Management System of Distribution Transformer Loading A. G. Leal, M.Sc
J. A. Jardini, Ph.D.
L. C. Magrini, Ph.D.
Abstract - This paper presents an Integrated Management System of Distribution1Transformer Loading using a relational database and loss of life estimation through artificial neural networks. This fully integrated software solution allows for technical personnel to carry out studies of transformers data, such as load curves and loss of life. It permits the analysis of these results by location and substitution urgency level. It also issues reports for the transformers that need substitution. This system was conceived to give a support to management activities of the distribution network. The system also supplies economical information for the substitution of transformers. Keywords - Database Management Systems, Distribution Transformers, Life Estimation, Life Cycle Costing, Load Management, Artificial Intelligence, Multilayer Perceptrons, Neural Network Applications, Power Distribution. I - Introduction
T
his paper presents an integrated management system for distribution transformer loading based on the methodology described in the references [1], [2] and [3]. This computational tool is applied to assist the management in about 61.500 distribution transformers, responsible for the energy supply of about two million consumers in 55 cities. The implemented methodology is based on the relationship between the aging of the isolation of the transformer and the operation temperature due to loading, that is, the transformer loss of life. Reference [6] supplies mathematical models for the calculus of the temperature of a transformer submerged in oil, more precisely, the temperature of the hottest part of the winding - the Hot-Spot temperature. This temperature is used for the evaluation of a thermal aging rate due to a cyclical load under ambient air temperature. This model [6] requests an interactive computer program, which is more time consuming, to be applied to all transformers individually. For checking transformers load adequacy, many authors use the KVAS function of the transformer (statistical kVA) that 1
A. G. Leal (e-mail:
[email protected]), J. A. Jardini (e-mail:
[email protected]) and L. C. Magrini (e-mail:
[email protected]) are with Department of Electrical Engineering at the University of São Paulo, São Paulo, SP, Brazil. 2
S. U. Ahn and D. Battani are with Bandeirante Energia S/A, São Paulo, SP, Brazil.
S. U. Ahn, Ph.D.
D. Battani
defines the maximum load current in a transformer considering the value of passer-by energy. However, it is known that the real condition to which a transformer is submitted doesn't always correspond to those conditions. In real conditions the transformer load varies during the day according to consumer's needs and to season. Besides, the load that a transformer can stand, it also suffers the influence of atmospheric conditions, such as the temperature of the room, the speed and direction of the wind, and the solar incidence. This methodology can be adapted to transformers with constant daily load curves, demanding just a few computational resources. However, for the distribution transformers that work with variable loads, calculations demand more time, mainly when the whole daily load curve is intended to be used. II - Information Flow Se Un Ahn proposed in his doctorate thesis [5] a methodology of thermal aging calculation, using the average and standard deviation load curves of a transformer and artificial neural networks (ANN). ANNs can "learn to accomplish the calculus" through examples where data, and responses are obtained through conventional calculations. In previous papers [5], the implementation and tests of this methodology were done through several separate programs. The time spent in data preparation and data formatting of just one case for the several programs, was excessively long. In order to have all steps coordinated to apply this new methodology, a central information system was built on a relational database and on concatenated programs of automatic execution (Fig.1). This system is denominated “Management System of Distribution Transformers” (MSDT). The developed system follows the client-server model, using a relational database server, in WINDOWS NT platform, physically connected to the corporative network of the company, so that all apt users can have access to stored data. The client module is implemented in the form of a program, which will provide an interface human-machine (IHM) to the user, being connected to the database server.
