Impact of seasonal weather on forecasting of power quality disturbances in distribution grids

Power supply disruptions, including short-time disturbances, can lead to large direct and indirect financial losses. The ability to predict the risk of these disturbances allows for preventive actions and increases the reliability of the supply. This paper investigates the impact of using seasonal data of combined common weather conditions on the power quality prediction in distribution grids. Our main contribution consists of weatherbased predictive models for three types of events that frequently occur in these grids, as well as an analysis of the influence of two training approaches: with either seasonal or all-year data, on their performance. All developed models score higher than arbitrary guessing; in several instances the improvement is considerable. It is demonstrated that in some cases the models improve when the training data is limited to a subset corresponding to a particular meteorological season. Examining variable importance values and distributions of the models’ data, it is shown that this situation takes place particularly when weather conditions correlated with the occurrence of power grid events vary across seasonsacceptedVersio

Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid

With the advancing integration of fluctuating renewables, a more dynamic demand-side, and a grid running closer to its operational limits, future power system operators require new tools to anticipate unwanted events. Advances in machine learning and availability of data suggest great potential in using data-driven approaches, but these will only ever be as good as the data they are based on. To lay the ground-work for future data-driven modelling, we establish a baseline state by analysing the statistical distribution of voltage measurements from three sites in the Norwegian power grid (22, 66, and 300 kV). Measurements span four years, are line and phase voltages, are cycle-by-cycle, and include all (even and odd) harmonics up to the 96 order. They are based on four years of historical data from three Elspec Power Quality Analyzers (corresponding to one trillion samples), which we have extracted, processed, and analyzed. We find that: (i) the distribution of harmonics depends on phase and voltage level; (ii) there is little power beyond the 13 harmonic; (iii) there is temporal clumping of extreme values; and (iv) there is seasonality on different time-scales. For machine learning based modelling these findings suggest that: (i) models should be trained in two steps (first with data from all sites, then adapted to site-level); (ii) including harmonics beyond the 13 is unlikely to increase model performance, and that modelling should include features that (iii) encode the state of the grid, as well as (iv) seasonality. View Full-Text Keywords: machine learning; power systems; harmonic distortion; power qualitypublishedVersio

The value of multiple data sources in machine learning models for power system event prediction

We describe a method for assessing the value of additional data sources used in the prediction of unwanted events (voltage dips, earth faults) in the power system. Using this method, machine learning models for event prediction using (combinations of) different data sources are developed. The value of each data source is the improvement in model performance it brings. In addition, feature importance is retrieved using SHapley Additive exPlanations (SHAP). The methodology is applied to models that predict faults based on power quality and weather data. We find that models that combine sources outperform models using either in isolation. They predict ground faults and voltage dips with AUCs (Area Under Curve) of 0.74 and 0.80, respectively. Meteorological data appears more valuable than power quality data and the most important features are dew point, month of the year, and the power spectral density at 4.7 HzacceptedVersio

A novel multistep mechanism for initial lymphangiogenesis in mouse embryos based on ultramicroscopy

Peer reviewe

Seal bypass at the Giant Gjallar Vent (Norwegian Sea): indications for a new phase of fluid venting at a 56-Ma-old fluid migration system

Highlights: • The Giant Gjallar Vent is still active in terms of fluid migration and faulting. • The Base Pleistocene Unconformity acts as a seal to upward fluid migration. • Seal bypass in at least one location leads to a new phase of fluid venting. The Giant Gjallar Vent (GGV), located in the Vøring Basin off mid-Norway, is one of the largest (~ 5 × 3 km) vent systems in the North Atlantic. The vent represents a reactivated former hydrothermal system that formed at about 56 Ma. It is fed by two pipes of 440 m and 480 m diameter that extend from the Lower Eocene section up to the Base Pleistocene Unconformity (BPU). Previous studies based on 3D seismic data differ in their interpretations of the present activity of the GGV, describing the system as buried and as reactivated in the Upper Pliocene. We present a new interpretation of the GGV’s reactivation, using high-resolution 2D seismic and Parasound data. Despite the absence of geochemical and hydroacoustic indications for fluid escape into the water column, the GGV appears to be active because of various seismic anomalies which we interpret to indicate the presence of free gas in the subsurface. The anomalies are confined to the Kai Formation beneath the BPU and the overlying Naust Formation, which are interpreted to act as a seal to upward fluid migration. The seal is breached by focused fluid migration at one location where an up to 100 m wide chimney-like anomaly extends from the BPU up to the seafloor. We propose that further overpressure build-up in response to sediment loading and continued gas ascent beneath the BPU will eventually lead to large-scale seal bypass, starting a new phase of venting at the GGV

Non-holistic coding of objects in lateral occipital complex with and without attention

A fundamental issue in visual cognition is whether high-level visual areas code objects in a part-based or a view-based (holistic) format. By examining the viewpoint invariance of object recognition, previous behavioral and neuroimaging studies have yielded ambiguous results, supporting both types of representational formats. A critical factor distinguishing the two formats could be the availability of attentional resources, as a number of studies have found greater viewpoint invariance for attended compared to unattended objects. It has therefore been suggested that attention is necessary to enable part-based representations, whereas holistic representations are automatically activated irrespective of attention. In this functional magnetic resonance imaging study we used a multivariate approach to probe the format of object representations in human lateral occipital complex (LOC) and its dependence on attention. We presented human participants with intact and half-split versions of objects that were either attended or unattended. Cross-classifying between intact and split objects, we found that the objectrelated information coded in activation patterns of intact objects is fully preserved in the patterns of split objects and vice versa. Importantly, the generalization between intact and split objects did not depend on attention. Our findings demonstrate that LOC codes objects in a non-holistic format, both in the presence and absence of attention

Incipient Fault Prediction in Power Quality Monitoring

European and global power grids are moving towards a Smart Grid architecture. Supporting this, advanced measurement equipment such as PQAs and PMUs are being deployed. These generate vast amounts of data upon which machine learning models capable of forecasting incipient faults can be built. We use live measurements from nine PQA nodes in the Norwegian grid to predict incipient interruptions, voltage dips, and earth faults. After training ensembles of gradient boosted decision trees on spectral decompositions of cycle-by-cycle voltage measurements, we evaluate their predictive performance. We find that interruptions are easiest to predict (95 % true positive, 20 % false positives). Earth faults and voltage dips are more challenging. Our models outperform naïve classifiers. We have explored forecast horizons of up to 40 seconds, but we have indications that forecast horizons of at least a few minutes are feasible.publishedVersio

Scattering suppression and confocal detection in multifocal multiphoton microscopy

Martini J, Andresen V, Anselmetti D. Scattering suppression and confocal detection in multifocal multiphoton microscopy. JOURNAL OF BIOMEDICAL OPTICS. 2007;12(3): 034010.We have developed a new descanned parallel (32-fold) pinhole and photomultiplier detection array for multifocal multiphoton microscopy that effectively reduces the blurring effect originating from scattered fluorescence photons in strongly scattering biological media. With this method, we achieve a fourfold improvement in photon statistics for detecting ballistic photons and an increase in spatial resolution by 21% in the lateral and 35% in the axial direction compared to single-beam non-descanned multiphoton microscopy. The new detection concept has been applied to plant leaves and pollen grains to verify the improvements in imaging quality. (C) 2007 Society of Photo-Optical Instrumentation Engineers