Dynamic measuring of frequency and frequency oscillations in multiphase power systems

IEEE Trans. Instrum. Meas.

2012

121

Abstract-A novel technique for online estimation of the fundamental frequency of unbalanced three-phase power systems is proposed. Based on Clarke's transformation and widely linear complex domain modeling, the proposed method makes use of the full second-order information within three-phase signals, thus promising enhanced and robust frequency estimation. The structure, mathematical formulation, and theoretical stability and statistical performance analysis of the proposed technique illustrate that, in contrast to conventional linear adaptive estimators, the proposed method is well matched to unbalanced system conditions and also provides unbiased frequency estimation. The proposed method is also less sensitive to the variations of the three-phase voltage amplitudes over time and in the presence of higher order harmonics. Simulations on both synthetic and real-world unbalanced power systems support the analysis.Index Terms-Augmented complex least mean square (CLMS) (ACLMS), complex noncircularity, frequency estimation, unbalanced three-phase voltage, widely linear modeling.

Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Widely Linear Adaptive Frequency Estimation of Unbalanced Three-Phase Power Systems

IEEE Trans. Instrum. Meas.

2012

121

“…Although the system frequency can be estimated directly from any of the three-phases in (1), the use of information from all three phases gives more robust frequency estimates [12], [13], [31]. To achieve this, the dimensionality of the signal is first reduced from R 3 in (1) to C via the (fixed-frame) αβ-transformed coordinates using the following Clarke transformation [50]…”

Section: Mathematical Modelling Of Unbalanced Power Systemsmentioning

confidence: 99%

Maximum Likelihood Parameter Estimation of Unbalanced Three-Phase Power Signals

Kanna

IEEE Trans. Instrum. Meas.

2018

Accurate detection of the system frequency in unbalanced three-phase power systems is a prerequisite for the operation of future smart grid. To this end, we introduce the Cramer-Rao Lower Bounds (CRLBs) for frequency estimation, based on the αβ-transformed unbalanced voltage contaminated with noise. Next, the maximum likelihood estimation (MLE) method for frequency estimation is introduced as a maximiser of an "augmented periodogram".The use of the augmented complex statistics caters for all the available second order information, including the noncircularity associated with unbalanced systems. To find the ML solution, Newton's iterative method is employed and its initialisation is implemented by a discrete Fourier transform (DFT) based dichotomous search technique.We show that the MLE of phases and amplitudes of both the positive and negative phase-sequence components within the αβ-transformed voltage can be generically derived based on the ML frequency estimates. In this way, a unified framework is provided to accurately detect voltage characteristics of the positive and negative phasesequence components within unbalanced three-phase power system when its frequency experiences off-nominal conditions. Simulations verify that the proposed MLE approaches theoretical Cramer-Rao Lower Bounds (CRLBs) for all parameters under consideration.

“…Deriving the system frequency from a single phase is a non-unique problem [12], and for robust frequency estimation it is desired to simultaneously consider all the three phase voltages. A classic approach is to use Clarke's αβ transformation to obtain a complex-valued signal from the three phase voltages [13], and derive system frequency from the phase of the transformed signal.…”

Section: The Need For Frequency Estimation In Smart Gridmentioning

confidence: 99%

Adaptive Frequency Estimation in Smart Grid Applications: Exploiting Noncircularity and Widely Linear Adaptive Estimators

Douglas

IEEE Signal Process. Mag.

2012

159

Signal processing is a crucial technology for the efficient use of limited and intermittent power resources in the smart grid of the future, and a number of challenges remain to be met. One major issue, as we move towards distributed energy production and use (microgrid) is real time estimation of power quality parameters (frequency, voltages, power factor). The accurate knowledge of frequency is a key parameter of a power system, and its optimal estimation becomes critical in the future smart grid, where the generation, loading and topology are all dynamically updated. In this work, we first consolidate the existing approaches to real-time frequency estimation in a three phase system, and then provide a unified framework for the estimation of the instantaneous frequency in both balanced and unbalanced conditions of a three phase power system. This is achieved by using recent developments in the statistics of complex variables (augmented statistics), by employing the associated widely linear models, and by rigorously accounting for the different degrees of noncircularity associated with various natures of frequency variations in real-world conditions. The usefulness of the proposed framework for frequency tracking in smart grids is illustrated in the context of two major issues in power quality control, namely the tracking of false frequency perturbations in the presence of unbalanced voltage sags (here both synthetic and real-world) and in adaptive frequency tracking in microgrids and islands where there is mismatch between production and consumption. THE NEED FOR FREQUENCY ESTIMATION IN SMART GRIDGovernments, utilities and consumers are all interested in making the ways we produce and use energy more efficient and sustainable. For the electrical power grid this involves fundamental paradigm shifts as we build a smart grid, adopt more renewable energy sources, and promote more energy efficient practices. A smart grid delivers electricity from suppliers to users using digital technology and has a number of properties, including incorporating all forms of energy generation and storage, using sensor information, enabling active participation by end users, being secure and reliable, and using optimization and control to make decisions [1]. This will require the interplay between sensor networks, generation systems, and the power grid, with key technologies from signal processing.It is estimated that the financial loss due to outages in the US economy approaches USD $45.7 billion annually, with power quality issues costing USD $6.7 billion annually [2,3]. Among them, voltage sags, that is, an increase in load current over up to few hundred cycles, are the most frequent problem [4] that severely affects medical centres, semiconductor plants, and broadcasting stations, among others [5]. Voltage sags are typically followed by frequency variations and occur due to switching between the main grid and microgrids, short circuits, motor starting, transformer inrush, fast reclosing of circuit breakers, unexpectedly large or ...