This paper presents the IDE4L project reference grid model developed to serve as a benchmark for studies on distribution grid dynamics within the project. The paper demonstrates a MATLAB/Simulink implementation of the reference grid to be used in real-time hardware-in-the-loop simulations. The simulations will be carried out to study distribution grid dynamics and to evaluate the techniques developed in IDE4L project for TSO/DSO interactions. Performance of the grid model is shown through sample real-time simulation results and a hardware-in-the-loop setup for PMU-based grid monitoring applications.
Flexibility and adaptability requirements of future electric power grids for integrating DERs call for the development of Wide-Area Monitoring, Protection And Control (WAMPAC) applications, utilizing synchrophasor measurements provided by the Phasor Measurement Units (PMUs).IEEE C37.118 is the most utilized protocol for real-time exchange of synchronized phasor measurement data. In order to fulfil some gaps not addressed in IEEE C37.118, and also to harmonize with the IEC 61850 power utility automation standard, the IEC 61850-90-5 technical report has been developed. IEC TR 61850-90-5 introduces a mechanism for transfer of digital states and time synchronized phasor measurement data over wide-area networks between Phasor Measurement Units (PMUs), Phasor Data Concentrators (PDCs) and WAMPAC applications in the context of IEC 61850.This work interprets the IEEE C37.118.2 and IEC 61850-90-5 Routed-Sampled Value and Routed-GOOSE protocols and describes the design and implementation of a library named Khorjin with the functionality of (1) an IEEE C37.118.2 to IEC 61850-90-5 gateway and protocol converter and, (2) an IEC 61850-90-5 subscriber and traffic parser.The main contribution of this work is the development of Khorjin library using only standard C libraries (i.e. independent from any operating system). This is allowing the use of the library in different platforms.The design requirements and functionality of the Khorjin library has been tested in the KTH SmarTS Lab Real-Time Hardware-in-the-Loop (HIL) simulation environment to assess its conformance to the functional requirements of IEEE C37.118.2 and IEC 61850-90-5 standards.
Real-time reduced steady state model synthesis of active distribution networks using PMU measurements.
IEEE Transactions on Power DeliveryAccess to the published version may require subscription. N.B. When citing this work, cite the original published paper.
Permanent link to this version:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192943 1 Abstract--Due to the increase of generation sources in distribution networks, it is becoming more and more complex to develop and maintain models of these networks. Network operators need to determine reduced models of distribution networks to be used in grid management functions. This paper presents a novel method that synthesizes steady state models of unbalanced active distribution networks by the use of dynamic measurements (time series) from PMUs. As PMU measurements may contain errors and bad data, the paper presents the application of a Kalman Filter technique for real-time data processing. In addition, PMU data captures the power system's response at different time-scales, which are generated by different types of power system events; the presented Kalman Filter has been improved to extract the steady state component of the PMU measurements to be fed to the steady state model synthesis application. Performance of the proposed methods has been assessed by real-time hardware-in-the-loop simulations on a sample distribution network.Index Terms-active distribution network, Kalman Filter (KF), model synthesis, PMU.
Real-time reduced steady state model synthesis of active distribution networks using PMU measurements.
IEEE Transactions on Power DeliveryAccess to the published version may require subscription. N.B. When citing this work, cite the original published paper.
Permanent link to this version:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192943 1 Abstract--Due to the increase of generation sources in distribution networks, it is becoming more and more complex to develop and maintain models of these networks. Network operators need to determine reduced models of distribution networks to be used in grid management functions. This paper presents a novel method that synthesizes steady state models of unbalanced active distribution networks by the use of dynamic measurements (time series) from PMUs. As PMU measurements may contain errors and bad data, the paper presents the application of a Kalman Filter technique for real-time data processing. In addition, PMU data captures the power system's response at different time-scales, which are generated by different types of power system events; the presented Kalman Filter has been improved to extract the steady state component of the PMU measurements to be fed to the steady state model synthesis application. Performance of the proposed methods has been assessed by real-time hardware-in-the-loop simulations on a sample distribution network.Index Terms-active distribution network, Kalman Filter (KF), model synthesis, PMU.
Data from phasor measurement units (PMUs) may be exploited to provide steady state information to the applications which require it. As PMU measurements may contain errors and missing data, the paper presents the application of a Kalman Filter technique for real-time data processing. PMU data captures the power system's response at different time-scales, which are generated by different types of power system events; the presented Kalman Filter methods have been applied to extract the steady state components of PMU measurements that can be fed to steady state applications. Two KF-based methods have been proposed, i.e., a windowing-based KF method and "the modified KF". Both methods are capable of reducing noise, compensating for missing data and filtering outliers from input PMU signals. A comparison of proposed methods has been carried out using the PMU data generated from a hardware-in-the-loop (HIL) experimental setup. In addition, a performance analysis of the proposed methods is performed using an evaluation metric.
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