Intelligent meter and ITK I iti ti f th ft t lKey Initiatives for theft control

BSES ExperienceArvind Gujral, Head (Operation), BSES Delhi

AFRICA UTILITY WEEKd h22nd to 24th May-2012

Reliance Energy : Leader in Private Sector DistributionReliance Energy : Leader in Private Sector Distribution

Mumbai Delhi Orissa

Serving over 7 million customers in Mumbai, Delhi and Orissa

Powering 2 out of 3 homes in Mumbai & Delhi and 3 out of 4 homes in Orissa

Distributing over 5,000 MW – the largest in India

Mumbai Delhi Orissa

Employs more than 30,000 personnel

Industrial, commercial and residential urban consumers

Largest customer base for a Private Sector Utility in India

June 2002- Delhi’s Electricity Distribution Scenario

Age Old NetworkPower availability –

Less than 75%

Insensitive CustomerService

High TheftHigh loss levels _ BYPL 62%

Major state revenue –To Run Power Dept.

High Equipment Burn-outs

Inadequate InvestmentGovt. Subsidy –12 Billion/ year

The biggest Challenge was very high losses Chandni Chowk, Delhi; June’2002

BSES Delhi Discoms – A Synopsis

Reliance Infrastructure Ltd. acquired 51% stake in July

NDPLNDPL

BRPLBYPL

acquired 51% stake in July 2002 in two out of three

Discoms

SN Particulars Unit BSES Delhi Discoms

MES NDMC

1 Area sq. km 950

2 Total Registered Customers Million 2.83 Peak Demand MW 3350

4 Consumption per year MU 17,500

5 Employees Nos. 7,218

6 Customer Density Cons/sq km 2,964

7 Revenue( as per ARR 2011-12) Billion USD 1.44

BSES Philosophy – Electricity Theft

How to control?Theft Theory ………..Any Abnormal condition resulting to

Study impact of theft rather than method of theft.

All theft leave evidence. Co-relate method with symptoms

• Slowing of meter• Switching OFF of meter• Can lead to data change

Are potential methods of theft Co relate method with symptoms.Are potential methods of theft

Kick Start ………..

As Abnormal conditions can result to meter tampering,

It can also damage the meters.g

Analysis of damaged / field removed meters can give vital clues.

Theft policing !!! key enablers

Data collectionEnergy meter Meter lab1 2 3

• Periodic down load

• Using AMR/CMRI

D t t t

• Source of information

• Memory & communication

A ti Th ft f t

• Failure analysis• Theft plotting• Theft trends

• Data storage system• Anti Theft feature

Analytics4

Energy Audit5

Analytics

• Logics and filter

Energy Audit

• Energy gaps• Identifying exception

• Generating leads

Energy gaps

• Area of high gap

Metering Systems

Anti theft features

Neutral current measurements

Parameters Captured• KWh, MDI, KVAh

• InstantaneousSealed – welded meter cover

Defined abnormalities logic

• Instantaneousvoltage, Current

• RTC & TOD Tariff

Hardware lock - calibration

Event logging

• Billing & Power On-Off History

•Load survey (3ph) Event logging

Data transfer logging

S i f b l fi ldAll meters have

large memory Inter Sensing of abnormal fieldslarge memory, Inter-face &

communication capabilitycapability.

Data Downloading

BSES has installed AMR modems for all premium consumers

Presently 15,000 consumers are covered through AMR

Plan to further extend AMR to 0.1 Million consumers

R t ll th d t i d l d d i CMRI/ PDSRest all consumers the data is down loaded using CMRI/ PDS.

Since 2006, All Consumers data is down loaded electronically.

Meter Test Lab

100% removed meter are tested in meter lab

Cause of failurePhysical condition

Accuracy

Cause of failure

Trend of failure

Functionality

Data down load

In front of consumers

Identify man made failure

Evidence - prosecution

Meter Photograph

Meter Test reportRating of consumers

Tracking movements

Meter Test report

Preventive action

Both lab NABL

Failure analysis Theft plotting

Meter Failure Analysis And Plotting The Theft Methods

Plotting theft on map

Sealing in b

Failure l i

Identifyi Feed backRemoval f

Cluster 1

Method A

bag analysis)

ng theftof meter

Cluster 5

Case 244

Method B

Case 1356Cluster:

Method D

Cluster 3

High A,DCluster 4

Method CCluster 8

Method D233 ases

Cluster

Method CCluster 7

Mrthod D

Method D

Types of Methods

Tampering by remote

Tampering by altering Ckt

External Burning & Hole

By External Methods By External Methods Tampering DevicesTampering Devices

Hi h T l fi ldHigh Tesla field

Remote control

High Frequency – RF field

High Voltage – Ignition coils

Spark Gun

Rare Earth magnet

M t Data

Theft Control Mechanism

Meter AnalyticsData download

Field removed

Meter S ifi ti Theft

removed meter

Specifications Theft plotting

Meter Lab- Analysis forfailure causes

Theft

leads

d

Theft method

Designing anti-theft features

Meter Technical Team

Energy Audit-High Gap

areas

Enforcement Cell

Effect of theft method

Anti Theft Feature In Meter

method

Immunity No effect

Anti Event

loggingDetection of

eventTheft

method

gg g

Direct Symptoms

Used Analytics Cell

event

Symptoms Cell

Helps to analytics

Indirect symptoms –

Addl analyticsAddln parameters

Use deterrent mode – check legality

Analytics – How to Identify Theft ?

