Ian Paul Gerber, Fredrick Mukundi Mwaniki, Hendrik Johannes Vermeulen
{"title":"Online estimation of wideband output impedance and control parameters of single-phase inverters using pseudo-random perturbation","authors":"Ian Paul Gerber, Fredrick Mukundi Mwaniki, Hendrik Johannes Vermeulen","doi":"10.1016/j.prime.2025.101126","DOIUrl":null,"url":null,"abstract":"<div><div>The growing deployment of single-phase inverters in residential low-voltage distribution networks poses new challenges to system stability and power quality. Accurate simulation models are essential for analysing these effects and enabling scenario assessment without costly and time-consuming physical testing. Wideband inverter models, in particular, are critical for capturing the inverter’s dynamic behaviour across a broad frequency range. The inverter’s output impedance profile plays a key role in identifying internal parameters, such as filter and control settings, typically not disclosed by manufacturers, and supports impedance-based stability analysis. This paper presents a methodology for online estimating an inverter’s wideband output impedance and internal control parameters. A pseudo-random impulse sequence is injected into the inverter AC terminals <em>in situ</em> to perturb the system, from which the output impedance is estimated. A case study on a standalone single-phase inverter supplying <span><math><mrow><mn>2</mn><mo>.</mo><mn>6</mn><mspace></mspace><msub><mrow><mi>A</mi></mrow><mrow><mtext>RMS</mtext></mrow></msub></mrow></math></span> demonstrates a strong correlation between the experimentally derived impedance and its analytical counterpart. The inverter’s impedance frequency response and time-domain output signals are further analysed to extract controller parameters using a three-step estimation process based on particle swarm optimisation. The approach is validated through both simulation and experimental results, confirming its accuracy and effectiveness in parameter identification.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101126"},"PeriodicalIF":0.0000,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772671125002323","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The growing deployment of single-phase inverters in residential low-voltage distribution networks poses new challenges to system stability and power quality. Accurate simulation models are essential for analysing these effects and enabling scenario assessment without costly and time-consuming physical testing. Wideband inverter models, in particular, are critical for capturing the inverter’s dynamic behaviour across a broad frequency range. The inverter’s output impedance profile plays a key role in identifying internal parameters, such as filter and control settings, typically not disclosed by manufacturers, and supports impedance-based stability analysis. This paper presents a methodology for online estimating an inverter’s wideband output impedance and internal control parameters. A pseudo-random impulse sequence is injected into the inverter AC terminals in situ to perturb the system, from which the output impedance is estimated. A case study on a standalone single-phase inverter supplying demonstrates a strong correlation between the experimentally derived impedance and its analytical counterpart. The inverter’s impedance frequency response and time-domain output signals are further analysed to extract controller parameters using a three-step estimation process based on particle swarm optimisation. The approach is validated through both simulation and experimental results, confirming its accuracy and effectiveness in parameter identification.