Common Information Model for Sensors - CiteSeerX

Common Information Model for Sensors Vinoth M. Mohan Mississippi State University [email protected]

Noel N. Schulz Mississippi State University [email protected]

Keywords: Common Information Model, Resource Description Framework, Unified Modeling Language, Data Integration, Semantic Web Abstract Sensors like current transformers and voltage transformers are used to monitor the power systems and data from these sensors are collected in appropriate computer applications to observe the state of the power system. Phasor Measurement Units (PMUs) get data from current and voltage transformers and time-stamp them thereby paving the way for wide-area monitoring of the power system. In wide-are monitoring, the dynamic-view of the power system may be observed across multiple utilities’ zones each using many different computer applications. To enable these computer applications to communicate with each other, a common language is required with the ability to overcome proprietary data constraints of power system applications. This paper focuses on the common data model that is required by sensors –CIM models and the storage format – CIM/XML that is used to share data between applications. 1.

INTRODUCTION Most Energy Management Systems (EMS) and Distribution Management Systems (DMS) developed by vendors cannot communicate with each other due to conflicting proprietary data formats. Utilities may have to use the same vendor for multiple packages even if another application with better features is available in the market due to the data-format constraints. Inter/Intra-communications between utilities can help to anticipate or mitigate any undesired contingencies in the power system and also lead to improved economic operation of utilities at times. The major hindrance to interchange of data is the heterogeneity of the software used by power companies and the variety of different formats used to store data. This representation of data in proprietary formats makes it difficult to exchange data between other applications thereby requiring translator programs to convert the data from one format to another. Due to the myriad of translators required between all the applications, a common format to cover all areas of data exchange in power electrical domain has been adopted by the Electric Power Research Institute (EPRI). 2.

COMMON INFORMATION MODEL Common Information Model (CIM) is a highly detailed object-oriented model of the power system developed using

Anurag K. Srivastava Mississippi State University [email protected]

Unified Modeling Language (UML). The Common Information Model for power systems falls under two standards: a. IEC 61970-301: This IEC standard describes the power system from an electrical point of view. The electrical properties of equipment and the relationship they share with other equipment are covered in this standard [1]. b. IEC 61968-11: This IEC standard describes supplementary features required in a power system like asset tracking, documentation work, consumers, and resource planning [2]. 2.1. Data Format issues Most power system applications are tailor-made for a particular functionality and they often use applicationspecific file formats. In a power system utility, many such applications are used and to enable communication between them, the utilities always have to stick with the same application vendor. In a de-regulated environment, the utilities often have to communicate with neighboring utilities that run applications developed by different vendors. This creates communication problems as the applications are normally built upon proprietary data formats. Often different versions of same application do not understand each other due to modifications made to the original file-formats. An example of this issue would be Microsoft Office Word 2007 and Word 2003. The native storage extension for Word 2007 is *.docx whereas Word 2003 uses *.doc. Word 2003 cannot open a *.docx document unless the Compatibility Pack is installed. 2.2. Need for Common Information Model In the power industry, numerous applications/systems are used and many are incompatible with other power system applications. As a stopgap approach, most utilities just use a point-to-point interface between applications. To prevent this point-to-point interface, the Control Center Application Program Interface (CCAPI) working group (WG) was set up by the Electric Power Research Institute (EPRI) to build the Common Information Model for power systems. As the scope of CIM models increased, the design of CIM was taken over by various International Electrotechnical Commissions (IEC) working groups primarily Technical Committee TC-57’s working groups 13 and 14. Common Information Model prescribes that utilities use the highly detailed customizable CIM format that is

compatible with many applications. Moreover, this format has the flexibility to add extra data to it in case an application needs them, without invalidating the data format [3].

