Small-signal Eigenvalue Analysis Research Articles

Varying Negative Sequence Virtual Impedance Adaptively for Enhanced Unbalanced Power Sharing Among DGs in Islanded AC Microgrids

Inverter based distributed generators (DGs) are increasingly being employed for interfacing renewable energy sources into the grid. Droop control is a very popular technique for ensuring parallel operation of these sources based on local measurements alone, in which the inverters are made to emulate the behaviour of synchronous generators of the power system. However, several drawbacks of the droop control technique are being addressed actively, over the years by various researchers. One of the prominent issues is the inaccurate power sharing among the distributed generators (DGs) due to the feeder/line impedances. The performance further deteriorates in the presence of unbalanced loads. This work presents an adaptive technique for adjusting the negative sequence (NS) virtual impedance of the DG, based on its output current, without the need of communication or extra sensors, to achieve enhanced unbalanced power sharing. The proposed control is shown to have the capability of seamless integration with the existing conventional and inverse droop techniques. The methodology is validated through simulations for different DG ratings, controls and on a modified 13 bus system. Small signal eigenvalue analysis about the operating point is used to determine the stable range of control parameters and the strategy is validated on an experimental setup in the laboratory.

Power-Synchronization Control for Ultra-Weak AC Networks: Comprehensive Stability and Dynamic Performance Assessment

Power-synchronization control (PSC) is a promising control strategy to improve the stability and performance of voltage-source converters (VSCs) in ultra-weak AC grids. However, evaluation of PSC to date has investigated performance only at single controller operating points, rather than holistically varying multiple controller gains. This paper develops a new methodology, based on small-signal eigenvalue analysis, to comprehensively analyze PSC-VSC stability. The maximum active power transfer of PSC is established across a broad range of controller tunings and the two-way and three-way couplings between the power-synchronization control, AC voltage control and high-pass current filter gains are quantified. A new stable tuning region is introduced, which represents the controller parameter space for stable operation. It is shown that PSC can achieve rated power transfer into an AC grid (short circuit ratio(SCR)=1) at multiple controller operating points, but dynamic performance varies significantly within this region. The robustness of this operating region to SCR changes is also investigated. The stability boundary and dynamic performance are validated using control hardware-in-the-loop experiments with a real-time digital simulator. Practical recommendations arising from this work are a set of controller gains that provide stability and good dynamic performance at high power transfer for ultra-weak grid-connected VSCs employing PSC.

Configuration and Model Order Selection of Frequency-Dependent π Models for Representing DC Cables in Small-Signal Eigenvalue Analysis of HVDC Transmission Systems

Eigenvalue-based analysis of small-signal dynamics in high-voltage direct current (HVdc) transmission systems requires cable models that are compatible with a state-space representation. While distributed parameter models accounting for frequency-dependent effects are inherently incompatible with a state-space representation, a conventional $\pi $ model can only represent the cable behavior accurately at a single frequency. Instead, a frequency-dependent $\pi $ (FD- $\pi $ ) model consisting of a lumped circuit representation with multiple parallel $RL$ branches in each $\pi $ -section can be utilized to reproduce the frequency dependence of the cable characteristics in a specified frequency range. Based on an evaluation of relevant error metrics for FD- $\pi $ models, this article demonstrates how the number of sections and the number of parallel branches in each section will influence the accuracy. From this starting point, an optimization algorithm for identifying the configuration that fulfills a specified set of accuracy requirements with the lowest possible model order is introduced. A similar algorithm for identifying the most accurate FD- $\pi $ model within a specified maximum model order is also proposed. Examples of numerical results for different cable lengths and cross sections are presented to highlight their effect on the model, and it is demonstrated how the cable model configuration can influence the results from small-signal eigenvalue analysis of HVdc transmission systems.

Frequency‐dependent cable modelling for small‐signal stability analysis of VSC‐HVDC systems

State-of-the-art time-domain models of power cables account for the frequency dependency of physical parameters to enable accurate transient simulations of high-voltage transmission schemes. Owing to their formulation, these models cannot be directly converted into a state-space form as required for small-signal eigenvalue analysis. Thus, dc cables are commonly represented in high-voltage direct current (HVDC) power system stability studies by cascaded pi-section equivalents that neglect the frequency-dependent effects. This study demonstrates how the conventional cascaded pi-section model is unable to accurately represent the damping characteristic of the cable and how this can lead to incorrect stability assessments. Furthermore, an alternative model consisting of cascaded pi-sections with multiple parallel branches is explored, which allows for a state-space representation while accounting for the frequency dependency of the cable parameters. The performance of the proposed model is benchmarked against state-of-the-art cable models both in the frequency domain and in the time domain. Finally, the study provides a comparative example of the impact of the cable modelling on the small-signal dynamics of a point-to-point voltage-source converter (VSC) HVDC transmission scheme.

Effects of various power system stabilizers on improving power system dynamic performance

To ensure the small-signal stability of a power system, power system stabilizers (PSSs) are extensively applied for damping low frequency power oscillations through modulating the excitation supplied to synchronous machines, and increasing interest has been focused on developing different PSS schemes to tackle the threat of damping oscillations to power system stability. This paper examines four different PSS models and investigates their performances on damping power system dynamics using both small-signal eigenvalue analysis and large-signal dynamic simulations. The four kinds of PSSs examined include the Conventional PSS (CPSS), Single Neuron based PSS (SNPSS), Adaptive PSS (APSS) and Multi-band PSS (MBPSS). A steep descent parameter optimization algorithm is employed to seek the optimal PSS design parameters. To evaluate the effects of these PSSs on improving power system dynamic behaviors, case studies are carried out on an 8-unit 24-bus power system through both small-signal eigenvalue analysis and large-signal time-domain simulations.

Triple diffused transistor for digital IC applications : Bogdan M. Wilamowski, Miroslaw Korzeniowski, Andrzej Sobkowiak, Barbara Welniak. Wladyslaw Wielich and Zbigniew Wierzbicki. Electron Technol.19 (3/4), 91 (1986)

To ensure the small-signal stability of a power system, power system stabilizers (PSSs) are extensively applied for damping low frequency power oscillations through modulating the excitation supplied to synchronous machines, and increasing interest has been focused on developing different PSS schemes to tackle the threat of damping oscillations to power system stability. This paper examines four different PSS models and investigates their performances on damping power system dynamics using both small-signal eigenvalue analysis and large-signal dynamic simulations. The four kinds of PSSs examined include the Conventional PSS (CPSS), Single Neuron based PSS (SNPSS), Adaptive PSS (APSS) and Multi-band PSS (MBPSS). A steep descent parameter optimization algorithm is employed to seek the optimal PSS design parameters. To evaluate the effects of these PSSs on improving power system dynamic behaviors, case studies are carried out on an 8-unit 24-bus power system through both small-signal eigenvalue analysis and large-signal time-domain simulations.