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Data-driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 874-885页

作者： wenlong liao Dechang Yang Qi Liu Yixiong Jia Chenxi Wang Zhe Yang Wind Engineering and Renewable Energy Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) the College of Information and Electrical Engineering China Agricultural University the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Electrical and Electronic Engineering (Energy Digitalization Laboratory) The University of Hong Kong the Department of Electrical Engineering The Hong Kong Polytechnic University

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods, which often lead to locally optimal solutions and require heavy online inference time consumption. To improve the quality of the solution and reduce the inference time burden, this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs(topology and power loads) and reactive power scheduling schemes of distribution networks, from a data-driven perspective. The graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix, a customized loss function, and the use of max-pooling layers. Additionally, a rulebased strategy is proposed to adjust infeasible solutions that violate constraints. Simulation results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load conditions. Moreover, its online inference time is significantly faster than traditional physical model based methods, particularly for large-scale distribution networks.

关键词： Distribution networks Reactive power Load modeling Transformers Machine learning algorithms Inference algorithms Static VAr compensators

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

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Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1274-1287页

作者： wenlong liao Jiejing Chen Qi Liu Ruijin Zhu Like Song Zhe Yang AAU Energy Aalborg UniversityAalborgDenmark School of Softwareand Microelectronics Peking UniversityBeijingChina Key Laboratory of Smart Gridof Ministry of Education Tianjin UniversityTianjinChina School of Electrical Engineering Tibet Agriculture and Animal Husbandry UniversityLinzhiChina Maintenance Branch of State Grid Jibei Electric Power Co. Ltd.BeijingChina

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.

关键词： Data-driven reactive power optimization distribution networks deep learning capsule networks

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

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Journal of Modern Power Systems and Clean Energy 2022年第4期10卷 964-976页

作者： wenlong liao Birgitte Bak-Jensen Jayakrishnan Radhakrishna Pillai Dechang Yang Yusen Wang Department of Energy Technology Aalborg UniversityAalborgDenmark College of Information and Electrical Engineering China Agricultural UniversityBeijingChina School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

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.

关键词： Data-driven missing data imputation wind farm deep learning context encoder

A Review of Graph Neural Networks and Their Applications in Power Systems

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Journal of Modern Power Systems and Clean Energy 2022年第2期10卷 345-360页

作者： wenlong liao Birgitte Bak-Jensen Jayakrishnan Radhakrishna Pillai Yuelong Wang Yusen Wang the Department of Energy Technology Aalborg UniversityAalborgDenmark the State Grid Tianjin Chengxi Electric Power Supply Branch TianjinChina the School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

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.

关键词： Machine learning power system deep neural network graph neural network artificial intelligence

Scenario Generation for Cooling,Heating,and Power Loads Using Generative Moment Matching Networks

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CSEE Journal of Power and Energy Systems 2022年第6期8卷 1730-1740页

作者： wenlong liao Yusen Wang Yuelong Wang Kody Powell Qi Liu Zhe Yang Department of Energy Technology Aalborg UniversityAalborg 9220Denmark School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSE-10044Sweden State Grid Tianjin Chengxi Electric Power Supply Branch Tianjin 300100China Department of Chemical Engineering University of UtahUT 84112America Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjin 300072China

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 highdimensional 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 multiclass 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.

关键词： Deep learning generative moment matching networks integrated energy systems scenario generations

Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1563-1575页

作者： wenlong liao Birgitte Bak-Jensen Jayakrishnan Radhakrishna Pillai Zhe Yang Yusen Wang Kuangpu Liu AAU Energy Aalborg UniversityAalborgDenmark School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

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.

关键词： Renewable energy source scenario generation implicit maximum likelihood estimation(IMLE) deep learning generative network

Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1100-1114页

作者： wenlong liao Shouxiang Wang Birgitte Bak-Jensen Jayakrishnan Radhakrishna Pillai Zhe Yang Kuangpu Liu AAU Energy Aalborg UniversityAalborg 9220Denmark Key Laboratory of Smart Grid of Ministry of Education School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China

Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power systems.However,the volatility and intermittence of wind power pose uncertainties to traditional point prediction,resulting in an increased risk of power system operation.To represent the uncertainty of wind power,this paper proposes a new method for ultra-short-term interval prediction of wind power based on a graph neural network(GNN)and an improved Bootstrap technique.Specifically,adjacent wind farms and local meteorological factors are modeled as the new form of a graph from the graph-theoretic perspective.Then,the graph convolutional network(GCN)and bi-directional long short-term memory(Bi-LSTM)are proposed to capture spatiotemporal features between nodes in the graph.To obtain highquality prediction intervals(PIs),an improved Bootstrap technique is designed to increase coverage percentage and narrow PIs effectively.Numerical simulations demonstrate that the proposed method can capture the spatiotemporal correlations from the graph,and the prediction results outperform popular baselines on two real-world datasets,which implies a high potential for practical applications in power systems.

关键词： Wind power graph neural network(GNN) bidirectional long short-term memory(Bi-LSTM) prediction interval Bootstrap technique

Fault Diagnosis of Power Transformers Using Graph Convolutional Network

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 241-249页

作者： wenlong liao Dechang Yang Yusen Wang Xiang Ren Department of Energy Technology Aalborg UniversityAalborg 9220Denmark College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology-StockholmSE-10044Sweden North China Electric Power Research Institute Co. Ltd.Beijing 100045China

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.

关键词： Power transformer fault diagnosis graph convolutional network similarity metrics

Robust Voltage Control Considering Uncertainties of Renewable Energies and Loads via Improved Generative Adversarial Network

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1104-1114页

作者： Qianyu Zhao wenlong liao Shouxiang Wang Jayakrishnan Radhakrishna Pillai the Key Laboratory of Smart Grid of Ministry of Education and Tianjin Key Laboratory of Power System Simulation and Control Tianjin UniversityTianjinChina the Department of Energy Technology Aalborg UniversityAalborgDenmark

The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution network.In this paper,a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network(IGAN).Firstly,both real and predicted data are used to train the IGAN consisting of a discriminator and a generator.The noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario reduction.Then,a new improved wolf pack algorithm(IWPA)is presented to solve the formulated robust voltage control model,since the accuracy of the solutions obtained by traditional methods is limited.The simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads,which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional methods.Furthermore,IWPA has a better performance than traditional methods in terms of convergence speed,accuracy,and stability for robust voltage control.

关键词： Robust voltage control uncertainty generative adversarial network wolf pack algorithm