A Novel Hybrid CNN-LSTM Compensation Model Against DoS Attacks in Power System State Estimation

Xu, Xiaoxin; Sun, Jia Lin; Wang, Chunye; Zou, Bin

doi:10.1007/s11063-021-10696-3

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…In general, existing machine learning methods, when applied to missing data recovery, fail to fully exploit retained information [14][15][16] or predefine certain aspects [17][18]. Whether from a data perspective or from a physical perspective, the retained information should be more closely related to the missing information.…”

Section: Missing Data Recovery For Transmission Network Using Graph C...mentioning

confidence: 99%

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Missing Data Recovery for Transmission Networks Using Graph Convolutional Networks with Location Mask

Li,

Zhang,

Liu

et al. 2024

Preprint

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As the integration of power systems and information systems has advanced, the significance of power data has progressively increased. However, data loss stemming from information attacks and other transmission failures diminishes the value of power data. Research on missing data recovery in transmission network nodes overlooks the stochastic characteristics of missing positions and random missing scales in missing information completion. Therefore, this paper proposes a graph convolution method with a location mask. This model introduces an input‒output matrix containing all node feature information to address random missing positions. The designed location mask and the class residual network structure formed by the graph convolution layers enhance the model's information mining and generalization capabilities, effectively addressing random missing positions and random missing scales. Subsequently, through comparison with other models and through testing across 61 scenarios involving various missing patterns and scales, the model's effectiveness and superiority are validated. Finally, a series of experiments are conducted to explore the role of the location mask during the data recovery task.

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Section: Missing Data Recovery For Transmission Network Using Graph C...mentioning

confidence: 99%

“…W construct a temporal prediction model for these specific nodes [16]. A.2) Machine learning methods based on spatial-temporal information.…”

Section: Missing Data Recovery For Transmission Network Using Graph C...mentioning

confidence: 99%

Missing Data Recovery for Transmission Networks Using Graph Convolutional Networks with Location Mask

Li,

Zhang,

Liu

et al. 2024

Preprint

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“…CNNs are currently being used to develop effective intrusion detection systems for IIoT networks. A hybrid CNN+LSTM model achieved 93.21% accuracy for binary classification and 92.9% for multi-class classification in detecting attacks on IIoT networks [112,113]. Another approach called IIoT-IDS used an inception CNN model to detect intrusions with high accuracy [114].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Overview of AI-Models and Tools in Embedded IIoT Applications

Dini,

Diana,

Elhanashi

et al. 2024

Electronics

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The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.

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“…With the rapid development of the economy, the demand for power is increasing, and the structure and working mode of power systems are becoming increasingly complex. In order to ensure the stability, economy, and security of power system operation, the automation level of the power system dispatching center needs to be constantly improved [2]. Power system state estimation is one of the most important application software in energy management systems, and its performance directly afects the reliability and correctness of the operation results of many other advanced application software [3].…”

Section: Introductionmentioning

confidence: 99%

Identification of Anomaly Detection in Power System State Estimation Based on Fuzzy C-Means Algorithm

Leng

Qiu

2023

International Transactions on Electrical Energy Systems

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In order to estimate the power system accurately and identify anomaly detection in real time, an identification method of anomaly detection in power system state estimation based on Fuzzy C-means algorithm is proposed. Considering the problems of scale and redundancy of power system measurement data, effective measurement data of the power system is extracted by the principal component analysis method. On this basis, the power system state estimation model established by particle swarm optimization support vector machines is used to judge the operational state of the power system. An anomaly detection identification method based on fuzzy C-means algorithm is proposed to cluster the measured data and identify the anomaly detection of power system. The experimental results show that this method can accurately estimate the state of the power system and has the highest identification accuracy for anomaly detection compared with similar methods. When the equivalent measurement data is affected by noise, the identification delay of this method for anomaly detection in a power system is 1 s, and the real-time performance is high.

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A Novel Hybrid CNN-LSTM Compensation Model Against DoS Attacks in Power System State Estimation

Cited by 9 publications

References 37 publications

Missing Data Recovery for Transmission Networks Using Graph Convolutional Networks with Location Mask

Missing Data Recovery for Transmission Networks Using Graph Convolutional Networks with Location Mask

Overview of AI-Models and Tools in Embedded IIoT Applications

Identification of Anomaly Detection in Power System State Estimation Based on Fuzzy C-Means Algorithm

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