Feasibility Study of Semantic Sensor Networks in the ...

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Feasibility Study of Semantic Sensor Networks in the context of Smart Grids Rubén F. Pérez, Óscar Pérez, Antonio de la Villa, Pedro Cruz, Gonzalo León and Rafael MartinezTomas  Abstract-- Since the research in Information and Communications Technologies has provided strong progress in the field of distributed sensor networks and geo-referenced systems, the fundamental sensor architectures and frameworks developed in Europe are reviewed in this work in order to look for their suitability to Smart Grids, providing with results of interest, drawbacks, and a proposal of general requirements for the identification of an appropriate integration framework. Additionally, a practical case of electrical network is analyzed (IEEE 14-Node Network) oriented to the estimation of fault sensors, describing aspects often studied separately in literature like sensor networks and electrical networks. In this sense, several scenarios have been considered including a simulated processing in order to reveal some of the potential advantages that can be found with the implementation of Semantic Services. Index Terms-- Sensor Web Enablement, Semantic Sensor Web, Sensor systems, Smart Grids, Power Grid, OGC Standards, IEEE 14-Node Network, Power Systems State Estimation Theory.

I. INTRODUCTION The electric power system has often been cited as the greatest and most complex machine ever built, consisting mainly of generators, substations and lines to generate, distribute and provide with electricity to all the consumers. Since the introduction of computers in the early 1960s, the industry has improved gradually the monitoring and control of the power system, which coupled with a modest use of sensors still remains less than ideal, being able at best to see the condition of the power system with a 20-second delay [1]. Given the complexity and dimension of current electrical networks, the constituents of the grid (i.e. power generators, impedances, etc) are not always known. Moreover, the temporal evolution of the constituents or their state (i.e. a new connection to an external grid or whether a component is damaged) are not known with accuracy or even in case of being known, the information required is not acquired Part of this work has been supported by project ENE2010-18867, financed by the Spanish Ministry of Science and Innovation. R.F. Pérez and O. Pérez are with the Department of Science and Earth Observation Processing Systems, GMV Aerospace and Defence, Tres Cantos, 28054, Madrid, Spain (e-mail: [email protected], [email protected]). G. León is developing his Masther's Thesis in the Artificial Intelligence Dpt. at the Escuela Técnica Superior de Informática - UNED (Madrid, Spain). A. Villa and P. Cruz are with the Department of Electrical Engineering at the University of Sevilla, Spain (e-mail: [email protected] ). R. Martinez-Tomas is with the Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia, Madrid, Spain (e-mail: [email protected]).

immediately. All those factors prevent from knowing or simulate the intensity and voltage in the nodes of the grid with accuracy. On the other hand, when the elements of the system are constantly changing like currents provided by renewable sources and the amount of renewable sources is considerable, the natural response may provoke a continuous noise in the signal or unpredictable behavior in some cases, which has been usually referred as a systemic difficulty of the traditional grid for managing renewable energies, limiting the capability of extending the grid integrating such power sources. This problem has been especially noted in Europe, where according to its energetic strategy it is planned to find effective solutions and policies in less than 10 years, allowing among other things to distribute power optimally between the different areas according to their generation profiles (e.g. wind in coastal areas, and solar in the Mediterranean, etc) [2]. Smart Grid represents the use of sensors, communications, computational ability and control in some form to enhance the overall functionality of the electric power delivery system. On the one hand, a great effort is being done to allow an accurate, fast and reliable control of the power. Several advanced devices are contributing to a better control of the transmission and distribution levels. Additionally, new architectures and computational models in Distributed Generation (DG) are allowing the integration of green energy with more accuracy, providing with a power signal as clean as possible [3]-[4]. An optimal synergy between the different sub-systems involved cannot be guaranteed without adding an information layer to the physical layer. The information layer of the grid provides with mechanisms to keep all the systems and actors involved with the appropriate information to make their best decisions and contributing at the same time to the overall efficiency of the system, comprising sensors, communication systems, information models and intelligent agents among others. Thus, the Power Grid becomes smarter by enhancing sensing, communicating, applying intelligence, exercising control and through feedback, continually adjusting and adapting to the changing conditions. II. SERVICE ORIENTED SOFTWARE ARCHITECTURES Service Oriented Architecture (SOA) was proposed by Gartner in 1996. In this architecture, services are loosely coupled avoiding dependency between the systems, being discoverable, autonomous, stateless, and easily reusable for their composition and orchestration [5]. In relation to Sensor Infrastructures, SOA has proved to be very efficient, providing interoperability, discoverability, and reusability of sensors and

