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Improvement of DT Load Estimation by Using

Typical Load ProfilesAndrew Charles D. Yang

1, Ronoel M. Dellota

2

Networks, Manila Electric Company

Pasig City, Philippines1acdyang@meralco.com.ph

2rmdellota@meralco.com.ph

Abstract This paper proposes a new methodology for singledistribution transformer (DT) load estimation using typicalcustomer load curves. The process described in this paper is an

improvement over regression-based formulas to properlyestimate DT loads. The results of the methodology are compared

and verified to the output of LP-capable meters installed at DTs.

Keywords distribution, transformer, meter, DT, TLMS, loadprofile

I. INTRODUCTION

Transformer load monitoring is an important aspect ofelectric distribution utility operation. An accurate and reliableload monitoring system enables system planners and operatorsto determine transformers that are overloaded, normally

loaded, under-loaded and idle.

If properly identified, overloaded transformers can be

readily scheduled for replacement. Hence, DT failures due to

overloading can be prevented. Under-loaded transformers can

be removed and replaced with the proper rating, therebymaximizing asset utilization. Idle transformers can be

removed and used as new stocks, thereby, saving purchasecosts for new transformers.

There are different methods of Transformer Loadmonitoring. The most accurate method to monitor the load of

transformers is to install indicating demand (ID) meters thatregister the kW or KVA demand. Because of the number

distribution transformers (DT) installed in the field, this is notpractical due to the significant investment required. However,

installation of ID meters can be justified in areas with high

incidence of DT failures due to overload.

Other methodologies of transformer load monitoring deal

with load estimation techniques. One particular method is byusing an empirical formula based on regression to estimate the

DT KVA loading given only the kWh consumption ofconnected customers. This formula categorizes transformers

into three groups: residential, mixed residential andcommercial. From this categorization, constants are obtained

for use with the formula. Currently, our experience with a

regression based DT load estimation methodology has shown

that the results are only 64% accurate. This necessitatesfurther load testing on-site to verify overloaded transformers,

which costs time and additional operational expense. Hence,

there is a need to explore other load estimation methodologiesthat give more accurate results.

In this study, the feasibility of using the typical customerload profiles for transformer load estimation will be explored.

KVA loading will be computed using the aggregate loading of

the connected customers.

II. RELATED WORKS

A load profile is a model of load characteristics representedby parameters such as customer types, day/season and

temperature. The load profile is used in power generation tomonitor and plan their generation schedule. For the

transmission system, load profiles are utilized for forecasting

demand and system planning. Furthermore, the distributionutilities make use of load profiles to enhance the operationefficiency and reliability of their facilities. Load profiles can

also be used for load balancing and customer billing in a

deregulated environment [1].

The importance and scope of the applications of load

profiling lead to numerous researches related to the subject.Most of these studies are targeted to seek out the most

accurate way to model the Typical Load Profile (TLP)

classification. We can categorize the classification techniques

into two. The first method is derived from the shape of theload curves while the second method is derived from a pre-

determined set of consumers. Most of the papers that fallunder the first category introduced a fuzzy clusteringtechnique to group similar load curves ([1], [2]). In [1],

Measured Load Profile (MLP) is classified according to

hierarchical clustering method and fuzzy logic. In [2], a twostage FCM (Fuzzy C-Means) is introduced by grouping

through load pattern and value. The second category wherein[3] and [4] belong retrieves data by sampling theory and

applies statistical analysis to develop the typical load profiles.

The distribution transformer is the most crucial component

of the distribution system, customer wise. It bridges the gapbetween the distribution utility and the consumer by stepping

down the distribution-level voltage to utilization voltage levels

that are required by the customer. Thus, the loading level of

the transformer should always be monitored in order tomaintain its good working condition and to ensure the

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continuous supply of power to customers. To monitoraccurately, the health and reliability of all distribution

transformers, an ID (Indicating Demand) meters can be used

to record the peak load. However, given the number of

distribution transformers in the field, to put up ID (IndicatingDemand) meters for each is not practical. Non-invasive digitalload loggers, on the other hand, can also be employed to

estimate the loading on the transformer with 800 kWhCons.>800 kWh

401-800 kWh401-800 kWh

301-400 kWh301-400 kWh

201-300 kWh201-300 kWh101-200 kWh101-200 kWh

71-100 kWh71-100 kWh

51-70 kWh51-70 kWh

1-50 kWh

GeneralServices(Commercial)

