Previous theoretical research efforts which were validated by experimental findings demonstrated the thermo-economic benefits of the hybrid concentrated photovoltaic-thermoelectric (CPV-TE) system over the stand-alone CPV. However, the operating conditions and TE material properties for maximum CPV-TE performance may differ from those required in a standalone thermoelectric module (TEM). For instance, a high-performing TEM requires TE materials with high Seebeck coefficients and electrical conductivities, and at the same time, low thermal conductivities (). Although it is difficult to attain these ideal conditions without complex material engineering, the low implies a high thermal resistance and temperature difference across the TEM which raises the PV backplate’s temperature in a hybrid CPV-TE operation. The increased PV temperature may reduce the overall system’s thermodynamic performance. To understand this phenomenon, a study is needed to guide researchers in choosing the best TE material for an optimal operation of a CPV-TE system. However, no prior research effort has been made to this effect. One method of finding the optimum TE material property is to parametrically vary one or more transport parameters until an optimum point is determined. However, this method is time-consuming and inefficient since a global optimum may not be found, especially when large incremental step sizes are used. This study provides a better way to solve this problem by using a multiobjective optimization genetic algorithm (MOGA) which is fast and reliable and ensures that the global optimum is obtained. After the optimization has been conducted, the best performing conditions for maximum CPV-TE energy, exergy, and environmental (3E) performance are selected using the technique for order performance by similarity to ideal solution (TOPSIS) decision algorithm. Finally, the optimization workflow is deployed for 7000 test cases generated from 10 features using the optimal machine learning (ML) algorithm. The result of the optimization chosen by the TOPSIS decision-making method generated an output power, exergy efficiency, and CO₂ saving of 44.6 W, 18.3%, and 0.17 g/day, respectively. Furthermore, among other ML algorithms, the Gaussian process regression was the most accurate in learning the CPV-TE performance dataset, although it required more computational effort than some algorithms like the linear regression model.

The induction of Electromagnetic Fields that are generated through the interaction of high-voltage transmission lines with neighboring buried metallic pipelines produce uncontrolled hazardous potential voltages, which can infringe safety limits. The paper presents the findings of the electromagnetic interference effects on water buried pipelines constructed within the vicinity of an Extra High-Voltage 380 kV transmission overhead line (OHL) in Riyadh-Salboukh route within the Saudi national grid power network. The presented case study showed that some segments of the buried pipelines under this line have not experienced voltages that exceeded the standard limits for the steady-state condition. However, in the event of L-G fault currents (short circuits), the pipelines experienced a voltage level that is above the local electric utility safety limits. Therefore, the work produced implemented the mitigation method of gradient control wires to reduce the potential voltages experienced by the pipelines to enforce the safety limit. The variation of wire resistance has been proven to be a feasible solution to reduce the excessive induced voltages. The comparison has shown that a 0.1 Ω is sufficient to maintain the safe limit for at least this line. These findings may vary depending on the OHL design and site topology.

The existing standard current-temperature calculations for overhead line (OHL) conductors have been adequate for conventional conductors and their operating temperatures. However, these calculations make assumptions and include simplifications about conductor geometry and aero-thermal-dynamics, introducing an error in the High-Temperature Low-Sag conductors operating temperatures. To quantify the error introduced by the shape of strands, the paper employs a Multi-Physics Finite Element Modeling approach that calculates the conjugate heat transfer for trapezoidal stranded OHL conductors. Furthermore, it proposes corrective equations to improve the accuracy of existing methods. The equations incorporate a new Nusselt number correlation for mixed convection and capture the surface area ignored by current calculations. The outer conductor geometry assumptions and the combined natural and forced convective cooling omission in the IEEE and CIGRE methods introduce an error at low (below 0.12 m/s) cross-flow wind speeds suggesting an underestimation of conductor temperature by up to 4%. In medium wind speeds, typically at 0.5 m/s–0.61 m/s, the standard methods overestimate the conductor temperature limiting its current-carrying capability. A 5% uprating for existing OHLs is potentially feasible, particularly for the trapezoidal stranded conductors, when removing the assumptions made in existing methods.

