NTL Detection of Electricity Theft and Abnormalities

for Large Power Consumers In TNB Malaysia

J. Nagi1,*

K.S. Yap2, F. Nagi

3

1Research Management Centre

UNITEN R&D Sdn. Bhd. University Tenaga Nasional, 43000 Kajang, Malaysia

{jawad,yapkeem,farrukh}@uniten.edu.my

S.K. Tiong1, S. P. Koh1, S. K. Ahmed2 2Dept. of Electronics and Communication Engineering

3Department of Mechanical Engineering

University Tenaga Nasional, 43000 Kajang, Malaysia

{siehkiong,johnnykoh,syedkhaleel}@uniten.edu.my

Abstract—Electricity consumer dishonesty is a problem faced by

all power utilities. Finding efficient measurements for detecting

fraudulent electricity consumption has been an active research area in recent years. This paper presents an approach towards

detection of Non-technical Losses (NTLs) of Large Power Consumers (LPC) in Tenaga Nasional Berhad (TNB) Malaysia. The main motivation of this study is to assist Tenaga Nasional

Berhad (TNB) Sdn. Bhd. in Malaysia to reduce its NTLs in the LPC distribution sector. Remote meters installed at premises of

LPC customers transmit power consumption data including

remote meter events wirelessly to TNB Metering Services Sdn. Bhd. The remote meter reading (RMR) consumption data for

TNB LPC customers is recorded based on half-hourly intervals. The technique proposed in this paper correlates the half-hourly

RMR consumption data with abnormal meter events. The correlated data provides information regarding consumption

characteristics i.e. load profiles of LPC customers, which helps to expose abnormal consumption behavior that is known to be

highly correlated with NTL activities and electricity theft. Pilot

testing results obtained from TNB Distribution (TNBD) Sdn. Bhd. for onsite inspection of LPC customers in peninsular

Malaysia indicate the proposed NTL detection technique is effective with a 55% detection hitrate. With the implementation of this intelligent system, NTL activities of LPC customers in

TNB Malaysia will reduce significantly. Keywords—Nontechnical loss, Electricity theft; Fuzzy logic;

Remote meters; Intelligent systems.

I. INTRODUCTION

OWER utilities lose large amounts of money each year

due to fraud by electricity consumers. Electricity fraud

can be defined as a dishonest or illegal use of electricity

equipment or service with the intention to avoid billing

charge. It is relatively difficult to distinguish between honest

and fraudulent customers. Realistically, electric utilities will

never be able to eliminate fraud, however, it is possible to take

measures to detect, prevent and reduce fraud [1].

Distribution losses in power utilities originating from

electricity theft and other customer malfeasances are termed as

Non-technical Losses (NTLs) [1]. Such losses mainly occur

due to meter tampering, meter malfunction, illegal connections

and billing irregularities [2].The problem of NTLs is not only

faced by the least developed countries in the Asian and African

regions, but also by developed countries such as the United

States of America and the United Kingdom. Specifically, high

rates of NTL activities have been reported in the majority of

developing countries in the Association of South East Asian

Nations (ASEAN) group, which include Malaysia, Indonesia,

Thailand, Myanmar and Vietnam [1]. As an example, in

developing countries such as Bangladesh, India, Pakistan and

Lebanon, an average between 20% to 30% of NTLs have been

observed [3], [4].

Investigations are undertaken by electric utility companies

to assess the impact of technical losses in generation,

transmission and distribution networks, and the overall performance of power networks [5], [6]. NTLs comprise one

of the most important concerns for electricity distribution

utilities worldwide. In 2004, Tenaga Nasional Berhad (TNB)

Sdn. Bhd. the sole electricity provider in Malaysia recorded

revenue losses as high as USD 229 million a year as a result of

electricity theft, billing errors and faulty metering [7].

In recent years, several data mining and research studies on

fraud identification and prediction techniques have been carried

out in the electricity distribution sector [8]. These include

Statistical Methods [9-10], Decision Trees [11], Artificial

Neural Networks (ANNs) [12], Knowledge Discovery in

Databases (KDD) [13], and Multiple Classifiers using cross

identification and voting scheme [14]. Among these, load

profiling is one of the most widely used [15], which is defined

as the pattern of electricity consumption of a customer [8].

