We propose GLassoformer, a novel and efficient transformer architecture leveraging group Lasso regularization to reduce the number of queries of the standard self-attention mechanism. Due to the sparsified queries, GLassoformer is more computationally efficient than the standard transformers. On the power grid post-fault voltage prediction task, GLassoformer shows remarkably better prediction than many existing benchmark algorithms in terms of accuracy and stability.
University of Tehran（德黑兰大学）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）；Institute for Research in Fundamental Sciences (IPM)（基础科学研究所（IPM））；Iranian Research Organization for Science and Technology (IROST)（伊朗科学技术研究组织（IROST））；Universitat Politecnica de Catalunya（加泰罗尼亚理工大学）
Deep Neural Networks (DNNs) are inherently computation-intensive and also power-hungry. Hardware accelerators such as Field Programmable Gate Arrays (FPGAs) are a promising solution that can satisfy these requirements for both embedded and High-Performance Computing (HPC) systems. In FPGAs, as well as CPUs and GPUs, aggressive voltage scaling below the nominal level is an effective technique for power dissipation minimization. Unfortunately, bit-flip faults start to appear as the voltage is scaled down closer to the transistor threshold due to timing issues, thus creating a resilience issue. T…
Nanyang Technological University（南洋理工大学）；Harbin Institute of Technology（哈尔滨工业大学）
As deep learning systems are widely adopted in safety- and security-critical applications, such as autonomous vehicles, banking systems, etc., malicious faults and attacks become a tremendous concern, which potentially could lead to catastrophic consequences. In this paper, we initiate the first study of leveraging physical fault injection attacks on Deep Neural Networks (DNNs), by using laser injection technique on embedded systems. In particular, our exploratory study targets four widely used activation functions in DNNs development, that are the general main building block of DNNs that crea…
Tsinghua University（清华大学）；Tencent（腾讯）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Tianjin University（天津大学）
With the fast evolvement of embedded deep-learning computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying neural networks (NNs) onto the devices under complex environments, there are various types of possible faults: soft errors caused by cosmic radiation and radioactive impurities, voltage instability, aging, temperature variations, and malicious attackers. Thus the safety risk of deploying NNs is now drawing much attention. In this paper, after the analysis of the possible faults in various types of NN accelerators, we formalize and imp…
Tsinghua University（清华大学）；VŠB - Technical University of Ostrava（俄斯特拉发技术大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
The detection and characterization of partial discharge (PD) are crucial for the insulation diagnosis of overhead lines with covered conductors. With the release of a large dataset containing thousands of naturally obtained high-frequency voltage signals, data-driven analysis of fault-related PD patterns on an unprecedented scale becomes viable. The high diversity of PD patterns and background noise interferences motivates us to design an innovative pulse shape characterization method based on clustering techniques, which can dynamically identify a set of representative PD-related pulses. Capi…
Predicting faults before they occur helps to avoid potential safety hazards. Furthermore, planning the required maintenance actions in advance reduces operation costs. In this article, the focus is on electrochemical cells. In order to predict a cell's fault, the typical approach is to estimate the expected voltage that a healthy cell would present and compare it with the cell's measured voltage in real-time. This approach is possible because, when a fault is about to happen, the cell's measured voltage differs from the one expected for the same operating conditions. However, estimating the ex…
PROTECT-90: A Fault Dataset for Power System Protection
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg（模式识别实验室，埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Institute of Electrical Energy Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg（电气能源系统研究所，埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Department of Electrical Engineering, Media and Computer Science（电气工程、媒体与计算机科学系）
Barcelona Supercomputing Center (BSC)（巴塞罗那超级计算中心）；IIIA - Artificial Intelligence Research Institute CSIC - Spanish National Research Council（西班牙国家研究委员会IIIA人工智能研究所）；Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯公司）；Starfleet Academy（星际舰队学院）
Voltage underscaling below the nominal level is an effective solution for improving energy efficiency in digital circuits, e.g., Field Programmable Gate Arrays (FPGAs). However, further undervolting below a safe voltage level and without accompanying frequency scaling leads to timing related faults, potentially undermining the energy savings. Through experimental voltage underscaling studies on commercial FPGAs, we observed that the rate of these faults exponentially increases for on-chip memories, or Block RAMs (BRAMs). To mitigate these faults, we evaluated the efficiency of the built-in Err…
Northeast Electric Power University（东北电力大学）；Technical University of Denmark（丹麦技术大学）
A fault diagnosis method for power electronics converters based on deep feedforward network and wavelet compression is proposed in this paper. The transient historical data after wavelet compression are used to realize the training of fault diagnosis classifier. Firstly, the correlation analysis of the voltage or current data running in various fault states is performed to remove the redundant features and the sampling point. Secondly, the wavelet transform is used to remove the redundant data of the features, and then the training sample data is greatly compressed. The deep feedforward networ…
Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究院）；Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada（格拉纳达大学计算机科学与人工智能系）
Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators
Center of Technology and Systems (UNINOVA-CTS)（技术与系统中心（UNINOVA-CTS））；Associated Lab of Intelligent Systems (LASI)（智能系统联合实验室（LASI））；Faculty of Engineering and Applied Sciences, University of Regina（里贾纳大学工程与应用科学学院）
Frequently recurring transient faults in a transmission network may be indicative of impending permanent failures. Hence, determining their location is a critical task. This paper proposes a novel image embedding aided deep learning framework called DeVLearn for faulted line location using PMU measurements at generator buses. Inspired by breakthroughs in computer vision, DeVLearn represents measurements (one-dimensional time series data) as two-dimensional unthresholded Recurrent Plot (RP) images. These RP images preserve the temporal relationships present in the original time series and are u…
Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, the authors successfully conducted 21 fault prognosis experiments, where fault precursors are introduced in the system to a degree enough to cause degradation in the waveform signals, but not enough to reach a real fault. Nine different machine learning techniques based on ensemb…