High-Temperature Low-Sag (HTLS) conductor technologies are often implemented, among many approaches, for optimizing the overhead lines’ utilization. The advancements of HTLS conductors are based on the differences in core and aluminum properties and installation procedures. This non-linear conductor behavior, although considered in the sag-tension calculations, by implementing the knee-point temperature, is often omitted from the aeolian vibration calculations. To understand the implications of the complex HTLS conductor structure on vibrations, a new calculation approach is proposed in this paper to quantify the effect of installation and operating conditions on the performance of any HTLS conductor. The proposed method conceives a prediction lay area that estimates where the actual field data are expected to lay and is validated with good accuracy against the field data from ACSR, ACCC, and GZTACSR. The developed model enables the prediction of non-homogeneous (composite) conductors and should be implemented at above knee-point operating temperatures with zero aluminum tensions.

Utilities aim to improve asset management strategies and enhance the utilization of their assets through low-risk reliable practices. Overhead lines and conductor designs have been evolving to increase systems’ power capacity and mechanical integrity, which have also extended asset lifetimes. Nevertheless, it is still challenging to predict a conductor’s fatigue stresses due to wind-induced vibrations that can help to estimate its useful life. A finite element model (FEM) has been established in COMSOL to study the free and forced wind-induced vibrations and the resultant fatigue on single multi-layer conductors considering their complex round and trapezoidal stranding patterns. The FEM analysis is based on multi-physics accounting for the conductor’s thermal and mechanical aspects as well as material and geometry properties. Consequently, the fatigue is quantified for both inter-layer and inter-wire interactions. The simulations show that free conductor vibrations are dictated by the conductor materials and tension distribution between the core and aluminum strands. The bigger the difference between the material properties of the core and aluminum, the lesser the conductor vibrations, especially when the aluminum becomes slack. In fact, a conductor equipped with carbon core (ACCC), has the best vibration resistance among other conductors with steel core (ACSR) and homogeneous (AAACs). Forced vibration simulations identified non-linear fatigue stresses for round and trapezoidal designs, which is more pronounced in larger conductor sizes. Larger trapezoidal ACSRs exhibit better fatigue resistance compared to smaller and round stranded AAACs.

Monitoring the battery state of health (SOH) has become increasingly important for electric vehicles (EVs), renewable storage systems, and consumer gadgets. It indicates the residual usable capacity and performance of a battery in relation to its original specifications. This information is crucial for the safety and performance enhancement of the overall system. This paper develops an explainable machine learning protocol with Bayesian optimization techniques trained on electrochemical impedance spectroscopy (EIS) data to predict battery SOH. Various robust ensemble algorithms, including HistGradientBoosting (HGB), Random Forest, AdaBoost, Extra Trees, Bagging, CatBoost, Decision Tree, LightGBM, Gradient Boost, and XGB, have been developed and fine-tuned for predicting battery health. Eight comprehensive metrics are employed to estimate the model’s performance rigorously: coefficient of determination (R²), mean squared error (MSE), median absolute error (medae), mean absolute error (MAE), correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and root mean squared error (RMSE). Bayesian optimization techniques were developed to optimize hyperparameters across all models, ensuring optimal implementation of each algorithm. Feature importance analysis was performed to thoroughly evaluate the models and assess the features with the most influence on battery health degradation. The comparison indicated that the GradientBoosting model outperformed others, achieving an MAE of 0.1041 and an R² of 0.9996. The findings suggest that Bayesian-optimized tree-based ensemble methods, particularly gradient boosting, excel at forecasting battery health status from electrochemical impedance spectroscopy data. This result offers an excellent opportunity for practical use in battery management systems that employ diverse industrial state-of-health assessment techniques to enhance battery longevity, contributing to sustainability initiatives for second-life lithium-ion batteries. This capability enables the recycling of vehicle batteries for application in static storage systems, which is environmentally advantageous and ensures continuity.