System and method for predictive foliage impingement and wildfire management using generative adversarial network

A system comprising:

• one or more processor;
• and
• a memory operatively coupled to the one or more processors, the memory having instructions stored thereon, wherein execution of the instructions by the one or more processors, cause the one or more processors to:
• provide, via a first classifier of a first trained neural network of a first GAN-based network, wherein the first GAN-based network is continuously configured using power line data set acquired by a first sensor equipment operatively placed at a low voltage-side at a first location of a utility power distribution circuit;
• provide via a second classifier of a second trained neural network of a second GAN-based network, wherein the second GAN-based network is continuously configured using power line data set comprising voltage, current, and/or power factor acquired by a second sensor equipment operatively placed at a low voltage-side at a second location of the utility power distribution circuit;
• determine, via the first trained neural network, a first predicted output indicative of a presence of the anomalous event in proximity to the first sensor device;
• determine, via the second trained neural network, a second predicted output indicative of a presence of the anomalous event in proximity to the second sensor device;
• and
• output, via a fault detection location operator, a location indicator of foliage impingement along the utility power distribution circuit;
• wherein the output is used to predict foliage impingement at the location in the utility power distribution circuit.

A non-transitory computer-readable medium having instructions stored thereon, wherein execution of the instructions by one or more processors of a remote-server or cloud-based analytical engine cause the one or more processors to:

• provide, via a first classifier of a first trained neural network of a first GAN-based network, wherein the first GAN-based network is continuously configured using power line data set acquired by a first sensor equipment operatively placed at a low voltage-side at a first location of a utility power distribution circuit;
• provide via a second classifier of a second trained neural network of a second GAN-based network, wherein the second GAN-based network is continuously configured using power line data set comprising voltage, current, and/or power factor acquired by a second sensor equipment operatively placed at a low voltage-side at a second location of the utility power distribution circuit;
• determine, via the first trained neural network, a first predicted output indicative of a presence of the anomalous event in proximity to the first sensor device;
• determine, via the second trained neural network, a second predicted output indicative of a presence of the anomalous event in proximity to the second sensor device;
• and
• output, via a fault detection location operator, a location indicator of foliage impingement along the utility power distribution circuit;
• wherein the output is used to predict foliage impingement at the location in the utility power distribution circuit.