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A. Sensor Web Enablement (SWE) The term Sensor Web was first used in 1997 [6] to describe a novel sensor system model where individual and autonomous nodes could act and coordinate as a whole performing stand alone observations or cooperative tasks. Sensor Web is commonly defined as 'Web-accessible sensor networks and archived sensor data that can be discovered and accessed using standard protocols and application interfaces' (Fig. 1). Sensor Web is progressively assuming great importance within several application domains, like large scale geographic information system (GIS), and all sensor systems working in accordance with complex business models that assume the cooperation of remote services. In this sense, the Open Geospatial Consortium (OGC) has played a main role in the development of services and standards since its foundation in 1994, from the definition of a consensus process that has yielded to the development of publicly available interface standards supporting interoperable solutions that enable SWE and geo-reference capabilities. Different OGC standards have been defined to support the management of geo-referenced sensor data allowing the definition of process data models for sensors (i.e. SensorML), dealing with whatever measurement and observation (i.e. O&M), modeling of services of sensors even if have been virtualized (i.e. SOS), plan the tasks and configuration for a sensor (i.e. SPS), managing alerts (i.e. SAS) and services for notifying the users (i.e. WNS) [7], [12]. The fundamental SOA for sensor management in Europe were developed in the context of ICT (6th Framework Programme) upon SWE, and contributed actively to the standards involved. On the one hand, OSIRIS (Open architecture for Smart and Interoperable networks in Risk management based on In-situ Sensors) covered a broad spectrum of sensors in several topics of interest mainly linked to the risk phases (i.e. Monitoring, Alert, and Risk Management) in line with GMES (Global Monitoring for Environment and Security). OSIRIS contributed actively to OGC standards (e.g. development of Mobile Sensors, SIR Services, SOR Services etc) [7]. Additionally, SANY project (Sensors Anywere) aimed at improving the interoperability of in-situ sensors and sensor networks, allowing quick and costefficient reuse of data and services. The architecture proposed (i.e. Sensor Service Architecture) allowed additional support of event processing with a particular focus on the access. One of the main contributions to OGC standards was the development of Cascading SOS (SOS-X) as a client for accessing underlying SOS services, providing with different secure alternatives for retrieving their data [8]. Since different types of geospatial data are becoming available on different sources through OGC Web Services, the interest to integrate them has increased with the time, along with the necessity to combine different types of data from various sources. Several solutions and frameworks have been proposed in literature for enabling integration of these Web Services. Particularly, an open source initiative of relevance started in 2006 (i.e. 52°North) which developed the OX-

Framework, which is a software framework whose architecture has probed to ease the utilization of OGC Web Services [9] in the aforementioned projects among others. Taking into account the public availability of such architectures and frameworks, and their demonstrated efficacy in sensor networks applied to several topics of interest (e.g. Weather Monitoring, Risk Management, Air Quality Monitoring etc), it should be feasible to apply them directly to several aspects of Risk Management and Monitoring of Power Grids (e.g. Outage Monitoring, Voltage Drops, Blackout Prevention, preventive maintenance etc), as supported by several studies that have highlighted SWE as a clear element in future Power Systems [10]. Additionally, as revealed from the results of OSIRIS and SANY, one of the practical problems of implementing sensor networks is their dependency with near power sources, which obviously represents a minor problem in the case of Power Grid. Service Oriented Software Architectures

Power Grid System of Systems

Power Grid

SENSORS SWE

their related data. The main approaches that are going to be reviewed in this work in relation to Smart Grids are Sensor Web Enablement and Semantic Sensor Web.

Internet

Fig. 1. Description of Power Grid System of Systems, and the information management from the point of view of Sensor Web Enablement.

