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Diagnostic Applications for Micro-Synchrophasor Measurements
This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems
Detection of Power Line Supporting Towers via Interpretable Semantic Segmentation of 3D Point Clouds
The inspection and maintenance of energy transmission networks are demanding and
crucial tasks for any transmission system operator. They rely on a combination of on-theground
staff and costly low-flying helicopters to visually inspect the power grid structure.
Recently, LiDAR-based inspections have shown the potential to accelerate and increase
inspection precision. These high-resolution sensors allow one to scan an environment and
store it in a 3D point cloud format for further processing and analysis by maintenance
specialists to prevent fires and damage to the electrical system. However, this task is
especially demanding to handle on time when we consider the extensive area that the
transmission network covers. Nonetheless, the transition to point cloud data allows us to
take advantage of Deep Learning to automate these inspections, by detecting collisions
between the grid and the revolving scene.
Deep Learning is a recent and powerful tool that has been successfully applied to a
myriad of real-life problems, such as image recognition and speech generation. With the
introduction of affordable LiDAR sensors, the application of Deep Learning on 3D data
emerged, with numerous methods being proposed every day to address difficult problems,
from 3D object detection to 3D point cloud segmentation. Alas, state-of-the-art methods
are remarkably complex, composed of millions of trainable parameters, and take several
weeks, if not months, to train on specific hardware, which makes it difficult for traditional
companies, like utilities, to employ them.
Therefore, we explore a novel mathematical framework that allows us to define tailored
operators that incorporate prior knowledge regarding our problem. These operators
are then integrated into a learning agent, called SCENE-Net, that detects power line supporting
towers in 3D point clouds. SCENE-Net allows for the interpretability of its results,
which is not possible in conventional models, it shows an efficient training and inference
time of 85 mn and 20 ms on a regular laptop. Our model is composed of 11 trainable
geometrical parameters, like the height of a cylinder, and has a Precision gain of 24%
against a comparable CNN with 2190 parameters.A inspeção e manutenção de redes de transmissão de energia são tarefas cruciais para
operadores de rede. Recentemente, foram adotadas inspeções utilizando sensores LiDAR
de forma a acelerar este processo e aumentar a sua precisão. Estes sensores são objetos de
alta precisão que conseguem inspecionar ambientes e guarda-los no formato de nuvens
de pontos 3D, para serem posteriormente analisadas por specialistas que procuram prevenir
fogos florestais e danos à estruta eléctrica. No entanto, esta tarefa torna-se bastante
difÃcil de concluir em tempo útil pois a rede de transmissão é bastasnte vasta. Por isso,
podemos tirar partido da transição para dados LiDAR e utilizar aprendizagem profunda
para automatizar as inspeções à rede.
Aprendizagem profunda é um campo recente e em grande desenvolvimento, sendo
aplicado a vários problemas do nosso quotidiano e facilmente atinge um desempenho
superior ao do ser humano, como em reconhecimento de imagens, geração de voz, entre
outros. Com o desenvolvimento de sensores LiDAR acessÃveis, o uso de aprendizagem
profunda em dados 3D rapidamente se desenvolveu, apresentando várias metodologias
novas todos os dias que respondem a problemas complexos, como deteção de objetos
3D. No entanto, modelos do estado da arte são incrivelmente complexos e compostos
por milhões de parâmetros e demoram várias semanas, senão meses, a treinar em GPU
potentes, o que dificulta a sua utilização em empresas tradicionais, como a EDP.
