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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
Monitoring and Fault Location Sensor Network for Underground Distribution Lines
One of the fundamental tasks of electric distribution utilities is guaranteeing a continuous
supply of electricity to their customers. The primary distribution network is a critical part of these
facilities because a fault in it could affect thousands of customers. However, the complexity of
this network has been increased with the irruption of distributed generation, typical in a Smart
Grid and which has significantly complicated some of the analyses, making it impossible to apply
traditional techniques. This problem is intensified in underground lines where access is limited. As a
possible solution, this paper proposes to make a deployment of a distributed sensor network along
the power lines. This network proposes taking advantage of its distributed character to support new
approaches of these analyses. In this sense, this paper describes the aquiculture of the proposed
network (adapted to the power grid) based on nodes that use power line communication and energy
harvesting techniques. In this sense, it also describes the implementation of a real prototype that
has been used in some experiments to validate this technological adaptation. Additionally, beyond
a simple use for monitoring, this paper also proposes the use of this approach to solve two typical
distribution system operator problems, such as: fault location and failure forecasting in power cables.Ministerio de Economía y Competitividad, Government of Spain project Sistema Inteligente Inalámbrico para Análisis y Monitorización de Líneas de Tensión Subterráneas en Smart Grids (SIIAM) TEC2013-40767-RMinisterio de Educación, Cultura y Deporte, Government of Spain, for the funding of the scholarship Formación de Profesorado Universitario 2016 (FPU 2016
A Review of Fault Diagnosing Methods in Power Transmission Systems
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field
Review of recent research towards power cable life cycle management
Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world
Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks
This paper develops a novel graph convolutional network (GCN) framework for
fault location in power distribution networks. The proposed approach integrates
multiple measurements at different buses while taking system topology into
account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus
benchmark system. Simulation results show that the GCN model significantly
outperforms other widely-used machine learning schemes with very high fault
location accuracy. In addition, the proposed approach is robust to measurement
noise and data loss errors. Data visualization results of two competing neural
networks are presented to explore the mechanism of GCN's superior performance.
A data augmentation procedure is proposed to increase the robustness of the
model under various levels of noise and data loss errors. Further experiments
show that the model can adapt to topology changes of distribution networks and
perform well with a limited number of measured buses.Comment: Accepcted by IEEE Journal on Selected Areas in Communicatio
Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning
This paper presents a spatiotemporal unsupervised feature learning method for
cause identification of electromagnetic transient events (EMTE) in power grids.
The proposed method is formulated based on the availability of
time-synchronized high-frequency measurement, and using the convolutional
neural network (CNN) as the spatiotemporal feature representation along with
softmax function. Despite the existing threshold-based, or energy-based events
analysis methods, such as support vector machine (SVM), autoencoder, and
tapered multi-layer perception (t-MLP) neural network, the proposed feature
learning is carried out with respect to both time and space. The effectiveness
of the proposed feature learning and the subsequent cause identification is
validated through the EMTP simulation of different events such as line
energization, capacitor bank energization, lightning, fault, and high-impedance
fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the
WSCC 9-bus system.Comment: 9 pages, 7 figure
Time domain analysis of switching transient fields in high voltage substations
Switching operations of circuit breakers and disconnect switches generate transient currents propagating along the substation busbars. At the moment of switching, the busbars temporarily acts as antennae radiating transient electromagnetic fields within the substations. The radiated fields may interfere and disrupt normal operations of electronic equipment used within the substation for measurement, control and communication purposes. Hence there is the need to fully characterise the substation electromagnetic environment as early as the design stage of substation planning and operation to ensure safe operations of the electronic equipment. This paper deals with the computation of transient electromagnetic fields due to switching within a high voltage air-insulated substation (AIS) using the finite difference time domain (FDTD) metho
System configuration, fault detection, location, isolation and restoration: a review on LVDC Microgrid protections
Low voltage direct current (LVDC) distribution has gained the significant interest of research due to the advancements in power conversion technologies. However, the use of converters has given rise to several technical issues regarding their protections and controls of such devices under faulty conditions. Post-fault behaviour of converter-fed LVDC system involves both active converter control and passive circuit transient of similar time scale, which makes the protection for LVDC distribution significantly different and more challenging than low voltage AC. These protection and operational issues have handicapped the practical applications of DC distribution. This paper presents state-of-the-art protection schemes developed for DC Microgrids. With a close look at practical limitations such as the dependency on modelling accuracy, requirement on communications and so forth, a comprehensive evaluation is carried out on those system approaches in terms of system configurations, fault detection, location, isolation and restoration
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