However, the application scope of Sensor Web may be limited mainly due to the lack of standardization of access infrastructures and data models, along with the inherent complexity of the Power Grid due to the heterogeneity of the systems, regulations and actors involved in the whole business. From the practical point of view, a weakness of SWE is the consequence of managing high volumes of sensors and data, like in the case of the Power Grid, where up to 30 measurements per second can be easily received by each Sensor. This may become a problem in implementations where the storage of high volumes of information received by the sensors is centralized, normally to be monitored or processed. These constraints shall be considered seriously in the applicability to Power Grids, since the information managed is strongly hierarchical and the volume of data and sensors can be huge. Therefore, in those cases where the interest is focused in self-efficiency mechanisms of the Power Grid and far from centralizing the information in order to respond to the situation as in the cases previously mentioned, it may be necessary to adopt a different implementation of those architectures, making use of a de-centralized approach to avoid bottlenecks and enhancing a semantic layer so the volume of information to be exchanged between the systems is more efficient and based in knowledge instead of measured data (i.e. Semantic Sensor Web). B. Semantic Sensor Web Semantic Sensor Web is considered as an evolving extension of Sensor Web that introduces a semantic layer in which semantics or meanings of information are formally defined. Semantics should integrate web-centric standard information infrastructures improving the capabilities of collecting, retrieving, sharing, manipulating and analyzing

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Therefore, the most appropriate framework for Power Grid shall be open to integrate an OGC semantic framework (e.g. OX-Framework) with specific Power Grid Knowledge, allowing transactions and different types of specific operations of the Power Grid, and being supported by e-commerce services (e.g. integrating E-Commerce frameworks). Additionally, the framework shall allow the dynamic management of clusters of sensors without loss of service (Fig. 2). E-COMMERCE SERVICES SMART GRID FRAMEWORK

sensor data (or associate phenomena) as well as potential interoperability between systems through semantic interactions [11]-[12]. An intense activity is being conducted in order to define the standards and necessary services that may be involved in spatial semantic services related to SWE. In this sense, the use of semantic web representation has extended considerably (i.e. RDF and OWL) [13], combined with the use of Spatial Data Infrastructures (SDI) and OGC services (e.g. next generation of OX-Framework) [14]. The OGC has defined a top-level interface standard called OWS Common [15] that can be shared by OGC Web services. It is expected that spatial semantic services will provide with a robust base upon it is possible to build intelligent applications that make use of shared spatial knowledge. However, spatial Ontology and spatial semantic services are not enough to deal with the problems usually found in Power Grid. An additional semantic layer is necessary in order to deal with the specific aspects of the Power Grid and their specific Ontology. According to A. Seth, SSW is described in the context of annotating sensor data with spatial (where), temporal (when) and thematic (what) metadata, which together constitute the semantic metadata [12]. In this sense, the temporal and spatial metadata could be managed in the context of OGC framework, and the rest of Metadata shall be integrated with them in the context of an integration framework. In relation to the interoperability, there are some additional requirements of the Power Grid that shall be taken in consideration in such framework. Assuming that a number of subsystems (clusters of sensors) can be defined in the Power Grid for monitoring and control its comprising parts, we propose the following aspects to be considered of interest:  Whatever sub-system shall be able to inter-operate with the rest of sub-systems making use of semantic enablement (i.e. to be able to ask for semantic information to others, to reason with knowledge, and to take their own actions in consequence).  The Framework shall support Transactional Operations between the subsystems if necessary. Otherwise, the framework wouldn´t allow basic operations in a secure way, like payment after exchanging information or energy of interest.  The system shall be able to perform Ultrafast Operations in some cases in relation to other subsystems, as the Power Grid requires a very fast response in some circumstances.  The definition of the system in terms of sub-systems (i.e. clusters and their comprising sensors) shall be as dynamic as possible (e.g. a sensor could be migrated virtually from a cluster to other if necessary and possible, providing with knowledge to the new Cluster). This would allow the administration of clusters of measurements and its reorganization.  Since the system is linked to a business model, several business operations shall be supported (e.g. B2B operations like Brokering, Negotiation, Mediation, Billing, Payment, etc).  It shall be possible to update the intelligence and related algorithms of any sub-system without affecting the behavior of the system. This would enhance the flexibility of the system.

GRID SYSTEMS ADMINISTRATION SERVICES OTHER SERVICES

POWER GRID SEMANTIC SERVICES

POWER GRID ONTOLOGY

OGC SEMANTIC SERVICES

SPACE ONTOLOGY TIME ONTOLOGY

SWE SERVICES POWER GRID SENSORS

OGC SEMANTIC FRAMEWORK

Fig. 2. Description of main services and their corresponding Ontology to be integrated in Smart Grid framework.

