Inteligente metros y de IT Inteligente metros e IT ...Intelligent meter and IT Key Initiatives for theft control BSES Experience Rajesh Bansal, Head (Meters), BSES Delhi Inteligente

28–30 September, 2010, Sao Paulo, BRAZIL

28 a 30 de Setembro de 2010Centro de Convenções Frei CanecaSão PauloBRASIL

Intelligent meter and ITKey Initiatives for theft control

BSES ExperienceRajesh Bansal, Head (Meters), BSES Delhi

Inteligente metros e ITPrincipais iniciativas para o controle de roubo

Experiência BSESRajesh Bansal, Head (Meters), BSES Delhi

Inteligente metros y de ITPrincipales iniciativas para el control de robo de

BSES ExperienciaRajesh Bansal, Head (Meters), BSES Delhi

Intelligent meter and ITKey Initiatives for theft control

BSES ExperienceRajesh Bansal, Head (Meters), BSES Delhi


Reliance Energy : Leader in Private Sector Distribu tionReliance Energy : Leader in Private Sector Distribu tion

� Serving over 7 million customers in Mumbai, Delhi a nd Orissa

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

� Distributing over 5,000 MW – the largest in India

� Employs more than 30,000 personnel

� Industrial, commercial and residential urban consum ers

Largest customer base for a Private Sector Utility in India

Mumbai Delhi Orissa


June 2002- Delhi’s Electricity Distribution Scenario

Age Old Network

Insensitive CustomerService

High TheftHigh loss levels _ BYPL 62%

Inadequate Investment

Major state revenue –To Run Power Dept.

Govt. Subsidy –12 Billion/ year

The biggest Challenge was very high losses

Power availability –Less than 75%

High Equipment Burn-outs

Chandni Chowk, Delhi; June’2002


NDPLNDPL

BRPLBRPL

BYPLBYPL

1301

4230

3312

5200

941

10.46

200

BYPL

2537

1360

3906

9100

1554

12.20

750

BRPL

3838

1964

7218

14300

2495

22.66

950

BSES

Delhi

1915Rs. Crs.Revenue( as per ARR for 2006-07)7

1667Cons/ sq. kmCustomer density6

3600Nos.Employees5

5700MUConsumption per year4

1050MWPeak Demand3

8.5LacsTotal Registered customers 2

510sq. kmArea1

NDPLUnitParticularsSN

REL acquired 51% stake in July

2002 in two out of three Discoms

Business Inheritance Marred by Multiple Maladies

Privatized Delhi DISCOMS – A Synopsis


BSES Philosophy – Electricity Theft

How to control?

Study impact of theft rather than method of theft.

All theft leave evidence. Co-relate method with symptoms.

Theft Theory ………..Any Abnormal condition resulting to

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

Are potential methods of theft

Kick Start ………..

As Abnormal conditions can result to meter tampering,

It can also damage the meters.

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


Data collection

• Periodic down load

• Using AMR/CMRI

• Data storage system

Energy meter

• Source of information

• Memory & communication

• Anti Theft feature

Meter lab

• Failure analysis

• Theft plotting

• Theft trends

Theft policing !!! key enablers

1 2 3

Analytics

• Logics and filter

• Identifying exception

• Generating leads

4Energy Audit

• Energy gaps

• Area of high gap

5


Anti theft features

• Neutral current measurements

• Sealed – welded meter cover

• Defined abnormalities logic

• Hardware lock - calibration

• Event logging

• Data transfer logging

• Sensing of abnormal fields

Parameters Captured

• KWh, MDI, KVAh

• Instantaneous voltage, Current

• RTC & TOD Tariff

• Billing & Power On-Off History

•Load survey (3ph)

All meters have large memory, Inter-

face & communication

capability.

Metering Systems


• BSES has installed AMR modems for all premium consum ers

• Presently 15,000 consumers are covered through AMR

• Plan to further extend AMR to 0.1 Million consumers

• Rest all consumers the data is down loaded using CMR I/ PDS.

Since 2006, All Consumers data is down loaded electronically.

Data Downloading


100% removed meter are tested in meter lab

Meter Test report

Meter Photograph

Data down load

Functionality

Accuracy

Physical condition

In front of

consumers

Cause of failure

Trend of failure

Identify man made failure

Preventive action

Rating of consumers

Evidence - prosecution

Both lab NABL

Tracking movements

Meter Test Lab


Cluster 5

Case 244

Method B

Cluster 1

Method A

Case 1356Cluster :

Method D

Cluster 3

High A,DCluster 4

Method C

233 asesCluster

Method CCluster 7

Mrthod D

Cluster 8

Method D

Failure analysis Theft plotting

Sealing in bag

Failure analys

is)