Consumers: type and monthly energy
Typical consumers’ daily Load profiles
Transformer Aggregated Daily Load Profile
Cluster Analysis
Alternative Procedures
Simulation statistical metodology
ANN Training
ANN Technique
Interpolation IHM
B. Loss of Life Calculation Alternatives: From the resulting load daily curves for the transformers, the user has as alternatives for the calculus of the loss of life: the analytical method (simulation) ANSI [6]; values interpolation for obtaining the estimate loss of life curve through the technique of artificial neural networks (ANN) [1]. 1) Simulation: The calculus for the simulation method is made following the procedure described in [1] generating from the mean daily curves and from the standard deviation of the load and the temperatures (11 load curves and 12 temperature curves). The composition of these load curves together with the one of temperature with the value of the transformer nominal power and weightings of result, define a point in the curve of loss of life because of the load. Multiplying this reference load by values in a range of e.g. 0.8 to 1.2, other points of loss of life are obtained (Fig. 3). Loss of Life (%)
40 Loss of Life Analysis
35 30
Selection of New Transformer Capacity
Economic Evaluation
Fig. 1 – MSDT Data Flow
25 20 15 10
A. Transformer Aggregate Load Curves: Transformers data and associate consumers data will be obtained monthly by the corporate database through an extractor program, resident in mainframe, that converts necessary information into a sequential text file. This database contains the daily load curve profile (average and standard deviation) of all types of consumers’ activity in the utility concession area (Fig.1). Through activity type and monthly consumption in consumers’ kWh that are linked to a certain transformer, the curve of joined load of this transformer can be calculated [2] in kVA. As discussed in reference [1], the curves will be stored in database in pu of the monthly average transformer demand (Fig.2). 1,6 1,4 1,2 1 0,8 0,6 0,4
mean load curve standard deviation curve
5 0 0,7 0,8 0,9
Fig. 2 – Mean load curve and standard deviation load curve (transf. pu )
1,1 1,2 1,3 1,4 1,5 1,6 1,7 Loading % rated power
Fig. 3 - Loss of life Curve due to Load 2) ANN: The classification of load curves of all transformers is necessary for the training of the neural network, which is done by using the technology of clusters analysis. In this analysis each transformer will have its load curve compared with the seeds of the various clusters through the calculus of the Euclidean distance among curves. The curve of the transformer will be allocated in the cluster where the Euclidean distance until the seed is shorter. The new curve resulting from the cluster starts then to be the arithmetic mean of all the curves of this cluster. The cluster analysis of clusters is made through the SAS statistical software [4]. The curves of the transformers, which are part of the cluster analysis, are composed by 96 points of the average and 96 standard deviation points (1 every 15 minutes). Table 1 shows the result of the calculus made for all of the transformers of the concession area of the São Paulo utility named “Bandeirante”. Table 1 – Simulation Results in 10 Clusters
0,2 0 0:00 2:30 5:00 7:30 10:00 12:30 15:00 17:30 20:00 22:30
1
Cluster 1
Freq. (# of transf.) 8293
Type of Load Commercial
2
31263
Residential
Characteristic Practically constant commercial hours 2.06pu peak at 20h15
Demand
at
3
169
Industrial Commercial
4 5 6
5165 1507 109
Residential --Industrial commercial
7
9283
Commercial
8
383
Industrial Commercial
9
1337
Industrial Commercial
10
3975
Total
Commercial
Practically constant Demand at commercial hours with a small decrease during lunch time 3.3pu peak at 20h25 2.8pu peak at 20h25 Practically constant Demand at commercial hours with a great decrease at lunch time Practically constant Demand at commercial hours Practically constant Demand at commercial hours with a great decrease at lunch time Practically constant Demand at commercial hours with a small decrease at lunch time Practically constant Demand at commercial hours with a small decrease at lunch time
61484
2,5 mean load curve standard deviation
2,0
1,5
1,0
0,5
0,0 0
2
4
6
8
10
12
14
16
18
20
22
24
Fig. 6 - Cluster 9 - Commercial/Industrial
2,5
mean load curve standard deviation curve
2,0
Starting from the separation of all load curves in clusters, a subset of these clusters will be used to compose the training vectors and test. Based on the number of load curves that will compose the training vector, inserted from the graphical interface (Fig.7), the transformers curves are randomly picked in the same proportion of the quantity of the elements of several clusters. The same is applied to the assembly of the test vector. Through established constraints for the table that stores the vectors, it is guaranteed that the curves of applied training transformers are different from those used for the ANN test.
1,5
1,0
0,5
0,0 0
2
4
6
8
10
12
14
16
18
20
22
24
Fig. 4 - Cluster 2 – Residential 2,5
mean load curve standard deviation curve
2,0
1,5
1,0
Fig. 7 - ANN Training Form 0,5
0,0 0
2
4
6
8
10
Fig. 5 - Cluster 7 - Commercial
12
14
16
18
20
22
24
Following, the transformers selected to be part of the training vectors and of the test, have their respective curves of loss of life annual percentage calculated by the simulation method, based on load rate. The calculus of the annual loss life will use the thermal parameters of the transformer, together with the curve of typical temperature for the 12 months of the year in the locality where the transformer is installed.
