A Type- and Control-Flow Analysis for System F: Technical Report

We present a monovariant flow analysis for System F (with recursion). The flow analysis yields both control-flow information, approximating the λ- and Λ-expressions that may be bound to variables, and type-flow information, approximating the type expressions that may instantiate type variables. Moreover, the two flows are mutually beneficial: the control flow determines which Λ-expressions may be applied to which type expressions (and, hence, which type expressions may instantiate which type variables), while the type flow filters the λ- and Λ-expressions that may be bound to variables (by rejecting expressions with static types that are incompatible with the static type of the variable under the type flow). As is typical for a monovariant control-flow analysis, control-flow information is expressed as an abstract environment mapping variables to sets of (syntactic) λ- and Λ-expressions that occur in the program under analysis. Similarly, type-flow information is expressed as an abstract environment mapping type variables to sets of (syntactic) types that occur in the program under analysis. Compatibility of static types (with free type variables) under a type flow is decided by interpreting the abstract environment as productions for a regular-tree grammar and querying if the languages generated by taking the types in question as starting terms have a non-empty intersection. This is a companion technical report, providing additional commentary and proof details, to a paper [11] appearing in Implementation and Application of Functional Languages: 24th International Symposium (IFL’12)

Modelling Type 1 and 2 Wind Turbines based on IEC 61400-27-1: Transient Response under Voltage Dips