1-50 kWh

Residential

kWh RangeRate ClasskWh RangeRate Class

Cons.>800 kWhCons.>800 kWh

401-800 kWh401-800 kWh

301-400 kWh301-400 kWh

201-300 kWh201-300 kWh101-200 kWh101-200 kWh

71-100 kWh71-100 kWh

51-70 kWh51-70 kWh

1-50 kWh

GeneralServices(Commercial)

1-50 kWh

Residential

kWh RangeRate ClasskWh RangeRate Class

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TABLEIIRATECLASSSTRATIFICATION(DEMAND)-INDUSTRIAL

10,000kW2,000 kW

201kW-2,000kW

41kW-200kW

5kW-40kW

LargeCommercial

kW Demand RangeRate Class

For each segment, customers are selected based on a

sample size computation of 90% confidence with a 10%margin of error. These customers have been installed with

load profile (LP) capable meters and their monthly kWhdemand recorded.

We let cnCand nNwhere N is the number of elements in

C. Also, we let h be any number between {1H} where H isequal to 24. The recorded load profile for each customer isnormalized using (1).

)max( 'h

hc

hkW

kWPU n =

(1)

The normalized load profiles of each customer segment are

averaged hourly to obtain a typical load profile to represent

that particular rate class as shown in (2).

N

PU

PU

N

n

c

h

h

n=

=1

(2)

The hourly average of all customers in a particular

customer group is normalized once again using (3) to obtain

the typical load profiles for use with the TLP DT estimationmethodology.

)max( 'h

h

hPU

PU

LP =(3)

A sample typical load profile (normalized) for residentialcustomers with monthly kWh consumption of 301 to 400 kWh

is shown in Fig. 3.

Residential, Cons. = 301 - 401 kWh

0

0.2

0.4

0.6

0.8

1

1.2

1:00

3:00

5:00

7:00

9:00

11:00

13:00

15:00

17:00

19:00

21:00

23:00

Time

PerUnit,P.U.

Fig. 3. Typical Load Profile (Residential, Consumption = 301-

400 kWh)

B. Application of Customer kWh Consumption to the TypicalLoad Profile

The process flow of the TLP methodology is shown in Fig.

4.

Monthly Customer

kWh Consumption

Aggregate DT Load

Profile

Divide by the number of billing

days to obtrain the approximate

Daily kWh Consumption

Apply the Daily k Wh Consumption

to the Typical Load Profile to get

Customer Hourly kW Load Profiles

Hourly kW

Streetlight Load

Profile

Aggregate all Customer Hourly kW

Load Profiles connected to the

transformer, the Streetlight Load

and System Loss

Take the Peak kW Demand from

the Aggregate DT Load Profile and

multiply it with the transformers

power factor

DT Peak KVA = DT

Load Estimate

Monthly Customer

kWh Consumption

Aggregate DT Load

Profile

Divide by the number of billing

days to obtrain the approximate

Daily kWh Consumption

Apply the Daily k Wh Consumption

to the Typical Load Profile to get

Customer Hourly kW Load Profiles

Hourly kW

Streetlight Load

Profile

Aggregate all Customer Hourly kW

Load Profiles connected to the

transformer, the Streetlight Load

and System Loss

Take the Peak kW Demand from

the Aggregate DT Load Profile and

multiply it with the transformers

power factor

DT Peak KVA = DT

Load Estimate

Fig. 4. Typical Load Profile (Residential, Consumption = 301-400 kWh)

The monthly kWh consumption of each customer

connected to a distribution transformer is taken from thecustomer load database. The monthly consumption is divided

by the number of billing days between meter readings to getthe daily kWh consumption, as shown in (4).

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daysbilling

kWhkWh

monthly

daily= (4)

The approximate load profile, that is, the hourly kW loadper customer for a 24-hour period, is obtained using (5).

