| 1. |
- Liao, Wenlong, et al.
(författare)
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A Review of Graph Neural Networks and Their Applications in Power Systems
- 2022
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Ingår i: Journal of Modern Power Systems and Clean Energy. - : Journal of Modern Power Systems and Clean Energy. - 2196-5625 .- 2196-5420. ; 10:2, s. 345-360
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Forskningsöversikt (refereegranskat)abstract
- 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.
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| 2. |
- Liao, Wenlong, et al.
(författare)
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An Open-Source Toolbox with Classical Classifiers for Electricity Theft Detection
- 2021
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Ingår i: 2021 IEEE 2nd China International Youth Conference on Electrical Engineering, CIYCEE 2021. - : Institute of Electrical and Electronics Engineers Inc..
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
- Liao, Wenlong, et al.
(författare)
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Data-driven Missing Data Imputation for Wind Farms Using Context Encoder
- 2022
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Ingår i: Journal of Modern Power Systems and Clean Energy. - : Journal of Modern Power Systems and Clean Energy. - 2196-5625 .- 2196-5420. ; 10:4, s. 964-976
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Liao, Wenlong, et al.
(författare)
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Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations
- 2022
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Ingår i: Journal of Modern Power Systems and Clean Energy. - : Journal of Modern Power Systems and Clean Energy. - 2196-5625 .- 2196-5420. ; 10:6, s. 1563-1575
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Liao, Wenlong, et al.
(författare)
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Simple Data Augmentation Tricks for Boosting Performance on Electricity Theft Detection Tasks
- 2023
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Ingår i: IEEE transactions on industry applications. - : Institute of Electrical and Electronics Engineers (IEEE). - 0093-9994 .- 1939-9367. ; 59:4, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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