Portanto, nós exploramos uma nova teoria matemática que nos permite definir operadores
especÃficos que incorporaram conhecimento sobre o nosso problema. Estes operadores
são integrados num modelo de aprendizagem prounda, designado SCENE-Net,
que deteta torres de suporte de linhas de transmissão em nuvens de pontos. SCENE-Net
permite a interpretação dos seus resultados, aspeto que não é possÃvel com modelos convencionais,
demonstra um treino eficiente de 85 minutos e tempo de inferência de 20
milissegundos num computador tradicional. O nosso modelo contém apenas 11 parâmetros
geométricos, como a altura de um cilindro, e demonstra um ganho de Precisão de
24% quando comparado com uma CNN com 2190 parâmetros
Predicting chattering alarms: A machine Learning approach
Abstract Alarm floods represent a widespread issue for modern chemical plants. During these conditions, the number of alarms may be unmanageable, and the operator may miss safety-critical alarms. Chattering alarms, which repeatedly change between the active and non-active states, are responsible for most of the alarm records within a flood episode. Typically, chattering alarms are only addressed and removed retrospectively (e.g. during periodic performance assessments). This study proposes a Machine-Learning based approach for alarm chattering prediction. Specifically, a method for dynamic chattering quantification has been developed, whose results have been used to train three different Machine Learning models – Linear, Deep, and Wide&Deep models. The algorithms have been employed to predict future chattering behavior based on actual plant conditions. Performance metrics have been calculated to assess the correctness of predictions and to compare the performance of the three models
Prognostic Model Development with Missing Labels - A Condition-Based Maintenance Approach Using Machine Learning
Condition-based maintenance (CBM) has emerged as a proactive strategy for determining the best time for maintenance activities. In this paper, a case of a milling process with imperfect maintenance at a German automotive manufacturer is considered. Its major challenge is that only data with missing labels are available, which does not provide a sufficient basis for classical prognostic maintenance models. To overcome this shortcoming, a data science study is carried out that combines several analytical methods, especially from the field of machine learning (ML). These include time-domain and time–frequency domain techniques for feature extraction, agglomerative hierarchical clustering and time series clustering for unsupervised pattern detection, as well as a recurrent neural network for prognostic model training. With the approach developed, it is possible to replace decisions that were made based on subjective criteria with data-driven decisions to increase the tool life of the milling machines. The solution can be employed beyond the presented case to similar maintenance scenarios as the basis for decision support and prognostic model development. Moreover, it helps to further close the gap between ML research and the practical implementation of CBM
See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation
Sampling discrepancies between different manufacturers and models of lidar
sensors result in inconsistent representations of objects. This leads to
performance degradation when 3D detectors trained for one lidar are tested on
other types of lidars. Remarkable progress in lidar manufacturing has brought
about advances in mechanical, solid-state, and recently, adjustable scan
pattern lidars. For the latter, existing works often require fine-tuning the
model each time scan patterns are adjusted, which is infeasible. We explicitly
deal with the sampling discrepancy by proposing a novel unsupervised
multi-target domain adaptation framework, SEE, for transferring the performance
of state-of-the-art 3D detectors across both fixed and flexible scan pattern
lidars without requiring fine-tuning of models by end-users. Our approach
interpolates the underlying geometry and normalizes the scan pattern of objects
from different lidars before passing them to the detection network. We
demonstrate the effectiveness of SEE on public datasets, achieving
state-of-the-art results, and additionally provide quantitative results on a
novel high-resolution lidar to prove the industry applications of our
framework. This dataset and our code will be made publicly available
SIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications
© IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TII.2022.3165890In this work, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS. The main role of the SIEMS is to maintain the state of charge at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this paper is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g. smart grid and microgrids, in order to produce perturbation for training data.Peer reviewe
INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks
Leveraging network information for predictive modeling has become widespread
in many domains. Within the realm of referral and targeted marketing,
influencer detection stands out as an area that could greatly benefit from the
incorporation of dynamic network representation due to the ongoing development
of customer-brand relationships. To elaborate this idea, we introduce
INFLECT-DGNN, a new framework for INFLuencer prEdiCTion with Dynamic Graph
Neural Networks that combines Graph Neural Networks (GNN) and Recurrent Neural
Networks (RNN) with weighted loss functions, the Synthetic Minority
Oversampling TEchnique (SMOTE) adapted for graph data, and a carefully crafted
rolling-window strategy. To evaluate predictive performance, we utilize a
unique corporate data set with networks of three cities and derive a
profit-driven evaluation methodology for influencer prediction. Our results
show how using RNN to encode temporal attributes alongside GNNs significantly
improves predictive performance. We compare the results of various models to
demonstrate the importance of capturing graph representation, temporal
dependencies, and using a profit-driven methodology for evaluation.Comment: 26 pages, 10 figure
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