Real time identification of coherent groups for controlled islanding based on graph theory

Gómez, Óscar; Ríos, Mario Alberto Gaviria

doi:10.1049/iet-gtd.2014.0865

Cited by 46 publications

(38 citation statements)

References 33 publications

(30 reference statements)

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“…9. The cutset of the minimal load-generation imbalance is {l [6][7][8] , l 6-9 , l 6-10 , l 6-28 , l [16][17] in island G 1 and 43.45 MW is the power flow disruption in island G 2 for the cutset = {l 6-8 , l 6-9 , l 6-10 , l 6-28 , l 10-17 , l [18][19] , l 23-24 } which is the result of minimal power flow disruption for these islands. Strong connection areas and islanding cutsets are exhibited in Fig.…”

Section: Ieee 30-bus Test Systemmentioning

confidence: 99%

“…Generators are divided into three groups of coherent generators, G 1 = {10, 12, 25, 26, 31}, G 2 = {46, 49, 54, 59, 61, 65, 66, 69} and G 3 = {80, 87, 89, 100, 103, 111} after several three-phase faults occurs at transmission lines [18] in this section. The data of the system can be found in Matpower, a power system analysis toolbox of MATLAB [25].…”

Section: ) Test Casementioning

confidence: 99%

“…The data of the system can be found in Matpower, a power system analysis toolbox of MATLAB [25]. Like [18], the system is operated in optimal state where all constraints are satisfied and the fuel cost is minimized. All threshold values g 0 1 , g 0 2 and g 0 3 are 1 for corresponding coherent groups in this case.…”

Section: ) Test Casementioning

confidence: 99%

“…Thus, this method is only effective on linearized systems. In [18], coherent generators and load buses are clustered by a modified k-means technique, which employs the distance between the load bus and the centroid of a coherent group. Sometimes, some buses may not be assigned to any island and this would cause a large load-generation imbalance.…”

Section: Introductionmentioning

confidence: 99%

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Three-stage method for intentional controlled islanding of power systems

Miao

2017

J. Mod. Power Syst. Clean Energy

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Power system controlled islanding is an emergency control to stop the propagation of disturbances and to avoid blackouts. This paper presents a three-stage method for intentional controlled islanding of power systems. It enables to search out reasonable islanding cutsets, which have the minimum load-generation imbalance or the minimal power flow disruption, without low-voltage problems. In the first stage, a self-adaptive graph simplification algorithm is proposed to obtain a two-terminal graph as a suitable islanding cutset search area from the original power network graph model. In the second stage, an islanding cutset search algorithm is designed to find all of islanding cutsets, including the minimum load-generation imbalance cutset, in the two-terminal graph. In the third stage, an islanding scheme checking algorithm is developed to examine the outputs of stage two. It uses the depth first search algorithm to determine reasonable islanding cutsets without low-voltage problems. The IEEE 30-bus system and the IEEE 118-bus system are utilized to demonstrate the proposed method. The simulation results show its validity and accuracy in large-scale power systems.

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Section: Ieee 30-bus Test Systemmentioning

confidence: 99%

Section: ) Test Casementioning

confidence: 99%

Section: ) Test Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Three-stage method for intentional controlled islanding of power systems

Miao

2017

J. Mod. Power Syst. Clean Energy

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“…E-mail: masa_hojo@tokushima-u.ac.jp *State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China **Department of Electrical and Electronic Engineering, Tokushima University, 2-1 Minami-josanjima Tokushima 770-8506, Japan of generator coherency under different disturbances, which is, however, inconsistent with practical cases. The measurement-based methods identify the generator coherency based on the hierarchical clustering method [11], independent component analysis [12,13], spectral clustering method [14,15], wavelet phase difference [16], robust principal component analysis [17], Koopman mode analysis [18], graph theory [19,20], and intelligent method [21,22]. These methods can adapt to various operation conditions, topology changes, and different disturbances by using the real-time measurement data because all the influences of these factors are reflected in the transient response of the power system.…”

Section: Introductionmentioning

confidence: 99%

A phase‐plane‐based dynamic coherency real‐time identification scheme for controlled islanding

Yang

Hojo

Zhang

2018

IEEJ Transactions Elec Engng

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Controlled islanding often acts as the last resort against a severe blackout. Generator coherency is the primary constraint to determine an effective controlled islanding strategy. This article proposes a scheme to identify the dynamic coherency of generators for controlled islanding. The generator coherency is identified based on the phase-plane trajectory vectors (PTVs) on the phase plane for generators (PPG). Then, a phase plane for buses (PPB) is proposed to assign the nongenerator buses to coherent generator groups following the minimum distance principle. The separated islands are formed by disconnecting certain transmission lines according to the identified coherent generators and areas. The case studies on the IEEE 39-bus 10-machine power system show that the proposed scheme cannot only adapt to different disturbances and changes of system conditions and network topology but also succeed in identifying the generator coherency at different times and developing proper islanding strategies according to current system states.

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A phase‐plane trajectory vector‐based method for real‐time identification of critical machines

Yang

Hojo

Zhang

2018

IEEJ Transactions Elec Engng

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The identification of critical machines (CMs) is the first and most important step of Equivalent‐Single Machine Infinite Bus based methods such as Extend Equal Area Criterion and SIngle Machine Equivalent (SIME) for transient stability assessment and control. This paper presents a novel real‐time CM identification method based on the Phase‐Plane Trajectory Vector (PTV) with Phase Measurement Unit (PMU) information support. The proposed method overcomes the drawbacks of conventional CM identification methods and enables the tracking of the change of CMs during the dynamic process. Only two sample points of all generators from PMUs are required to obtain the PTVs at each moment, and the computation of the feature matrix, as well as the k‐means clustering, is fast and accurate. The application of the PTV‐based method is verified through cases studied in the IEEE 39 bus New England power system. Results are compared with those obtained by conventional methods. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Real time identification of coherent groups for controlled islanding based on graph theory

Cited by 46 publications

References 33 publications

Three-stage method for intentional controlled islanding of power systems

Three-stage method for intentional controlled islanding of power systems

A phase‐plane‐based dynamic coherency real‐time identification scheme for controlled islanding

A phase‐plane trajectory vector‐based method for real‐time identification of critical machines

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