III. POWER GRID SENSORS Traditionally the sensors employed in power grids are current and voltage transformers, deployed all over the network. They allow to measure the current through the lines and the voltages at the nodes of the grid (moreover, other sensors located inside electrical machines, like generators and transformers, play a more specific role taking care of the integrity of the particular device). Current and voltage measurements are performed with a different rate depending on the end use of the measurements. If they are used for protection relays, the rate is several dozens of measurements per second. On the other hand, if they are used for energy billing the rate is much lower. A third application of current and voltage measurements is to feed the Energy Management System (EMS) or the Distribution Management System (DMS), located at the control centre of the electrical transmission and distribution networks respectively, in order to serve as inputs to important applications needed for the proper operation of the grid. Taking into account the big number of current and voltage sensors deployed through the whole grid, the amount of measurements that reach the EMS and DMS of utilities is huge. This situation has been getting worse for two reasons: the increasing complexity of the network due to the integration of distribution sources that require more measurements and the presence of new sensors, like PMUs (Phasor Measurement Units) that measure the phase difference between two nodes of the grid, allowing important advantages, like the improvement of state estimators, the robustness of transient stability and the dynamic rating of power lines. Moreover, during the last decades other sensors have been proposed to improve the reliability and security of the network, in particular to increase the dynamic rating of overhead lines (sensors to measure conductor and ambient temperatures, solar radiation, clearance conductor-ground, sag, mechanical tension) [16], or to detect incipient mechanical failures in towers (accelerometers) [17]. Other types of new

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sensors are more related with the environmental impact of electrical installations, like the vibration sensors on ground wire of overhead lines to detect the collision of birds [18]. Another example of new generation sensors, that nowadays are a reality, are the smart meters, that can transmit to the control centre the energy measurements from the consumers, and can receive from the control centre instructions to shape the load. Regarding the transmission media several possibilities are available. The most important are copper, optical fibre and radio. As most of the sensors are concentrated in substations a traditional solution has been the copper wire. To avoid interferences an alternative is the use of optical fibre. To transmit all the data from the substations to the control centre and between substations it was customary the use of PLC (Power Line Carrier) technology, replaced progressively by optical fibre inside the ground wire. In relation to the new sensors described previously to monitor the condition of overhead lines, wireless communication is the most suitable technology, due to the wide deployment of sensors along the lines. Regarding the smart meters, wireless and PLC are the most employed. A. Wireless Sensor Networks Several devices are available in the market which can be used to make up a physical wireless sensor network. Although the choice of the devices is related to the requirements of the sensor network, the preference is to use systems as autonomous as possible, designed to receive sensor data from a wide range of other sensor devices, process them and be able to send them to other network devices. Additionally, specific devices are available in big environments in order to aggregate the information of those devices, with smarter functionalities like filtering, local storage, processing, or capabilities to send data to external sytems via ethernet, Wifi or GPRS connections. In this sense, the Zigbee protocol is especially suitable for creating local-area wireless sensor networks. A number of reasons has made it a very popular protocol: full addressing of devices, many power-saving options, optimizations for efficiency in low-bandwidth applications, a layered approach to communications, secure communications, routing, ad-hoc network creation, and error recovery options like the self-healing process, that reconfigure the network if any node of link is broken. On the other hand, the use of GPS has become necessary in these devices, in order to fuse the information received from the different sensors with georeference attributes easing the latter monitoring and processing of the data. The available devices are usually updateable, being possible to change their firmware once installed in order to change the device capabilities to improve current functionalities or add new ones [19]. B. PLC Sensor Networks Several technologies are available for sharing internet through the power line. Since the emergence of HomePlug specification in 2003, the PLC network equipment market has continued to grow. Several vendors offer heterogeneous solutions and various network modes (i.e. peer-to-peer, master-slave, centralized) to cover almost whatever network infrastructure, including High Voltage, Medium Voltage and Low Voltage. Since PLC is more coupled to the Power Grid

than Wireless equipments, different hybrid solutions can be arranged by means of specific routers [20]. IV. SPECIFIC PROBLEM DISCUSSION After an introduction of main technologies and systems related to sensor networks, a specific problem is analyzed, along with the possible benefits of Semantic Services in the context of Service Oriented Architecture. For this purpose we consider the IEEE 14-node network shown in Fig. 3 in combination with Power Systems State Estimation Theory, which requires of processing of the measurements received from the different sensors. For the sake of simplicity, the objective has been limited to the identification of wrong sensors, leaving other aspects of interest to the scope of a different work (e.g. e-business inter-operability, distributed search between networks etc). Moreover, the terms network and cluster will refer by default to the electrical network and sensor network respectively which may follow different though related topologies.

