11 research outputs found
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Using data from connected thermostats to track large power outages in the United States
The detection of power outages is an essential activity for electric utilities. A large, national dataset of Internet-connected thermostats was used to explore and illustrate the ability of Internet-connected devices to geospatially track outages caused by hurricanes and other major weather events. The method was applied to nine major outage events, including hurricanes and windstorms. In one event, Hurricane Irma, a network of about 1000 thermostats provided quantitatively similar results to detailed utility data with respect to the number of homes without power and identification of the most severely affected regions. The method generated regionally uniform outage data that would give emergency authorities additional visibility into the scope and magnitude of outages. The network of thermostat-sensors also made it possible to calculate a higher resolution version of outage duration (or SAIDI) at a level of customer-level visibility that was not previously available
Transfer Learning for Power Outage Detection Task with Limited Training Data
Early detection of power outages is crucial for maintaining a reliable power
distribution system. This research investigates the use of transfer learning
and language models in detecting outages with limited labeled data. By
leveraging pretraining and transfer learning, models can generalize to unseen
classes.
Using a curated balanced dataset of social media tweets related to power
outages, we conducted experiments using zero-shot and few-shot learning. Our
hypothesis is that Language Models pretrained with limited data could achieve
high performance in outage detection tasks over baseline models. Results show
that while classical models outperform zero-shot Language Models, few-shot
fine-tuning significantly improves their performance. For example, with 10%
fine-tuning, BERT achieves 81.3% accuracy (+15.3%), and GPT achieves 74.5%
accuracy (+8.5%). This has practical implications for analyzing and localizing
outages in scenarios with limited data availability.
Our evaluation provides insights into the potential of few-shot fine-tuning
with Language Models for power outage detection, highlighting their strengths
and limitations. This research contributes to the knowledge base of leveraging
advanced natural language processing techniques for managing critical
infrastructure
Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee
Line outage identification in distribution grids is essential for sustainable
grid operation. In this work, we propose a practical yet robust detection
approach that utilizes only readily available voltage magnitudes, eliminating
the need for costly phase angles or power flow data. Given the sensor data,
many existing detection methods based on change-point detection require prior
knowledge of outage patterns, which are unknown for real-world outage
scenarios. To remove this impractical requirement, we propose a data-driven
method to learn the parameters of the post-outage distribution through gradient
descent. However, directly using gradient descent presents feasibility issues.
To address this, we modify our approach by adding a Bregman divergence
constraint to control the trajectory of the parameter updates, which eliminates
the feasibility problems. As timely operation is the key nowadays, we prove
that the optimal parameters can be learned with convergence guarantees via
leveraging the statistical and physical properties of voltage data. We evaluate
our approach using many representative distribution grids and real load
profiles with 17 outage configurations. The results show that we can detect and
localize the outage in a timely manner with only voltage magnitudes and without
assuming a prior knowledge of outage patterns.Comment: 12 page
Analysis of the electric power outage data and prediction of electric power outage for major metropolitan areas in Texas using Machine Learning and Time Series Methods
With growing energy usage, power outages affect millions of households. This case study focuses on gathering power outage historical data, modifying the data to attach weather attributes, and gathering ERCOT energy market conditions for Dallas-Fort Worth and Houston metropolitan areas of Texas. The transformed data is then analyzed using machine learning algorithms including, but not limited to, Regression, Random Forests and XGBoost to consider current weather and ERCOT features and predict power outage percentage for locations. The transformed data is also trained using time series models and serially correlated models including Autoregression and Vector Autoregression. This study also focuses on traditional machine learning models that assume sample independence when compared to those that assume serial correlation. The results show machine learning models that utilize both weather features and ERCOT data yield a lower RMSE and higher prediction accuracy than using one feature-set exclusively. In addition, multivariate Vector Autoregressive models have lower RMSE compared to univariate Auto-Regressive, univariate Random Forest and univariate neural network models when weather and ERCOT data are included to predict power outages. Top performing traditional machine learning models are packaged into an external facing web application for public use in determining current power outage risk
A survey on the development status and application prospects of knowledge graph in smart grids
With the advent of the electric power big data era, semantic interoperability
and interconnection of power data have received extensive attention. Knowledge
graph technology is a new method describing the complex relationships between
concepts and entities in the objective world, which is widely concerned because
of its robust knowledge inference ability. Especially with the proliferation of
measurement devices and exponential growth of electric power data empowers,
electric power knowledge graph provides new opportunities to solve the
contradictions between the massive power resources and the continuously
increasing demands for intelligent applications. In an attempt to fulfil the
potential of knowledge graph and deal with the various challenges faced, as
well as to obtain insights to achieve business applications of smart grids,
this work first presents a holistic study of knowledge-driven intelligent
application integration. Specifically, a detailed overview of electric power
knowledge mining is provided. Then, the overview of the knowledge graph in
smart grids is introduced. Moreover, the architecture of the big knowledge
graph platform for smart grids and critical technologies are described.
Furthermore, this paper comprehensively elaborates on the application prospects
leveraged by knowledge graph oriented to smart grids, power consumer service,
decision-making in dispatching, and operation and maintenance of power
equipment. Finally, issues and challenges are summarised.Comment: IET Generation, Transmission & Distributio
A Resilience Toolbox and Research Design for Black Sky Hazards to Power Grids
A structured collection of tools for engineering resilience and a research approach to improve the resilience of a power grid are described in this paper. The collection is organized by a two-dimensional array formed from typologies of power grid components and business processes. These two dimensions provide physical and operational outlooks, respectively, for a power grid. The approach for resilience research is based on building a simulation model of a power grid which utilizes a resilience assessment equation to assess baseline resilience to a hazards’ profile, then iteratively selects a subset of tools from the collection, and introduces these as interventions in the power grid simulation model. Calculating the difference in resilience associated with each subset supports multicriteria decision-making to find the most convenient subset of interventions for a power grid and hazards’ profile. Resilience is an emergent quality of a power grid system, and therefore resilience research and interventions must be system-driven. This paper outlines further research required prior to the practical application of this approac
Data Challenges and Data Analytics Solutions for Power Systems
L'abstract è presente nell'allegato / the abstract is in the attachmen