its performance, both robust and deterministic formulations were run for a 70-bus system, and the obtained outcomes were compared with those reported in a reference study. The outcomes prove that the proposed approach significantly reduces daily energy losses under normal and uncertain operating conditions compared with the reference method.
Meisam Mahdavi, Abdullah G. Alharbi, A. BaQais et al.· IEEE Canadian Journal of Ele...· 0 citations
