An extended Kalman filter approach for accurate instantaneous dynamic phasor estimation

This paper proposes the application of a non-linear Extended Kalman Filter (EKF) for accurate instantaneous dynamic phasor estimation. An EKF-based algorithm is proposed to better adapt to the dynamic measurement requirements and to provide real-time tracking of the fundamental harmonic components and power system frequencies. This method is evaluated using dynamic compliance tests defined in the IEEE C37.118.1-2011 synchrophasor measurement standard, providing promising results in phasor and frequency estimation, compliant with the accuracy required in the case of off-nominal frequency, amplitude and phase angle modulations, frequency ramps, and step changes in magnitude and phase angle. An important additional feature of the method is its capability for real-time detection of transient disturbances in voltage or current waveforms using the residual of the filter, which enables flagging of the estimation for suitable processing

Three-Phase Synchrophasor Estimation Through Taylor Extended Kalman Filter

Synchronized phasor and frequency measurements are key tools for the monitoring and management of modern power systems. In a dynamic scenario, it is fundamental to define algorithms that allow accurately measuring time-varying signals, with short latencies and high reporting rates. A dynamic phasor model can help the design of these algorithms and, in particular, of those based on Kalman filtering approach. In this paper, an Extended Kalman filter formulation that considers the Taylor expansions of amplitudes and phase angles in three-phase signals is introduced. The proposed dynamic model takes into account the inherent relationship among the phases and includes harmonics in a effective way. The performance of the method permitting both synchrophasor and frequency measurements are assessed by simulations, considering also the combined effect of dynamics and disturbances. The algorithm shows tracking capabilities and the flexibility which is mandatory to deal with different conditions

Dynamic synchrophasor estimation by extended Kalman filter

Fast synchronized measurements of the phasor, frequency, and rate of change of frequency are expected to be very important for the automated control actions in the smart grid context. In this regard, measurement latency must be kept as short as possible for an effective control implementation when networks characterized by extremely fast dynamics are concerned. Kalman filter (KF)-based estimation algorithms appear to be attractive in this context; however, the conventional implementations suffer from significant limitations in their ability to deal with different types of dynamic conditions due to approximations in the model and in the associated uncertainty. This article proposes an innovative solution, based on an extended KF algorithm using a Taylor model, which is shown to provide improved tracking ability in a vast range of dynamic conditions. A novel element in the proposed technique is the representation of model uncertainty, which takes into account the intrinsic correlation among errors that appear in the state–space description under dynamic conditions. A compatibility check between the forecast and measurement result is also introduced as an effective and metrologically sound approach to detect large unexpected changes in the tracked parameters in order to achieve a fast response of the algorithm also under those conditions. The performance of the algorithm is thoroughly investigated by means of simulation to demonstrate the significant improvement compared to other KF solutions in some conditions of practical relevance

Phasor Parameter Modeling and Time-Synchronized Calculation for Representation of Power System Dynamics

The electric power grid is undergoing sustained disturbances. In particular, the extreme dynamic events disrupt normal electric power transfer, degrade power system operating conditions, and may lead to catastrophic large-scale blackouts. Accordingly, control applications are deployed to detect the inception of extreme dynamic events, and mitigate their causes appropriately, so that normal power system operating conditions can be restored. In order to achieve this, the operating conditions of the power system should be accurately characterized in terms of the electrical quantities that are crucial to control applications. Currently, the power system operating conditions are obtained through SCADA system and the synchrophasor system. Because of GPS time-synchronized waveform sampling capability and higher measurement reporting rate, synchrophasor system is more advantageous in tracking the extreme dynamic operating conditions of the power system. In this Dissertation, a phasor parameter calculation approach is proposed to accurately characterize the power system operating conditions during the extreme electromagnetic and electromechanical dynamic events in the electric power grid. First, a framework for phasor parameter calculation during both electromagnetic and electromechanical dynamic events is proposed. The framework aims to satisfy both P-class and M-class PMU algorithm design accuracy requirements with a single algorithm. This is achieved by incorporating an adaptive event classification and algorithm model switching mechanism, followed by the phasor parameter definition and calculation tailored to each identified event. Then, a phasor estimation technique is designed for electromagnetic transient events. An ambient fundamental frequency estimator based on UKF is introduced, which is leveraged to adaptively tune the DFT-based algorithm to alleviate frequency leakage. A hybridization algorithm framework is also proposed, which further reduces the negative impact caused by decaying DC components in electromagnetic transient waveforms. Then, a phasor estimation technique for electromechanical dynamics is introduced. A novel wavelet is designed to effectively extract time-frequency features from electromechanical dynamic waveforms. These features are then used to classify input signal types, so that the PMU algorithm modeling can be thereafter tailored specifically to match the underlying signal features for the identified event. This adaptability of the proposed algorithm results in higher phasor parameter estimation accuracy. Finally, the Dissertation hypothesis is validated through experimental testing under design and application test use cases. The associated test procedures, test use cases, and test methodologies and metrics are defined and implemented. The impact of algorithm inaccuracy and communication network distortion on application performance is also demonstrated. Test results performance is then evaluated. Conclusions, Dissertation contributions, and future steps are outlined at the end