=

=H

h

h

hdailyhour

LP

LPxkWhkW

1

(5)

The load profile to be used in the above formula

corresponds to the rate class of the customer, as obtained from(3). Note that the load profile represents a typical day only.

In addition to customer loads, the effect of streetlight loadsand system loss on the total transformer loading must also be

taken into account.

Streetlight loads connected to the transformer, if any, are

assumed to be 70W high pressure sodium (HPS) luminariesfor residential transformers and 250W HPS luminaries for

commercial transformers. Streetlight loads are assumed tooperate between the hours of 1800H to 600H and the total

streetlight load is distributed within this period. Thus the

streetlight consumption is added to the hourly kW load of theDT for the said range. The hourly streetlight load is denotedas STLhour.

The technical loss of the transformer should consider theconsumption of the connected customers and the incident

streetlight load. For the study, a technical loss of 2% is

assumed. Hence, the calculation of the hourly losses isexpressed as in (6):

LossDTxSTLkWLosses hourhourhour )(+=

(6)

The hourly streetlight load and DT losses obtained in (6) areto be used in the TLP load estimation:

C. Aggregation of Demand Profiles and DT Load EstimationThe total hourly kW loading is obtained as the aggregate of

the hourly kW load, the streetlight load, if any and the hourly

kW loss, also known as the Aggregate DT load profile. Thisis also expressed as in (7).

hourhourhourhourLossesSTLkWkWTotal ++= (7)

One may begin to obtain a DT load estimate from theaggregate DT load profile. To do so, the Peak kVA load of

the transformer is computed by converting the max. totalkWhour load to its kVA load equivalent, using (8).

pf

kWTotalPeakkVAPeak

hour= (8)

For (8), a power factor (pf) of 0.85 lag is assumed in the

calculation. As DT load estimates are commonly expressed asa percentage of total transformer capacity, the Peak DT kVAload (%) is taken from (9).

100% XkVADTRated

kVAPeakkVADTPeak = (9)

The Peak kVA value from (8) is analogous to the peak kVA

demand of the transformer, whereas the percentage obtained

in (9) is equivalent to the percent ratio of the peak DT kVA

demand to the rated DT capacity. Hence, this is the resultant

DT load estimate.

IV.RESULTS AND EVALUATION

In developing the typical load profiles, load profile datafrom 1,730 customers installed with LP-capable meters are

collected and analyzed. The gathered data was recorded for a

period of one (1) year from November 2004 to October 2005,and from these, typical load profiles for each rate class have

been developed. Fig. 5, Fig. 6, Fig. 7 and Fig. 8 show thetypical load profiles for the residential, general services

(commercial), large commercial and industrial rate classes

respectively.

Typical Load Profile of Residential Custome rs

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1:00

3:00

5:00

7:00

9:00

11:00

13:00

15:00

17:00

19:00

21:00

23:00

Time

PerUnitP.U.

0-50 kWh

51-70 kWh

71-100 kWh

101-200 kWh

201-300 kWh

301-400 kWh

401-800 kWh

Cons. >800

kWh

Fig. 5. Typical Load Profiles for Residential Customers

Typical Load Profile for General Services (Commercial)

Customers

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1:00

3:00

5:00

7:00

9:00

11:00

13:00

15:00

17:00

19:00

21:00

23:00

Time

PerUnitP.U.

0-50 kWh

51- 70 kWh

71-100 kWh

101-200 kWh

201-300 kWh

301-400 kWh

401-800 kWh

Cons. >800kWh

Fig. 6. Typical Load Profiles for General Services Customers

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Typical Load Profile of Large Commercial Customers

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1:00

4:00

7:00

10:00

13:00

16:00

19:00

22:00

Time

PerUnitP.U.