Figure 1. Traditional Data Integration approach

Figure 2. CIM Data Integration The detailed UML diagrams for the power system provide the data format. This format combined with eXtensible Markup Language (XML) and Resource Description Framework (RDF) forms the CIM/XML language that can be used as the communication language between any two non-compatible applications. Figures 1 and 2 indicate the simplification provided by CIM integration [4]. 2.3. CIM Class Relationships

The Common Information Model when expressed in eXtensible Markup Language along with the Resource Description Format is a Formal Language. The basic grammar for this language is set forth by IEC Technical Committee TC-57’s Working Groups 13 and 14 in the form

of CIM/UML class diagrams. The Working Group 13 (WG13) mainly deals with CIM for Energy Management Systems (EMS) and Working Group 14 (WG14) deals with CIM for Distribution Management Systems (DMS). The unique feature of the CIM grammar is that it is abstract and generic. This generic property enables it to be compatible with any application used in the power system industry. Moreover, it provides the option of customization for any application that needs additional properties. In essence, the grammar can be extended to accommodate the specific needs of individual applications without affecting the core grammar. As the Common Information Model is based on Object Oriented Design (OOD) modeling [4], the basic object oriented modeling concepts used in the CIM are described below : Class: A class is a kind of blueprint for any physical or abstract thing in the world. It includes the characteristics and behavior of that particular thing. Object: An object is a particular instance of a class. For example, for the class “Car”, “Ferrari 550 Barchetta” is a particular instance and so it is an object. The important relationships that one class shares with other classes are as follows: Inheritance: Inheritance enables sub-classes to inherit characteristics and behavior from parent classes. Association: Association describes the relationship between classes wherein one is not the subset of another class. Aggregation: Aggregation describes relationship where one class is a collection of another class. For example, a “website” is a collection of individual “web-pages”. Figure 3 is the CIM model for AC/DC line segments and it illustrates the various relationships between the classes. The vertical flow of diagram from bottom to top describes, ‘AC/DC line segments’ are a type of ‘Conductor’; ‘Conductor’ is a type of ‘Conducting Equipment’; ‘Conducting Equipment’ is a type of ‘Equipment’; ‘Equipment’ is a ‘Power System Resource’ [5]. The relationship between the classes ‘Conductor’ and ‘Conducting Equipment’ is Inheritance (Generalization). It means the class ‘Conductor’ is a subclass of the class ‘Conducting Equipment’. It inherits all the characteristics of the parent class and in addition has some extra characteristics of its own. The relationship between ‘Conductor Type’ and ‘Conductor’ is Association. The class ‘Conductor’ does not contain the class ‘Conductor Type’ whereas it shares a special association relation. The multiplicity at the ends of the line denotes that the class ‘Conductor’ can be associated with zero or one ‘Conductor Type’ class. Similarly the class ‘Conductor Type’ can be associated with zero or many number of ‘Conductor’ classes. The relationship between the classes ‘Equipment Container’ and Equipment is Aggregation. Here it describes

Power System Resource

Equipment Container

Equipment

Conducting Equipment

Conductor Type

0..* 0..1

DCLine Segment

Conductor

ACLine Segment

Figure 3. CIM for AC/DC Line Segments that the class ‘Equipment Container’ “contains” the class Equipment. Conversely, the class equipment is “part-of” the class ‘Equipment Container’. 2.4. CIM Class Usage Even though, the CIM UML model has lots of classes/attributes, not all the classes or attributes have to be used. For example, all the classes in figure 3 may be required by one application and another application might need only one class. That means, ACLine Segment can appear as a separate class of its own or it can be considered as a Conductor, a Conducting Equipment, Equipment, Power System Resource etc as needed [3]. Even if additional classes are added to the same class diagram, it is not going to affect the applications that are already using the existing class diagrams thereby enabling customization of the CIM class diagram according to the requirements of specific applications. 3.