[EN] Wind power plants depend greatly on weather conditions, thus being considered intermittent, uncertain and non-dispatchable. Due to the massive integration of this energy resource in the recent decades, it is important that transmission and distribution system operators are able to model their electrical behaviour in terms of steady-state power flow, transient dynamic stability, and short-circuit currents. Consequently, in 2015, the International Electrotechnical Commission published Standard IEC 61400-27-1, which includes generic models for wind power generation in order to estimate the electrical characteristics of wind turbines at the connection point. This paper presents, describes and details the models for wind turbine topologies Types 1 and 2 following IEC 61400-27-1 for electrical simulation purposes, including the values for the parameters for the different subsystems. A hardware-in-the-loop combined with a real-time simulator is also used to analyse the response of such wind turbine topologies under voltage dips. The evolution of active and reactive powers is discussed, together with the wind turbine rotor and generator rotational speeds.This work was partially supported by the Spanish Ministry of Economy and Competitiveness and the European Union -FEDER Funds, ENE2016-78214-C2-1-R-; and the Spanish Ministry of Education, Culture and Sports -ref. FPU16/04282-.García-Sánchez, TM.; Muñoz-Benavente, I.; Gómez-Lázaro, E.; Fernández-Guillamón, A. (2020). Modelling Type 1 and 2 Wind Turbines based on IEC 61400-27-1: Transient Response under Voltage Dips. Energies. 13(16):1-19. https://doi.org/10.3390/en13164078S1191316Fernández-Guillamón, A., Villena-Lapaz, J., Vigueras-Rodríguez, A., García-Sánchez, T., & Molina-García, Á. (2018). An Adaptive Frequency Strategy for Variable Speed Wind Turbines: Application to High Wind Integration Into Power Systems. Energies, 11(6), 1436. doi:10.3390/en11061436Fernández-Guillamón, A., Das, K., Cutululis, N. A., & Molina-García, Á. (2019). Offshore Wind Power Integration into Future Power Systems: Overview and Trends. Journal of Marine Science and Engineering, 7(11), 399. doi:10.3390/jmse7110399Fernández-Guillamón, A., Gómez-Lázaro, E., Muljadi, E., & Molina-García, Á. (2019). Power systems with high renewable energy sources: A review of inertia and frequency control strategies over time. Renewable and Sustainable Energy Reviews, 115, 109369. doi:10.1016/j.rser.2019.109369Cardozo, C., van Ackooij, W., & Capely, L. (2018). Cutting plane approaches for frequency constrained economic dispatch problems. Electric Power Systems Research, 156, 54-63. doi:10.1016/j.epsr.2017.11.001Fernández-Guillamón, A., Martínez-Lucas, G., Molina-García, Á., & Sarasua, J. I. (2020). An Adaptive Control Scheme for Variable Speed Wind Turbines Providing Frequency Regulation in Isolated Power Systems with Thermal Generation. Energies, 13(13), 3369. doi:10.3390/en13133369Global Wind Report 2019https://gwec.net/global-wind-report-2019/Muñoz-Benavente, I., Hansen, A. D., Gómez-Lázaro, E., García-Sánchez, T., Fernández-Guillamón, A., & Molina-García, Á. (2019). Impact of Combined Demand-Response and Wind Power Plant Participation in Frequency Control for Multi-Area Power Systems. Energies, 12(9), 1687. doi:10.3390/en12091687Villena-Ruiz, R., Lorenzo-Bonache, A., Honrubia-Escribano, A., Jiménez-Buendía, F., & Gómez-Lázaro, E. (2019). Implementation of IEC 61400-27-1 Type 3 Model: Performance Analysis under Different Modeling Approaches. Energies, 12(14), 2690. doi:10.3390/en12142690Kumar, D., & Chatterjee, K. (2016). A review of conventional and advanced MPPT algorithms for wind energy systems. Renewable and Sustainable Energy Reviews, 55, 957-970. doi:10.1016/j.rser.2015.11.013Hansen, A. D., Iov, F., Blaabjerg, F., & Hansen, L. H. (2004). Review of Contemporary Wind Turbine Concepts and Their Market Penetration. Wind Engineering, 28(3), 247-263. doi:10.1260/0309524041590099Liang, X. (2017). Emerging Power Quality Challenges Due to Integration of Renewable Energy Sources. IEEE Transactions on Industry Applications, 53(2), 855-866. doi:10.1109/tia.2016.2626253Calif, R., & Schmitt, F. G. (2014). Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm. Nonlinear Processes in Geophysics, 21(2), 379-392. doi:10.5194/npg-21-379-2014Calif, R., Schmitt, F. G., & Huang, Y. (2013). Multifractal description of wind power fluctuations using arbitrary order Hilbert spectral analysis. Physica A: Statistical Mechanics and its Applications, 392(18), 4106-4120. doi:10.1016/j.physa.2013.04.038Fernández‐Guillamón, A., Vigueras‐Rodríguez, A., & Molina‐García, Á. (2019). Analysis of power system inertia estimation in high wind power plant integration scenarios. IET Renewable Power Generation, 13(15), 2807-2816. doi:10.1049/iet-rpg.2019.0220Heredia, F.