NTLs for Large Power Consumers (LPCs) appear to have

never been adequately studied, and to date there is no published

evidence of research on detection of NTLs in the LPC

distribution sector of the electricity supply industry. In [3], [7]–

[9], we proposed a NTL detection model using Support Vector

Machines (SVMs) and the Fuzzy Inference System (FIS) for

detection of NTLs in the Ordinary Power Consumer (OPC)

distribution sector of TNB Malaysia. Currently TNB Malaysia is focusing on reducing its NTLs,

in the LPC distribution sector. At present, customer installation

inspections are carried out without any specific focus due to the

remote meter reading (RMR) generating a large number of

event logs for LPC customers. The huge amount of meter event

logs causes confusion in detecting and shortlisting possible

suspects from the RMR data. The approach proposed in this

paper models an intelligent system for assisting TNBD Strike

P

This project is supported by Tenaga Nasional Berhad (TNB) Research in

collaboration with UNITEN R&D Sdn. Bhd. of University Tenaga Nasional, Malaysia.

Proceedings of 2010 IEEE Student Conference on Research and Development (SCOReD 2010), 13 - 14 Dec 2010, Putrajaya, Malaysia

978-1-4244-8648-9/10/$26.00 ©2010 IEEE 202

Engagement Against Losses (SEAL) teams to increase

effectiveness of their operation in reducing NTLs in the LPC

distribution sector. The proposed intelligent system will

increase electricity theft detection hitrate for onsite remote

meter inspection of LPC customers and reduce operational

costs due to onsite inspection in monitoring NTL activities. The rest of this paper is organized as follows. Section II

presents a brief review of NTLs. Section III presents the

framework used for development of the LPC NTL detection

model. In Section IV, the pilot testing results obtained from

TNBD Sdn. Bhd. are presented and discussed. Finally,

conclusions are presented in Section V.

II. NON-TECHNICAL LOSSES

NTLs are mainly related to electricity theft and customer

management processes in which there exist a number of means

of consciously defrauding the utility concerned [3]. In most

developing countries, transmission and distribution (T&D)

losses account for a large portion of NTLs, which implies that electric utilities have to concentrate on reducing NTLs prior to

reducing technical losses [16]. NTLs generally include the

following activities [17], [18]:

1) Tampering with meters so that meters record lower

rates of consumption;

2) Stealing by bypassing the meter or otherwise making

illegal connections;

3) Arranging false readings by bribing meter readers;

4) Arranging billing irregularities with the help of internal

employees by means of such subterfuges as making out

lower bills, adjusting the decimal point position on bills, or

just ignoring unpaid bills. By default, the amount of electrical energy generated

should equal the amount of energy registered as consumed.

However, in reality, this situation is different because losses are

an integral result of energy transmission and distribution [11].

As some power loss is inevitable, steps can be taken to ensure

that it is minimized. Several measures have been applied to this

end, including those based on technology and those that rely on

human effort and ingenuity [4]. Reduction of LPC NTLs is crucial for electricity utility

companies, as these losses are concentrated in the high voltage

(HV) network and are most critical at higher levels in industrial

and large commercial sectors such as factories. As the current

method of dealing with NTLs imposes high operational costs

due to onsite inspection and requires extensive use of human

resources [17]; therefore, this study aims to reduce LPC NTLs

and operational costs in monitoring NTL activities.

III. METHODOLOGY

A. Problem Background

At present there are about 80,000 of low-voltage LPC

customers and 6,000 high-voltage LPC customers that are

generating approximately 80% of the total revenue of TNB

Malaysia. In the effort of executing TNB Distribution (TNBD)

Division's key initiative in reducing NTLs in the LPC

distribution sector, remote meters were installed by TNBD

SEAL teams for all LPC customer premises. The current

actions taken by the NTL Group of TNB to reduce LPC NTLs

include: stepping up meter checking, reporting on irregularities,

and monitoring unbilled accounts and meter reading and sales.

The intelligent system proposed in this research study provides tools for multiple classification and detection of LPC

NTL activities based on the available monthly RMR

consumption data and RMR event logs of LPC customers. This

NTL detection system is an enhancement of the previous NTL

detection systems that were developed for Sabah Electricity

Sdn. Bhd. (SESB), Malaysia [19], [20] and TNB OPC metered

customers in peninsular Malaysia [3], [8], [9].

