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In practical engineering, electricity theft detection is usually performed on highly imbalanced datasets (i.e., the number of fraudulent samples is much smaller than the benign ones), which limits the accuracy of the classifier. To alleviate the data imbalance problem, this article proposes simple data augmentation tricks (SDAT) to boost performance on electricity theft detection tasks. SDAT includes five simple but powerful operations: adding noises to electricity consumption readings, drifting values of electricity consumption readings, quantizing electricity consumption readings to a level set, adding a fixed value to electricity consumption readings, and adding changeable values to electricity consumption readings. In addition, eight potential tricks are also mentioned. Numerical simulations are conducted on a real-world dataset. The simulation results show that SDAT can significantly boost the performance of different classifiers, especially for small datasets. Besides, specific suggestions on how to select parameters of SDAT are provided for its migration use to other datasets., QC 20230824

Scenario generations of cooling, heating, and power loads are of great significance for the economic operation and stability analysis of integrated energy systems. In this paper, a novel deep generative network is proposed to model cooling, heating, and power load curves based on generative moment matching networks (GMMNs) where an auto-encoder transforms high-dimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples. After training the model, the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN. Unlike the explicit density models, the proposed GMMN does not need to artificially assume the probability distribution of the load curves, which leads to stronger universality. The simulation results show that the GMMN not only fits the probability distribution of multi-class load curves very well, but also accurately captures the shape (e.g., large peaks, fast ramps, and fluctuation), frequency-domain characteristics, and temporal-spatial correlations of cooling, heating, and power loads. Furthermore, the energy consumption of generated samples closely resembles that of real samples., QC 20230215

Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems. This paper proposes a deep generative network based method to model time-series curves, e.g., power generation curves and load curves, of renewable energy sources and loads based on implicit maximum likelihood estimations (IMLEs), which can generate realistic scenarios with similar patterns as real ones. After training the model, any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of IMLEs. The proposed approach does not require any model assumptions or prior knowledge of the form in the likelihood function being made during the training process, which leads to stronger applicability than explicit density model based methods. The extensive experiments show that the IMLEs accurately capture the complex shapes, frequency-domain characteristics, probability distributions, and correlations of renewable energy sources and loads. Moreover, the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures., QC 20230314

The penetrations of wind power and photovoltaic system in distribution network are increasing year by year. The randomness and fluctuation of their output powers bring great challenges to the planning and operation of distribution networks. Aimed at the uncertainty of output power of renewable energy, a stochastic scenarios generation method for photovoltaic and wind power based on generative moment matching network (GMMN) is proposed. In this method, the maximum mean discrepancy is adopted as the loss function of the generator, and the auto-encoder is adopted to reduce the dimension of the generated stochastic scenarios, so as to solve the low dimensional manifold problem of the high-dimensional power curves. According to the characteristics of the power curves, the network structure suitable for the stochastic scenarios generation of renewable energy is designed. The effectiveness and adaptability of the proposed method are verified by real data. The simulation results show that the proposed GMMN can not only simulate the shape characteristics, probability distribution characteristics, fluctuation, and spatial-temporal correlation of the photovoltaic and wind power curves, but also has universality. It can be applied to the random field generation tasks for different generation units only by adjusting the structure and parameters of the network., QC 20221027

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., QC 20220907

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNN structures, e.g., graph convolutional networks, are summarized. Key applications in power systems such as fault scenario application, time-series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed., QC 20220506

The construction of advanced metering infrastructure and the rapid development of deep learning technology make it possible to quickly find the optimal strategy for reactive power optimization by mining historical data and prior knowledge instead of relying on physical models. Therefore, a method for reactive power optimization based on graph convolutional network (GCN) is proposed. Through representing the topology information between nodes in distribution network with the adjacency matrix, the proposed algorithm can effectively mine the correlation between the node loads, mapping the complex nonlinear relationship between the power equipment status and the load data with the deep graph convolutional architecture. The simulation results show that the accuracy and robustness of the GCN are better than that of the existing data-driven methods such as the convolutional neural network, the multi-layer perceptron and the case-based reasoning. Its solution time is much shorter than the traditional heuristic algorithm, which can meet the real-time demand of the reactive power optimization in distribution networks., QC 20220321