Fig. 3. IEEE 14-node network.

The Network consists of two levels of voltages, 14 electric nodes, 15 lines, 2 single-phase transformers and one threephase transformer. To describe the state of the network it is required 28 state variables (magnitude and phase of nodes voltages). A. Unique Cluster Scenario In this scenario, all the Nodes are considered to be monitored with a unique cluster of sensors and we assume that the information from the sensors is received by the control center. From the semantic point of view, a minimal knowledge is necessary to describe at least the network, its different nodes and features, and the cluster of sensors deployed for its monitoring. In this sense, the use of the appropriated Ontology would help to use this knowledge [21]-[22]. In this example, this knowledge is related to this Network and is managed by the control center, which processes the information received from the sensors, obtain the results (i.e. a wrong sensor identified), and from its knowledge is able to infer the actions to be taken. Thus, it constitutes a traditional centralized system where the knowledge is managed in the control center. In this particular electrical network a deployment of current and

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voltage sensors provide 54 measurements of voltage magnitudes, active and reactive power flows. The state estimator is an algorithm that reads all the measurements and, based on the Weighted Least Squares method, provides the best estimation of the voltages (magnitude and phase) at the nodes of the network, taking into account that the measurements have an error. In this particular example we assume that all sensor measurements are exact with the exception of the voltage magnitude of node 3, whose value is intentionally increased a 5 % in relation to its exact value. In the real practice all the measurements have an error, but with this example we want to highlight the possibilities of state estimation to detect measurements that have abnormal errors, beyond the unavoidable stochastic errors. The results of the estimation allow knowing the values of the normalized residuals associated with each measurement. Normalized residuals are a by-product of state estimation process. Their values allow the detection and identification of measurements that can be considered as erroneous. If the value of a normalized residual is higher than a determined threshold (customarily equal to 3), the measurement associated with such residual is considered erroneous or wrong, and suppressed from the estimation process. Otherwise it is considered to be correct. In this example the residuals with higher values are shown in Table I. The measurements shown are the voltage magnitude at node 3 (V3), the real power between nodes 3 and 4 (P3-4) and the reactive power between nodes 3 and 4 (Q3-4) and between nodes 4 and 5 (Q4-5). As we indicated previously, according to the state estimation theory the measurement error can be detected if corresponding value of the normalized residual is higher than 3, what is verified. Moreover the measurement device with error is identified if the value of its normalized residual is the highest. However, in this case the measurement device with error cannot be identified because there are three measurements with the same value of normalized residual. Thus, it is necessary to add new sensors to the cluster in order to obtain more data as described in next scenario. B. Unique Cluster Scenario with PMU Sensors After adding 3 additional PMU measurements at nodes 1, 2 and 5, we have the synchrophasors measurements of voltage at the three nodes (magnitude and phase) and current at the lines connected to the three nodes (real and imaginary parts). Therefore we have 21 new measurements of the system available in the control centre. With these new data, the results of the state estimation algorithm allow to identify the erroneous measurement device, the voltage sensor V3, as can be seen in Table I. From the semantic point of view, as soon as the wrong sensor (i.e. sensor 3 in this case) is identified by the control center, the level of reliability of the measurements can be added to the own measurements (i.e. “PMU-reliable” or “Not PMU-reliable”) as semantic annotations, which helps to latter filtering and processing. Finally, since the volume of information has increased by 39 % by adding only 3 sensors, next objective is to analyze the feasibility of dividing the unique cluster in smaller and more intelligent clusters, along with its inter-operability as described in next scenario.

