Hypertuned-YOLO for interpretable distribution power grid fault location based on EigenCAM

Ensuring the reliability of electrical distribution networks is a pressing concern, especially given the power outages due to surface contamination on insulating components. Surface contamination can elevate surface conductivity, thereby resulting in failures that can lead to power shutdowns. Addressing this challenge, this paper proposes an approach for real-time monitoring of electrical distribution grids to prevent such incidents. A hypertuned version of the you only look once (YOLO) model is tailored for this application. We refine the model's hyperparameters by integrating a genetic algorithm to maximize its detection performance. The EigenCAM technique enhances the visual interpretability of the model's outcomes, providing operators with actionable insights for maintenance and monitoring tasks. Benchmark tests reveal that the proposed Hypertuned-YOLO outperforms Detectron (Masked R-CNN), YOLOv5, and YOLOv7 models. The Hypertuned-YOLO achieves an F1-score of 0.867 and a [email protected] of 0.922, validating its robustness and efficacy

Optimized hybrid YOLOu-Quasi-ProtoPNet for insulators classification

To ensure the electrical power supply, inspections are frequently performed in the power grid. Nowadays, several inspections are conducted considering the use of aerial images since the grids might be in places that are difficult to access. The classification of the insulators' conditions recorded in inspections through computer vision is challenging, as object identification methods can have low performance because they are typically pre-trained for a generalized task. Here, a hybrid method called YOLOu-Quasi-ProtoPNet is proposed for the detection and classification of failed insulators. This model is trained from scratch, using a personalized ultra-large version of YOLOv5 for insulator detection and the optimized Quasi-ProtoPNet model for classification. For the optimization of the Quasi-ProtoPNet structure, the backbones VGG-16, VGG-19, ResNet-34, ResNet-152, DenseNet-121, and DenseNet-161 are evaluated. The F1-score of 0.95165 was achieved using the proposed approach (based on DenseNet-161) which outperforms models of the same class such as the Semi-ProtoPNet, Ps-ProtoPNet, Gen-ProtoPNet, NP-ProtoPNet, and the standard ProtoPNet for the classification task

Optimized hybrid ensemble learning approaches applied to very short-term load forecasting

The significance of accurate short-term load forecasting (STLF) for modern power systems’ efficient and secure operation is paramount. This task is intricate due to cyclicity, non-stationarity, seasonality, and nonlinear power consumption time series data characteristics. The rise of data accessibility in the power industry has paved the way for machine learning (ML) models, which show the potential to enhance STLF accuracy. This paper presents a novel hybrid ML model combining Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGBoost), k-Nearest Neighbors (kNN), and Support Vector Regression (SVR), examining both standalone and integrated, coupled with signal decomposition techniques like STL, EMD, EEMD, CEEMDAN, and EWT. Through Automated Machine Learning (AutoML), these models are integrated and their hyperparameters optimized, predicting each load signal component using data from two sources: The National Operator of Electric System (ONS) and the Independent System Operators New England (ISO-NE), boosting prediction capacity. For the 2019 ONS dataset, combining EWT and XGBoost yielded the best results for very short-term load forecasting (VSTLF) with an RMSE of 1,931.8 MW, MAE of 1,564.9 MW, and MAPE of 2.54%. These findings highlight the necessity for diverse approaches to each VSTLF problem, emphasizing the adaptability and strength of ML models combined with signal decomposition techniques

Enhancing hydroelectric inflow prediction in the Brazilian power system: A comparative analysis of machine learning models and hyperparameter optimization for decision support

Electricity generation in Brazil heavily depends on hydroelectric power, making it vulnerable to fluctuations due to its reliance on weather patterns. Accurately forecasting water inflow into hydroelectric plants is vital for the National Electric System Operator to make decisions regarding the monthly scheduling and operation of the power system. In this paper, an evaluation of predicted flows for a 14-day horizon are evaluated for the Tucuruíhydroelectric plant, located in the Tocantins river in the North of Brazil. The temporal fusion transformer (TFT), long short-term memory (LSTM), and temporal convolutional networks (TCN) are compared. The findings demonstrate that the TFT is a more suitable alternative than LSTM and TCN models for predicting inflows for the next 14 days. The TFT model is hypertuned by Optuna to achieve an optimized structure (h-TFT). The h-TFT had a mean absolute percentage error of 13.1 and a Nash–Sutcliffe of 0.96, outperforming its initial version and even the bidirectional LSTM model in benchmarking. Based on the error results, h-TFT showed promise for flow forecasting and provides insights into decision-making processes in the Brazilian electricity sector

Hybrid-YOLO for classification of insulators defects in transmission lines based on UAV

Transmission power lines are essential to supply electrical energy to consumption centers. Keeping a reliable transmission system requires the early identification of faults. Image-based inspection of transmission lines makes fault identification faster and more accessible since it can be carried out using unmanned aerial vehicles (UAVs) in hard-to-reach places. In this paper, it is proposed to use a hybrid version of the You Only Look Once (YOLO) using ResNet-18 classifier, for power system inspection based on real images of failed components recorded by UAVs. This work assumed a dataset including 1,593 power grid inspection pictures for a supervised training. Based on YOLOv5x, with an mAP of 0.99262, the proposed method was superior to YOLOv5n, YOLOv5s, YOLOv5m, and YOLOv5l for object detection. For the multiclassification task, with an F1_score result of 0.96216, the proposed Hybrid-YOLO was superior to distinct architectures as the VGG-11, VGG-13, VGG-16, VGG-19, ResNet-34, ResNet-50, ResNet-152, DenseNet-121, DenseNet-161, DenseNet-169, DenseNet-201, YOLOv5, YOLOv6, and YOLOv7 versions

Evaluation of visible contamination on power grid insulators using convolutional neural networks

The contamination of insulators increases their surface conductivity, resulting in a higher chance of shutdowns occurring. To measure contamination, equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) are used. In this paper, the VGG-11, VGG-13, VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-152, DenseNet-121, DenseNet-161, DenseNet-169, and DenseNet-201 convolutional neural networks (CNNs) were considered to classify the visible contamination of pin-type distribution power grid insulators. The NSDD presents more visual variation than ESDD when artificial contamination is evaluated. Comparing the CNNs, the ResNet-50 had the best performance for classifying visible contamination using unbalanced data with an accuracy of 99.242% and an F1-score of 0.97436, respectively. In benchmarking, the ResNet-50 outperformed well-established classifiers such as the multilayer perceptron, support vector machine, k-nearest neighbors, decision tree, ensemble bagged trees, and quadratic discriminant

Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting

A wind power forecast is an useful support tool for planning and operating wind farm production, facilitating decisions regarding maintenance and load share. This paper presents an evaluation of a cooperative method, which uses a time series pre-processing strategy, artificial neural networks, and multi-objective optimization to forecast wind power generation. The proposed approach also evaluates the accuracy of the hybridization of variational mode decomposition (VMD) with bootstrap aggregation and extreme learning machine model for forecasting very short and short-term wind power generation. Multi-objective strategy aggregates the VMD-based components and obtains the final forecasting. The results imply that the presented algorithm has better forecasting performance compared to bootstrap stacking, other machine learning approaches, and statistical models, with a reduction of root mean squared error of approximately 12.76%, 25.25%, 31.91%, and 34.76%, respectively, for out-of-sample predictions. The forecasting results indicate that the presented approach can improve generalizability and accuracy in cases of very short and short-term wind energy generation