Traditional methods for transformer fault diagnosis are difficult to fit the complex nonlinear relationship between dissolved gas and fault type accurately, and their diagnosis accuracy is limited. To improve the accuracy of transformer fault diagnosis, a method of transformer fault diagnosis based on capsule network (CapsNet) is proposed. The convolutional layers with strong feature extraction ability are used to map the dissolved gas data into the feature space to realize the automatic extraction of key features. Furthermore, the mapping relationship between feature and fault type is established by primary capsule layers and digital capsule layers. Dynamic routing algorithm and back propagation algorithm are used to complete the training process of CapsNet. The simulation results show that the fault diagnosis performance of CapsNet is better than that of the traditional methods such as convolutional neural network, multi-layer perceptron, support vector machine, extreme gradient boosting tree, and light gradient lifting machine in different input characteristics and data scales, which can effectively meet the needs of diagnosis accuracy and provide guidance for the operation pattern of relay. The research can provide references for the fault diagnosis of the transformer., QC 20220118

Existing methods for transformer fault diagnosis either train a classifier to fit the relationship between dissolved gas and fault type or find some similar cases with unknown samples by calculating the similarity metrics. Their accuracy is limited, since they are hard to learn from other algorithms to improve their own performance. To improve the accuracy of transformer fault diagnosis, a novel method for transformer fault diagnosis based on graph convolutional network (GCN) is proposed. The proposed method has the advantages of two kinds of existing methods. Specifically, the adjacency matrix of GCN is utilized to fully represent the similarity metrics between unknown samples and labeled samples. Furthermore, the graph convolutional layers with strong feature extraction ability are used as a classifier to find the complex nonlinear relationship between dissolved gas and fault type. The back propagation algorithm is used to complete the training process of GCN. The simulation results show that the performance of GCN is better than that of the existing methods such as convolutional neural network, multi-layer perceptron, support vector machine, extreme gradient boosting tree, k-nearest neighbors and Siamese network in different input features and data volumes, which can effectively meet the needs of diagnostic accuracy., QC 20210503

Recently, there is increasing interest in detecting electricity thieves for economic benefits for power companies, and many works aim to improve the accuracy of electricity theft detection. Nevertheless, a core obstacle that currently hinders the direct comparison of classifiers for electricity theft detection is the lack of a standard and public dataset, since fraudulent power load profiles are usually difficult to collect for various reasons, including cost, cumber, and confidentiality. Therefore, this paper presents an open-source toolbox, which generates different kinds of fraudulent power load profiles from attack models, and integrates classical classifiers (e.g., support vector machine, multi-layer perceptron, convolutional neural network, long short-term memory, bidirectional long short-term memory) with different performance as baselines for the comparison with new algorithms. Users can easily generate datasets and modify parameters of classical classifiers guided by user friendly interactive interfaces. The codes, toolbox, and user manuals are available online and it is free to use and extend them., Part of proceedings: ISBN 978-1-6654-0064-0QC 20220816

There are few transformer faults, which makes the methods of transformer fault diagnosis based on machine learning lack of data. For this reason, a method based on improved auto-encoder (IAE) is proposed to augment transformer fault data. Firstly, to solve the problem of limited data and lack of diversity in the traditional automatic encoder, an improved strategy for generating samples for transformer faults is proposed. Secondly, considering that the traditional convolutional neural network will lose a lot of feature information in the pooling operation, the improved convolutional neural network (ICNN) is constructed as the classifier of fault diagnosis. Finally, the effectiveness and adaptability of the proposed method are verified by the actual data. The simulation results show that IAE can take into account the distribution and diversity of data at the same time, and the generated transformer fault data can improve the performance of the classifier better than the traditional augmentation methods such random over-sampling method, synthetic minority over-sampling technique, and auto-encoder. Compared with traditional classifiers, ICNN has higher fault diagnosis accuracy before and after data augmentation., QC 20220523