C. Clusters Division Scenario In this scenario, two clusters (1:nodes 1-5 and 2:nodes 6-14) are defined with their corresponding sensors, according to the voltage level. Note that it would be possible to define the clusters sharing some sensors as required. Since the erroneous measurement is in node 3, we consider only the sensors in cluster 1 by simplicity. In this case, we assume that the information can be aggregated and processed by a local device (e.g. wireless board) before sending the necessary data and added semantic annotations to the control center. The number of measurements (conventional and PMUs) now is 39, and we can assume that the control center has been wholly released from receiving those data and its related processing. The results of the estimation process are shown in Table I, being also possible the identification of the erroneous measurement of voltage sensor V3. TABLE I HIGHEST NORMALIZED RESIDUALS Unique Cluster (A) Sensor Normalized residual V3 11.3 P3-4 11.3 Q3-4 11.3 Q4-5 1.8

Unique Cluster & PMU (B) Sensor Normalized residual V3 11.3 Q3-4 3.2

Clusters Division (C) Sensor Normalized residual V3 11.5 Q3-4 3.0

From those results, we have checked that it is possible to perform a preprocessing stage in the different clusters, annotating semantic data to the measurements (i.e. “Not PMUreliable”) and releasing the control center from the corresponding acquisition and processing. Moreover, several options are available for inter-operability. On the one hand, the measured and semantic data could be requested by the control center on demand (i.e. by search query) to a service deployed in the local board. In this sense, the control center would assume a new role as an orchestrator of services offered by the different clusters. On the other hand, it would be feasible to do without the control center in order to make the clusters to interact with each other, which would represent a pure de-centralized system, where both clusters could make use of the services offered by each one. V. CONCLUSIONS A gap in the technologies currently used for sensor networks in the context of Power Grid has been identified in relation to other fields (e.g. Climate Change, Weather Monitoring, etc) where Service Oriented Architectures, OGC services, and geo-referenced data have been exploited intensely during the last years. The study of feasibility for the application of Semantic Sensor Technologies to Power Grid has revealed a number of basic constraints that should be taken into account in the scope of Smart Grids. The main constraints that apply are related to the management of hierarchical and massive information in a distributed and decentralized context, where the systems (semantic sensor networks) may interact between them in order to obtain the necessary information in a simple way. The scenarios described have helped to justify the requirements proposed for an appropriated integration framework, highlighting the benefits of semantics in

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combination with sensor clustering to distribute the processing of information and add annotations that improve the interoperability in a de-centralized context. Considering that the sensor network (i.e. Sensor Clusters) shall make reference to the underlying electrical network, it has been revealed the necessity for an Ontology describing the electrical network and its topology in order to be used by the semantic annotations, which requires of further research at the moment.

VII. BIOGRAPHIES Rubén Francisco Pérez Moreno received his Master degree in Physics at the University of Valencia in 1998, Spain, and is currently studying a PhD in Artificial Intelligence at the National University for Distance Education, Spain. His employment experience includes the SchlumbergerSema, and GMV Aerospace and Defence. His special fields of interest include OGC standards, semantics of sensor networks, systems architecture, and methods to support decision-making.

VI. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

[15] [16] [17] [18]

[19] [20] [21] [22]

