Estimation Of Power Quality Indices Research Articles

Smart meter for the estimation of power quality indices based on second-order generalized integrator.

With the increasing integration of renewable energy sources and nonlinear loads in the grid, it is necessary to compute energy and power quality parameters for monitoring as well as analyzing the power consumption indices (PCI) and power quality indices (PQI). Smart meters (SMs) can monitor the PCI and PQI at the consumer end. SM necessitates the measurement of voltage and current, which can be further processed by an algorithm for estimating different parameters through signal processing and arithmetic operations. This paper proposes the combined dual second-order generalized integrator (SOGI) and proportionate least mean square (PLMS) algorithm-based PCI and PQI estimation for smart metering. Dual SOGIs individually process voltage and current signals to extract the respective fundamental in-phase and quadrature components. To account the variations in grid frequency, the SOGI processing the voltage also incorporates a frequency locked loop. This prevents performance degradation on account of frequency variations. The PLMS algorithm then process the output of SOGI processing the current for extracting the fundamental active and reactive components of current. Moreover, the PLMS algorithm results in further attenuation of harmonics. Also, only two separate values of learning rate are required, which is easy to tune for better dynamic performance. With only two quantities to be determined, this results in largely reduced computation. These computations facilitate in PCI and PQI computations. The proposed smart meter calculates peak value, RMS value, and phase angle for the fundamental components of voltage and current for each phase. Additionally, it assesses fundamental power factor, total harmonic distortion (THD), distortion factor, true power factor, active power, reactive power, and apparent power for each phase. The performance validation of the proposed combined dual-SOGI-PLMS algorithm-based PCI and PQI estimation for smart metering is carried out in MATLAB/SIMULINK for fifteen different operating scenarios. Further, the real-time implementation of the proposed methodology is carried out on dSPACE MicroLab Box 1202. Comparative analysis is also presented, which reveals the computational simplicity and other merits of the proposed scheme over the earlier reported scheme.

Time Dependency of Current Harmonics for Switch-Mode Power Supplies

This paper presents the time-dependent variance in the current harmonics emission by power supplies during power quality measurements. Power quality problems are becoming more significant with the adoption of power electronic-based circuits such as power supplies. The switch-mode power supplies are widespread as industrial, commercial, and domestic electrical loads. They draw non-sinusoidal current from the utility and inject current harmonics. Therefore, they are the reason for poor power quality and reduction in the power factor. The current harmonics emission from these power supplies depends on the circuit topology, operating conditions, and filter inside them. The harmonic emission estimations are critical for network operators; however, various uncertainties have made it a complicated task. The time-dependent stability affects the magnitude and phase angle of the harmonic current measurements and estimation of power quality indices. This paper investigates the variation in current harmonics emitted by the power supply during the initial unstable period under constant load and operating conditions.

Global Harmonic Parameters for Estimation of Power Quality Indices: An Approach for PMUs

For wide-area measurement systems and smart grids, phasor measurement units (PMUs) have become key elements since they provide synchronized information related to the fundamental frequency components of voltages and currents. In recent years, some works have extended the concept of PMU to harmonic analysis due to the proliferation of nonlinear loads. In this work, as a first contribution, the reference model for P-class and M-class PMUs provided by the IEEE Standard C37.118.1 is expanded with the aim of obtaining the harmonic information and electric power quantities. Additionally, as a second contribution, the approach of global harmonic parameters (GHPs) for PMUs is proposed. Specifically, GHPs are introduced in this work as unified quantities regarding the overall harmonic content of voltages and currents signals. With the help of these parameters, the estimation of power quality indices (PQIs) according to the IEEE Standard 1459 can be carried out but with an important advantage, i.e., a reduced amount of data, which reduces the requirements of management, storage, and analysis. Finally, the mathematical formulations for PQIs using the proposal are also presented. It is important to mention that they are equivalent to classical formulations that use individual harmonic information; however, they exploit the advantage of PMUs that require a reduced amount of data. Several tests with synthetic and real signals are carried out to validate the proposal. Results demonstrate the effectiveness and usefulness of the proposed approach.

A New Fast Discrete S-Transform for Power Quality Disturbance Monitoring

In this paper a new fast discrete S-transform (ST) has been proposed for the estimation of power quality (PQ) disturbance signal parameters in an electric power system. The proliferation of non-stationary non-linear loads and various PQ disturbances such as voltage dips, swells, transients, interruptions, voltage flicker, and spikes, etc., necessitate high PQ level in micro and smart grids. The proposed algorithm is based on the generalised Fourier algorithm that combines down sampling and signal cropping to produce a fast discrete ST. The technique is based on Heisenberg’s uncertainty principle, which limits the time-frequency resolution of ST and employs a trade-off between the time resolution and frequency resolution. In ST the window width decreases at higher frequencies with a reduction in frequency resolution and conversely at low frequencies with wider windows. Hence the signal can be down sampled at low frequencies and cropped at high frequencies to result in fewer samples to be evaluated. The algorithm models the typical time-varying signal and the accompanied distortions due to harmonics and random noise in a manner that will be suitable for real-time PQ indices estimation. Various PQ disturbance waveforms like voltage sag, swell, harmonics and interharmonics, and oscillatory transients are considered for tracking and estimation of PQ indices.

Estimation of power quality indices in distributed generation systems during power islanding conditions

This paper presents a new, fast Modified Recursive Gauss–Newton (MRGN) method for the estimation of power quality indices in distributed generating systems during both islanding and non-islanding conditions. A forgetting factor weighted error cost function is minimized by the well known Gauss–Newton algorithm and the resulting Hessian matrix is approximated by ignoring the off-diagonal terms. This simplification produces a decoupled algorithm, for the fundamental and harmonic components and results in a large reduction of computational effort, when the power signal contains a large number of harmonics. Numerical experiments have shown that the proposed approach results in higher speed of convergence, accurate tracking of power signal parameters in the presence noise, waveform distortion, etc., which are suitable for the estimation of power quality indices. In the case of a distribution network, power islands occur when power supply from the main utility is interrupted due to faults or otherwise and the distributed generation system (DG) keeps supplying power into the network. Further, due to unbalanced load conditions the DG is subject to unbalanced voltages at its terminals and suffers from increased total harmonic distortion (THD). Thus, the power quality indices estimation, along with the power system frequency estimation will play a vital role in detecting power islands in distributed generating systems. Extensive studies, both on simulated and real, benchmark hybrid distribution networks, involving distributed generation systems reveal the effectiveness of the proposed approach to calculate the power quality indices accurately.

Two-Stage Improved Recursive Newton-Type Algorithm for Power-Quality Indices Estimation

In the paper a new improved recursive Newton type algorithm (IRNTA) suitable for various measurement applications in the electric power systems is presented. Here it is applied for power quality indices estimation according to the IEEE Standard 1459-2000. Basically the algorithm is a recursive nonlinear estimator, improved with a strategy of sequentially tuning of the forgetting factor. This approach generally improves the algorithm performance: immunity to random noise, accuracy and the speed of convergence. The IRNTA consider the power frequency as unknown model parameter and takes into account distortion of voltages and currents. Therefore, it is suitable for the real-time power quality monitoring. The algorithm has two stages. In the first stage the input signal spectra and fundamental frequency are estimated, whereas in the second stage the unknown power quality indices are calculated. To demonstrate the efficiency of the proposed algorithm, the results of computer simulations and laboratory testing are presented.