-J., Cuadrado, M. D., & Corchero, C. (2018). On optimal participation in the electricity markets of wind power plants with battery energy storage systems. Computers & Operations Research, 96, 316-329. doi:10.1016/j.cor.2018.03.004Zhang, W., & Fang, K. (2017). Controlling active power of wind farms to participate in load frequency control of power systems. IET Generation, Transmission & Distribution, 11(9), 2194-2203. doi:10.1049/iet-gtd.2016.1471Honrubia-Escribano, A., Gómez-Lázaro, E., Fortmann, J., Sørensen, P., & Martin-Martinez, S. (2018). Generic dynamic wind turbine models for power system stability analysis: A comprehensive review. Renewable and Sustainable Energy Reviews, 81, 1939-1952. doi:10.1016/j.rser.2017.06.005Moschitta, A., Carbone, P., & Muscas, C. (2011). Generalized Likelihood Ratio Test for Voltage Dip Detection. IEEE Transactions on Instrumentation and Measurement, 60(5), 1644-1653. doi:10.1109/tim.2011.2113110Moschitta, A., Carbone, P., & Muscas, C. (2012). Performance Comparison of Advanced Techniques for Voltage Dip Detection. IEEE Transactions on Instrumentation and Measurement, 61(5), 1494-1502. doi:10.1109/tim.2012.2183436Gallo, D., Landi, C., Luiso, M., & Fiorucci, E. (2014). Survey on Voltage Dip Measurements in Standard Framework. IEEE Transactions on Instrumentation and Measurement, 63(2), 374-387. doi:10.1109/tim.2013.2278996Ipinnimo, O., Chowdhury, S., Chowdhury, S. P., & Mitra, J. (2013). A review of voltage dip mitigation techniques with distributed generation in electricity networks. Electric Power Systems Research, 103, 28-36. doi:10.1016/j.epsr.2013.05.004Hossain, M. J., Pota, H. R., Ugrinovskii, V. A., & Ramos, R. A. (2010). Simultaneous STATCOM and Pitch Angle Control for Improved LVRT Capability of Fixed-Speed Wind Turbines. IEEE Transactions on Sustainable Energy, 1(3), 142-151. doi:10.1109/tste.2010.2054118Hossain, M. J., Pota, H. R., & Ramos, R. A. (2011). Robust STATCOM control for the stabilisation of fixed-speed wind turbines during low voltages. Renewable Energy, 36(11), 2897-2905. doi:10.1016/j.renene.2011.04.010Hossain, M. J., Pota, H. R., & Ramos, R. A. (2012). Improved low-voltage-ride-through capability of fixed-speed wind turbines using decentralised control of STATCOM with energy storage system. IET Generation, Transmission & Distribution, 6(8), 719. doi:10.1049/iet-gtd.2011.0537Wessels, C., Hoffmann, N., Molinas, M., & Fuchs, F. W. (2013). StatCom control at wind farms with fixed-speed induction generators under asymmetrical grid faults. IEEE Transactions on Industrial Electronics, 60(7), 2864-2873. doi:10.1109/tie.2012.2233694Obando-Montaño, A., Carrillo, C., Cidrás, J., & Díaz-Dorado, E. (2014). A STATCOM with Supercapacitors for Low-Voltage Ride-Through in Fixed-Speed Wind Turbines. Energies, 7(9), 5922-5952. doi:10.3390/en7095922Moghadasi, A., Sarwat, A., & Guerrero, J. M. (2016). A comprehensive review of low-voltage-ride-through methods for fixed-speed wind power generators. Renewable and Sustainable Energy Reviews, 55, 823-839. doi:10.1016/j.rser.2015.11.020Heydari-doostabad, H., Khalghani, M. R., & Khooban, M. H. (2016). A novel control system design to improve LVRT capability of fixed speed wind turbines using STATCOM in presence of voltage fault. International Journal of Electrical Power & Energy Systems, 77, 280-286. doi:10.1016/j.ijepes.2015.11.011Fortmann, J., Engelhardt, S., Kretschmann, J., Feltes, C., & Erlich, I. (2014). New Generic Model of DFG-Based Wind Turbines for RMS-Type Simulation. IEEE Transactions on Energy Conversion, 29(1), 110-118. doi:10.1109/tec.2013.2287251Goksu, O., Altin, M., Fortmann, J., & Sorensen, P. E. (2016). Field Validation of IEC 61400-27-1 Wind Generation Type 3 Model With Plant Power Factor Controller. IEEE Transactions on Energy Conversion, 31(3), 1170-1178. doi:10.1109/tec.2016.2540006Honrubia-Escribano, A., Jiménez-Buendía, F., Gómez-Lázaro, E., & Fortmann, J. (2016). Validation of Generic Models for Variable Speed Operation Wind Turbines Following the Recent Guidelines Issued by IEC 61400-27. Energies, 9(12), 1048. doi:10.3390/en9121048Honrubia-Escribano, A., Jimenez-Buendia, F., Gomez-Lazaro, E., & Fortmann, J. (2018). Field