Efficient SPF approach based on regression and correction models for active distribution systems

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Efficient SPF approach based on regression and correction models for active distribution systems. / Mahmoud, Karar; Abdel-Nasser, Mohamed.

In: IET RENEWABLE POWER GENERATION, Vol. 11, No. 14, 13.12.2017, p. 1778-1784.

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@article{8eea3951f5e24debafad51d21017eb9f,
title = "Efficient SPF approach based on regression and correction models for active distribution systems",
abstract = "This study proposes efficient methods for sequential power flow (SPF) analysis of distribution systems with intermittent photovoltaic (PV) units and fluctuated loads. The proposed methods are based on machine learning techniques; more specifically, they use a regression trees (RTs) algorithm to construct a model for voltage estimation. This model is trained using synthetic data generated by a number of PV generation and load demand scenarios. The SPF methods that utilise iterative techniques have a high computational burden. In turn, the proposed method, which is called SPF-RT, is fast and accurate. Furthermore, the authors combine SPF-RT with a correction method to develop a new method, called SPF-RTC, which significantly reduces the estimation error of the RT model. The proposed methods are tested using a 33-bus distribution test system interconnected with two PV units. To assess the performance of the proposed methods, they conducted several experiments at different resolutions of day/year data. The proposed methods are compared with the iterative SPF methods and validated using the OpenDSS software. The simulation results demonstrate that the proposed methods outperform the other methods in terms of the computational speed. The SPF-RT and SPF-RTC methods are useful for real-time assessment of distribution systems with PV units.",
keywords = "Electric load flow, Learning systems, Photovoltaic systems, Real time systems, Trees (mahematics)",
author = "Karar Mahmoud and Mohamed Abdel-Nasser",
year = "2017",
month = "12",
day = "13",
doi = "10.1049/iet-rpg.2017.0300",
language = "English",
volume = "11",
pages = "1778--1784",
journal = "IET RENEWABLE POWER GENERATION",
issn = "1752-1416",
publisher = "Institution of Engineering and Technology",
number = "14",

}

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TY - JOUR

T1 - Efficient SPF approach based on regression and correction models for active distribution systems

AU - Mahmoud, Karar

AU - Abdel-Nasser, Mohamed

PY - 2017/12/13

Y1 - 2017/12/13

N2 - This study proposes efficient methods for sequential power flow (SPF) analysis of distribution systems with intermittent photovoltaic (PV) units and fluctuated loads. The proposed methods are based on machine learning techniques; more specifically, they use a regression trees (RTs) algorithm to construct a model for voltage estimation. This model is trained using synthetic data generated by a number of PV generation and load demand scenarios. The SPF methods that utilise iterative techniques have a high computational burden. In turn, the proposed method, which is called SPF-RT, is fast and accurate. Furthermore, the authors combine SPF-RT with a correction method to develop a new method, called SPF-RTC, which significantly reduces the estimation error of the RT model. The proposed methods are tested using a 33-bus distribution test system interconnected with two PV units. To assess the performance of the proposed methods, they conducted several experiments at different resolutions of day/year data. The proposed methods are compared with the iterative SPF methods and validated using the OpenDSS software. The simulation results demonstrate that the proposed methods outperform the other methods in terms of the computational speed. The SPF-RT and SPF-RTC methods are useful for real-time assessment of distribution systems with PV units.

AB - This study proposes efficient methods for sequential power flow (SPF) analysis of distribution systems with intermittent photovoltaic (PV) units and fluctuated loads. The proposed methods are based on machine learning techniques; more specifically, they use a regression trees (RTs) algorithm to construct a model for voltage estimation. This model is trained using synthetic data generated by a number of PV generation and load demand scenarios. The SPF methods that utilise iterative techniques have a high computational burden. In turn, the proposed method, which is called SPF-RT, is fast and accurate. Furthermore, the authors combine SPF-RT with a correction method to develop a new method, called SPF-RTC, which significantly reduces the estimation error of the RT model. The proposed methods are tested using a 33-bus distribution test system interconnected with two PV units. To assess the performance of the proposed methods, they conducted several experiments at different resolutions of day/year data. The proposed methods are compared with the iterative SPF methods and validated using the OpenDSS software. The simulation results demonstrate that the proposed methods outperform the other methods in terms of the computational speed. The SPF-RT and SPF-RTC methods are useful for real-time assessment of distribution systems with PV units.

KW - Electric load flow

KW - Learning systems

KW - Photovoltaic systems

KW - Real time systems

KW - Trees (mahematics)

UR - http://www.scopus.com/inward/record.url?scp=85037693939&partnerID=8YFLogxK

U2 - 10.1049/iet-rpg.2017.0300

DO - 10.1049/iet-rpg.2017.0300

M3 - Article

VL - 11

SP - 1778

EP - 1784

JO - IET RENEWABLE POWER GENERATION

JF - IET RENEWABLE POWER GENERATION

SN - 1752-1416

IS - 14

ER -

ID: 35289782