5-40 kW

41-200 kW

201-2,000 kW

2,000 kW< Demand

Fig. 7. Typical Load Profiles for Large Commercial Customers

Typical Load Profile of Industrial Customer s

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1:00

4:00

7:00

10:00

13:00

16:00

19:00

22:00

Time

PerUnitP.U. 5-40 kW

41-200 kW

201-2,000 kW

2,001-10,000 kW

10,000 kW< Demand

Fig. 8. Typical Load Profiles for Industrial Customers

To validate the accuracy of the developed load profiles

against the actual measured transformer loading, the TLPLoad Estimation methodology is applied to a representative

transformer and the resultant aggregate transformer load

profile is superimposed against a plot of the actual meteredtransformer loading, shown in Fig. 9. From observation, the

TLP demand profile closely approximates the shape of theactual metered DT loading, although the most crucial point for

the load estimate, which is the peak demand occurring at23:00 shows a deviation of around 13.2% from the actual

transformer peak demand, as is with the test case.

Comparison of TLP vs. Actual Metered DT Loading

4

6

8

10

12

14

16

1:00

3:00

5:00

7:00

9:00

11:00

13:00

15:00

17:00

19:00

21:00

23:00

Time

kWDemand

Metered

Demand

TLP

Demand

Fig. 9. Comparison of TLP Methodology with Actual Metered Load

In comparing the accuracy of the TLP methodology against

the regression formula, the monthly transformer demandestimates of both techniques were compared to the actualmetered demand of four transformers installed with LP-

capable meters over a period of 6 months. The summary of

the findings are shown in Table IV.

TABLEIVVRESULTSCOMPARISON(TLP VS REGRESSION)

18.76% - 24.02%2.88% - 8.44%Commercial4

15.37% - 26.00%77.18% - 130.27%Commercial3

6.34% - 19.81%0.08% - 17.94%Residential2

0.18% - 14.31%16.83% - 29.2%Residential1

TLP

Methodology

Regression

Formula

TypeTrafo

% Deviation from Actual Metered DT

Demand (Over the Study Period)

18.76% - 24.02%2.88% - 8.44%Commercial4

15.37% - 26.00%77.18% - 130.27%Commercial3

6.34% - 19.81%0.08% - 17.94%Residential2

0.18% - 14.31%16.83% - 29.2%Residential1

TLP

Methodology

Regression

Formula

TypeTrafo

% Deviation from Actual Metered DT

Demand (Over the Study Period)

Based on our side-by-side comparison of the demand

estimates of both techniques against the actual metered

transformer peak demand, on the average, the results of the

TLP methodology are more precise than the regression

formula. Although there are instances where the regressionformula is more accurate than the TLP methodology and vice-

versa, the TLP methodology provides more consistent resultsthat range within 20% of the actual DT loading value for

residential DTs and 26% of the actual DT loading for

commercial DTs. The regression formula on the other hand,provides results that range from 30% of the actual DT loading

for residential DTs, and 130% of the actual DT loading forcommercial DTs.

V. CONCLUSIONS

The load profile methodology for DT load estimationshows a lot of potential based on the results of our data

analysis.

Overall, the LP methodology is more precise than the

regression formula for DT load estimation, as the results are

much closer on the average to the actual metered demand thanthe regression formula. But still, there is a lot of room forimprovement of the methodologys accuracy, which shall be

the subject of future studies.

For instance, the load profiles of commercial and industrial

customers could be stratified into different categories based on

the type of establishment and load behavior.

Furthermore, introducing load profiles for weekday and

weekend loads and load profiles that differentiate loadbehavior between wet and dry seasons may make the LPmethodology more accurate.

However, it should be noted that proper facility-customer

connectivity in the customer load database is a primerequirement for either technique to be accurate as well, as

customer kWh consumption, which is one of the requisite datafor both estimation techniques, comes from the load database.

ACKNOWLEDGMENT

The authors would like to thank the following: Jona D.Villanueva and Mercedo P. Delgado for being the initial

proponents of this project; the Meralco Metering Services

Asset Management group for lending their technical expertise

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and use of their facilities; Manuel M. Galvan, Tomas A.Javellana, Carl G. Aquino and Ireneo Acua for their

endorsement and support for the project; Ricardo V.

Buencamino and the staff of OH-Networks for their unending

support; and Ma.Victoria B. Que, for her support andguidance in the completion of this study.

REFERENCES

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