Validation of a Standard Type 3 Wind Turbine Model for Power System Stability, According to the Requirements Imposed by IEC 61400-27-1. IEEE Transactions on Energy Conversion, 33(1), 137-145. doi:10.1109/tec.2017.2737703Lorenzo-Bonache, A., Honrubia-Escribano, A., Jiménez-Buendía, F., Molina-García, Á., & Gómez-Lázaro, E. (2017). Generic Type 3 Wind Turbine Model Based on IEC 61400-27-1: Parameter Analysis and Transient Response under Voltage Dips. Energies, 10(9), 1441. doi:10.3390/en10091441Honrubia-Escribano, A., Jiménez-Buendía, F., Sosa-Avendaño, J. L., Gartmann, P., Frahm, S., Fortmann, J., … Gómez-Lázaro, E. (2019). Fault-Ride Trough Validation of IEC 61400-27-1 Type 3 and Type 4 Models of Different Wind Turbine Manufacturers. Energies, 12(16), 3039. doi:10.3390/en12163039Wang, L., Zhang, Z., Long, H., Xu, J., & Liu, R. (2017). Wind Turbine Gearbox Failure Identification With Deep Neural Networks. IEEE Transactions on Industrial Informatics, 13(3), 1360-1368. doi:10.1109/tii.2016.2607179Hansen, A. D., & Hansen, L. H. (2007). Wind turbine concept market penetration over 10 years (1995–2004). Wind Energy, 10(1), 81-97. doi:10.1002/we.210IEC 61400-27-1. Electrical Simulation Models—Wind Turbines; Technical Reporthttps://webstore.iec.ch/publication/21811Vázquez-Hernández, C., Serrano-González, J., & Centeno, G. (2017). A Market-Based Analysis on the Main Characteristics of Gearboxes Used in Onshore Wind Turbines. Energies, 10(11), 1686. doi:10.3390/en10111686Duong, M., Grimaccia, F., Leva, S., Mussetta, M., & Le, K. (2015). Improving Transient Stability in a Grid-Connected Squirrel-Cage Induction Generator Wind Turbine System Using a Fuzzy Logic Controller. Energies, 8(7), 6328-6349. doi:10.3390/en8076328Cheng, M., & Zhu, Y. (2014). The state of the art of wind energy conversion systems and technologies: A review. Energy Conversion and Management, 88, 332-347. doi:10.1016/j.enconman.2014.08.037Pinar Pérez, J. M., García Márquez, F. P., Tobias, A., & Papaelias, M. (2013). Wind turbine reliability analysis. Renewable and Sustainable Energy Reviews, 23, 463-472. doi:10.1016/j.rser.2013.03.018Sumathi, S., Ashok Kumar, L., & Surekha, P. (2015). Wind Energy Conversion Systems. Green Energy and Technology, 247-307. doi:10.1007/978-3-319-14941-7_4Fernández-Guillamón, A., Sarasúa, J. I., Chazarra, M., Vigueras-Rodríguez, A., Fernández-Muñoz, D., & Molina-García, Á. (2020). Frequency control analysis based on unit commitment schemes with high wind power integration: A Spanish isolated power system case study. International Journal of Electrical Power & Energy Systems, 121, 106044. doi:10.1016/j.ijepes.2020.106044Liu, J., Gao, Y., Geng, S., & Wu, L. (2017). Nonlinear Control of Variable Speed Wind Turbines via Fuzzy Techniques. IEEE Access, 5, 27-34. doi:10.1109/access.2016.2599542Margaris, I. D., Hansen, A. D., Sørensen, P., & Hatziargyriou, N. D. (2010). Illustration of Modern Wind Turbine Ancillary Services. Energies, 3(6), 1290-1302. doi:10.3390/en3061290Wan, S., Cheng, K., Sheng, X., & Wang, X. (2019). Characteristic Analysis of DFIG Wind Turbine under Blade Mass Imbalance Fault in View of Wind Speed Spatiotemporal Distribution. Energies, 12(16), 3178. doi:10.3390/en12163178Boukhezzar, B., & Siguerdidjane, H. (2011). Nonlinear Control of a Variable-Speed Wind Turbine Using a Two-Mass Model. IEEE Transactions on Energy Conversion, 26(1), 149-162. doi:10.1109/tec.2010.2090155Chu, Yuan, Hu, Pan, & Pan. (2019). Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine. Energies, 12(18), 3429. doi:10.3390/en12183429Villena-Ruiz, R., Honrubia-Escribano, A., Fortmann, J., & Gómez-Lázaro, E. (2020). Field validation of a standard Type 3 wind turbine model implemented in DIgSILENT-PowerFactory following IEC 61400-27-1 guidelines. International Journal of Electrical Power & Energy Systems, 116, 105553. doi:10.1016/j.ijepes.2019.105553Ekanayake, J. B., Holdsworth, L., & Jenkins, N. (2003). Comparison of 5th order and 3rd order machine models for doubly fed induction generator (DFIG) wind turbines. Electric Power Systems Research, 67(3), 207-215. doi:10.1016/s0378-7796(03)00109-3Brandl, R. (2017). Operational Range of Several Interface Algorithms for Different Power Hardware-In-The-Loop Setups. Energies, 10(12), 1946. doi:10.3390/en10121946Matar, M., Karimi, H., Etemadi, A., & Iravani, R. (2012). A High Performance Real-Time Simulator for Controllers Hardware-in-the-Loop Testing. Energies, 5(6), 1713-1733. doi:10.3390/en506171

