Asupra calitatii energiei electrice: Quality of Measurement Data Support for Power Quality by Apetrei D. , Pintea V.


 

 

Apetrei D. , Pintea V.

– ELECTRICA S.A., str. Grigore Alexandrescu, nr. 9, sector 1, Bucureşti, telefon: 2085250, e-mail:dan.apetrei@electrica.ro

– EGL Romania, nb. 14, Helesteului Str. RO-011988 Bucharest 1 phone +40 21 2303323/4092912 fax+40 2303335 mobile +40 746 054 089, E-mail viorel.pintea@egl.ro

More and more, managing data is treated as a strategic asset by Companies. Usually, driven by governmental regulations or by the need for greater organizational agility, more attention is being paid to data management. Unfortunately, starting a data governance program is not a formalized issue. Even worse,   gaining support for data governance projects is often difficult.

During 2008, ANRE (Romanian Regulator) tests the Distribution Quality Regulation [4] in order to start enforcing it from 2009. One of the problems related to this issue is the data management of the quality database [8]. The paper presents the case study of developing a Quality analysis system related to: quality of the service and quality of the power. Main items to be presented are: Better quality of the data leads to better quality of the services; Data sources, communication channels & data validation process• Practical approach to information flows in Romanian energy market

The paper presents a: Short introduction of the data ecosystem (EGL, ELECTRICA, electricity market); New QoS: information flows; regulator’s requirements; data management requirements; Common project EGL/Electrica (alpha P+ equipment capabilities; database structure; data validation process.)Instead of conclusion, a draft action plan is presented.

 

Keywords: QoS, data, information, knowledge

 

 


1.      INTRODUCTION

 

Main idea of this paper is that better quality of the data should lead to a better quality of the services. In order to analyze the way quality of the data influences the quality of the service it is useful to show the contract relation between market participants.

One key point in increasing the data quality is to define the data owner as the person interested in maintaining data quality [5]. With the particular example on QoS, the communication channels and the validation procedures are presented in short.

 

2.      ELECTRICITY MARKET ECOSYSTEM

In economics, a market is a social structure for exchange of rights, which enables people, firms and products to be evaluated and priced.

In figure 1 the main market flows are presented:

·         energy flow;

·         money flow;

·         data flow.

The energy flow is the one established from the producer to the client. The financial flow, goes from the client to the producer.

The data flow has at least two components: administrative data and billing data. The administrative data are the one used to calculate the metered values according to the changes in the market.

                According to the regulation in force, the information owner in different stages of the process is:

·         the customer

·         the supplier

·         the distributor

·         the billing operator

·         the metering operator.

Regarding the customer, there is a special observation that has to be made. From the legal point of the customer is the owner of the information regarding his energy consumption.

 fig17

Figure 1 main flows in the energy market

The information could not be made public without his agreement and its disclosure could affect customers economic interest.

In figure 2, the contract relation between the parties in the market are presented. Participating in the market, there are: a distributor, a supplier and a customer. Between the distributor and the supplier there is a distribution contract and between customer and supplier there is a supply contract. In special cases, when a customer hase more than one supplier, a direct contract between distributor and customer is accepted.

fig21

Figure 2 contracts in the market

The contract of the distributor specifies the level of QoS (quality of service). Beginning in Jully 2007, the Romanian electricity market is 100% opened.

 

3.      QoS INFORMATION FLOWS

 

Figure 3, presents the main aspects of the QoS (quality of service) related to distribution of the electricity.

Main drivers to QoS are:

·         rules established by the regulator [4],[6];

·         the will to reduce costs like penalties, losses, credibility damages

This paper look at QoS as sum of outages and power quality [7]. Commercial quality is a separate subject that has to be analysed in a different context.

 fig31

Figure 3 QoS definition, drivers,  data&information

Data quality of QoS leads to information quality. Among basic characteristics of information in order to measure its quality is: accuracy, objectivity, believability, reputation, contextual relevancy, timeliness, completeness, accessibility, security, interpretability.

QoS data comes from:

·         metering system;

·         AMR/SCADA application

·         call center

·         customer complaints

QoS is based on data storage and administration. Data goes to information as queries are launched. Figure 4 presents the process of data transformation into information and then getting knowledge from information.