Energy meter data analysis

To study of consumer meter data

Consumption analysis

T t d th ti t dTo study of consumer meter data for abnormalities To study the consumption trend

Analytics

To study data to identify theft

Billing database analysis Secondary database analysisBilling database analysis

To study billing parameters

Secondary database analysis

To study the survey data

How Analytics Works?

Collection of meter data

Conversion of dataConversion of data

Filtration on defined logics

2nd level filtration (Analysis) Development of new logics

Theft leads Meter Test Lab

Quality cases

Assessment cases

Meter Team

Vendor

Inputsfrom

Basic Concept

To find the relation between

LogicsLogics are the correlation between deviation of

Energy Meter Data Analysis

To find the relation between theft method And

its effect on meter parameter

Energy = V I CosØ t

gbasic electrical rules and with method of theft

Using software identify events which satisfy suchLogics.

V lt Ci it Tgy

Theft Method

Voltage Circuit TamperLogic : Voltage < Vth And Current > Vth

AbnormalDeviation in

Basic ElectricalMeter Data

Basic Electrical Engineering

rules

Potential Missing in R & Y Phase

BSES has developed a library of logics

Billing pattern study

B t d t dConsumption Graph Year-2008

By trend studymonth by month

0100200300400500

KWH Units

Year-2009

Fall in consumption in same 0

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep

Oct

Nov

DecMonth

Fall in consumption in same month for different year

By trend studyof 24 hrs.

Domestic consumer, but no Consumption in night hours.

Predefined ratio of consumption / MD for different categoryDomestic : 96 units/ MDCommercial : 165 units / MDIndustrial : 150 units / MD

T t d th t l ti / d fi d b h k

Consumption Analysis

Benchmarking

To study the actual consumption v/s predefined benchmarks

By Survey By Grouping

Hotels Industry ….. BTS ATM …..y ….. …..

AC Rooms I d t T

Benchmark decided by the average consumptionAC Rooms

OccupancyAmbience---------

Industry TypeWorking Hrs.---------

of similar type of consumers

* Wide variation found in different hotels.* Consumption of CNG pumps in Mumbai found double as compare to Delhi.

Secondary data collected from various sources.

Secondary Data Analysis

The data available in the secondary data are reconciled in billing database to conclude unbilled cases.

For example , through internet sites of Reserve bank of India & all For example , through internet sites of Reserve bank of India & all other banks operating in India, list of all bank branches operating in our service area was obtained.

This list was reconciled with the billing database to confirm that all b k b h b i bill dbank branches were being billed.

To our surprise we found around 1% of the bank branches were not in the billing net.

Secondary data analysis – a useful tool for tariff misuse

Logics development is a continuous exercise

Energy Audit – A Very Powerful Tool

Grid Substation

M1

11 kV Feeder Feeding to DTs and HT Consumers

M4

DT 1

M2

M3 HT Consumer

Analysis using HV Energy Audit ReportsDT 2

Anslysis Using HV Energy Audit Reports

Summary of Feeder to DT + HT Reports

S. No. Division Feeder Name Feeder Sum ofDT/HT Gap (Units) Gap (%)S. No. Division Feeder Name Energy DT/HTEnergy

Gap (Units) Gap (%)

1 Nehru Place S/S NO. 6 OKHLA PH-III 175782 175374 408 0.23

2 Nehru Place O/G TELEPHONE EXCHANGE 277284 273945 3339 0.20

3 R K Puram NIRYAT BHAWAN 676500 38844 637656 94.26

Error in Multiplying factor of 20

Secondary Data Analysis

26 no. flats indulged ini di t lusing direct supply

Electricity Theft Policing - BSES

Detection

Action (Raid)Prevention

ProsecutionProsecution

AT&C Loss Reduction Performance

BSES DELHI BYPL BRPL

61.88

54.29

47.445 06

43.8850.12

50.00

60.00

70.00

Year analytics initiative was

started

29.5

23.2211

45.0640.64

35.53

29.9227.1

211

39.03

30.00

40.00

50.00 started

21.1 1921.1 19.217

10.00

20.00

2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-112002 03 2003 04 2004 05 2005 06 2006 07 2007 08 2008 09 2009 10 2010 11

AT&C Loss in % including collection efficiency

Any Queries ?

ThanksGraciasObrigadoThanks

Arvind Gujral91-11-39999959, Mobile - 0091 9350718353

E il i d j l@ li dEmail: arvind.gujral@relianceada.com