On the Flow-level Dynamics of a Packet-switched Network

The packet is the fundamental unit of transportation in modern communication networks such as the Internet. Physical layer scheduling decisions are made at the level of packets, and packet-level models with exogenous arrival processes have long been employed to study network performance, as well as design scheduling policies that more efficiently utilize network resources. On the other hand, a user of the network is more concerned with end-to-end bandwidth, which is allocated through congestion control policies such as TCP. Utility-based flow-level models have played an important role in understanding congestion control protocols. In summary, these two classes of models have provided separate insights for flow-level and packet-level dynamics of a network

Combining behavioural types with security analysis

Today's software systems are highly distributed and interconnected, and they increasingly rely on communication to achieve their goals; due to their societal importance, security and trustworthiness are crucial aspects for the correctness of these systems. Behavioural types, which extend data types by describing also the structured behaviour of programs, are a widely studied approach to the enforcement of correctness properties in communicating systems. This paper offers a unified overview of proposals based on behavioural types which are aimed at the analysis of security properties

Power Flow Modelling of Dynamic Systems - Introduction to Modern Teaching Tools

As tools for dynamic system modelling both conventional methods such as transfer function or state space representation and modern power flow based methods are available. The latter methods do not depend on energy domain, are able to preserve physical system structures, visualize power conversion or coupling or split, identify power losses or storage, run on conventional software and emphasize the relevance of energy as basic principle of known physical domains. Nevertheless common control structures as well as analysis and design tools may still be applied. Furthermore the generalization of power flow methods as pseudo-power flow provides with a universal tool for any dynamic modelling. The phenomenon of power flow constitutes an up to date education methodology. Thus the paper summarizes fundamentals of selected power flow oriented modelling methods, presents a Bond Graph block library for teaching power oriented modelling as compact menu-driven freeware, introduces selected examples and discusses special features.Comment: 12 pages, 9 figures, 4 table

