Efficient methods for identifying weak nodes in electrical power networks

Chen, Yuan-Lin; Chang, Chun Ming; Liu, Chun-Chang

doi:10.1049/ip-gtd:19951728

Cited by 38 publications

(19 citation statements)

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“…The weakest bus may be identified through a static analysis, even though the final consequence is the loss of dynamic stability [16]. The weakest bus indicates that the bus with the most sensitive voltage and relatively heavy load [17], which is also the starting point of system voltage collapse.…”

Section: A．pqv Characteristics At Load Busmentioning

confidence: 99%

“…From [17], [19]- [20], it can be concluded that the weak buses rankings using the different methods for the normal condition according to the different approaches are similar. The profiles of the system voltage under the specific contingencies are the major concern of this paper.…”

Section: Identification Of the Weakest Busesmentioning

confidence: 99%

“…The weakest bus is of the largest VCI. The VCPI is based on a power flow Jacobian matrix J, which is calculated at the current operating point of the system [1]- [2], [17]. The system nodes are ranked in the order of weakness based on reactive power index VCPI Q or apparent power index VCPI S .…”

Section: Identification Of the Weakest Busesmentioning

confidence: 99%

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Power system reliability based on voltage weakest bus identification

Qin

Zhang

Wang

et al. 2011

2011 IEEE Power and Energy Society General Meeting

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The weakest bus in a network is the first point where voltage collapses in a severe contingency. The weakest bus may change with system configurations and operating conditions. This paper proposes a technique to evaluate the weakest bus and system reliability based on system voltage stability under various system contingencies and operation conditions. A power system is divided into safety, margin, risk, and collapse zones according to the practical voltage limitations and voltage collapse point. The three voltage collapse indicators of Minimum Voltage Index (MVI), voltage change index (VCI) and voltage collapse proximity indicator (VCPI) are adopted for determining the voltage distance to the collapse point and weakest bus identification. The IEEE 30-bus system has been modified and analyzed to illustrate the proposed technique. The results provide system planners and operators very important information for the weakest points at different contingencies.Index Terms-Weakest bus, power system reliability, voltage collapse

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Section: A．pqv Characteristics At Load Busmentioning

confidence: 99%

Section: Identification Of the Weakest Busesmentioning

confidence: 99%

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Power system reliability based on voltage weakest bus identification

Qin

Zhang

Wang

et al. 2011

2011 IEEE Power and Energy Society General Meeting

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“…They are based on multiple load flow solution such as the Voltage Instability Proximity Index (VIPI) and Voltage Collapse Proximity Indicator (VCPI). Evaluation techniques based on load flow full of or reduced Jacobian analysis such as singular value decomposition, eigenvalue calculation, sensitivity factors and modal analysis are time consuming for a large power system [6][7][8]. More robust and efficient index for determining the set of low-voltage solutions closest to the operatable solution is also used.…”

Section: Introductionmentioning

confidence: 99%

Artificial immune system based machine learning for voltage stability prediction in power system

Suliman

Rahman

2010

2010 4th International Power Engineering and Optimization Conference (PEOCO)

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Voltage instability has recently become a challenging problem for many power system operators. This phenomenon has been reported to be responsible for severe low voltage condition leading to major blackouts. This paper presents the application of Artificial Immune Systems (AIS) for online voltage stability evaluation that could be used as early warning system to the power system operator so that necessary action could be taken in order to avoid the occurrence of voltage collapse. Key features of the proposed method are the implementation of clonal selection principle that has the capability in performing pattern recognition task. The proposed technique was tested on the IEEE 30 bus power system and the results shows that fast performance with accurate evaluation for voltage stability condition has been obtained.

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“…They are based on multiple load flow solutions and give only global picture [5,6]. The transmission proximity index that specifies the weakest transmission part of the system based on voltage phasor approach necessitate the scanning of the whole power system structure for several time which the time consuming approach [7].…”

Section: Introductionmentioning

confidence: 99%

An optimized fast voltage stability indicator

Belhadj

Abido²

PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376)

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This paper proposes a simulated annealing optimization technique for optimal voltage stability profile through out the whole power network. The technique is applied to control the power elements of major influence on the voltage stability profile. Elements such as generator reactive generation, adjustable shunt compensation devices, transformer tap settings are optimally adjusted at each operating point to reach the objective of minimizing the voltage stability index at each individual bus as well minimizing the global voltage stability indicator. Because of the optimal setting of the control elements, the maximum possible MVA voltage stable loading has been achieved and a the best voltage profile was obtained. Results of tests conducted on the 6 bus Wale and Hale system are presented and discussed.

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Efficient methods for identifying weak nodes in electrical power networks

Cited by 38 publications

References 0 publications

Power system reliability based on voltage weakest bus identification

Power system reliability based on voltage weakest bus identification

Artificial immune system based machine learning for voltage stability prediction in power system

An optimized fast voltage stability indicator

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