Distributed IoT based sensor analytics for power line diagnosis

1. A method of predicting electrical line issues, the method comprising:

a) collecting through a processor, data associated with at least one electrical line reading from one or more sensors capable of local computation of time series data of electrical sensors transmitted over a communication network;

i) wherein the data collected is over a finite time period and transmitted to a machine learning engine, and

ii) wherein the machine learning engine is associated with a computer database hosting real time and historical data;

b) visualizing, through a processor, at least one electrical line issue based on an analysis through a big data engine;

c) determining the at least one electrical line issue based on one or more computations;

d) indicating the at least one electrical line issue through a user interface dynamic; and,

e) setting an alarm, through a processor, for the at least one electrical line issue;

wherein a computation engine enables the one or more computations;

wherein the alarm is set through at least one of a rule based engine and multi-classification machine learning engine;

wherein the user interface dynamic is a predictive maintenance circular gauge; and

wherein the electrical line issue includes at least one of swell, surge, harmonics, sag and flickering.

2. A distributed power line diagnosis systenciin which at least one power line issue is determined based on one or more computations comprising;

a) a firmware to receive a plurality of electrical line data over a communications network;

b) a real time data processing system associated with distributed databases;

c) a local firmware board;

d) a data hub;

e) an IoT server;

f) a multi-classification machine learning engine associated with the IoT server;

g) a display module associated with one or more processors and user interface; wherein a power line issue is mapped onto a depiction on a user interfaces; and wherein the power line issue is determined based on a computation through at least one of the local firmware board, the data hub, and the IoT server; and

h) an alarm module to raise an alarm when a pre-set condition is breached; wherein the alarm module is associated with the multi-classification machine learning engine.

3. The system of claim 2 , wherein the IoT server further comprises a computation engine, operatively connected to the data processing system, the distributed databases, the data hub and the learning engine enabling one or more computations.

4. The system of claim 3 , wherein the distributed computation engine computes in one or more of:

i) the local firmware board;

ii) the data hub; and

iii) the IoT server.

5. The system of claim 4 , wherein the alarm is set through at least one of a rule based engine and a multi-classification machine learning engine;

wherein the depiction on the user interface is a predictive maintenance circular gauge;

wherein the communication network is one of Wi-Fi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet, Zwave, or a combination thereof;

wherein the alarm is raised over the communication network through one of a notification on the mobile application, Short Message Service (SMS), email or a combination thereof.

6. A method of diagnosing distributed power line issues comprising:

a) receiving at least one electrical reading at a firmware board computation engine;

b) transmitting an output of firmware board computation engine to a data hub computation engine over a communication network;

c) transmitting an output of the data hub computation engine to a big data server, wherein a machine learning engine is associated with at least one of the big data server and a computer database hosting historical data;

d) determining at least one electrical line issue based on one or more computations on the big data server using the historical data;

e) visualizing at least one electrical line issue based on an analysis through the big data server;

f) indicating at least one electrical line issue through a user interface dynamic; and

g) setting, through a processor, an alarm for at least one electrical line issue through at least one of a rule based engine and a multi-classification machine learning engine;

wherein the alarm is set through at least one of a rule based engine and multi-classification machine learning engine;

wherein the user interface dynamic is a predicative maintenance circular gauge;

wherein the communications network is one of WiFi, 2G, 3G, 4G, GPRS, EDGE, Bluetooth, ZigBee, Piconet of BLE, Zwave or a combination thereof;

wherein the alarm is raised over the communication network through one of a notification on the mobile application, Short Message Service (SMS), email or a combination thereof.