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Skidding is a surface hazard for mobile robots' navigation and traction control systems when operating in outdoor environments and uneven terrains due to the wheel–terrain interaction. It could lead to large trajectory tracking errors, losing the robot's controllability, and mission failure occurring. Despite research in this field, the development of a real‐world feasible in situ skid estimation system with the capability of operating in harsh and unforeseen environments using low‐cost/power and ease of integrating sensors is still an open problem in terramechanics. This paper presents a novel velocity‐based definition for skidding that enables a real‐world feasible estimation for the mobile robot's undesired skidding at the vehicle‐level in outdoor environments. The proposed technique estimates the undesired skidding using a combination of two proprioceptive sensors (e.g., inertial measurement unit and wheel encoder) and deep learning. The practicality of a velocity‐based definition and the performance of the proposed undesired skid estimation technique are evaluated experimentally in various outdoor terrains. The results show that the proposed technique performs with less than 11.79 mm/s mean absolute error and estimates the direction of undesired skidding with approximately 98% accuracy. [ABSTRACT FROM AUTHOR]

Accurate power flow analysis is essential to system operators for planning, design, analysis, and control of power networks. The accuracy of power flow analysis can be increased significantly by including the weather-dependent characteristics of the system. In this paper, a novel weather-dependent power flow algorithm is proposed and studied in comparison to the very well-known conventional power flow. The weather-dependent power flow algorithm is novel in the sense that it is explicitly parameterized in terms of typically available measured weather parameters (ambient temperature, solar irradiance, wind speed, and wind angle) to perform a fully coupled weather-dependent power flow analysis. Using this algorithm, the IEEE 30-bus power network was studied utilizing real weather data by performing three year-long steady-state time-series power flow analyses. The study demonstrates that the proposed weather-dependent power flow algorithm accurately estimates the branch resistances, the system states (current and voltages), the power losses, the branch flows, and the branch loadings. These are made possible because the proposed algorithm accurately estimates branch conductor temperature due to the coupling of power flow with the nonlinear heat balance model. An analysis of the computational complexity of the proposed algorithm is also presented. [ABSTRACT FROM AUTHOR]