Characteristic Study of Solar Photovoltaic Array under Different Partial Shading Conditions

© The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Photovoltaic (PV) systems are frequently exposed to partial or complete shading phenomena. Partial shading has a profound impact on the performance of solar power generation. The operational performance of PV arrays under partial shading shows multiple maximum power point peaks, therefore it is challenging to identify the actual maximum power point. This paper investigates the impact of partial shading location on the output power of solar photovoltaic arrays with various configurations. Multiple photovoltaic strings, in both parallel and series configurations, are considered. Different random shading patterns are considered and analyzed to determine which configuration has higher maximum power point. The sensitivity of the partial shading can change according to the partial shading types, shading pattern, and the configuration used to connect all PV modules. Moreover, the study also investigates the output of the PV array with shading two random models, two consecutive models, and three random and consecutive modules. Experimental results validate the analysis and demonstrate the effect of various partial shading on the eficiency and performance of the PV system.Peer reviewe

Diagnosis of Stator Turn-to-Turn Fault and Stator Voltage Unbalance Fault Using ANFIS

An induction machine is a highly non-linear system that poses a great challenge because of its fault diagnosis due to the processing of large and complex data. The fault in an induction machine can lead to excessive downtimes that can lead to huge losses in terms of maintenance and production. This paper discusses the diagnosis of stator winding faults, which is one of the common faults in an induction machine. Several diagnostics techniques have been presented in the literature. Fault detection using traditional analytical methods are not always possible as this requires prior knowledge of the exact motor model. The motor models are also susceptible to inaccuracy due to parameter variations. This paper presents Adaptive Neuro-fuzzy Inference system (ANFIS) based fault diagnosis of induction motors. The distinction between the stator winding fault and supply unbalance is addressed in this paper. Experimental data is collected by shorting the turns of a health motor as well as creating unbalance in the stator voltage. The data is processed and fed to an ANFIS classifier that accurately identifies the faulted condition and unbalanced supply voltage conditions. The ANFIS provides almost 99% accurate and computationally efficient output in diagnosing the faults and unbalance conditions.DOI:http://dx.doi.org/10.11591/ijece.v3i1.185

Optimal Torque Allocation for All-Wheel-Drive Electric Vehicles Using a Reinforcement Learning Algorithm

A novel reinforcement learning-based algorithm is proposed in this paper for the optimal torque allocation among the four wheels of an all-wheel-drive (AWD) electric vehicle (EV) through a direct yaw moment control approach. A hierarchical structure was utilized for the control procedure, in which a linear quadratic regulator (LQR) controller is exploited for the high-level controller to generate yaw moments and a novel deep deterministic policy gradient (DDPG) algorithm is employed for the low-level controller. The DDPG agent possesses the ability to interact with the environment and learn to optimally split torque among four wheels. The vehicle is modeled via a nonlinear model with seven degrees of freedom (7 DOF), while the reference signals are generated by a bicycle model with two degrees of freedom (2 DOF). For enhanced precision in vehicle modeling, the tire model is characterized by the Pacejka Magic Formula (MF), which offers a rigorous and empirically validated representation of tire behavior. The proposed model is verified through a scenario of the response of the vehicle while circumnavigating a curve on a slippery road. The obtained results depict improved performance and enhanced dynamic stability compared to the conventional model with the average torque distribution method. Control over the yaw behavior and increased dynamic stability are achieved, while the understeer and oversteer are avoided. Index Terms—Direct yaw control, Electric vehicle, Reinforcement learning, Torque vectoring, Vehicle dynamics

Design of Advanced Atmospheric Water Vapor Differential Absorption Lidar (DIAL) Detection System

The measurement of atmospheric water vapor is very important for understanding the Earth's climate and water cycle. The lidar atmospheric sensing experiment (LASE) is an instrument designed and operated by the Langley Research Center for high precision water vapor measurements. The design details of a new water vapor lidar detection system that improves the measurement sensitivity of the LASE instrument by a factor of 10 are discussed. The new system consists of an advanced, very low noise, avalanche photodiode (APD) and a state-of-the-art signal processing circuit. The new low-power system is also compact and lightweight so that it would be suitable for space flight and unpiloted atmospheric vehicles (UAV) applications. The whole system is contained on one small printed circuit board (9 x 15 sq cm). The detection system is mounted at the focal plane of a lidar receiver telescope, and the digital output is read by a personal computer with a digital data acquisition card

A novel optimized neutrosophic k-means using genetic algorithm for skin lesion detection in dermoscopy images

This paper implemented a new skin lesion detection method based on the genetic algorithm (GA) for optimizing the neutrosophic set (NS) operation to reduce the indeterminacy on the dermoscopy images. Then, k-means clustering is applied to segment the skin lesion regions