SENSORS FOR GRID MONITORING: The present day power system is monitored with numerous measuring and control devices. Potential Transformers (PT), Current Transformers (CT), relays, and

Phasor Measurement Units (PMU) are the important sensors used in power system networks. PTs and CTs measure the high level voltages and currents and convert them to safe operating level signals. Relays and PMUs are fed by CTs and PTs. The voltages and currents sampled are converted into digital format for further processing using relays. Relays analyze these signals to provide the necessary protection. Relays also calculate the sequence currents, real and reactive power measurements. A conventional measuring device measures the quantities across the power system at different instances of time. The measurements have to be synchronized to get an accurate picture of the power system. The measurements from the devices may not be accurate due to improper calibration, aging of the devices. Error may also be introduced due to noise in communication networks when the data is transmitted from the field to control center. The parameters of the sensors like accuracy, accuracy class and total vector error are used by the state estimator program to obtain a better estimate of the power system states and measurements. State Estimation plays a major role in calculating all the states of the power system. 3.1. Wide-Area Monitoring Using Phasor Measurement Units (PMU) Traditionally, SCADA systems provide voltage, power flow, frequency measurements and other values typically taken once every two or four seconds. Moreover, SCADA systems can monitor only small areas, as measurements from far-away locations would have a time-lag associated with them leading to loss of synchronization. Phasor Measurement Units output voltages, currents, their respective angles, and frequency rate-of-change at a very fast rate (ex: 40 samples/second) and are time-synchronized using GPS time pulses [6]. By installing PMUs at salient positions of the power grid, the measurements from current transformers and potential transformers connected to each PMU can be time-tagged and sent to the control center. Due to the time-synchronization of the measurements, wide-area monitoring of the power system is possible [7]. Moreover as the rate of data output is very fast, the dynamic-view of the system can be had at all times leading to fast responses from human operators or machine. 3.2. CIM For Sensors As part of the research at Mississippi State University, a Sensor Web is used to perform online monitoring and control of power system. The Sensor Web is a semantic web which is an evolving extension of World Wide Web (www) [8]. In the semantic web, machine-understandable data is used to perform automated tasks. As part of our requirements, the sensor web is required to perform state estimation, contingency analysis and other tasks using data from the sensors. To perform state estimation, we require a) equipment characteristics of Current Transformers, Potential Transformers and PMUs and b) measurements from sensors.

Power System Resource 1

* Measurement Type

1

*

Measurement

*

1

Measurement Limit

1

* Measurement Value sensorAccuracy timeStamp

Figure 4. Measurement CIM classes [9] In traditional CIM, current transformers and potential transformers are substituted with measurement class CIM. In our case, the equipment characteristics like accuracy, accuracy class, and total vector error are required. These can be taken from the IEC 61968-11 asset models. Moreover, additional attributes are required by the sensor web for documentation purposes. IEC 61968-11 already has the CIM UML model for Current Transformers and Potential Transformers. The measurement CIM also exists in the 61970-301. This means only the PMU CIM model needs to be developed. Figure 4 shows the Measurement CIM/UML model. Having ‘sensorAccuracy’ and ‘timeStamp’ available with every ‘Measurement Value’ should make the accuracy value obtained from the asset CIM redundant. But in case the measurement value is from a PMU, just using the ‘sensorAccuracy’ attribute of ‘Measurement Value’ class would lead to error. This is because, the PMU gets its inputs from the current transformers and/or voltage transformers and so the output of the PMU device not only has the native accuracy error associated with the PMU device, it also carries the error that got introduced when the measurement was originally made in the current or voltage transformer. This means CIM asset models for current transformer, voltage transformer and PMU would be useful while performing State Estimation and other analysis methods. A draft PMU model built using the UML software Enterprise Architect is shown in the figure 5. The attributes chosen for the classes in the diagram were chosen using SEL-421 relay that can double up as PMU [10]. 4.