B. Data Acqusition

Remote meter reading (RMR) consumption data and meter

event logs for all LPC (LV and HV) customers for a period of

one month (30 days) were acquired from TNB Metering

Services Sdn. Bhd. The RMR consumption data and meter

event logs for all customers are transmitted wirelessly to TNB

Metering Services database. The RMR data is recorded based

on half-hourly intervals, as indicated in Figure 1. The RMR

event logs shown in Figure 2 are triggered by abnormal meter

events such as AC Power Down, Voltage Cut, Reverse Current and Current Zero Sequence etc.

Fig. 1. Remote meter reading (RMR) consumption data

C. Data Preprocessing

For the purpose of load profiling and NTL detection, the

load consumption characteristics of the LPC customers need to

be obtained along with their respective meter events. Hence,

the RMR consumption data in Figure 1 and RMR event logs in

Figure 2 for each LPC customer are correlated based on the

half-hourly intervals with respect to the RMR consumption

data, as shown in Figure 3.

203

Fig. 2. Remote meter reading (RMR) event logs

D. Load Profiling

The monthly correlated data provides valuable information

regarding the consumption characteristics of LPC customers,

which helps to expose abnormal consumption behavior that is known to be highly correlated with NTL activities. A load

profile indicating the one month consumption period of 30

days (1440 load values) of an LPC customer with abnormal

meter events is shown in Figure 4.

Fig. 3. Monthly correlated RMR data

E. NTL Detection Framework

The monthly correlated data of the LPC customers is used to model a NTL detection framework as shown in Figure 5.

Based on the information provided by the NTL Group of

TNBD, confirmed LPC suspects after onsite inspection are

commonly found performing electricity theft and fraud for

smaller periods of time, generally lasting a few hours within

the time frame of a week. Using this human knowledge with

other intelligence from the NTL Group of TNBD, 5 types of

NTL detection categories (Type-1 through Type-5) are

developed to form the NTL detection framework in Figure 5.

The 5 types of NTL detection categories are determined by

analyzing the monthly correlated RMR data and load profiles

of LPC customers previously confirmed as fraud by TNBD

customer onsite Strike Engagement Against Losses (SEAL) teams.

Fig. 4. Monthly load profile (30 days) of LPC customer

The 5 types of NTL detection mechanisms in Figure 5

emulate the reasoning process that human experts (TNBD

SEAL teams) undertake in detecting abnormalities and fraud

activities. Type-1 and Type-2 detections shortlist customer

suspects with high possibilities of electricity theft and fraud

while, Type-3 to Type-5 detections shortlist customer suspects

with abnormalities such as abnormal meter events. The NTL

detection methodology for the 5 types is elaborated as follows:

1) Type-1: If the customer has � or more continuous or

discontinuous point(s) of the meter events: “AC Power

Down or Voltage Cut” with the condition that main (M)

and check (C) meter for the customer have the same

phenomenon.

2) Type-2: If the customer has � or more continuous or

discontinuous point(s) of the meter events: “Current

Zero Sequence or Cover Opened”.

3) Type-3: If the customer has � or more continuous or

discontinuous point(s) of the meter event: “Reverse

Current” where all occurrences within one interval

(half hour) are counted as a single point only.

4) Type-4: If customer is a Full Day Operation (FDO)

customer and the correlated data has a consumption

drop with the meter conditions that no “AC Power Down or Voltage Cut” event occurs before the drop,

where the drop is for ‘� points’ or more with the

consumption during the drop being in the range of �� to

�� percent of the maximum customer consumption.

5) Type-5: If customer is a Half Day Operation (HDO)

customer and the correlated data has a consumption

drop with meter conditions that no “AC Power Down or

Voltage Cut” event occurs before the drop, where the

drop is for ‘�points’ or more with the consumption

during the drop being in the range of �� to �� percent of

the maximum customer consumption.

where �, �� and �� represent integer values. FDO and

HDO LPC customers in Type-4 and Type-5 detection

mechanisms are classified using a simple logic-based approach.

204

If a customer load profile satisfies the condition that it has a

constant consumption with a tolerance level within the range of

20% peak-to-peak (pp) kWh for duration of 336 points (one

week) or more, then that customer is classified as a FDO

customer. The load profile of a FDO LPC customer is shown in

Figure 6. Alternatively, customers that do not satisfy the FDO

condition are considered as HDO customers. The load profile of a HDO LPC customer is shown in Figure 7.