Identifying theft

Feed back

Removal of meter

Plotting theft on map

Meter Failure Analysis And Plotting The Theft Methods


Tampering by remote

Tampering by altering Ckt

External Burning & Hole

Types of Methods


By External Methods By External Methods Tampering DevicesTampering Devices

High Tesla field

High Frequency – RF field

Spark Gun

Remote control

High Voltage – Ignition coils

Rare Earth magnet


Meter Specifications

Meter

Theft plotting

Meter Technical Team

Energy Audit-High Gapareas

Enforcement Cell

Meter Lab- Analysis forfailure causes

AnalyticsData download

Cause &

symptoms

Theft

leads

Theft method

Field removed meter

Designing anti-theft features

Theft Control Mechanism


Anti Theft method

Effect of theft method

Immunity No effect

Event logging

Direct Symptoms

Used Analytics Cell

Helps to analytics

Indirect symptoms –

Addlnparameters

Detection of event

Use deterrent mode – check legality

Anti Theft Feature In Meter


Analytics

To study data to identify theft

Energy meter data analysis

To study of consumer meter data for abnormalities

Consumption analysis

To study the consumption trend

Billing database analysis

To study billing parameters

Secondary database analysis

To study the survey data

Analytics – How to Identify Theft ?


Collection of meter data

Conversion of data

Filtration on defined logics

2nd level filtration (Analysis)

Theft leads

Quality cases

Assessment cases

How Analytics Works?

Development of new logics

Meter Test Lab

Meter Team

Vendor

Inputsfrom


Basic Concept

To find the relation between theft method And

its effect on meter parameter

Energy = V I Cos Ø t

Logics

� Logics are the correlation between deviation of basic electrical rules and with method of theft

� Using software identify events which satisfy suchLogics.

Theft Method

AbnormalMeter Data

Deviation inBasic Electrical

Engineeringrules

BSES has developed a library of logics

Voltage Circuit TamperLogic : Voltage < Vth And Current > Vth

Potential Missing in R & Y Phase

Energy Meter Data Analysis


Billing pattern study

By trend studymonth by month

By trend studyof 24 hrs.

Consumption Graph

0100200300400500

Jan

Feb

Mar

Apr

May Jun

Jul

Aug Sep Oct

Nov

DecMonth

KWH Units

Year-2008

Year-2009

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

Fall in consumption in same month for different year

Domestic consumer, but no Consumption in night hours.


Benchmarking

By Survey By Grouping

Hotels Industry ….. BTS ATM …..

� AC Rooms� Occupancy� Ambience� ---------

� Industry Type� Working Hrs.� ---------

Benchmark decided by the average consumption

of similar type of consumers

To study the actual consumption v/s predefined benchmarks

Consumption Analysis

* Wide variation found in different hotels.

* Consumption of CNG pumps in Mumbai found double as compare to Delhi.


� Secondary data collected from various sources.

� The data available in the secondary data are reconc iled in billing database to conclude unbilled cases.

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

� This list was reconciled with the billing database to confirm that all bank branches were being billed.

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

Secondary Data Analysis

Secondary data analysis – a useful tool for tariff m isuse

Logics development is a continuous exercise


Analysis using HV Energy Audit Reports

Grid Substation

11 kV Feeder Feeding to DTs and HT Consumers

M3

M4

HT Consumer

DT 1

DT 2

M2

M1

Energy Audit – A Very Powerful Tool


0.203339273945277284O/G TELEPHONE EXCHANGE

Nehru Place

2

94.2663765638844676500NIRYAT BHAWAN

R K Puram

3

0.23408175374175782S/S NO. 6 OKHLA PH-III

Nehru Place

1

Gap (%)Gap (Units)Sum ofDT/HTEnergy

FeederEnergy

Feeder NameDivisionS.

No.

Summary of Feeder to DT + HT Reports

Error in Multiplying factor of 20

Anslysis Using HV Energy Audit Reports


26 no. flats indulged inusing direct supply

Secondary Data Analysis


Detection

Action (Raid)Prevention

Prosecution

Electricity Theft Policing - BSES


BSES BSES BSES BSES DELHIDELHIDELHIDELHI

61.88

54.29

29.5

23.221.1

17

47.445.06

40.64

35.53

29.9227.1

21.1 19.215

39.03

43.8850.12

10.0010.0010.0010.00

20.0020.0020.0020.00

30.0030.0030.0030.00

40.0040.0040.0040.00

50.0050.0050.0050.00

60.0060.0060.0060.00

70.0070.0070.0070.00

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

BYPLBYPLBYPLBYPL BRPLBRPLBRPLBRPL

Year analytics

initiative was started

AT&C Loss in % including collection efficiency

AT&C Loss Reduction Performance


Rajesh Bansal011-39999425, Mobile - 0091 9350261602Email: [email protected]

Email : [email protected]

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Inteligente metros y de IT Inteligente metros e IT ...Intelligent meter and IT Key Initiatives for theft control BSES Experience Rajesh Bansal, Head (Meters), BSES Delhi Inteligente