CIM/XML MESSAGES To have the final communication language, the CIM diagrams have to be implemented using XML. XML is used

to define metadata, which means ‘data about data’. For example, John has two tags and which is used to denote the name of a person “John”. The main drawback of XML is its limitation to be expressed only in the form of a parentchild relationship. This means it cannot be used to express the association or aggregation relationship that one class may share with another. To enable the XML to share relationship with other classes, the Resource Description Schema (RDF) is required. In RDF schema, the elements are denoted using triplets: subject, predicate and object [11]. The CIM/UML models available in the IEC are data models and not database schema. This means using the CIM/UML models of the assets CT, PT and PMU and measurement CIM model, the database has to be created in a relational database and the tables need to be populated. The tables were populated and a tool was written in C# to pull up the data from the tables and write them in CIM/XML format. For this CIM/XML data to be valid, it needs to be validated against a relevant RDF schema. To validate the document, RDF schema has to be generated for the CIM/UML models of CTs, PTs and PMUs and measurement CIM. This was achieved using the CIMtool plug-in [12] available for the popular software Eclipse. The CIM *.XMI file was generated using Enterprise Architect and this *.XMI file was used to generate the RDF schema in Eclipse/CIMtool. The CIM/ XML messages were generated for the assets CT, PT and PMU and also the associated measurements. These CIM/XML files were validated against the relevant RDF schemas that were earlier generated. Figure 6 shows the steps that are involved in the generation of a valid CIM/XML file. The validation of these CIM/XML files was incorporated into the sensor Web which reads the CIM/XML and validates it. Figure 7 shows the entire process from generation of CIM/XML models to where they are used. Using the ‘Data Exporter’, power system raw data are read and written in the CIM/XML format. Now this CIM/XML is fed into the sensor web, which first validates it using a suitable parser. The parsed data is then stored in spatial database or in sensorML files. These data are fed into the Sensor Observation Service (SOS) server which runs the semantic web services. As of now, it is programmed to perform State Estimation and Contingency Analysis using the data that are present in the SensorML files and Spatial Database. Figure 8 shows the CIM/XML file that was generated for the asset ‘Phasor Measurement Unit’. This CIM/XML was generated using the CIM model for PMU that was developed (Figure 5). The CIM/XML has information about three classes, ‘PMU’, ‘PMUTypeAsset’ and ‘PMUProperties’. The values contained by each of the attributes are just dummy values. This CIM/XML message is

Figure 5. Draft CIM asset model for PMU

Enterprise Architect

CIM/UML

State Estimation Power System Data

UML to RDF conversion Data Exporter RDF schema validates

Power System Data

CIM/XML

CIM/XML files

Contingency Analysis

Semantic Web Service

SOS server

SensorML/ Spatial Database

Figure 7. Sensor Web architecture Figure 6. Steps Involved In CIM/XML Generation

52 Demodulated +/-2 1 Phasor Measurement Unit 52 Demodulated 15 +/-0.2 1 PMUTypeAsset 10 40-60 UTC –179.99° to 180° 20 30-150 PMUProperties Figure 8. CIM/XML Messages For Phasor Measurement Device Asset Model validated by the sensor web, and then the dummy data is extracted from this file and stored in the appropriate database to perform various pre-programmed tasks. At present, many utilities just shy away from CIM/XML as it is an overhead to enable CIM/XML for existing legacy applications. Alternatively, many application vendors now incorporate CIM features in their latest products so as to ease the burden off the utilities that choose to go with latest edition of an application. 5.

SUMMARY A common language is required by applications that wish to communicate with each other. EPRI initiated the development of Common Information Model for power systems to enable application integration. The CIM/UML model and CIM/RDF file accomplish the task of forming the common language between applications thereby enabling ‘plug-and-play’ capability for the power system applications. In power systems where wide-area monitoring is done, CIM messages have to be exchanged between applications

operating under different utilities. The existing CIM models for CT and PT were used and new model for PMU was developed. These asset models in addition to the measurement CIM is used by the sensor web to perform automated tasks like state estimation, contingency analysis and other tasks. 6.

ACKNOWLEDGEMENT This research was funded through United States Department of Homeland Security/ Oak Ridge National Lab Funds: 63886, Office of Naval Research (ONR) Grant No. N00014-02-1-0623 and N00014-08-1-0080. 7. REFERENCES [1] “Common Information Model (CIM)”: CIM 10 Version, EPRI, Palo Alto, CA: 2001, 1001976. [2] http://www.ucaiug.org/CIMug/default.aspx. Official site for Common Information Modeling for power systems.

