The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 04, 2021

Filed:

Apr. 09, 2018
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Amar P. Azad, Bangalore, IN;

Manikandan Padmanaban, Bangalore, IN;

Kalyan Dasgupta, Bangalore, IN;

Shivkumar Kalyanaraman, Bangalore, IN;

Jagabondhu Hazra, Bangalore, IN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H02S 50/10 (2014.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
H02S 50/10 (2014.12); G06N 3/0445 (2013.01); G06N 3/08 (2013.01);
Abstract

Methods, systems, and computer program products are provided herein in connection with IoT-enabled solar PV health monitoring and advising related thereto. A computer-implemented method includes obtaining current-voltage samples corresponding to solar photovoltaic modules by triggering switch circuitry between (i) an inverter attributed to the solar photovoltaic modules and (ii) a current-voltage tracer; detecting one or more anomalies in the obtained current-voltage samples by applying machine learning techniques to the obtained current-voltage samples; automatically performing a root cause analysis on the detected anomalies by (i) converting the obtained current-voltage samples to sequential data, (ii) applying a sequence classifier to the sequential data, and (iii) identifying a pre-determined anomaly class comparable to the sequential data based on the application of the sequence classifier; and automatically generating and outputting a suggestion for remedial action based on the identified pre-determined anomaly class.


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