Systematic Framework for Integration of Weather Data into Prediction Models for the Electric Grid Outage and Asset Management Applications

This paper describes a Weather Impact Model (WIM) capable of serving a variety of predictive applications ranging from real-time operation and day-ahead operation planning, to asset and outage management. The proposed model is capable of combining various weather parameters into different weather impact features of interest to a specific application. This work focuses on the development of a universal weather impacts model based on the logistic regression embedded in a Geographic Information System (GIS). It is capable of merging massive data sets from historical outage and weather data, to real-time weather forecast and network monitoring measurements, into a feature known as weather hazard probability. The examples of the outage and asset management applications are used to illustrate the model capabilities

Monitoring of Power System Topology in Real-Time.

Abstrac

Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast

A novel method is proposed for real-time solar generation forecast using weather data, while exploiting both spatial and temporal structural dependencies. The network observed over time is projected to a lower-dimensional representation where a variety of weather measurements are used to train a structured regression model while weather forecast is used at the inference stage. Experiments were conducted at 288 locations in the San Antonio, TX area on obtained from the National Solar Radiation Database. The model predicts solar irradiance with a good accuracy (R2 0.91 for the summer, 0.85 for the winter, and 0.89 for the global model). The best accuracy was obtained by the Random Forest Regressor. Multiple additional experiments were conducted to characterize influence of missing data and different time horizons providing evidence that the new algorithm is robust for data missing not only completely at random but also when the mechanism is spatial, and temporal

Ligand-Independent Activation of the EGFR by Lipid Raft Disruption

Normal and immortalized keratinocytes demonstrate large aggregates of lipid rafts, detectable by membrane staining with fluorescently tagged cholera toxin (CTx). As lipid rafts are known to regulate the function of many surface receptors, we wished to investigate their impact on the EGFR in HaCaT cells. When rafts were disrupted by cholesterol sequestration with methyl-β-cyclodextrin (MβCD) or filipin III, EGFR rearranged into approximately micrometer large clusters outside the CTxbright raft aggregates. These clusters contained high concentrations of activated, tyrosine-phosphorylated EGFR exhibiting greatly reduced mobility in the fluorescence recovery after photobleaching experiments. EGFR activation led to the stimulation of extracellular signal-regulated kinase 2, the phosphorylated form of which translocated to the nucleus and stimulated growth of the MβCD-treated cells. Experiments with the specific antagonistic antibody proved that the activation of EGFR by lipid raft disruption occurred without the participation of the ligand. We hypothesize that cholesterol depletion leads to the release of EGFR from the damaged rafts into the small confined areas of the membrane, where the receptor molecules are likely to be spontaneously activated owing to a very high density and/or separation from the inhibitory factors remaining in the surrounding portions of the membrane

Training Machine Learning Models with Simulated Data for Improved Line Fault Events Classification From 3-Phase PMU Field Recordings

Utilizing data analytics and machine learning (ML) on phasor measurement units (PMUs) data to analyze faults automatically is the focus of this paper. Insufficient labels and natural uneven distribution of different types of line fault events found in field-recorded PMU data make supervised ML model development challenging. To address this issue, we train off-the-shelf Support Vector Machine (SVM) ML models for line fault classification using simulated PMU data obtained from a combination of 12 physical and virtual PMUs placed on a synthetic IEEE 14-bus system as well as using this simulated data unified with field recordings. A conducted sensitivity study is focused on three factors, 1) the number of PMUs used to train the ML model, 2) the voltage level at which the model is trained, and 3) the vicinity of PMUs to transmission line faults. The ML models trained with simulated and field data are evaluated on one-year field-recorded data collected from 38 PMUs sparsely located in the US Western interconnection. We demonstrate that when training ML models with only simulated data, the performance varies significantly with different number of PMUs, voltage level, and PMU placement in separate areas of the synthetic grid (F1 score of 0.78 to 0.92). We obtained an F1 score of 0.94 using the simulated dataset integrated with field recordings. The performance of a ML model developed using simulated data is also evaluated on the three-phase voltage signals extracted from 188 PMUs in the Eastern interconnection accompanied by imprecise labels, where the majority of the labels do not identify the fault type. On this extremely challenging task, we achieved 77% accuracy solely using synthetic data for ML training

The Use of System in the Loop, Hardware in the Loop, and Co-modeling of Cyber-Physical Systems in Developing and Evaluating New Smart Grid Solutions

This paper deals with two issues: development of some advanced smart grid applications, and implementation of advanced testbeds to evaluate these applications. In each of the development cases, the role of the testbeds is explained and evaluation results are presented. The applications cover the synchrophasor systems, interfacing of microgrids to the main grid, and cybersecurity solutions. The paper hypothesizes that the use of the advanced testbeds is beneficial for the development process since the solution product-to-market cycle may be shortened due to early real-life demonstrations. In addition, solution users’ feedback to the testbed demonstration can be incorporated at an early stage when making the changes is not as costly as doing it at more mature development stages

Fast Extraction and Characterization of Fundamental Frequency Events from a Large PMU Dataset using Big Data Analytics

A novel method for fast extraction of fundamental frequency events (FFE) based on measurements of frequency and rate of change of frequency by Phasor Measurement Units (PMU) is introduced. The method is designed to work with exceptionally large historical PMU datasets. Statistical analysis was used to extract the features and train Random Forest and Catboost classifiers. The method is capable of fast extraction of FFE from a historical dataset containing measurements from hundreds of PMUs captured over multiple years. The reported accuracy of the best algorithm for classification expressed as Area Under the receiver operating Characteristic curve reaches 0.98, which was obtained in out-of-sample evaluations on 109 system-wide events over 2 years observed at 43 PMUs. Then Minimum Volume Enclosing Ellipsoid Algorithm was used to further analyze the events. 93.72% events were correctly characterized, where average duration of the event as seen by the PMU was 9.93 sec

Spatially Aware Ensemble-Based Learning to Predict Weather-Related Outages in Transmission

This paper describes the implementation of prediction model for real-time assessment of weather related outages in the electric transmission system. The network data and historical outages are correlated with variety of weather sources in order to construct the knowledge extraction platform for accurate outage probability prediction. An extension of logistic regression prediction model that embeds the spatial configuration of the network was used for prediction. The results show that developed algorithm has very high accuracy and is able to differentiate the outage area from the rest of the network in 1 to 3 hours before the outage. The prediction algorithm is integrated inside weather testbed for real-time mapping of network outage probabilities using incoming weather forecast