Schneider Electric Systems USA, Inc.

Schneider Electric Industries Sas

Bartosz Boguslawski

Matthieu Boujonnier

Loryne Bissuel-beauvais

Fahd Saghir

Diego Alberto

Reynaldo Espana Rey

Giorgio Colangelo

Jacky Hoareau

Xavier Pasbeau

Loryne Bissuel Beauvais

Helenio Gilabert

Costin Vasile

Matthieu Favre

A method for classifying a partial discharge in an insulator of an electrical conductor of a medium voltage or high voltage electrical device, the method allowing a partial discharge to be classified from between at least one first class and a second class distinct from the first class. The method includes: obtaining a set of samples each corresponding to at least one partial discharge, determining a classification model by automatic learning based on at least one statistical quantity of the samples of the set, acquiring a new sample corresponding to at least one partial discharge, and determining the class of the partial discharge associated with the new sample acquired using the classification model.

Method for classifying a partial discharge in an electrical conductor of a medium voltage electrical device

Systems and methods for real-time monitoring and control of well operations at a well site use machine learning (ML) based analytics at the well site. The systems and methods perform ML-based analytics on data from the well site via an edge device directly at the well site to detect operations that fall outside expected norms and automatically respond to such abnormal operations. The edge device can issue alerts regarding the abnormal operations and take predefined steps to reduce potential damage resulting from such abnormal operations. The edge device can also anticipate failures and a time to failure by performing ML-based analytics on operations data from the well site using normal operations data. This can help decrease downtime and minimize lost productivity and cost as well as reduce health and safety risks for field personnel.

Predicting well site failure modes and times using machine learning analytics and dynacard classifications

Well site machine learning based analytics

A method and system for determining a tripping range of an electrical circuit switching device designed to supply power to an electrical installation. The system includes an electronic calculation module which is configured to receive data from at least one vibration sensor integrated into said switching device and which is configured to select, based on spectrograms calculated from temporal vibration signals acquired, a predetermined subset of operational characteristics; apply a prediction model parameterized by supervised machine learning to the values of the operational characteristics so as to obtain an estimated value of cut-off current, and determine, as a function of the estimated value of cut-off current, a tripping range of the switching device amongst: normal tripping, overload tripping, and tripping following a short circuit.

Method and system for determining a tripping range of an electrical circuit switching device

Systems/methods for real-time monitoring and control of a well site provide an event monitor and detector for progressing cavity pump (PCP) operations at the well site. The event monitor and detector uses machine learning (ML) based anomaly detection to detect operations that fall outside normal PCP operating space. The event monitor and detector then computes novelty scores for the anomalies and checks whether the novelty scores exceed a threshold novelty score. If the number of novelties detected within a given detection window exceeds a minimum threshold count, then the event monitor and detector flags an 'event' and automatically responds accordingly. The event monitor and detector also provides an explanation with the alerts that quantifies the extent to which various PCP parameters contributed to the event. The event monitor and detector further performs drift detection to determine whether an event may be due to operator-initiated adjustments to PCP parameters.

Detecting events in progressing cavity pump operation and maintenance based on anomaly and drift detection

Systems and methods for real-time monitoring and control of well site operations employ well site edge analytics to detect abnormal operations. The systems and methods use machine learning (ML) based analytics on an edge device directly at the well site to detect possible occurrence of abnormal events and automatically respond to such events. The event detection may be based on trends identified in the data acquired from the well site operations in real time. The trends may be identified by correlation and by fitting line segments to the data and analyzing the slopes of the line segments. Upon detecting an unusual event, the edge device can issue alerts regarding the event and take predefined steps to reduce potential damage resulting from such event. This can help decrease downtime and minimize lost productivity and cost as well as reduce health and safety risks for field personnel.

Autonomous failure prediction and pump control for well optimization

A method for detecting an abnormal event in an insulator of an electrical conductor of a medium-voltage or high-voltage electrical device. The method includes: acquiring a set of successive samples of a vibration signal associated with the electrical conductor; determining a modelled value of a following sample based on the acquired set of samples and on a prediction model, the prediction model being obtained through machine learning of the vibration signal based on a set of samples acquired in reference conditions in which the electrical conductor is free from any abnormal event; acquiring the following sample of the vibration signal; calculating a difference between the value of the acquired sample and the modelled value; and if the calculated difference is greater than a predetermined threshold, detecting an abnormal event in the electrical conductor.

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Grenoble, France

Bartosz Boguslawski