Synchrophasor Measurement Using Substation Intelligent Electronic Devices: Algorithms and Test Methodology

This dissertation studies the performance of synchrophasor measurement obtained using substation Intelligent Electronic Devices (IEDs) and proposes new algorithms and test methodology to improve and verify their performance when used in power system applications. To improve the dynamic performance when exposed to sinusoidal waveform distortions, such as modulation, frequency drift, abrupt change in magnitude, etc, an adaptive approach for accurately estimating phasors while eliminating the effect of various transient disturbances on voltages and currents is proposed. The algorithm pre-analyzes the waveform spanning the window of observation to identify and localize the discontinuities which affect the accuracy of phasor computation. A quadratic polynomial signal model is used to improve the accuracy of phasor estimates during power oscillations. Extensive experimental results demonstrate the advantages. This algorithm can also be used as reference algorithm for testing the performance of the devices extracting synchronized phasor measurements. A novel approach for estimating the phasor parameters, namely frequency, magnitude and angle in real time based on a newly constructed recursive wavelet transform is developed. This algorithm is capable of estimating the phasor parameters in a quarter cycle of an input signal. It features fast response and achieves high accuracy over a wide range of frequency deviations. The signal sampling rate and data window size can be selected to meet desirable application requirements, such as fast response, high accuracy and low computational burden. In addition, an approach for eliminating a decaying DC component, which has significant impact on estimating phasors, is proposed using recursive wavelet transform. This dissertation develops test methodology and tools for evaluating the conformance to standard-define performance for synchrophasor measurements. An interleaving technique applied on output phasors can equivalently increase the reporting rate and can precisely depict the transient behavior of a synchrophasor unit under the step input. A reference phasor estimator is developed and implemented. Various types of Phasor Measurement Units (PMUs) and PMU-enabled IEDs (Intelligent Electronic Devices) and time synchronization options have been tested against the standards using the proposed algorithm. Test results demonstrate the effectiveness and advantages

Online Neuro-Adaptive Learning For Power System Dynamic State Estimation

With the increased penetration of renewable generation in the smart grid , it is crucial to have knowledge of rapid changes of system states. The information of real-time electro-mechanical dynamic states of generators are essential to ensuring reliability and detecting instability of the grid. The conventional SCADA based Dynamic State Estimation (DSE) was limited by the slow sampling rates (2-4 Hz). With the advent of PMU based synchro-phasor technology in tandem with Wide Area Monitoring System (WAMS), it has become possible to avail rapid real-time measurements at the network nodes. These measurements can be exploited for better estimates of system dynamic states. In this research, we have proposed a novel Artificial Intelligence (AI) based real-time neuro-adaptive algorithm for rotor angle and speed estimation of synchronous generators. Generator swing equations and power flow models are incorporated in the online learning. The algorithm learns and adapts in real-time to achieve accurate estimates. Simulation is carried out on 68 bus 16 generator NETS-NYPS model. The neuro-adaptive algorithm is compared with classical Kalman Filter based DSE. Applicability and accuracy of the proposed method is demonstrated under the influence of noise and faulty conditions

Exploiting phasor measurement units for enhanced transmission network operation and control

This thesis was submitted for the degree of Doctor of Engineering and awarded by Brunel UniversityIn order to achieve binding Government targets towards the decarbonisation of the electricity network, the GB power system is undergoing an unprecedented amount of change. A series of new technologies designed to integrate massive volumes of renewable generation, predominantly in the form of offshore wind, asynchronously connecting to the periphery of the transmission system, are transforming the requirements of the network. This displacement of traditional thermal generation is leading to a significant reduction in system inertia, thus making the task of system operation more challenging. It is therefore deemed necessary to develop tools and technologies that provide far greater insight into the state of the power system in real-time and give rise to methods for improving offline modelling practices through an enhanced understanding of the systems performance. To that extent PMUs are seen as one of the key enablers of the Smart Grid, providing accurate time-synchronised measurements on the state of the power system, allowing the true dynamics of the power system to be captured and analysed. This thesis provides an analysis of the existing PMU deployment on the GB transmission system with a view to the future system monitoring requirements. A critical evaluation and comparison is also provided on the suitability of a University based Low Voltage PMU network to further enhance the visibility of the GB system. In addition a novel event detection algorithm based on Detrended Fluctuation Analysis is developed and demonstrated, designed to determine the exact start time of a transmission event, as well as the suitability of such an event for additional transmission system analysis, namely inertia estimation. Finally, a reliable method for the estimation of total system inertia is proposed that includes an estimate of the contribution from residual sources, of which there is currently no visibility. The proposed method identifies the importance of regional inertia and its impact to the operation of the GB transmission system.Engineering and Physical Sciences Research Council (EPSRC) and National Grid