Reactive Power Optimization of Distribution Network Based on Case-Based Reasoning

Liao, Wenlong; Wang, Shouxiang; Liu, Qi; Shu, Xin

doi:10.1109/pesgm.2018.8586373

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…To illustrate the superiority of the proposed RPOGAT, simulations are analyzed in comparison with CNN [12], MLP [10], GA, CBR [7], support vector machine (SVM), random forest (RF), and more recent and advanced GCN [17]. Note that node 0 is a slack node, so it is not considered as an input to the model.…”

Section: A Simulation Setupmentioning

confidence: 99%

“…Specifically, the similarity-based algorithms calculate the distance between the current case and historical cases to find the historical case closest to the current one, and then assign the historical solution to the current case. For instance, the case-based reasoning (CBR) and principal component analysis are integrated to screen historical cases for the RPO problem in [7]. The work in [8] applies the Apriori algorithm to search for the most suitable scheduling solution for the current case from the historical data based on the association rule learning and frequent itemset mining.…”

Section: Introductionmentioning

confidence: 99%

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

Liao,

Yang,

Liu

et al. 2024

Journal of Modern Power Systems and Clean Energy

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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.

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Section: A Simulation Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liao,

Yang,

Liu

et al. 2024

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

show abstract

“…The conclusions of the matched dispatching strategy are sent out to the system operators [13]. CBR searches historical cases similar to current cases, and then uses them to determine the dispatching strategy of unknown samples [14]. Generally, although these similarity-based methods make good use of prior knowledge by calculating distances between historical samples and current samples, they have difficulty in mining the complex non-linear relationship between power loads and dispatching strategies, resulting in their limited accuracy for reactive power optimization.…”

Section: Nomenclature δ Ijmentioning

confidence: 99%

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

Liao

Chen

Liu

et al. 2022

Journal of Modern Power Systems and Clean Energy

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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.

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“…The scenario generation is a popular method to capture the uncertainty of power load and renewable energy by generating a series of possible daily power load profiles [1]. Taking a large number of daily load profiles as the input of the Newton-Raphson method, the probability distribution of voltage and power loss is obtained, which is of great significance for the economic operation and stability analysis of the distribution network [2]- [4].…”

Section: Introduction a Backgroundmentioning

confidence: 99%

Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network

Liao

Wang

et al. 2020

IEEE Access

Self Cite

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The daily load profiles modeling is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a flow-based generative network is proposed to model daily load profiles of the distribution network. Firstly, the real samples are used to train a series of reversible functions that map the probability distribution of real samples to the prior distribution. Then, the new daily load profiles are generated by taking the random number obeying the Gaussian distribution as the input data of these reversible functions. Compared with existing methods such as explicit density models, the proposed approach does not need to assume the probability distribution of real samples, and can be used to model different loads only by adjusting the structure and parameters. The simulation results show that the proposed approach not only fits the probability distribution of real samples well, but also accurately captures the spatial-temporal correlation of daily load profiles. The daily load profiles with specific characteristics can be obtained by simply classification.INDEX TERMS Daily load profiles, distribution network, generative network. BIRGITTE BAK-JENSEN (Senior Member, IEEE) received the M.Sc. degree in electrical engineering and the Ph.D. degree in modeling of high voltage components from the

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Reactive Power Optimization of Distribution Network Based on Case-Based Reasoning

Cited by 3 publications

References 9 publications

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

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

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

Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network

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