Automatic Discontinuity Classification of Wind-turbine Blades Using A-scan-based Convolutional Neural Network

Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems. However, traditional intelligent methods limit the use of the physical structures and data information of power networks. To this end, this study proposes a fault diagnostic model for distribution systems based on deep graph learning. This model considers the physical structure of the power network as a significant constraint during model training, which endows the model with stronger information perception to resist abnormal data input and unknown application conditions. In addition, a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability. This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults. In addition, a multi-task learning framework is constructed for fault location and fault type analysis, which improves the performance and generalization ability of the model. The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model. Finally, different fault conditions, topological changes, and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model. Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods.

“…The space-based GCN primarily originates from the convolutional operation of traditional CNNs [27]. A brief introduction to spectral graph theory is presented in [28].…”

Section: A Spectral Convolution On Graphsmentioning

confidence: 99%

Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods

Chen

et al. 2023

“…MLP, DBN, and CNN construct neural networks using dense layers, restricted Boltzmann machines, and convolutional layers, respectively, to represent the relationship between power loads and dispatching strategies. Compared with MLP and DBN, CNN has higher accuracy in reactive power optimization, since the convolutional layers have more powerful feature extraction ability than dense layers and restricted Boltzmann machines [17]. However, the pooling operation of CNN will lose rich information of power loads, which restricts its accuracy to be further improved [18].…”

Section: Nomenclature δ Ijmentioning

confidence: 99%

Data-driven Reactive Power Optimization for Distribution Networks Using Capsule Networks

Liao

Chen

Liu

et al. 2022

The construction of advanced metering infrastructure and the rapid evolution of artificial intelligence bring opportunities to quickly searching for the optimal dispatching strategy for reactive power optimization. This can be realized by mining existing prior knowledge and massive data without explicitly constructing physical models. Therefore, a novel datadriven approach is proposed for reactive power optimization of distribution networks using capsule networks (CapsNet). The convolutional layers with strong feature extraction ability are used to project the power loads to the feature space to realize the automatic extraction of key features. Furthermore, the complex relationship between input features and dispatching strategies is captured accurately by capsule layers. The back propagation algorithm is utilized to complete the training process of the CapsNet. Case studies show that the accuracy and robustness of the CapsNet are better than those of popular baselines (e.g., convolutional neural network, multi-layer perceptron, and casebased reasoning). Besides, the computing time is much lower than that of traditional heuristic methods such as genetic algorithm, which can meet the real-time demand of reactive power optimization in distribution networks.

“…Its emergence has greatly promoted the development of artificial intelligence. Because of its powerful feature extraction capabilities, CNN has been widely used in various fields such as fault diagnosis, object detection, speech recognition, and semantic segmentation [28]. Therefore, CNN is chosen to build the encoder, decoder, and discriminator.…”

Section: A Encoder-decoder-discriminator Pipelinementioning

confidence: 99%

Data-driven Missing Data Imputation for Wind Farms Using Context Encoder

Liao

Bak‐Jensen

Pillai

et al. 2022

High-quality datasets are of paramount importance for the operation and planning of wind farms. However, the datasets collected by the supervisory control and data acquisition (SCADA) system may contain missing data due to various factors such as sensor failure and communication congestion. In this paper, a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder (CE), which consists of an encoder, a decoder, and a discriminator. Through deep convolutional neural networks, the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly. The proposed method can not only fully use the surrounding context information by the reconstructed loss, but also make filling data look real by the adversarial loss. In addition, the correlation among multiple missing attributes is taken into account by adjusting the format of input data. The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks, large valleys, and fast ramps. Moreover, the CE shows stronger generalization ability than traditional methods such as auto-encoder, K-means, k-nearest neighbor, back propagation neural network, cubic interpolation, and conditional generative adversarial network for different missing data scales.