[3] Dr Alan W. McMorran: "An Introduction to IEC 61970301 & 61968-11: The Common Information Model” available at www.cimphony.org/cimphony/cim-intro.pdf [4] Jian Wu – “Data Modeling for Shipboard Power System” , MS Thesis, Department of Electrical & Computer Engineering, Mississippi State University, 2004, http://library.msstate.edu/content/etd/show.asp?etd=etd03252004-220340 [5] Xiaofeng Wang: “Data Modeling and Data Exchange for Electrical Distribution Networks” – Dissertation for Doctor of Philosophy, Michigan Technological University, Nov 2001 [6] RTDMS training available at http://www.naspi.org/resources/training/2008_0122/rtdms_tr aining_package.pdf [7] Reynaldo Francisco Nuqui: "State Estimation and Voltage Security Monitoring Using Synchronized Phasor Measurements", Department of Electrical & Computer Engineering, Virginia Tech, 2001 http://scholar.lib.vt.edu/theses/available/etd-07122001030152/unrestricted/rnuqui_dissertation.pdf [8] http://www.w3.org/2001/sw/ . W3C official website for semantic web related activities. [9] Robin Podmore, David Becker, Rob Fairchild, Marck Robinson: “Common Information Model – A Developer’s Perspective” in Proc. System Sciences, 1999. HICSS-32. Proceedings of the 32nd Annual Hawaii International Conference, pp-6 [10] http://www.selinc.com/sel-421.htm. SEL-421 Instruction Manual available at Schweitzer Engineering Laboratories (SEL) website. [11] http://www.w3.org/TR/rdf-concepts/. W3C website explaining RDF concepts. [12] www.cimtool.org. Website for the Eclipse plug-in CIMtool. 8.

BIOGRAPHIES:

Vinoth M. Mohan is an M.S.E.E. student at Mississippi State University. He received his B.E degree in Electrical and Electronics Engineering from Panimalar Engineering College, India in 2005. He worked as a software programmer in Infosys Technologies Limited, India for around 2 years before pursuing his MS degree. His interests include electrical generation, transmission, distribution networks and quantum mechanics.

Noel N. Schulz received her B.S.E.E. and M.S.E.E. degrees from Virginia Polytechnic Institute and State University in 1988 and 1990, respectively. She received her Ph.D. in EE from the University of Minnesota in 1995. She has been an Associate Professor in the ECE department at Mississippi State University since July 2001.She currently holds the TVA endowed professorship in power systems engineering. Her research interests are in computer applications in power system operations including artificial intelligence techniques. She is a NSF CAREER award recipient. She has been active in the IEEE Power & Energy Society and served as Secretary for 2004-2007 and Treasurer for 2008-2009. She was the 2002 recipient of the IEEE/PES Walter Fee Outstanding Young Power Engineer Award. Dr. Schulz is a member of Eta Kappa Nu and Tau Beta Pi. Anurag K. Srivastava received his Ph.D. degree from Illinois Institute of Technology (IIT), Chicago, in 2005, M. Tech. from Institute of Technology, India in 1999 and B. Tech. in Electrical Engineering from Harcourt Butler Technological Institute, India in 1997. He has been working as Assistant research professor at Mississippi State University since September 2005. Before that, he worked as research assistant and teaching assistant at IIT, Chicago, USA and as Senior Research Associate at Electrical Engineering Department at the Indian Institute of Technology, Kanpur, India as well as Research Fellow at Asian Institute of Technology, Bangkok, Thailand. His research interest includes real time simulation, power system modeling, power system security, power system deregulation and artificial intelligent application in power system. Dr. Srivastava is member of IEEE, IET, Power & Energy Society, Sigma Xi and Eta Kappa Nu. He is recipient of several awards and serves as reviewer for IEEE Transactions, international journals and conferences.