Abstract
The massive integration of distributed energy resources in distribution level has added more complexity in operation, control and protection of Medium Voltage Networks (MV-Networks). Therefore, Distribution Phasor Measurement Units (D-PMUs) and Micro-Synchrophasors Measurement Units (μPMUs) has emerged as high resolution and accurate measurement devices to improve not only the situational awareness but also post-mortem and distributed generation (DG) integration analysis toward MV-Networks. The present work describes the SynchroPhasorial Monitoring System (SPSM) for MV-Networks in order to carry out acquisition, storage, aftertreatment and visualization of synchrophasorial data in accordance with std. IEEE C37.118-2011 and std. IEEE C37.244-2013. This platform consists in different open-sources applications based on Python Programming Language. Then, the structure of this monitoring platform is conformed by applications for real-time monitoring, post-mortem analysis and off-line analysis. The core of this platform is the Phasorial Data Concentrator (PDC) which accomplishes the following functions: data aggregation, data communications, data validation, data format and coordinate conversion, configuration, performance monitoring, redundant data handling and duplicate data handling. Synchrophasorial data buffering into the PDC allows to display real-time monitoring by a Human-Machine Interface (HMI), that also permit to interact with PMUs settings and communication streams. Moreover, synchrophasorial data concentrated is sent to storage server which enables post-mortem and off-line analysis. Based on synchrophasorial data stored, there were developed algorithms and methodologies for disturbance detection analysis and GD integration analysis. First one corresponds to a methodology that correlates Supervisory, Control And Data Acquisition (SCADA) alarms with synchrophasorial data. After that using the rate of change of voltage and frequency (ROCOV and ROCOF), disturbances are identified through established limits violations. Finally, the algorithm present series time plots of electrical variables and CSVs with all registered operative violations. Second one was developed to analyze the impact of excessive GD integration because of this phenomenon could affect protection system, voltage feeder performance, power quality and total power losses in a MV-Network. Using synchrophasorial data of a MV-networks without GD integration and fast decoupled power flow algorithm, the methodology evaluates the power system under different GD penetration levels in order to find the optimal capacity and location for GD integration. Both methodologies were validated with real events and data registered by SCADA system of a MV-network.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Monitoring and Control of Electrical Power Systems Using Machine Learning Techniques
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
