BSES Experience Arvind Gujral, Head (Operation), BSES Delhi

Transcription

1 Intelligent meter and IT Key Initiatives for theft control BSES Experience Arvind Gujral, Head (Operation), BSES Delhi AFRICA UTILITY WEEK 22 nd to 24 th May-2012

2 Reliance 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 Employs more than 30,000 personnel Industrial, commercial and residential urban consumers Largest customer base for a Private Sector Utility in India

3 June Delhi s Electricity Distribution Scenario Age Old Network Power availability Less than 75% High Theft High loss levels _ BYPL 62% Insensitive Customer Service High Equipment Burn-outs Major state revenue To Run Power Dept. Govt. Subsidy 12 Billion/ year Inadequate Investment Chandni Chowk, Delhi; June 2002 The biggest Challenge was very high losses

4 BSES Delhi Discoms A Synopsis NDPL BRPL BYPL Reliance Infrastructure Ltd. acquired 51% stake in July 2002 in two out of three Discoms MES NDMC SN Particulars Unit BSES Delhi Discoms 1 Area sq. km Total Registered Customers Million Peak Demand MW Consumption per year MU 17,500 5 Employees Nos. 7,218 6 Customer Density Cons/sq km 2,964 7 Revenue( as per ARR ) Billion USD 1.44

5 BSES Philosophy Electricity Theft Theft Theory.. Any Abnormal condition resulting to Slowing of meter Switching OFF of meter Can lead to data change Are potential methods of theft How to control? Study impact of theft rather than method of theft. All theft leave evidence. Co-relate method with symptoms. 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.

6 Theft policing!!! key enablers Energy meter Data collection Meter lab Source of information Memory & communication Anti Theft feature Periodic down load Using AMR/CMRI Data storage system Failure analysis Theft plotting Theft trends 4 Analytics 5 Energy Audit Logics and filter Identifying exception Generating leads Energy gaps Area of high gap

7 Metering Systems Parameters Captured KWh,MDI,KVAh Instantaneous voltage, Current RTC&TODTariff Billing & Power On- Off History Anti theft features Neutral current measurements Sealed welded meter cover Defined abnormalities logic Hardware lock - calibration Load survey (3ph) Event logging All meters have Data transfer logging large memory, Inter- Sensing of abnormal fields face & communication capability.

8 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 Rest all consumers the data is down loaded d using CMRI/ PDS. Since 2006, All Consumers data is down loaded electronically.

9 Meter Test Lab 100% removed meter are tested in meter lab Physical condition Accuracy Functionality Data down load Meter Photograph Meter Test report In front of consumers Both lab NABL Cause of failure Trend of failure Identify man made failure Evidence - prosecution Tracking movements Rating of consumers Preventive action

10 Meter Failure Analysis And Plotting The Theft Methods Failure analysis Theft plotting Plotting theft on map Removal of meter Sealing in bag Failure analysi s) Identifyi ng theft Feed back Cluster 1 Method A Case 1356 Cluster: Method D Cluster 5 Case 244 Method B Cluster 8 Method D Cluster 3 High A,D Cluster Cluster 4 Method C 233 ases Cluster 7 Method C Mrthod D

11 Types of Methods Tampering by remote Tampering by altering Ckt External Burning & Hole

12 By External Methods Tampering Devices High Tesla field Remote control High Frequency RF field High Voltage Ignition coils Spark Gun Rare Earth magnet

13 Theft Control Mechanism Meter Data download Analytics Field removed meter Meter Specifications Meter Lab- Analysis for failure causes Theft plotting Theft leads Designing anti-theft features Theft method Meter Technical Team Energy Audit- High Gap areas Enforcement Cell

14 Effect of theft method Anti Theft Feature In Meter Immunity No effect Anti Theft method Event logging g Direct Symptoms Detection of event Used Analytics Cell Indirect symptoms Addln parameters Helps to analytics Use deterrent mode check legality

15 Analytics How to Identify Theft? Energy meter data analysis To study of consumer meter data for abnormalities Consumption analysis To study the consumption trend Analytics To study data to identify theft Billing database analysis To study billing parameters Secondary database analysis To study the survey data

16 How Analytics Works? Collection of meter data Conversion of data Filtration on defined logics 2 nd level filtration (Analysis) Development of new logics Theft leads Quality cases Assessment cases Meter Test Lab Meter Team Vendor Inputs from

17 Basic Concept To find the relation between theft method And its effect on meter parameter Energy = V I CosØ t Energy Meter Data Analysis Logics Logics are the correlation between deviation of basic electrical rules and with method of theft Using software identify events which satisfy such Logics. Voltage Circuit it Tamper Logic : Voltage < Vth And Current > Vth Theft Method Abnormal Meter Data Deviation in Basic Electrical Engineering rules Potential Missing in R & Y Phase BSES has developed a library of logics

18 Billing pattern study By trend study month by month Fall in consumption in same month for different year KWH 500Units Jan Feb Mar Consumption Graph Apr May Jun Jul Aug Sep Oct Nov Year-2008 Year-2009 Dec Month By trend study of 24 hrs. Domestic consumer, but no Consumption in night hours. Predefined ratio of consumption / MD for different category Domestic : 96 units/ MD Commercial : 165 units / MD Industrial : 150 units / MD

19 Consumption Analysis To study the actual consumption v/s predefined d benchmarks Benchmarking By Survey By Grouping Hotels Industry.. BTS ATM.. AC Rooms Industry Type Occupancy Working Hrs. Ambience * Wide variation found in different hotels. Benchmark decided by the average consumption of similar type of consumers * Consumption of CNG pumps in Mumbai found double as compare to Delhi.

20 Secondary Data Analysis Secondary data collected from various sources. 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 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 bank 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

21 Energy Audit A Very Powerful Tool Grid Substation M1 DT 1 11 kv Feeder Feeding to DTs and HT Consumers M2 M4 M3 HT Consumer DT 2 Analysis using HV Energy Audit Reports

22 Anslysis Using HV Energy Audit Reports Summary of Feeder to DT + HT Reports S. No. Division Feeder Name Feeder Energy Sum of DT/HT Gap (Units) Gap (%) Energy 1 Nehru Place S/S NO. 6 OKHLA PH-III Nehru Place O/G TELEPHONE EXCHANGE R K Puram NIRYAT BHAWAN Error in Multiplying factor of 20

23 Secondary Data Analysis 26 no. flats indulged in using direct supply

24 Electricity Theft Policing - BSES Detection Prevention Action (Raid) Prosecution

25 AT&C Loss Reduction Performance BSES DELHI BYPL BRPL started Year analytics initiative was AT&C Loss in % including collection efficiency

26 Any Queries? Gracias Obrigado Thanks Arvind Gujral , Mobile arvind.gujral@relianceada.com li d