In this paper, an obstacle avoidance and target tracking method for both indoor and outdoor mobile robots in dynamic environment is presented, and it aims to enhance autonomous navigation capability of such robots. In the proposed method, image processing and machine-learning approaches are considered. Since obstacles have differences in color and texture and in order to identify non-navigable areas, a monocular onboard camera is used to capture the road lanes by dealing with an image processing technique. The position of the robot with respect to road lane center during navigation is calculated on the basis of a proposed fuzzy logic rules set. In order to provide fast and robust computation, the Haar cascade classifier-based machine-learning technique has been exploited to detect the different sizes and shapes of the obstacles faced by the robot during its movement from source to destination. The dynamic model of a four-wheel mobile robot is initially developed using bond graph theory and then, the proposed obstacle-avoidance strategy is applied. The effectiveness and performance of the proposed method are tested under various simulation and experimentation scenarios. The behavior of the mobile robot for detecting and avoiding static obstacle for single and double road lane change environment is analyzed and discussed.

This study addresses the identification and optimization of parameters in the single-diode photovoltaic (PV) model (SDM), a widely adopted approach for simulating the electrical behavior of PV modules. The five key parameters, namely, the photo-generated current (I_ph), the reverse saturation current (I₀), the series resistance (R_s), the shunt resistance (R_sh), and the diode ideality factor (n), were initially estimated under Standard Test Conditions (STC) using manufacturer-provided data and the PV System toolbox in MATLAB. Based on these initial values, simulations of the current–voltage (I–V) and power–voltage (P–V) characteristics were conducted under real operating conditions using the Lambert W function and the Newton–Raphson method. An optimization procedure was then applied, combining the Newton–Raphson technique with two optimization algorithms: the Genetic Algorithm (GA) and the Levenberg–Marquardt (LM) method. The performance of each method was evaluated using three statistical indicators: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Among the tested approaches, the Genetic Algorithm achieved the highest accuracy, with an RMSE of 0.0359 A, a MAE of 0.0270 A, and an R² value of 0.9993. Finally, the analysis of environmental influence confirmed the significant impact of temperature and irradiance on module performance, particularly on the open-circuit voltage, maximum power output, and overall energy generation.

Wind turbine prognostics and health management (PHM) remains challenging because supervisory control and data acquisition (SCADA) measurements reflect both operating conditions and turbine health. Weather and control actions can modify monitored variables in ways that resemble early component degradation, particularly before failure when degradation may produce only weak and progressive changes in multivariate SCADA time series. This paper introduces a unified PHM framework that separates operating effects from health-related changes before diagnosis. The framework establishes the healthy behaviour expected for each monitored variable under current meteorological and operational conditions. Differences between observations and their expected healthy behaviour form multivariate sequences of contextualized deviations, whose temporal persistence and joint patterns are analysed to distinguish operating changes from emerging degradation and characterize component fault signatures. Because documented failures provide limited labelled information, previously unlabeled observations enter diagnostic learning only when assigned a health state with sufficiently high confidence. During diagnosis, uncertainty prevents a decision when health information is insufficient. The resulting evidence identifies the variables and physical components supporting each decision, linking early detection to interpretable maintenance information. Computational efficiency is assessed against diagnostic improvement. The framework is validated chronologically on real wind farm data with documented failure events, including direct comparison with existing degradation detection and early warning approaches evaluated on the same turbines and failures.