                As could be seen in the figure, from the process, data gathered using metering equipment or customer call center, are aggregated into specific connection point data. Theese data, correlated with network data are stored in a specific database. Through queries, from the database are extracted information. The information are used to take action or to make a decision. Some special cases allow us to innovate or learn from the information obtained. In this case we are obtaining knowledge.

 fig41

Figure 4 Data, information, knowledge pyramid

Final purpose of a database deposit is to provide information in support of the decision leading to action. Besides that, knowledge obtained from multiple interrogation leads to a change in strategy.

 

4.      QoS PROJECT

Choosing EGL as partner in this project was an easy decision for Electrica because:

·         specialists in EGL had  good level of expertise in metering;

·         an important number of metering points is spread all over the country (as could be seen in figure 5

·         most of the meters used are state of the art PQM activated type;

In figure 5 blue labeled counties are the ones that EGL has metering point for electricity and gas consumption; red labeled counties are the ones that EGL has not metering point for electricity; unlabeled counties are the ones that EGL has metering point for electricity.

 fig52

Figure 5 distribution of metering points of EGL

Compared to other meters, the ones used in this application has most of the common capabilities of quality monitoring.

As could be seen in table 1, Electrica uses five types of meters that are different in terms of quality survey.

Common monitored characteristics are [9],[12]:

·         outage record;

·         frequency;

·         voltage level;

·         current level;

·         harmonics.

The meter use in this pilot QoS application is ALPHA P+. The meter is generally integrated in an AMR application.

Table 1 meter capabilities [1],[2],[3]

meter type

ALPHA

APLHA P+

CEET15

ENERLUX

Indigo+

outage record

yes

yes

yes

yes

yes

outage record per phase

no

yes

no

yes

yes

frequency

no

yes

no

yes

yes

frequency threshold

no

yes

no

yes

yes

measurement and display of the voltage per phase

no

yes

no

yes

yes

measurement and display of the current per phase

no

yes

no

yes

Yes

voltage threshold

no

yes

no

no

yes

current threshold

no

yes

no

no

yes

voltage sag recoder

no

yes

no

no

no

harmonics

no

yes

no

no

no

An example of county level architecture of the AMR structure is presented in figure 6.

 

The system is based on a local PABX network that provides the communication channels for data transmission. Basically every quarter of hour the communication server, tries to make a connection to the modem in the substation.

If connection is successful, then the meters in the substation are read. That leads to a good resolution of the quality data since this are volatile data.

If the requirements are limited to energy consumption, outage recording and snapshot quality information, then meter reading once a day could be more than enough.

All the data that was read from the meter goes to a database. Depending on the application used, this database could be ACCESS or SQL Server.

To obtain a higher level of integration, there is a replication process between databases.

In figure 7, the replication process is presented. We’ve chosen four servers to explain the replication process:

·         two servers are ARGUS based applications;

·         two servers are GALAXY based applications.

Main idea of the replication process is to take specific records from one database and to copy to the corresponding records of the other database.

 

 fig61

Figure 6 county level architecture of AMR application

The communication process is initiated by the client database that has to communicate the records is asking for.

 fig71

Figure 7 database replication

In this stage of the development of the application, the replication is done once a day after midnight. The data that is going to be exchanged between the databases is manually introduced and has to be synchronised by hand. The process is difficult and not always successful. For instance, the first data settlement in the beginning of the market took more than five month to be concluded.

Figure 8 shows a validation process that was designed for the AMR application.

                First of all the data that comes from the process goes through the normal hardware/middleware validation process:

·         serial interface validation using control character

·         class validation inside the meter using checksum control

·         communication channel correction/validation using standard protocol like MNP5

·         serial validation at the computer interface.

Besides these hardware/middleware validation, a software validation was designed.

First of all there is a subjective validation made by users through web browser. Then there is a banch of test in order to check data validity:

·         there is a histogram and standard deviation test to check statistical validity of the data;

·         there is a second balance test that compares aggregated values coming from different sources;

·         in the end there is a contour validation test

Any of the tests failed triggers an alarm.

 fig81

Figure 8 validation process

The elements presented until now, there are working and could be used for the QoS pilot. The pilot has three beneficiaries:

          Customers – usually makes a statement on the quality based on a subjective evaluation

          A.N.R.E (Romanian National Authority for Energy) – asks for yearly reports based on The Standard of Performance for Distribution Service

          Internal audit of the distributor

Its purpose is to improve the administration of the data recorded by ALPHA P+ meters installed in settlement spots. As a consequence it will be easier to identify the troublesome network points. Main requirement of the project is that supplier would access primary data. In order to reduce the cost of interpretation we will need an uniform  programming of the meters that are used.

