Photovoltaic (PV) systems are vulnerable to failures due to undesired conditions. Faults can occur unpredictably and remain challenging to recognize. In this study, inverter fault, voltage sag, partial shading, and open circuit were considered as the PV faults. However, the faults become eight target classes since each fault considers two different conditions: intermediate power point tracking and maximum power point tracking. Machine learning techniques have recently become the most interesting methods for solving PV failures. This paper proposes a random forest and modified independent component analysis (RF-MICA) to detect the occurrence of PV faults. The MICA was developed for dimensionality reduction for enhanced performance, whereas previous studies only focused on principal component analysis. Two strategies are introduced to address an imbalanced dataset: the synthetic minority oversampling technique as scenario 1 and random undersampling as scenario 2 for oversampling and undersampling methods, respectively. These techniques are essential for investigation because PV fault data can become unbalanced, and they have not been fully addressed in previous studies. The results indicated that the proposed model performs better than other algorithms, with high accuracy and low computational time. RF-MICA yielded accuracy rates of 99.88% and 99.43% for scenarios 1 and 2, respectively.
A photovoltaic (PV) system is one of the renewable energy resources that can help in meeting the ever-increasing energy demand. However, installation of PV systems is prone to faults that can occur unpredictably and remain challenging to detect. Major PV faults that can occur are line-line and open circuits faults, and if they are not addressed appropriately and timely, they may lead to serious problems in the PV system. To solve this problem, this study proposes a voting-based ensemble learning algorithm with linear regression, decision tree, and support vector machine (EL-VLR-DT-SVM) for PV fault detection and diagnosis. The data acquisition is performed for different weather conditions to trigger the nonlinear nature of the PV system characteristics. The voltage-current characteristics are used as input data. The dataset is studied for a deeper understanding, and pre-processing before feeding it to the EL-VLR-DT-SVM. In the pre-processing step, data are normalized to obtain more feature space, making it easy for the proposed algorithm to discriminate between healthy and faulty conditions. To verify the proposed method, it is compared with other algorithms in terms of accuracy, precision, recall, and F-1 score. The results show that the proposed EL-VLR-DT-SVM algorithm outperforms the other algorithms.
Real-time mission planning for Un-manned Aerial Vehicles (UAVs) is an important application that requires implementation of computer vision algorithms such as LocallyNormalized Cross Correlation (LNCC), with greater accuracy and throughput. Although the LNCC algorithm is the prime choice for image matching, its real time hardware implementation has proved to be a real challenge for hardware designers mainly due to its computational complexity. In this paper, a novel architecture for real time implementation of this scheme using Field Programmable Gate Array (FPGA) platform is proposed. In the proposed design, multiple computations are concurrently executed by incorporating parallelism in architectures through 4 FPGAs. The FPGAs are efficiently multiple synchronized along with efficient memory architecture to optimize match point results in minimum possible time. The exploration of the parallelism and reusability of results made it possible to meet timing constraints imposed in this research.
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