IV. EXPERIMENTAL RESULTS

Pilot testing results obtained from TNBD SEAL teams for

manual onsite inspection of LPC NTL activities provided

feedback that an average detection hitrate of 55% was

achieved. The detection hitrate obtained is for inspections

carried out in the states of Selangor, Penang and Johor in

peninsular Malaysia. Apart from detection of customers with

electricity theft and fraud activities, the proposed system can

identify LPC meters with the following abnormalities:

1) Voltage Tolerance

2) Low Battery Condition

3) No Internal Consumption

4) No External Consumption 5) Voltage Swell

Fig. 6. Monthly load profile of full day operation (FDO) customer

Fig. 7. Monthly load profile of half day operation (HDO) customer

RMR

Consumption

Data

RMR Event

Logs

RMR Data less

than a month

Read RMR

Consumption Data

into Database

Correlate RMR

Consumption Data

with RMR Event Logs

Check for Incomplete Data

Type-3

Type-4

Type-1

Type-2

Detection Report (Type-1 and Type-2)

Detection Report Summary

Abnormality Report (Type-3, Type-4 and Type-5)

Incomplete Customer

Data Report

Type-5

No

Yes

Input

Output

(Reports)

NTL Detection and

Classification

Data Preprocessing

Fig. 5. NTL detection framework for LPC customers

Abnormality Report Summary

Check for Abnormalities Check for Fraud Activities

205

The LPC Remote Meter Abnormality Detection System

(RMADS) (in Figure 8) developed in this research study uses

the NTL detection and classification framework is shown in

Figure 5. The RMADS (intelligent system) will assist TNBD

SEAL teams to increase the effectiveness of their operation in

reducing NTL activities in the LPC distribution sector.

V. CONCLUSION

This paper presents an approach for detection of NTLs for LPC customers in TNB Malaysia. More specifically, this

study develops a NTL detection framework for detection of

fraudulent and abnormal load consumption patterns using

RMR consumption data and meter event logs.

Testing results obtained from TNBD Sdn. Bhd. Indicate that

the proposed framework can be used for reliable detection of

fraudulent electricity consumers and abnormalities. The

current actions taken by the NTL Group of TNB in order to

reduce LPC NTLs include: stepping up meter checking,

reporting on irregularities, and monitoring unbilled accounts

and meter reading and sales.

With the implementation of the developed intelligent system (in Figure 8) an average detection hitrate of 55% will be

achievable. This will benefit TNB not only in improving its

handling of NTLs, but will complement their existing ongoing

practices, and it is envisaged that significant savings will

result from the use of this system.

Fig. 8. Remote Meter Abnormality Detection System (RMADS)

REFERENCES

[1] A. H. Nizar, and Z. Y. Dong, “Identification and detection of electricity

customer behavior irregularities,” in Proc. of IEEE/PES Power Systems

Conference and Exposition, Seattle, Washington, United States, Mar.

15-18, 2009, pp. 1–10.

[2] A.H. Nizar, J.H. Zhao, and Z.Y. Dong, “Customer Information System

Data Pre-Processing with Feature Selection Techniques for Non-

Technical Losses Prediction in an Electricity Market,” in Proc. of the

International Conference on Power System Technology (PowerCon) 2006, Chongqing, China, Oct. 22-26 2006, pp. 1–7.

[3] J. Nagi, K. S. Yap, S. K. Tiong, S. K. Ahmed, M. Mohamad,

“Nontechnical loss detection for metered customers in power utility using support vector machines”, IEEE Trans. Power Del., vol. 25, no. 2,

pp. 1162–1171, Apr. 2010.

[4] A. H. Nizar, Z. Y. Dong, and Y. Wang, “Power utility nontechnical loss analysis with extreme learning machine model”, IEEE Trans. Power

Syst., vol. 23, no. 3, pp. 946–955, Aug. 2008.

[5] C. R. Paul, “System loss in a Metropolitan utility network” IEEE Power

Engineering Journal, pp. 305–307, Sept. 1987.

