Power line issue diagnostic methods and apparatus using distributed analytics

Abstract

Apparatus, system, and method for diagnosing status of electrical line performance by receiving and analyzing a plurality of electrical line data from a plurality of lines includes a collection of internet of things sensors, a communication network, local firmware boards, a data hub, a computation engine, a machine learning engine, and an internet of things server operatively connected to the machine learning engine.

The following is claimed: 
     
         1 . A method of providing predicted electrical line issues including phase current imbalance or phase current harmonics to a high number of end users having interest in the reliability of the line, comprising:
 a) providing a single board computer processor;   b) connecting an electrical line to be analyzed for the presence or absence of phase current imbalance and phase current harmonics to the single board computer processor;   c) using the single board computer processor to collect and extract metadata indicative of phase current imbalance or phase current harmonics from any high frequency current or voltage present in said electrical line;   d) processing the metadata using either a machine learning support vector machine or a rule engine, which is equipped with a baseline dataset;   e) providing the processed metadata to a big data cloud data service database; and   f) making the results of the metadata processing available to a high number of end users communicating with the big data cloud data service database.   
     
     
         2 . Apparatus for diagnosing status of electrical line performance by receiving and analyzing a plurality of electrical line data from a plurality of electrical lines, comprising:
 a) a collection of Internet of things sensors, one operatively connected to each of the electrical lines, each sensor sensing one or more electrical parameters including current, voltage, power factor, harmonic distortion, swell, surge, sag, active power, reactive power and frequency of electrical power carried by the associated electrical line;   b) a communications network;   c) a collection of local firmware boards, one for each of the internet of things sensors and being connectedly associated therewith, for receiving sensed electrical line data from the internet of things sensors;   d) a data hub operatively connected to the local firmware boards and receiving therefrom the data sensed by the internet of things sensors;   e) a computation engine operatively connected to the local firmware boards, the data hub, and an internet of things server, for classifying different fault types in the electrical line data using quadratic hyperplanes in transformed variable space at the local firmware boards, at the data hub, or at the internet of things server, and computing at the local firmware boards, at the data hub, and at the internet of things server data parameter values pre-defined by a user;   f) a machine learning engine;   g) the internet of things server being operatively connected to the machine learning engine for analyzing the sensed electrical line data by comparing the sensed data to pre-defined prior condition data indicative of acceptable operation and raising an alarm if the sensed data deviates from the prior data indicative of acceptable operation by more than a preselected amount;   h) a gauge for displaying a visual indication of the performance state of a selected one of the parameters, the gauge accepting user-based intuition about the parameter state to affect the displayed visual indication.   
     
     
         3 . A method for determining status of electrical line performance comprising:
 a) positioning a plurality of internet of things sensors in operative communication with a plurality of electrical lines, one internet of things sensor for each line;   b) sensing electrical line data with the collection of internet of things sensors, each sensor sensing one or more electrical parameters, including current, voltage, power factor, harmonic distortion, swell, surge, sag, active power, reactive power and frequency of electrical power carried by the associated electrical line;   c) providing a collection of local firmware boards, one for each of the internet of things sensors and being connectedly associated therewith, for receiving sensed electrical line data from the internet of things sensors;   d) receiving from the local firmware boards the electrical line data sensed by the internet of things sensors on a data hub operatively connected to the local firmware boards;   e) providing a cloud-based internet of things server and computation engine operatively connected to the data hub;   f) classifying different fault types in the electrical line data using quadratic hyper planes in transformed variable space at the local firmware boards, at the data hub, or at the internet of things server;   g) computing at the local firmware boards, and/or at the data hub, and/or at the internet of things server preselected data parameters using quadratic hyperplanes in transformed variable space;   h) analyzing the sensed electrical line data by comparing the sensed data to pre-defined parameter values of prior data established using quadratic hyperplanes in transformed variable space that are indicative of acceptable operation;   i) raising an alarm if the sensed data deviates from the parameter values of the prior data by more than a preselected amount;   j) displaying a visual indication of the performance state of selected ones of the parameters, on a gauge accepting user-based intuition about the parameter state to affect the displayed visual indication.