LEs based framework for transient instability prediction and mitigation using PMU data

Rashidi, Mehran; Farjah, Ebrahim

doi:10.1049/iet-gtd.2015.1482

Cited by 22 publications

(17 citation statements)

References 33 publications

(12 reference statements)

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“…The PMU measurements from different substations in different areas are collected and time aligned by the phasor data concentrator, then submitted to the monitoring centre. However, preserving full network observability may not be practically possible in large power systems [27]. In our case, the optimal PMU placement is based on the maximisation of the determinant of the empirical observability Gramian in [28], where the obtained optimal PMU placements for generators can still guarantee good observability under small or big disturbance.…”

Section: Transient Instability Detection Based On the Grey Verhulst Smentioning

confidence: 99%

Wide‐area measurement system‐based model‐free approach of post‐fault rotor angle trajectory prediction for on‐line transient instability detection

Huang

Yang

Chen

et al. 2018

IET Generation, Transmission & Distribution

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Section: Transient Instability Detection Based On the Grey Verhulst Smentioning

confidence: 99%

Wide‐area measurement system‐based model‐free approach of post‐fault rotor angle trajectory prediction for on‐line transient instability detection

Huang

Yang

Chen

et al. 2018

IET Generation, Transmission & Distribution

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“…For breaking these restrictions, data mining model-based methodologies, which leave out power system physical model, have been proposed as the model-free methods. Curve fitting measures and machine learning techniques are representative for these methodologies [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], which are reviewed below. iii.…”

Section: Introductionmentioning

confidence: 99%

“…Their applications in power system transient security assessment and control have demonstrated promising performance, e.g. artificial neural network [15][16][17], decision tree [18][19][20], support vector machine (SVM) [21,22], core vector machine [23], and extreme learning machine (ELM) [24,25]. The two main predict objects of these methods are transient stability degree (e.g.…”

Section: Introductionmentioning

confidence: 99%

Hybrid method for power system transient stability prediction based on two‐stage computing resources

Tang

Feng

Wang

et al. 2018

IET Generation, Transmission & Distribution

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“…A comprehensive method for analyzing the suitability of limited candidate PMU locations, enhancing situational awareness of the system operator, and ensuring full system observability is proposed. Further, a Lyapunov exponent has been utilized for transient instability prediction and mitigation using wide area measurement systems data, but it does not ensure full network observability . By considering the possibility of variation in each substation, a method for PMU placement is presented, and several methods based on mathematical and heuristic algorithms have been suggested for OPP problem .…”

Section: Introductionmentioning

confidence: 99%

Economical placement of PMUs considering observability and voltage stability using binary coded ant lion optimization

Manoharan

Subramanian

Ganesan³

et al. 2018

Int Trans Electr Energ Syst

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Summary Phasor measurement unit (PMU) plays a key role in concurrent power system state estimation and security enhancement processes. Considering the cost of PMU as an important concern, confined number of PMUs is located at hazardous locations for power system observability. The PMU placements can be explored further for various power system operational studies. One promising indicator is that not only the PMUs should be informative, but also they should be able to perceive the voltage stability levels under critical loading conditions. In topical eons the role of voltage stability is essential which causes severe difficulty. Regrettably, this results in enhancement and impractical vision because of same cost of consumption of PMUs. It is sufficient to reduce the deployment cost provided that the factors such as reliability of the measurement system, observability can be certain for effective placement of PMUs. In line with the above concern, to solve the problem efficiently, a realistic multiobjective model that includes 3 objectives such as (1) system observability with minimum number of PMUs, (2) envisaging the communication cost involved in these placements, and (3) enhancing the voltage stability levels of the system is proposed. The developed model is solved as a nonlinear and constrained optimization problem in multiobjective framework. The binary coding scheme is conventionally preferred for solving PMU placement problem, hence, binary‐coded ant lion optimizer has been developed and is applied for the first time to solve the developed multiobjective PMU placement model. The standard IEEE test systems (14 bus, 30 bus, and 57 bus systems) and a large scale system containing 2383 buses are used to ratify the proposed model and the envisioned optimization tool. Numerical outcome specifies that binary‐coded ant lion optimizer is robust and effective in finding the best compromise solution.

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LEs based framework for transient instability prediction and mitigation using PMU data

Cited by 22 publications

References 33 publications

Wide‐area measurement system‐based model‐free approach of post‐fault rotor angle trajectory prediction for on‐line transient instability detection

Wide‐area measurement system‐based model‐free approach of post‐fault rotor angle trajectory prediction for on‐line transient instability detection

Hybrid method for power system transient stability prediction based on two‐stage computing resources

Economical placement of PMUs considering observability and voltage stability using binary coded ant lion optimization

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