Topic
OpenPDC
About: OpenPDC is a research topic. Over the lifetime, 17 publications have been published within this topic receiving 207 citations. The topic is also known as: Open Source Phasor Data Concentrator.
Papers
TL;DR: A fault location (FL) identification method for smart distribution network is presented and validated using a digital real-time simulator (DRTS) and an accurate estimation of the FL (over 90% of the cases) is achieved.
Abstract: A fault location (FL) identification method for smart distribution network is presented and validated using a digital real-time simulator (DRTS) The method can accurately identify the FL in a distribution network in the presence of distributed generation (DG) This method is based on state estimation (SE) algorithm which uses real-time data from simulated phasor measurement units (PMUs), placed in the distribution network SE needs the fault currents of the generators and voltage measurements of an optimal number of nodes to perform the FL algorithm The method was validated using the IEEE 37 node test feeder with DGs PMUs are placed on the real-time model of the system The real-time model was implemented on a DRTS which streams phasor data over the Internet using C37118 protocol OpenPDC is used to collect real-time PMU data coming from the DRTS Microsoft SQL is used as a database management server to store data coming from OpenPDC In the last step of the FL process, data stored in OpenPDC is fed into a FL identification algorithm to locate the fault Both balanced and unbalanced fault types are applied to different nodes and an accurate estimation of the FL (over 90% of the cases) is achieved
73 citations
5 Oct 2015
TL;DR: A novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform is presented.
Abstract: Phasor Measurement Units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronised measurements. The devices high data reporting rates present major computational challenges, in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain, from the aspects of speedup, scalability and accuracy. The speedup of the PDFA in computation is initially analysed through Amdahl's Law, a revision to the law is then proposed, suggesting enhancements to its capability to analyse the performance gain in computation when parallelizing data intensive applications in a cluster computing environment
63 citations
TL;DR: An advanced phasor data concentrators (APDC) capable of counteracting the communication impairments and improving the quality of monitoring of distributed energy resources (DERs) in microgrids is proposed.
Abstract: Synchrophasor networks are subject to communication delays and packet dropout that can compromise data integrity and, thus, jeopardize control and monitoring of smart microgrids. This paper proposes an advanced phasor data concentrators (APDC) capable of counteracting the communication impairments and improving the quality of monitoring of distributed energy resources (DERs) in microgrids. The proposed APDC utilizes an adaptive compensation scheme to achieve an effective estimate of missing data elements. Moreover, a monitoring unit is proposed to reliably detect frequency excursions and identify the DERs affected by islanding events. The performance of the proposed APDC is evaluated based on realistic phasor measurement unit data aligned and processed with real-time OpenPDC software. The experimental results confirm a high-level data integrity under both normal and disturbed conditions of microgrids. Moreover, fast and reliable detection of islanding events is achieved due to the significant improvement in detection time even under severe data losses.
37 citations
25 Jul 2010
TL;DR: The high rate of data samples reported by devices that support PMU functionality forces the use of non-traditional methods in order to attempt realtime anomaly detection, two methods discussed are offline machine learning and a realtime sliding window procedure.
Abstract: The high rate of data samples reported by devices that support PMU functionality forces the use of non-traditional methods in order to attempt realtime anomaly detection. Two methods discussed are offline machine learning and a realtime sliding window procedure. In using machine learning techniques it is possible to assert a classifier algorithm, which to a certain degree of accuracy can flag incoming data for further operation when applied in realtime. The open source project Hadoop provides the storage architecture for large datasets (petabyte scale) as well as the MapReduce computational framework for distributed computing to produce these classifiers. Additionally, a sliding window of realtime data can be used to present a longer data sample window than the device report rate allowing for a heuristic hysteresis approach. The open source openPDC promotes the implementation of the classifier and sliding window in a realtime environment operating on new measurements thirty times a second.
37 citations
24 Jul 2011
TL;DR: Test results show that the performance can meet the requirements of wide area power system visualization, real-time power system monitoring and the potential angle separation can be predicted by the monitoring system.
Abstract: This paper presents an online monitoring system for operator situation awareness using real-time synchrophasor measurements developed under a DOE R&D and demonstration project. The system provides wide area power system visualization, near real-time event replay and early warning of potential stability problems. The technologies implemented in this system include the memory residence object oriented database, special synchrophasor data transfer from application server to each user's computer, event oriented database using Binary Large Object (BLOB), data partitioning, and modal analysis using synchrophasor data. This system has been interfaced with the openPDC and tested using the real time or simulated synchrophasor measurements. The system architecture and the technologies involved are described in detail in the paper. The system is tested using simulated 179 PMUs in the WECC system. Test results show that the performance can meet the requirements of wide area power system visualization, real-time power system monitoring and the potential angle separation can be predicted by the monitoring system.
25 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2019 | 2 |
| 2018 | 1 |
| 2017 | 3 |
| 2016 | 2 |
| 2015 | 4 |
| 2014 | 2 |