The training vectors and test vectors consist of a set of inputs and outputs.
5 and 50% the signal is "Yellow" indicating that it should be in the maintenance list; above 50% the signal is "Red" indicating the need of an urgent change;
The 192 input variables are constituted by: o o
96 points of the load mean curve normalized by the maximum demand of the mean curve; 96 points of the load standard deviation curve, also normalized.
D. Economic Calculation: This function has the purpose of analyzing the transformers change when they are over loaded. This module produces the total annual cost of the transformers curve of several nominal powers due to an annual load rate growth (Fig. 9).
The 11 exit variables are: o
11 points of transformer life expectancy curve due to load rate (maximum demand / nominal power).
The training vector is processed by ANN, generating a file, which contains the weights of the trained network. Next, the test vector is processed and a histogram of relative error among the loss of life curves estimated by ANN and calculated by the simulation method is presented (Fig. 8).
Fig. 9 - Annual cost in kVA
Fig. 8 – Relative Error Screen Histogram In case users find that the results are acceptable, they can save the file, which contains ANN weights in the database, for subsequent use. Otherwise, one can train the neural network with another set of parameters such as number of neurons in hidden layers, number of training curves, and tolerance, optimizing the training. With the neural network adequately trained, it is possible to accomplish the monthly management of the transformers. C. Actual Life Estimation: After the estimate of the curve of loss of life of the transformers, the loss of life in the present condition of the transformer is calculated with current load by the linear interpolation of the loss of life curve. The remaining time of transformer operation, which is the inverse of the annual percentage loss of life, is also calculated and stored in an appropriate table. Additionally, an indicator denominated "Signal" is defined. In case the loss of life is below 0.5% a year, there will be a "White" signal indicating that the transformer is sub-loaded; between 0.5 and 5% the signal is "Green" indicating the ideal range; between
The annual total cost of the transformers include the following [3]: cost of purchase and installation of the new transformer; cost of removal of the old; cost of the losses in the iron and in the copper; cost associate to the loss of additional life. This calculation is meant for typical curves of the several clusters and with different rate growth. E. Summary: Once the calculus of the loss of life for all of the transformers of the company is concluded, those that have a Red or Yellow signal, should be analyzed for substitution (or eventually load relocation). The function verifies to which cluster the transformer belongs to. With this result and with the present transforming capacity, the standard rate growth of the place, the function will consult the "Substitute” table to give back a more appropriate value to the new transformer capacity. The table "Substitute" presents the viable economical sequence of transformers substitution.
III. Global Visualization of Results After the processing of all modules the user can search the condition of transformers load of the various localities and capacities (Fig. 10). With the possibility of having it in ascending or descending order by loss of life or load, the user can check the overloaded transformers, or the ones that are sub loaded. Thus it is possible relocate sub loaded transformers of larger capacity to substitute for another one of lower power that it is overloaded. It is also possible to click in any transformer of this list, to verify the information of the transformer, as well as the graphic of the Load curves (Fig.11), loss of life (Fig.12) and total annual cost of the transformer. This way, the user can analyze the need of the substitution of the current transformer to suit load conditions of average month. The transformer is a three-phase with a capacity of 75kVA, a maximum demand of 30 kVA, and average demand of 19.8 kVA in April of 2001. Analyzing graphic 12, one notices that the loss of life of the transformer only begins to be relevant when the maximum demand of the transformer reaches 1.4 times the nominal power. Additionally the user can analyze the growth of the annual cost for a certain load growth. In the present case, the transformer is sub-loaded allowing for an increase of load or location change with a 45kVA transformer, which is overloaded.
Fig. 11 - Transformer Information Form
Fig. 12 - Loss of Life Curve IV - Conclusion
Fig. 10 - List Distribution Transformers Form
The first calculations made for the town of Jundiaí (countryside of São Paulo State, Brazil) presented good results. Because it is a system integrated around a relational database, generating training vectors and storing vectors of trained ANN, it is easier and faster. The best ANN training configurations for the calculation of the loss of life are then easily done by enabling the consultation of the values of transformers loss of life of the company. Sub loaded and overloaded transformers are avoided. Once we have the load transformer report, they can be managed by following the loss of life.
