With the rapid growth of power systems measurements in terms of size and complexity, discovering statistical patterns for a large variety of real-world applications such as renewable energy prediction, demand response, energy disaggregation, and state estimation is considered a crucial challenge In recent years, deep learning has emerged as a novel class of machine learning algorithms that represents power systems data via a large hypothesis space that leads to the state-of-the-art performance compared to most recent data-driven algorithms This study explores the theoretical advantages of deep representation learning in power systems research We review deep learning methodologies presented and applied in a wide range of supervised, unsupervised, and semi-supervised applications as well as reinforcement learning tasks We discuss various settings of problems solved by discriminative deep models including stacked autoencoders and convolutional neural networks as well as generative deep architectures such as deep belief networks and variational autoencoders The theoretical and experimental analysis of deep neural networks in this study motivates long- term research on optimizing this cutting-edge class of models to achieve significant improvements in the future power systems research

Deep learning in power systems research: A review

Tese de Mestrado Integrado. Engenharia Electrotecnica e de Computadores (Area de Especializacao de Energia). Faculdade de Engenharia. Universidade do Porto. 2012

Diagnosing faults in power transformers with autoassociative neural networks and mean shift

This paper focuses on a powerful and comprehensive overview of Deep Learning (DL) techniques on Distribution Automation System (DAS) applications to provide a complete viewpoint of modern power systems. DAS is a crucial approach to increasing the reliability, quality, and management of distribution networks. Due to the importance of development and sustainable security of DAS, the use of DL data-driven technology has grown significantly. DL techniques have blossomed rapidly, and have been widely applied in several fields of distribution systems. DL techniques are suitable for dynamic, decision-making, and uncertain environments such as DAS. This survey has provided a comprehensive review of the existing research into DL techniques on DAS applications, including fault detection and classification, load and energy forecasting, demand response, energy market forecasting, cyber security, network reconfiguration, and voltage control. Comparative results based on evaluation criteria are also addressed in this manuscript. According to the discussion and results of studies, the use and development of hybrid methods of DL with other methods to enhance and optimize the configuration of the techniques are highlighted. In all matters, hybrid structures accomplish better than single methods as hybrid approaches hold the benefit of several methods to construct a precise performance. Due to this, a new smart technique called Learning-to-learning (L2L) based DL is proposed that can enhance and improve the efficiency, reliability, and security of DAS. The proposed model follows several stages that link different DL algorithms to solve modern power system problems. To show the effectiveness and merit of the L2L based on the proposed framework, it has been tested on a modified reconfigurable IEEE 32 test system. This method has been implemented on several DAS applications that the results prove the decline of mean square errors by approximately 12% compared to conventional LSTM and GRU methods in terms of prediction fields.

/pdf/a-survey-on-deep-learning-role-in-distribution-automation-18uarrhi.pdf

A Survey on Deep Learning Role in Distribution Automation System: A New Collaborative Learning-to-Learning (L2L) Concept

Abstract Fault diagnosis plays a vital role in assessing the health management of industrial robots and improving maintenance schedules. In recent decades, artificial intelligence-based data-driven approaches have made significant progress in machine fault diagnosis using monitoring data. However, current methods pay less attention to correlations and internal differences in monitoring data, resulting in limited diagnostic performance. In this paper, a data-driven method is proposed for the fault diagnosis of industrial robot reducers, that is, a dual-module attention convolutional neural network (DMA-CNN). This method aims to diagnose the fault state of industrial robot reducer. It establishes two parallel convolutional neural networks with two different attentions to capture the different features related to the fault. Finally, the features are fused to obtain the fault diagnosis results (normal or abnormal). The fault diagnosis effect of the DMA-CNN method and other attention models are compared and analyzed. The effectiveness of the method is verified on a dataset of real industrial robots. 

https://link.springer.com/content/pdf/10.1007/s43684-022-00031-5.pdf

Fault diagnosis of industrial robot based on dual-module attention convolutional neural network

This article presents a novel health management method of the dry-type transformer to diagnose the early unhealthy behavior and evaluate the transformer's health condition by health score. The health condition diagnosis implemented by a proposed dynamic-weighted-feed-back broad learning system (BLS) (DW-FB-BLS) method, which helps to determine the BLS network structure effectively, and adjusts the weight of features in the online application to avoid reduction of accuracy caused by concept drift. Then, a rational score rule is set to evaluate the health condition of the dry-type transformer by health score, which allows intuitive presentation and preservation of transformer's health condition over a long period. Finally, the effectiveness and validity of the proposed method are verified based on the real field data of dry-type transformer. Satisfactory results for unhealthy behavior diagnosis and health evaluation are obtained, it shows that health management of this article can reflect the real health condition of dry-type transformer appropriately. 

