A robust data clustering method for probabilistic load flow in wind integrated radial distribution networks

Sadeghian, Omid; Oshnoei, Arman; Kheradmandi, Morteza; Khezri, Rahmat; Mohammadi‐Ivatloo, Behnam

doi:10.1016/j.ijepes.2019.105392

Cited by 30 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The k-means method allocates the remaining individual data between clusters so that the Euclidean distance of an individual vector data (m i ) from its corresponding cluster centroid (c k ) is minimized as shown in (1). The k-means clustering method has been widely used for data management of power systems related problems [29][30][31][32]:…”

Section: The K-means Clustering Methodsmentioning

confidence: 99%

A Chance-Constrained Multistage Planning Method for Active Distribution Networks

Koutsoukis

Georgilakis

2019

Energies

View full text Add to dashboard Cite

This paper introduces a multistage planning method for active distribution networks (ADNs) considering multiple alternatives. The uncertainties of load, wind and solar generation are taken into account and a chance constrained programming (CCP) model is developed to handle these uncertainties in the planning procedure. A method based on a k-means clustering technique is employed for the modelling of renewable generation and load demand. The proposed solution methodology, which is based on a genetic algorithm, considers multiple planning alternatives, such as the reinforcement of substations and distribution lines, the addition of new lines, and the placement of capacitors and it aims at minimizing the net present value of the total operation cost plus the total investment cost of the reinforcement and expansion plan. The active network management is incorporated into planning method in order to exploit the control capabilities of the output power of the distributed generation units. To validate its effectiveness and performance, the proposed method is applied to a 24-bus distribution system.

show abstract

Section: The K-means Clustering Methodsmentioning

confidence: 99%

A Chance-Constrained Multistage Planning Method for Active Distribution Networks

Koutsoukis

Georgilakis

2019

Energies

View full text Add to dashboard Cite

show abstract

“…P m = T e ω rm = T e ω p p r + T e ω c p r = P p + P c (24) where P p and P c are the power of the primary and secondary windings, i.e., the power and control windings, respectively, and P m and T e are the mechanical power and electromag-netic torque of the machine, respectively. Additionally, the synchronous speed (ω syn ) is as follows [44]:…”

Section: Statormentioning

confidence: 99%

“…It also can be used in applications, such as turbomachinery, adjustable-speed constantfrequency wind, and hydro-power applications, air conditioning, commercial heating, and large pump drives [20,21]. The application of BDFRM in wind turbines especially offshore ones, has accelerated the research studies on BDFRM [22], due to the advantages of wind turbines in viewpoints of zero energy generation cost and environmental issues [23,24]. Due to the application and the importance of BDFRM, the requirements of BDFRM-based wind turbines are discussed in [6].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Brushless Doubly-Fed Reluctance Machine

et al. 2021

Self Cite

View full text Add to dashboard Cite

The Brushless Doubly-Fed Reluctance Machine (BDFRM) has been widely investigated in numerous research studies since it is brushless and cageless and there is no winding on the rotor of this emerging machine. This feature leads to several advantages for this machine in comparison with its induction counterpart, i.e., Brushless Doubly-Fed Induction Machine (BDFIM). Less maintenance, less power losses, and also more reliability are the major advantages of BDFRM compared to BDFIM. The design complexity of its reluctance rotor, as well as flux patterns for indirect connection between the two windings mounted on the stator including power winding and control winding, have restricted the development of this machine technology. In the literature, there is not a comprehensive review of the research studies related to BDFRM. In this paper, the previous research studies are reviewed from different points of view, such as operation, design, control, transient model, dynamic model, power factor, Maximum Power Point Tracking (MPPT), and losses. It is revealed that the BDFRM is still evolving since the theoretical results have shown that this machine operates efficiently if it is well-designed.

show abstract

“…The well‐known way to increase the MCS computational procedure and decrease the execution costs is data clustering methodology. Data clustering has been developed by researchers in the previous studies 7‐12 for different problems such as total transfer capability 7,8 and power flow 9‐12 . Data clustering method is effectively able to manage a dense of input data and extract the necessary information from the data.…”

Section: Background and Motivationmentioning

confidence: 99%

A clustering‐based technoeconomic analysis for wind farm and shunt capacitor allocation in radial distribution systems

Sadeghian

Oshnoei

Tarafdarhagh

et al. 2020

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

A robust data clustering method for probabilistic load flow in wind integrated radial distribution networks

Cited by 30 publications

References 33 publications

A Chance-Constrained Multistage Planning Method for Active Distribution Networks

A Chance-Constrained Multistage Planning Method for Active Distribution Networks

A Comprehensive Review on Brushless Doubly-Fed Reluctance Machine

A clustering‐based technoeconomic analysis for wind farm and shunt capacitor allocation in radial distribution systems

Contact Info

Product

Resources

About