1,581 research outputs found
Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter
International audienc
Smart Power Grid Synchronization With Fault Tolerant Nonlinear Estimation
Effective real-time state estimation is essential for smart grid synchronization, as electricity demand continues to grow, and renewable energy resources increase their penetration into the grid. In order to provide a more reliable state estimation technique to address the problem of bad data in the PMU-based power synchronization, this paper presents a novel nonlinear estimation framework to dynamically track frequency, voltage magnitudes and phase angles. Instead of directly analyzing in abc coordinate frame, symmetrical component transformation is employed to separate the positive, negative, and zero sequence networks. Then, Clarke\u27s transformation is used to transform the sequence networks into the αÎČ stationary coordinate frame, which leads to system model formulation. A novel fault tolerant extended Kalman filter based real-time estimation framework is proposed for smart grid synchronization with noisy bad data measurements. Computer simulation studies have demonstrated that the proposed fault tolerant extended Kalman filter (FTEKF) provides more accurate voltage synchronization results than the extended Kalman filter (EKF). The proposed approach has been implemented with dSPACE DS1103 and National Instruments CompactRIO hardware platforms. Computer simulation and hardware instrumentation results have shown the potential applications of FTEKF in smart grid synchronization
PMU-Based ROCOF Measurements: Uncertainty Limits and Metrological Significance in Power System Applications
In modern power systems, the Rate-of-Change-of-Frequency (ROCOF) may be
largely employed in Wide Area Monitoring, Protection and Control (WAMPAC)
applications. However, a standard approach towards ROCOF measurements is still
missing. In this paper, we investigate the feasibility of Phasor Measurement
Units (PMUs) deployment in ROCOF-based applications, with a specific focus on
Under-Frequency Load-Shedding (UFLS). For this analysis, we select three
state-of-the-art window-based synchrophasor estimation algorithms and compare
different signal models, ROCOF estimation techniques and window lengths in
datasets inspired by real-world acquisitions. In this sense, we are able to
carry out a sensitivity analysis of the behavior of a PMU-based UFLS control
scheme. Based on the proposed results, PMUs prove to be accurate ROCOF meters,
as long as the harmonic and inter-harmonic distortion within the measurement
pass-bandwidth is scarce. In the presence of transient events, the
synchrophasor model looses its appropriateness as the signal energy spreads
over the entire spectrum and cannot be approximated as a sequence of
narrow-band components. Finally, we validate the actual feasibility of
PMU-based UFLS in a real-time simulated scenario where we compare two different
ROCOF estimation techniques with a frequency-based control scheme and we show
their impact on the successful grid restoration.Comment: Manuscript IM-18-20133R. Accepted for publication on IEEE
Transactions on Instrumentation and Measurement (acceptance date: 9 March
2019
P and M class phasor measurement unit algorithms using adaptive cascaded filters
The new standard C37.118.1 lays down strict performance limits for phasor measurement units (PMUs) under steady-state and dynamic conditions. Reference algorithms are also presented for the P (performance) and M (measurement) class PMUs. In this paper, the performance of these algorithms is analysed during some key signal scenarios, particularly those of off-nominal frequency, frequency ramps, and harmonic contamination. While it is found that total vector error (TVE) accuracy is relatively easy to achieve, the reference algorithm is not able to achieve a useful ROCOF (rate of change of frequency) accuracy. Instead, this paper presents alternative algorithms for P and M class PMUs which use adaptive filtering techniques in real time at up to 10 kHz sample rates, allowing consistent accuracy to be maintained across a ±33% frequency range. ROCOF errors can be reduced by factors of >40 for P class and >100 for M class devices
Performance Improvement of Wide-Area-Monitoring-System (WAMS) and Applications Development
Wide area monitoring system (WAMS), as an application of situation awareness, provides essential information for power system monitoring, planning, operation, and control. To fully utilize WAMS in smart grid, it is important to investigate and improve its performance, and develop advanced applications based on the data from WAMS. In this dissertation, the work on improving the WAMS performance and developing advanced applications are introduced.To improve the performance of WAMS, the work includes investigation of the impacts of measurement error and the requirements of system based on WAMS, and the solutions. PMU is one of the main sensors for WAMS. The phasor and frequency estimation algorithms implemented highly influence the performance of PMUs, and therefore the WAMS. The algorithms of PMUs are reviewed in Chapter 2. To understand how the errors impact WAMS application, different applications are investigated in Chapter 3, and their requirements of accuracy are given. In chapter 4, the error model of PMUs are developed, regarding different parameters of input signals and PMU operation conditions. The factors influence of accuracy of PMUs are analyzed in Chapter 5, including both internal and external error sources. Specifically, the impacts of increase renewables are analyzed. Based on the analysis above, a novel PMU is developed in Chapter 6, including algorithm and realization. This PMU is able to provide high accurate and fast responding measurements during both steady and dynamic state. It is potential to improve the performance of WAMS. To improve the interoperability, the C37.118.2 based data communication protocol is curtailed and realized for single-phase distribution-level PMUs, which are presented in Chapter 7.WAMS-based applications are developed and introduced in Chapter 8-10. The first application is to use the spatial and temporal characterization of power system frequency for data authentication, location estimation and the detection of cyber-attack. The second application is to detect the GPS attack on the synchronized time interval. The third application is to detect the geomagnetically induced currents (GIC) resulted from GMD and EMP-E3. These applications, benefited from the novel PMU proposed in Chapter 6, can be used to enhance the security and robust of power system
On Phasor Estimation for Voltage Sags Detection in a Smart Grid Context
International audienceThe advent of smart grids have urged a radical reappraisal of distribution networks and power quality requirements, and effective use of the network are indexed as the most important keys for smart grid expansion and deployment regardless. One of the most efficient ways of effective use of these grids would be to continuously monitor their conditions. This allows for early detection of power quality degeneration facilitating therefore a proactive response, prevent a fault ride-through the renewable power sources, minimizing downtime, and maximizing productivity. In this smart grid context, this paper proposes the evaluation of signal processing tools, namely the Hilbert transform and the linear Kalman filter to estimate voltage phasor for voltage sags detection
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