Show simple item record

dc.contributor.authorRadhoush, Sepideh
dc.contributor.authorVannoy, Trevor
dc.contributor.authorLiyanage, Kaveen
dc.contributor.authorWhitaker, Bradley M.
dc.contributor.authorNehrir, Hashem
dc.date.accessioned2023-08-02T18:22:13Z
dc.date.available2023-08-02T18:22:13Z
dc.date.issued2023-06
dc.identifier.citationRadhoush S, Vannoy T, Liyanage K, Whitaker BM, Nehrir H. Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization. Applied Sciences. 2023; 13(12):6938. https://doi.org/10.3390/app13126938en_US
dc.identifier.issn2076-3417
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/18023
dc.description.abstractDistribution System State Estimation (DSSE) techniques have been introduced to monitor and control Active Distribution Networks (ADNs). DSSE calculations are commonly performed using both conventional measurements and pseudo-measurements. Conventional measurements are typically asynchronous and have low update rates, thus leading to inaccurate DSSE results for dynamically changing ADNs. Because of this, smart measurement devices, which are synchronous at high frame rates, have recently been introduced to enhance the monitoring and control of ADNs in modern power networks. However, replacing all traditional measurement devices with smart measurements is not feasible over a short time. Thus, an essential part of the grid modernization process is to use both traditional and advanced measurements to improve DSSE results. In this paper, a new method is proposed to hybridize traditional and advanced measurements using an online machine learning model. In this work, we assume that an ADN has been monitored using traditional measurements and the Weighted Least Square (WLS) method to obtain DSSE results, and the voltage magnitude and phase angle at each bus are considered as state vectors. After a period of time, a network is modified by the installation of advanced measurement devices, such as Phasor Measurement Units (PMUs), to facilitate ADN monitoring and control with a desired performance. Our work proposes a method for taking advantage of all available measurements to improve DSSE results. First, a machine-learning-based regression model was trained from DSSE results obtained using only the traditional measurements available before the installation of smart measurement devices. After smart measurement devices were added to the network, the model predicted traditional measurements when those measurements were not available to enable synchronization between the traditional and smart sensors, despite their different refresh rates. We show that the regression model had improved performance under the condition that it continued to be updated regularly as more data were collected from the measurement devices. In this way, the training model became robust and improved the DSSE performance, even in the presence of more Distributed Generations (DGs). The results of the proposed method were compared to traditional measurements incorporated into the DSSE calculation using a sample-and-hold technique. We present the DSSE results in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for all approaches. The effectiveness of the proposed method was validated using two case studies in the presence of DGs: one using a modified IEEE 33-bus distribution system that considered loads and DGs based on a Monte Carlo simulation and the other using a modified IEEE 69-bus system that considered actual data for loads and DGs. The DSSE results illustrate that the proposed method is better than the sample-and-hold method.en_US
dc.language.isoen_USen_US
dc.publisherMDPI AGen_US
dc.rightscc-byen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subjectdistribution system state estimationen_US
dc.subjectweighted least squareen_US
dc.subjectSCADA measurementsen_US
dc.titleDistribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernizationen_US
dc.typeArticleen_US
mus.citation.extentfirstpage1en_US
mus.citation.extentlastpage17en_US
mus.citation.issue12en_US
mus.citation.journaltitleApplied Sciencesen_US
mus.citation.volume13en_US
mus.identifier.doi10.3390/app13126938en_US
mus.relation.collegeCollege of Engineeringen_US
mus.relation.departmentElectrical & Computer Engineering.en_US
mus.relation.universityMontana State University - Bozemanen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

cc-by
Except where otherwise noted, this item's license is described as cc-by

MSU uses DSpace software, copyright © 2002-2017  Duraspace. For library collections that are not accessible, we are committed to providing reasonable accommodations and timely access to users with disabilities. For assistance, please submit an accessibility request for library material.