C. Gellings, The Smart Grid: Enabling Energy Efficiency and Demand Response, Lilburn: The Fairmont Press, 2009, pp. 2-12. Green Paper: Towards a Secure, Sustainable and Competitive European Energy Network., Commission of the European Communities COM (2008)782, 2008. A. Keyhani, M. Marwali, and M. Dai, Integration of Green and Renewable Energy in Electric Power Systems, New Yersey: John Wiley & Sons, 2010, pp. 8-22. F. Sissine, S. M. Kaplan, and T. Net, Smart grid: modernizing electric power transmission and distribution, Washington: TheCapitol.Net, 2009, p. 360-361. N. M. Josuttis, SOA in Practice: The art of Distributed System Design, Sebastopol: O’Reilly, 2007, pp. 25-45. M. Botts and A. Robin, "Bringing the sensor web together," Geosciences, pp. 46-53, 1997. S. Jirka, A. Bröring, and C. Stasch, "Discovery Mechanisms for the Sensor Web," Sensors, vol. 9, pp. 2661-2681, 2009. D. Havlik, G. Schimak, and T. Bleier, "Cascading and replicating the OGC Sensor Observation Service," presented at the 18th Int. Conf. World IMACS / MODSIM, Cairns, Australia, 2009. A. Bröring, E. H. Jürrens, S. Jirka, and C. Stasch, "Development of Sensor Web Applications with Open Source Software," presented at the 1st Int. Conf. OSGIS, Nottingham, United Kingdom, 2009. V. Madani, "Strategies to meet grid challenges for safety and Reliability," International Journal of Reliability and Safety, vol. 2, no. 1/2, pp. 146-165, 2008. W. Ke, S. A. Ayyash, and T. Little, "Semantic Internetworking of Sensor Systems," presented at the IEEE Int. Conf. on Mobile Ad-hoc and Sensor Systems (MASS), Fort Lauderdale, USA, 2004. A. Seth, C. Henson, and S. S. Sahoo, "Semantic Sensor Web," IEEE Internet Computing, pp. 78-83, July/August. 2008. A. Korotij and J. Kiss-Gulyas, "A Cognitive Approach to Modelling Semantic Sensor Web Solutions," presented at the 2nd Int. Workshop on Semantic Sensor Web, Valencia, Spain, 2010. K. Janowicz, C. Keßler, A. Bröring, C. Stasch, and S. Schade, " Towards Semantic Enablement for Spatial Data Infrastructures," presented at the 4th European Conference on Smart Sensing and Context (EuroSSC), Guildford, United Kingdom, 2009. A. Whiteside, "OGC Web Services Common Specification. OpenGIS Implementation Specification," Open Geospatial Consortium: 06-121r3, 2007. J. S. Engelhardt, S. P. Basu, "Design, installation, and field experience with an overhead transmission dynamic line rating system," in Proc. 1996 IEEE Transmission and Distribution Conf., pp. 366 - 370. R. A. León, Vijay Vittal and G. Manimaran, “Application of sensor network to secure electric energy infrastructure”, IEEE Transactions on Power Delivery, vol. 22, no. 2, April 2007. M. Masri and R. Therkelsen (2003, Oct.). Bird Strike Indicator/Bird Activity Monitor and Field Assessment of Avian Fatalities. Electric Power Research Institute (EPRI), Palo Alto, CA. [Online]. Available: http://www.energy.ca.gov/reports/2004-03-05_500-03-107F.pdf I. F. Akyildiz and M. C. Vuran, Wireless Sensor Networks, West Sussex: John Wiley & Sons, 2010, pp. 365-384. H. Hrasnica, A. Haidine, and R. Lehnert, Broadband powerline communications networks: network design, West Sussex: John Wiley & Sons, 2004, pp. 9-35, 120-135. J.Q. Feng, Q. H. Wu, and J. Fitch, "An Ontology For Knowledge Representation In Power Systems," in Proc. 2004 Control Programme Conf., ID035. D. Küçük, O. Salor, T. Inan, I. Çadirci, and M. Ermis, "PQONT: A domain Ontology for electrical power quality," Elsevier Advanced Engineering Informatics, vol. 24, pp. 84-95, Jan. 2010.

Óscar Pérez Navarro got a Master degree in Mathematics (CC Computación) by Universidad Complutense of Madrid in 1997. He has got an MBA by IESE Business School as part of University Of Navarra in 2009. Mr Pérez Navarro work experience were linked to Meta4 in USA, Wunderman Cato Jonhson and GMV Aerospace & Defence. His area of expertise is Data Processing, both in situ and satellite data. He has been very active involved within the development of ESA and CNES mission ground segment facilities at ESA and CNES premises. Gonzalo León Manzano received the Information Systems Engineering Bachelor degree from the Escuela Politécnica de Albacete and Master degree from UNED University. He is currently studying a PhD in Artificial Intelligence at the National University for Distance Education, Spain. He is currently an IT government employee. His research interests include data mining, machine learning, high-dimensional data analysis, robotics, and sensor networks. Rafael Martínez-Tomás received his degree in Physics from the University of Valencia, Spain, in 1983, and received her Ph.D. from the Department of Artificial Intelligence of the National University for Distance Education, Spain, in 2000. Since 2001, he is an Assistant Professor with the Department of Artificial Intelligence of the National University for Distance Education, Spain. His research interests are in knowledge engineering, knowledge based systems, semantic web and semantic technologies, description logics and video-sequence semantic interpretation. Antonio de la Villa Jaén was born in Riotinto (Spain) in 1960. He received his PhD degree in Electrical Engineering at the University of Sevilla in 2001. He is currently Associate Professor at the same university. His primary areas of interest are computer methods for power systems state estimation problems, power systems protection and waves energy.

Pedro Cruz Romero was born in Sevilla (Spain) in 1967. He received his Master and PhD degrees in Electrical Engineering at the University of Sevilla in 1993 and 2000 respectively. He is currently Associate Professor at the same university. His primary areas of interest are magnetic field mitigation and application of sensors to power systems. He is a member of cigré and IEEE.