V. REFERENCES [1] J. A. Jardini, H. P. Schmidt, C. M. V. Tahan, C. B. de Oliveira, and S. U. Ahn, “Distribution transformer loss of life evaluation: A novel approach based on daily load profiles”, IEEE Transactions on Power Delivery, vol. 15, no. 1, pp 361-366, January 2000. [2] J. A. Jardini, C. M. V. Tahan, M. R. Gouvea, S. U. Ahn, and F. M. Figueiredo, “Daily load profiles for residential, commercial and industrial low voltage consumers”, IEEE Transactions on Power Delivery, vol. 15, no. 1, pp 375-380, January 2000. [3] J. A. Jardini, C. M. V. Tahan, S. U. Ahn and E. L. Ferrari, “Selection of transformers based on economic criteria”, International Conference on Electricity Distribution - CIRED ’97, Conference Publication N° 438, pp 6.14.1-6.14.5, IEE 1997 [4] J. A. Jardini Et Alii., “Selection and Management of Distribution Transformers Using Artificial Neural Networks”, International Conference on Electricity Distribution - CIRED ’99, Brussels, 1999. [5] S. U. Ahn, Methodology for selection and management of distribution transformers - an ANN approach. Ph.D. Thesis, Escola Politécnica da Universidade de São Paulo, São Paulo, Brazil, 1997 (in Portuguese). [6] American National Standard Institute - ANSI, “Guide for loading minimal-oil-immersed overhead and pad mounted distribution transformers (rated 500 kVA and less) with 65°C or 55°C average winding temperature rise”, ANSI C.57.91, 1981.
VI - Biographies Adriano Galindo Leal was born in São Paulo, Brazil, in September 19th, 1971. Graduated at EPUSP - The Polytechnic School of São Paulo University in 1996 (Electrical Engineering). From the same institution he received the MSc degree in 1999. For the past 5 years he has been working as a researcher at EPUSP in the Department of Energy Engineering and Electric Automation group - GAGTD - where he has developped computational systems aiming at distribution system planning and operation. His interests are Database Applications, Systems Information, Distribution Systems, Data Storage and Systems Integration. José Antonio Jardini, born March 27 1941, graduated at EPUSP- The Polytechnic School of São Paulo University in 1963. MSc in 1970 and PhD in 1973. Associate Professor in 1991 and full professor in 1999, all of them at PEA (The Department of Energy Engineering and Electric Automation). Worked at Themag Engineering Ltd in the area of power systems studies – lines projects and automation. At the moment he is a professor at the Department of Energy Engineering and Electric Automation where he teaches “Automation of the Generation,Transmission and Distribution of Electric Energy”. Represented Brazil at SC38 of CIGRE, CIGRE member, Fellow Member of IEEE and Distinguished Lecturer of IAS/IEEE. Luiz Carlos Magrini was born in São Paulo, Brazil, on May 3rd, 1954. He graduated from Escola Politécnica da Universidade de São Paulo in 1977 (Electrical Engineering). From the same institution he received the MSc and PhD degrees in 1995 and 1999, respectively. For 17 years he worked for Themag Engenharia Ltda, a leading consulting company in Brazil. He is currently a researcher at Escola Politécnica da Universidade de São Paulo GAGTD group. Se Un Ahn was born in Inchon, Korea, in 1957. He graduated from Escola de Engenharia Mackenzie (São Paulo) in 1981. He received the MSc and PhD degrees from Escola Politécnica da Universidade de São Paulo in 1993 and 1997, respectively. Since 1986 he has been a researcher engineer at Empresa Bandeirante de Energia (former Eletropaulo), one of São Paulo State Electricity Utilities, where he works in the distribution department. His professional activities include load forecasting and underground distribution systems.
Durval Battani was born in Mogi das Cruzes in 1963. He graduated in Computation at the University of Mogi das Cruzes, São Paulo, in 1989. He is currently working on his MSc. and works for Bandeirante Energia, an Electrical Utility Company of São Paulo. His research interests are in the field of tools development for analysis and data banks, to be applied in several projects of the distribution system and planning.