Health Management of Dry-Type Transformer Based on Broad Learning System

Dissolved gas analysis (DGA) is an effective way to diagnose the internal faults of transformer. This paper proposes a deep belief sparse autoencoder (DBSAE), which can be performed on DGA data to detect the transformer fault. The proposed method is constructed by two layers of sparse autoencoder and one layer of back propagation network. DBSAE adopts the unsupervised training to collect the features of DGA raw data, and the back propagation network with the supervised training could realize the transformer faults diagnosis. The experiments are performed on a real DGA dataset. The experimental results verify the effectiveness of the proposed method, which can extract features from the original data, and achieve a superior correct differentiation rate which reached 95.4% in average accuracy on transformer fault diagnosis.

Transformer Fault Diagnosis based on Deep Brief Sparse Autoencoder

Silicone rubber (SR) is widely used as insulation for cable accessories and silicone grease severs as a lubricant during its installation. However, the absorption of silicone grease during long-term operation can lead to swollen SR and interfacial cavity defects, whose effects can be severe and unpredictable. This study aims to investigate the changes in dielectric and mechanical properties of SR caused by silicone grease absorption. We measure the dielectric properties by frequency domain dielectric spectroscopy (FDS) and dynamic mechanical analysis (DMA) is applied to evaluate the complex modulus and stress relaxation. The effects of molecular polarity and absorption content of silicone grease are specifically considered and evaluated. As a consequence, silicone grease absorption reduces storage modulus and loss modulus, and accelerates stress relaxation of SR insulation, probably leading to insufficient interfacial pressure which is considered a major concern for cable accessories. In addition, changes in dielectric properties are dependent on the absorption content and molecular polarity of silicone grease. The deterioration effect of fluorinated silicone grease is stronger than that of phenyl methyl silicone grease. These results can be attributed to the occupation of free volume and weakening of intermolecular forces in SR, which provides an understanding of deterioration mechanism of SR caused by silicone grease absorption.

Changes in Dielectric and Mechanical Properties of Silicone Rubber Caused by Silicone Grease Absorption

Moisture is one of the most serious causes of cable joint failures in the distribution network, which can invade cable joints through the composite interface structures. Enhancing the sealing performance of interface structures is an important initiative that should be focused on, but the relevant aspects are lacking in the design and selection of cable accessories. In this work, a detection and evaluation method applicable to the water tightness of composite interface structures in distribution cables is proposed, and the water ingress process at different temperatures and interfacial pressures is measured. Two indexes, namely the time constant of penetration Tp and the water resistance coefficient Kr, are defined and calculated to evaluate the water tightness of cable joints. Experimental results show that increasing temperature and decreasing interfacial pressure have accelerated effects on the water ingress process, resulting in a worse water tightness of cable joints. When the temperature increases from 25°C to 75°C, Tp decreases to a minimum of 16.7% and Kr decreases to 49.8%. As the interfacial pressure decreases from 0.14 MPa to 0.015 MPa, Tp decreases to a minimum of 7.22% and Kr decreases to 55.2%. Similar results are obtained for water immersion environment and humid environment with 95% relative humidity. The proposed method is effective and accurate for testing and evaluating the water tightness of cable joints and can provide important guidance for the design and selection of cable accessories.

Water Tightness Testing and Evaluation of Composite Interface Structures in Cable Joints

Silicone grease helps lubricate and seal the cable joint interface during cable installation. However, silicone grease causes silicone rubber to swell during cable operation, which is an important factor| causing silicone rubber deterioration and equipment failure. In this paper, experiments are designed to study the effect of silicone grease on the dielectric properties of silicone rubber in cable joints. First, the dielectric properties of silicone grease absorbing different silicone rubbers in a wide frequency range were measured by using a broadband dielectric spectrum. Then, the experimental results are analyzed from the microscopic point of view by molecular dynamics simulation. It is found that dimethyl silicone grease can improve the charging properties of silicone rubber. Fluorinated silicone grease and long-chain alkanes deteriorate the charging properties of silicone rubber, and fluorinated silicone grease has the most obvious effect on the deterioration of the properties of silicone rubber. As the viscosity of dimethyl silicone grease decreases, its improvement on the dielectric properties of silicone rubber is more obvious. Therefore, the influence of silicone grease on the dielectric properties of silicone rubber is not only related to the molecular polarity of the silicone grease itself, but also to the swelling properties of the silicone grease.

Effect of Silicone Grease on the Dielectric Property of Silicone Rubber for Cable Joint

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Papers

Transformer Fault Diagnosis based on Deep Brief Sparse Autoencoder

Changes in Dielectric and Mechanical Properties of Silicone Rubber Caused by Silicone Grease Absorption

Water Tightness Testing and Evaluation of Composite Interface Structures in Cable Joints

Effect of Silicone Grease on the Dielectric Property of Silicone Rubber for Cable Joint