FAST : a fault detection and identification software tool

The aim of this work is to improve the reliability and safety of complex critical control systems by contributing to the systematic application of fault diagnosis. In order to ease the utilization of fault detection and isolation (FDI) tools in the industry, a systematic approach is required to allow the process engineers to analyze a system from this perspective. In this way, it should be possible to analyze this system to find if it provides the required fault diagnosis and redundancy according to the process criticality. In addition, it should be possible to evaluate what-if scenarios by slightly modifying the process (f.i. adding sensors or changing their placement) and evaluating the impact in terms of the fault diagnosis and redundancy possibilities. Hence, this work proposes an approach to analyze a process from the FDI perspective and for this purpose provides the tool FAST which covers from the analysis and design phase until the final FDI supervisor implementation in a real process. To synthesize the process information, a very simple format has been defined based on XML. This format provides the needed information to systematically perform the Structural Analysis of that process. Any process can be analyzed, the only restriction is that the models of the process components need to be available in the FAST tool. The processes are described in FAST in terms of process variables, components and relations and the tool performs the structural analysis of the process obtaining: (i) the structural matrix, (ii) the perfect matching, (iii) the analytical redundancy relations (if any) and (iv) the fault signature matrix. To aid in the analysis process, FAST can operate stand alone in simulation mode allowing the process engineer to evaluate the faults, its detectability and implement changes in the process components and topology to improve the diagnosis and redundancy capabilities. On the other hand, FAST can operate on-line connected to the process plant through an OPC interface. The OPC interface enables the possibility to connect to almost any process which features a SCADA system for supervisory control. When running in on-line mode, the process is monitored by a software agent known as the Supervisor Agent. FAST has also the capability of implementing distributed FDI using its multi-agent architecture. The tool is able to partition complex industrial processes into subsystems, identify which process variables need to be shared by each subsystem and instantiate a Supervision Agent for each of the partitioned subsystems. The Supervision Agents once instantiated will start diagnosing their local components and handle the requests to provide the variable values which FAST has identified as shared with other agents to support the distributed FDI process.Per tal de facilitar la utilització d'eines per la detecció i identificació de fallades (FDI) en la indústria, es requereix un enfocament sistemàtic per permetre als enginyers de processos analitzar un sistema des d'aquesta perspectiva. D'aquesta forma, hauria de ser possible analitzar aquest sistema per determinar si proporciona el diagnosi de fallades i la redundància d'acord amb la seva criticitat. A més, hauria de ser possible avaluar escenaris de casos modificant lleugerament el procés (per exemple afegint sensors o canviant la seva localització) i avaluant l'impacte en quant a les possibilitats de diagnosi de fallades i redundància. Per tant, aquest projecte proposa un enfocament per analitzar un procés des de la perspectiva FDI i per tal d'implementar-ho proporciona l'eina FAST la qual cobreix des de la fase d'anàlisi i disseny fins a la implementació final d'un supervisor FDI en un procés real. Per sintetitzar la informació del procés s'ha definit un format simple basat en XML. Aquest format proporciona la informació necessària per realitzar de forma sistemàtica l'Anàlisi Estructural del procés. Qualsevol procés pot ser analitzat, només hi ha la restricció de que els models dels components han d'estar disponibles en l'eina FAST. Els processos es descriuen en termes de variables de procés, components i relacions i l'eina realitza l'anàlisi estructural obtenint: (i) la matriu estructural, (ii) el Perfect Matching, (iii) les relacions de redundància analítica, si n'hi ha, i (iv) la matriu signatura de fallades. Per ajudar durant el procés d'anàlisi, FAST pot operar aïlladament en mode de simulació permetent a l'enginyer de procés avaluar fallades, la seva detectabilitat i implementar canvis en els components del procés i la topologia per tal de millorar les capacitats de diagnosi i redundància. Per altra banda, FAST pot operar en línia connectat al procés de la planta per mitjà d'una interfície OPC. La interfície OPC permet la possibilitat de connectar gairebé a qualsevol procés que inclogui un sistema SCADA per la seva supervisió. Quan funciona en mode en línia, el procés està monitoritzat per un agent software anomenat l'Agent Supervisor. Addicionalment, FAST té la capacitat d'implementar FDI de forma distribuïda utilitzant la seva arquitectura multi-agent. L'eina permet dividir sistemes industrials complexes en subsistemes, identificar quines variables de procés han de ser compartides per cada subsistema i generar una instància d'Agent Supervisor per cadascun dels subsistemes identificats. Els Agents Supervisor un cop activats, començaran diagnosticant els components locals i despatxant les peticions de valors per les variables que FAST ha identificat com compartides amb altres agents, per tal d'implementar el procés FDI de forma distribuïda.Postprint (published version