Sas Institute Inc.

Mark Joseph Konya

Thomas Dale Anderson

Priyadarshini Sharma

David Pope

Yuwei Liao

Glenn D Good

Tae Yoon Lee

Tom Anderson

Emily Jean Forney

Bradley Lawson, Jr

Jennifer Short Whaley

Blanden Scott Chisum

James M Caton

Sen-Hao Lai

Brett Farina

A computer monitors a status of grid devices using sensor measurements. Sensor data is clustered using a predefined grouping distance value to define one or more sensor event clusters. A plurality of monitored devices is clustered using a predefined clustering distance value to define one or more asset clusters. A location is associated with each monitored device of the plurality of monitored devices. A distance is computed between each sensor event cluster and each asset cluster. When the computed distance is less than or equal to a predefined asset/sensor distance value for a sensor event cluster and an asset cluster, an asset identifier of the asset cluster associated with the computed distance is added to an asset event list. For each asset cluster included in the asset event list, an asset location of an asset is shown on a map in a graphical user interface presented in a display.

Grid status monitoring system

Operational events associated with a target physical device can be detected for mitigation by implementing some aspects described herein. For example, a system can apply a sliding window to received sensor measurements at successive time intervals to generate a set of data windows. The system can determine a set of eigenvectors associated with the set of data windows by performing principal component analysis on a set of data points in the set of data windows. The system can determine a set of angle changes between pairs of eigenvectors. The system can generate a measurement profile by executing an integral transform on the set of angle changes. One or more trained machine-learning models are configured to detect an operational event associated with the target physical device based on the measurement profile and generate an output indicating the operational event.

Diagnostic techniques for monitoring physical devices and resolving operational events

An apparatus includes a processor to: assign each value of each set of values of an initial supply meter data and of an initial load meter data to one of multiple buckets based on weather conditions and/or time and date; for each bucket, generate upper and lower bounds of power provision and power consumption values, and use the upper and lower bounds to identify outlier values assigned to the bucket; for each set of values within the initial supply meter data and within the initial load meter data, generate a naive model from the non-outlier values, and use interpolation and the naive model to fill in gaps, thereby generating cleansed supply meter data and cleansed load meter data; and store the cleansed supply meter data and cleansed load meter data together as merged meter data for use in making predictions.

Electric power grid supply and load prediction using cleansed time series data

Various embodiments are generally directed to systems for multi-stage measurement data analysis (MMDA), such as for evaluation and/or validation of data received from a measurement device, for instance. Some embodiments are particularly directed to a MMDA system that utilizes event stream processing (ESP) to provide near real-time validation of measurement data, at least in part, by detecting losses in the measurement data. In many embodiments, the MMDA system may detect technical losses (e.g., due to equipment malfunction) and/or non-technical losses (e.g., due to compromised equipment). For example, the MMDA system may receive measurement data generated by an electrical meter and determine the electrical meter is malfunctioning by detecting a technical loss in the measurement data. In many embodiments, the MMDA system may utilize both direct and indirect measurement data transmitted via separate communication paths to provide near real-time validation of measurement data.

Techniques for multi-stage analysis of measurement data with event stream processing

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Chesterfield, MO, United States of America

Mark Joseph Konya