[6] I. E. Davidson, A. Odubiyi, M. O. Kachienga, and B. Manhire,

“Technical Loss Computation and Economic Dispatch Model in T&D

Systems in a Deregulated ESI”, IEEE Power Eng. Journal, Apr. 2002.

[7] J. Nagi, K.S. Yap, S.K. Tiong, and S.K. Ahmed, “Detection of

Abnormalities and Electricity Theft using Genetic Support Vector

Machines”, in Proc. of the IEEE Region 10 Conference (TENCON)

2008, Nov. 18-21, 2008, Hyderabad, India, pp. 1–6.

[8] J. Nagi, K.S. Yap, S.K. Tiong, A. M. Mohammad, and S. K. Ahmed,

“Non-Technical Loss Analysis for Detection of Electricity Theft using

Support Vector Machines”, in Proc. of the 2nd IEEE International Power

and Energy Conference (PECON) 2008, Dec. 1-3, 2008, Johor Bahru,

Malaysia, pp. 907–912.

[9] J. Nagi, K.S. Yap, S.K. Tiong, S.K. Ahmed, F. Nagi, “Improving SVM-based Nontechnical Loss Detection in Power Utility Using Fuzzy

Inference System”, accepted for publication in IEEE Trans. Power Del.,

June 2010. Manuscript ID: PESL-00108-2009.R2. [10] J. W. Fourie and J. E. Calmeyer, “A statistical method to minimize

electrical energy losses in a local electricity distribution network” in

Proc. of the 7th IEEE AFRICON Conference Africa, Sept. 2004. [11] A. H. Nizar, Z. Y. Dong, J. H. Zhao, P. Zhang, “A Data Mining Based

NTL Analysis Method”, IEEE PES General Meeting, pp. 1–8, 2007.

[12] J. R. Galvan, A. Elices, A. Munoz, T. Czernichow, and M. A. Sanz-

Bobi, “System for Detection of Abnormalities and Fraud in Customer Consumption” in Proc. of the 12th Conference on the Electric Power

Supply Industry, Nov 1998.

[13] A. H. Nizar, Z. Y. Dong, M. Jalaluddin, and M. J. Raffles, “Load Profiling Non-Technical Loss Activities in a Power Utility” in Proc. of

First International Power and Energy Conference (PECON), Nov. 2006.

[14] R. Jiang, H. Tagaris, A. Lachsz, and M. Jeffrey, “Wavelet Based Feature

Extraction and Multiple Classifiers for Electricity Fraud Detection” in

Proc. of IEEE/PES T&D Conference and Exhibition 2002: Asia Pacific,

Vol. 3, pp. 2251–2256.

[15] D. Gerbec, S. Gasperic, I. Smon, and F. Gubina, “Allocation of the load

profiles to consumers using probabilistic neural networks” IEEE Trans.

on Power Systems, vol. 20, no. 2, pp. 548–555, May 2005.

[16] Hydro and Thermal Power Business Team, Transmission and

Distribution Loss Reduction Services #167 Samsung-dong, Kangnamgu,

Seoul, 135-791, Korea.

[17] A. H. Nizar, Z. Y. Dong, M. Jalaluddin, and M. J. Raffles, “Load

profiling method in detecting non-technical loss activities in a power

utility,” in Proc. of the 1st IEEE International Power and Energy

Conference (PECON) 2006, Putrajaya, Malaysia, Nov. 28–29, 2006, pp.

82–87.

[18] A. H. Nizar, Z. Y. Dong, and J. H. Zhao, “Load profiling and data

mining techniques in electricity deregulated market,” presented at the

IEEE Power Eng. Soc. General Meeting, Montreal, QC, Canada, Jun.

18–22, 2006.

[19] H.L. Pok, K.S. Yap, I.Z. Abidin, A.H. Hashim, Z.F. Hussien, and A.M.

Mohamad, “Abnormalities and Fraud Electric Meter Detection using

Hybrid Support Vector Machine and Modified Genetic Algorithm”, in

Proc. of the 19th International Conference on Electricity Distribution

(CIRED) 2007, Vienna, May 21–24 2007.

[20] A.R. Ahmad, A.M. Mohamad, “Intelligent System for Detection of Abnormalities and Probable Fraud by Metered Customers”, in Proc. of

the 19th International Conference on Electricity Distribution (CIRED)

2007, Vienna, May 21–24 2007.

206