The benefits of the project are:

·         intermediary processing of data is eliminated being replaced by local validation operated by supplier;

·         the number of complaints is reduced and the time needed to solve the remaining ones decreases since only the important one will reach the distributor;

·         start time and duration of the event gets a fair recognition from both parties;

·         the distributor get a tool to decide priorities in intervention to the network improvement.

Using the equipments described integrated into the remote meter reading system, the following parameters could be recorded:

·         Outages -total duration of long term outages

·         High voltage duration

·         Low voltage duration

The project does not check conformance to the standard at the first stage. It will be a source of qualitative information regarding particular network points.

Figure 9 presents the classical Deming management loop applied to our case.

 fig91

Figure 9 Continuous loop of quality improvement

First step in quality project is to define SMART objectives (specific, measurable, acceptable, realistic and time defined). The detailed objective is:

          S – establishes the most convenient information flow between the distributor, the supplier and the customer

          M – reducing by 10% of the customers complaints concerning the quality of the supplied  energy

          A – the distributor and the supplier agreed that the most convenient data management generates useful information for both of them

          R- the practical application of the project does not involve additional costs, relying on an existing assets; mainly changes in workflow

          T – the project is meant to prepare the introduction of the new Standard of Performance for Distribution Service by 01.01.2009

Figure 10 presents the supplier activities with QoS system.

 

Figure 10 supplier activities

Data sources are: meters, customers complaints and other equipments. The process flow with the supplier starts with the data acquisition. Then we got the node code assignment task. After this there is a first validation of the event. This step has to establish the root of the event. In the end the event is written in database and then used to make reports.

Figure 11 presents the calendar of actions of the project.

 

 

Figure 11 Gant chart of the project

As could be seen, the project is supposed to end in December 2008.

 

5.      CONCLUSION

 

          The process of data quality management is a classical management process

          Technical means could help in improving process of data quality management

          Going from project based quality efforts to quality management program is a must

 

 

6.      REFERENCES

 

Books:

[1]

,, – „Manual contor Alpha”, – SC Elster Rometrics   SRL , 2005

[2]

,, – „Manual contor CEET15, Enerlux”, – SC AEM SA , 2006

[3]

,, – „Manual contor Indigo+”, – SC energobit SRL , 2003

Regulator documents:

[4]

 

 

,, – „Codul retelelor electrice de distributie – document de discutie”, – ANRE , 2006

Articles:

[5]

Hans De Keulenaer,, – „The hidden cost of poor power quality”, – http://www.leonardo-energy.org , 2003

[6]

Hermina ALBERT, Nicolae GOLOVANOV,, – „Indicatori semnificativi de fiabilitate in alimentarea consumatorilor. În Energetica,53, nr.4, 2005;”, – ENERGETICA 53 nr 4 , 2005

[7]

Ricardo Gonzales Mantero EREN,, – „Electricity service quality – Continuity of supply, quality of product and customer care”, – Ente Regional de la Energia de Castilia e Leon , 2002

[8]

Tibor TERSZTYANSZKY,, – „FIRST RESULTS OF PERFORMANCE BASED REGULATION OF SUPPLY QUALITY INHUNGARY”, – CIRED -Torino , 2005

Normes:

[9]

,, – „EN 50160 Voltage characteristics of electricity supplied by public distribution systems”, – CENELEC , 1999

[10]

,, – „PE 143/2001 Normativ privind limitarea regimului deformant si nesimetric in retelele electrice”, – CNTEE Transelectrica – document de discutie , 2001

[11]

,, – „PE 142 – Normativ privind limitarea fluctuaţiilor de tensiune şi a efectului de flicker în reţelele electrice de distribuţie „, –  ,

[12]

,, – „IEC 61000-4-30 Ed. 1: Electromagnetic compatibility (EMC) – Part 4-30: Testing andmeasurement techniques – Power quality measurement methods”, – CEI , 2003

 

 

 

 

 

 

 

 

 

 

 

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