In recent years, ultralow-frequency oscillation has repeatedly occurred in asynchronously connected regional power systems and brought serious threats to the operation of power grids. This phenomenon is mainly caused by hydropower units because of the water hammer effect of turbines and the inappropriate Proportional-Integral-Derivative (PID) parameters of governors. In practice, hydropower and solar power are often combined to form an integrated photovoltaic (PV)-hydro system to realize complementary renewable power generation. This paper studies ultralow-frequency oscillations in integrated PV-hydro systems and analyzes the impacts of PV generation on ultralow-frequency oscillation modes. Firstly, the negative damping problem of hydro turbines and governors in the ultralow-frequency band was analyzed through the damping torque analysis. Subsequently, in order to analyze the impact of PV generation, a small-signal dynamic model of the integrated PV-hydro system was established, considering a detailed dynamic model of PV generation. Based on the small-signal dynamic model, a two-zone and four-machine system and an actual integrated PV-hydro system were selected to analyze the influence of PV generation on ultralow-frequency oscillation modes under different scenarios of PV output powers and locations. The analysis results showed that PV dynamics do not participate in ultralow-frequency oscillation modes and the changes of PV generation to power flows do not cause obvious changes in ultralow-frequency oscillation mode. Ultra-low frequency oscillations are mainly affected by sources participating in the frequency adjustment of systems.
State-of-charge (SOC) estimation of lithium-ion (Li-ion) batteries with good accuracy is of critical importance for battery management systems. For the model-based methods, the electrochemical model has been widely used due to its accuracy and ability to describe the internal behaviors of the battery. However, the uncertainty of parameters and the lack of correction from voltage also induce errors during long-time calculation. This paper proposes a particle filter (PF) based method to estimate Li-ion batteries’ SOC using electrochemical model, with sensitive parameter identification achieved using the particle swarm optimization (PSO) algorithm. First, a single particle model with electrolyte dynamics (SPME) is used in this work to reduce the computational burden of the battery electrochemical model, whose sensitive parameters are selected through the elementary effect test. Then, the representative sensitive parameters, which are difficult to measure directly, are adjusted by PSO for a high efficiency. Finally, a model-based SOC estimation framework is constructed with PF to achieve accurate Li-ion battery SOC. Compared with extended Kalman filter and equivalent circuit model, the proposed method shows high accuracy under three different driving cycles.
Oscillations caused by the interaction between voltage source converters (VSCs) and weak grids are vital threats to the stability of power systems. Determining the appropriate parameters for the control of VSCs is essential to prevent the occurrence of oscillations in advance. To achieve this goal, a quantitative evaluation method of system stability for VSCs is proposed in this article to specify the stability boundary of control parameters. Then, an active damping controller for current control and a parameter optimization method for the phase-locked loop (PLL) is proposed, and the related parameters are designed based on the guidance of the proposed evaluation method. With planting the parameters optimization in the control of VSC, the stability boundary of control parameters of VSC is extended, so that the stability of VSC can be significantly improved. Finally, simulations are presented to verify the effectiveness of the theoretical analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.