IWOS Inhand Wireless Overhead Line Sytem

Smart Distribution Network

InHand Wireless Overhead Line System

InHand Networks Inc.

Pioneering Industrial IoT for 17 Years

Global & Public company Beijing, China Virginia, US

Listed on NEEQ 430642 Backed by Top Silicon Valley VC

2001

2018

Delivered industrial cellular modem for China State Grid

Founded as M2M startup, Pioneer of IoT

2014 2012

2005

2007

Developed industrial router and remote monitoring solution for GE Healthcare; Now a global supplier of GEHC

Berlin, Germany

Became Schneider CAPP Technology Partner and Rockwell Encompass Product Partner

Complete IoT products and solutions proven by world class customers

Total Solutions for Industrial IoT IoT Products

Industrial Cellular Router

Cellular Data Terminal

Industrial Switch

Industrial Computer

Device Network Cloud Platform

Cloud Platform

Remote Maintenance and Service Platform

Solutions (include some products lines)

Distribution Overhead Line Analytic System

Smart Vending & Operation Platform

Machine Remote Monitoring System

France | Energy Energy Management Service

UK | Transportation Traffic Signal Control

Germany | Power Grid Distributed Energy Monitoring

China | Power Grid Distribution Automation Overhead Line Monitoring

Schneider Electric

Siemens

E.ON Energy, RWE

China State Grid

Used worldwide.

Proven worldwide.

4

US | Healthcare Italy | Energy Generating Medical Equipment Monitoring Steam Boilers Preventive Maintenance Monitoring & Control

South Africa Industrial Automation

India | Oil & Gas Natural Gas Pipeline System Monitoring

GE Healthcare

Schneider Electric

Gail

Viessmann

01 / How IWOS works

Contents

02 / Solution Highlights 03 / Summary

60,000+ Line Sensors Deployed

6,000+ Power Lines Covered

15 Million+ Power Line Events & Waveforms

20,000+ Line Faults Detected 6 All figures are by the end of 2017.

InHand Wireless Overhead-Line System (IWOS) Fault Detection & Location • Grid Analytics • Preventive Maintenance

IWOS Analytics Platform • • •

How it works Wireless VPN / Fiber Communication

Line Status Monitoring

Line Status Analysis Fault Detection Interface to DMS Big-data Analysis

Panorama Monitoring

DMS Center Utility Pole # 25

Pole # 30

LinePole Sensors #

Pole # 177 Pole # 5

Pole # 94

Feeder Lines 110KV Substation

Pole #

Pole # 174

Pole # 2

Concentrator Auto-triggered Waveform-recording Accurate Fault Locating

Pole # 28

InHand Networks

Notification to Line Patrol Team

Preventive Grid Analytics Powered by AI

• Integration Model based on Neural Network • Oscillography waveform classification based on AI algorithms • Accurately identify line faults and events including ground faults, short circuit faults, inrush current, power outage and etc.

• Preventive analysis and pre-fault alerts

8

Basic Setup • 3 Line Sensors form one group • Line Sensors communicate with concentrator via short range RF • Concentrator utilizes LTE/SSN to communicate with headend system

Site Configuration

A

Design principle & Benefits

Line Sensors

B

RF

Concentrator

C

• Simplify line sensors, leave complexity to concentrator • Flexibility • Optimization • Time synchronization • Low cost • Easy to install & maintain • Future proof

Key Features

Line Sensor

Line Sensor

• High precision current measurement: 0-800A ±1% • Electric-field strength measurement: ±1% • High sampling rate of oscillography waveforms: 12.8KHz (256 pts per cycle) • High precision time synchronization: 30us, enabling zerosequence current synthesis • Power optimization operation - Full-function mode: all features including waveform recording enabled; powered by line and supercapacitors - Low-power mode: waveform recording disabled; powered by line and battery • Power harvesting: 1A to support full-function mode; supercapacitors back for 12h • Water proof: IP67 • Light weight: < 1.25kg • Wide operation temperature: -40 ~ +70 °C

Concentrator Key Features • • • • • • • • Model A: Pole-mounted

WAN: LTE Field Area Network: RF, BLE Location and time synchronization: GPS Fault detection based on zero-sequence current synthesis Main power: 20W/30W solar panel Backup power: Lead–acid/Lithium battery IP55 protection Remote upgrade & maintenance

Concentrator Key Features

Model B: Line-mounted

• • • • • • • •

WAN: LTE Field Area Network: RF, BLE Location and time synchronization: GPS Fault detection based on zero-sequence current synthesis Main power: energy harvesting from the line, 6A Backup power: 24h Water proof: IP67 Light weight: <3kg

Field Installation

Headend System Key Features • Sensor/concentrator management • GIS-based real-time monitoring of line status and events • Deep Learning analytic system • Oscillography waveform classification • Fault alarms and preventive analysis • Reports • Load trend & statistics • power quality analysis • Fault statistics • System management

Artificial Intelligence is the Core Technology behind IWOS

Work condition classification flow

Power restore

Some features used by AI

Time domain

Frequency domain

Wavelet domain

Waveform signatures

Power Restore

Ground Fault Restore

Power Outage

Lightning Strike

Waveform signatures

Short Circuit Fault

Inrush

Other Condition

Other Condition

Accuracy of classification[1]

Total # of verified conditions: 2,924 sets Ground Faults: 522 sets Short Circuit Faults: 236 sets Inrush Current: 560 sets Lightning: 601 sets Power Restore: 524 sets Power Outage: 293 sets Other Conditions: 188 sets

Power restore

[1] Smart Distribution Network Operating Condition Recognition Based on Big Data Analysis. Fan Min , Zhang Bo , Yao Qiang , Zhang Jianliang , Huang darong , Han Qi. IEEE Conference on Data Driven Control and Learning Systems Conference(DDCLS), 2017.

Ground fault location process ① Test check if the Ground-fault event can be located

② Selection of fault line Select the fault line by comparison between lines

③ Fault location Locate the fault segment by comparison between nodes

故障定位模型结构

Integration Model based on DNN

Waveforms Preprocess

Output

故障定位模型结构

Training process

110kV Substation

Ground fault location example

Accuracy of ground fault location Result by ML

Not located

Full data set

3.56%

3.41%

Missing:

3.56%

False:

4.68%

Success:

92.76%

Locatable data set (94.92% of full data set): Success 89.35%

1.67%

located

Failed 3.01%

Missing:

3.71%

False:

3.14%

Success:

93.15%

Fault Can be located

Cannot be located

Our Achievements

Proved by installations in 18 China provinces and cities â&#x153;&#x201C; 20,000+ sets â&#x153;&#x201C; 60,000+ line sensors

China State Grid China Southern Grid

Achievements by Q2 2017 Statistic of IWOS systems under InHand Networks Support & Service (part of total IWOS installations) By Q2 2017 No. Power Utilities (Regional Level) No, of Substations

65 462

No. of Feeder Lines

2,159

No. of Installations

6,038

No. of Sensors No. of Power Line Events, with waveform data

No. of Detection of Short Circuit Faults No. of Detection of Ground Faults

18,114 15,099,165

2,817 10,115

IWOS is: • an advanced and field-proven solution for fault location • optimized for cost and flexibility • enabled and highlighted by Machine Learning tech • keep evolving towards predictive maintenance

Wangjing Science Park

3900 Jermantown Road, Suite 150

Beijing, China

Fairfax, VA 22030

+86 (10) 64391099

+1 (703) 348 2988

Our Customers Smart Grid

Industrial Automation

Transportation & Security

29

Healthcare

Smart Vending

Finance