3,259 research outputs found
Low-Resolution Fault Localization Using Phasor Measurement Units with Community Detection
A significant portion of the literature on fault localization assumes (more
or less explicitly) that there are sufficient reliable measurements to
guarantee that the system is observable. While several heuristics exist to
break the observability barrier, they mostly rely on recognizing
spatio-temporal patterns, without giving insights on how the performance are
tied with the system features and the sensor deployment. In this paper, we try
to fill this gap and investigate the limitations and performance limits of
fault localization using Phasor Measurement Units (PMUs), in the low
measurements regime, i.e., when the system is unobservable with the
measurements available. Our main contribution is to show how one can leverage
the scarce measurements to localize different type of distribution line faults
(three-phase, single-phase to ground, ...) at the level of sub-graph, rather
than with the resolution of a line. We show that the resolution we obtain is
strongly tied with the graph clustering notion in network science.Comment: Accepted in IEEE SmartGridComm 2018 Conferenc
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Phasor Measurement Units Optimal Placement and Performance Limits for Fault Localization
In this paper, the performance limits of faults localization are investigated using synchrophasor data. The focus is on a non-trivial operating regime where the number of Phasor Measurement Unit (PMU) sensors available is insufficient to have full observability of the grid state. Proposed analysis uses the Kullback Leibler (KL) divergence between the distributions corresponding to different fault location hypotheses associated with the observation model. This analysis shows that the most likely locations are concentrated in clusters of buses more tightly connected to the actual fault site akin to graph communities. Consequently, a PMU placement strategy is derived that achieves a near-optimal resolution for localizing faults for a given number of sensors. The problem is also analyzed from the perspective of sampling a graph signal, and how the placement of the PMUs i.e. the spatial sampling pattern and the topological characteristic of the grid affect the ability to successfully localize faults. To highlight the superior performance of presented fault localization and placement algorithms, the proposed strategy is applied to a modified IEEE 34, IEEE-123 bus test cases and to data from a real distribution grid. Additionally, the detection of cyber-physical attacks is also examined where PMU data and relevant Supervisory Control and Data Acquisition (SCADA) network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations
Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms
The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods.publishedVersio
PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels
Electric faults may trigger blackouts or wildfires without timely monitoring and control strategy. Traditional solutions for locating faults in distribution systems are not real-time when network observability is low, while novel black-box machine learning methods are vulnerable to stochastic environments. We propose a novel Physics-Preserved Graph Network (PPGN) architecture to accurately locate faults at the node level with limited observability and labeled training data. PPGN has a unique two-stage graph neural network architecture. The first stage learns the graph embedding to represent the entire network using a few measured nodes. The second stage finds relations between the labeled and unlabeled data samples to further improve the location accuracy. We explain the benefits of the two-stage graph configuration through a random walk equivalence. We numerically validate the proposed method in the IEEE 123-node and 37-node test feeders, demonstrating the superior performance over three baseline classifiers when labeled training data is limited, and loads and topology are allowed to vary
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward
Advancements in digital automation for smart grids have led to the
installation of measurement devices like phasor measurement units (PMUs),
micro-PMUs (-PMUs), and smart meters. However, a large amount of data
collected by these devices brings several challenges as control room operators
need to use this data with models to make confident decisions for reliable and
resilient operation of the cyber-power systems. Machine-learning (ML) based
tools can provide a reliable interpretation of the deluge of data obtained from
the field. For the decision-makers to ensure reliable network operation under
all operating conditions, these tools need to identify solutions that are
feasible and satisfy the system constraints, while being efficient,
trustworthy, and interpretable. This resulted in the increasing popularity of
physics-informed machine learning (PIML) approaches, as these methods overcome
challenges that model-based or data-driven ML methods face in silos. This work
aims at the following: a) review existing strategies and techniques for
incorporating underlying physical principles of the power grid into different
types of ML approaches (supervised/semi-supervised learning, unsupervised
learning, and reinforcement learning (RL)); b) explore the existing works on
PIML methods for anomaly detection, classification, localization, and
mitigation in power transmission and distribution systems, c) discuss
improvements in existing methods through consideration of potential challenges
while also addressing the limitations to make them suitable for real-world
applications
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