Identification of generator criticality and transient instability by supervising real-time rotor angle trajectories employing RBFNN

Soni, Bhanu Pratap; Saxena, Akash; Gupta, Vikas; Surana, S.L.

doi:10.1016/j.isatra.2018.08.008

Cited by 12 publications

(11 citation statements)

References 32 publications

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“…This paper used the Radial Basis Function Neural Networks (RBFNN) [60,61] model, which has three structural layers, including the input layer, hidden layer, and output layer, to measure the contributions of the driving factors to CUT (Figure 2). The input layer node passes the input signal to the hidden layer first, and then to the output layer, which entails a distinct process and target [62,63]. According to the principles, this paper used the driving factors as the input layer and indices of CUT, cropland functional transition and cropland spatial transition as the output layer, and calculated the contributions of various factors to CUT.…”

Section: Analysis Of Driving Factor Contributionmentioning

confidence: 99%

“…According to the principles, this paper used the driving factors as the input layer and indices of CUT, cropland functional transition and cropland spatial transition as the output layer, and calculated the contributions of various factors to CUT. The detailed calculation steps of the RBFNN model can be found in the literature [32,62,63]. The effects of the input layer on the output layer, which is the contributions of various factors to CUT, were calculated according to the following steps [32]:…”

Section: Analysis Of Driving Factor Contributionmentioning

confidence: 99%

“…Although both subjective and objective methods have been applied, the underlying relationships between original driving factors and evaluation targets have been ignored, which undermines the analysis of the contributions of driving factors to CUT under different contexts. In comparison, the utility of the driving factors calculated by the RBFNN model not only considers the original efficiency of the index factors, but also the internal relationship and target orientation of CUT [61,62], which ensures that our analysis and results are comprehensive and reliable. Each factor's contribution differs under different goals, which reflects the various driving forces of cropland spatial and functional transition.…”

Section: Suitability Of Rbfnn Modelmentioning

confidence: 99%

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Cropland Use Transitions and Their Driving Factors in Poverty-Stricken Counties of Western Hubei Province, China

Xiang

Song

2019

Sustainability

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Agriculture is important for economic development in most poverty-stricken areas in China, but cropland use is facing challenges due to rapid industrialization and urbanization, causing serious issues for poverty alleviation and sustainable socioeconomic development. Cropland Use Transition (CUT) is one way to alleviate poverty and develop the economy in poverty-stricken areas. This paper chose 16 typical poverty-stricken counties in Western Hubei province as the case area. A morphology index system was established to evaluate CUT, and geographic information system software was used to analyze the temporal-spatial variations in CUT. Using the Radial Basis Function Neural Network (RBFNN) model, contributions of driving factors of population, economy, and industrial structure to CUT were analyzed. The results show that: (1) cropland use morphology can be divided into functional morphology and spatial morphology; (2) the spatial distribution of CUT was high in the north and low in the south, the temporal variation of CUT from 1995 to 2013 showed fluctuations, and the coefficient of CUT changed from 0.460 to 0.649 with a growth rate of 41%; (3) for the driving factors, population factors most significantly contributed to CUT, followed by industrial structure and economic factors. The results obtained in this study are in line with the findings of previous studies. The RBFNN model is suitable for evaluating the contributions of driving factors, which can solve the deficiency in previous studies caused by ignoring the internal relationship and target orientation of driving factors. This study suggests that poverty-stricken counties should narrow the urban–rural divide, encourage balanced labor and investment flow into cropland by formulating relevant economic policies, motivate farmers’ agricultural engagement, and use science and technology to promote CUT and the growth of the agricultural economy, poverty alleviation, and to coordinate urban–rural development.

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Section: Analysis Of Driving Factor Contributionmentioning

confidence: 99%

Section: Analysis Of Driving Factor Contributionmentioning

confidence: 99%

Section: Suitability Of Rbfnn Modelmentioning

confidence: 99%

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Cropland Use Transitions and Their Driving Factors in Poverty-Stricken Counties of Western Hubei Province, China

Xiang

Song

2019

Sustainability

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“…In this paper, to generate the dataset, a large number of contingencies are simulated under different operating conditions. Rotor angle deviation‐based transient stability index (TSI) [22] has been employed to identify the system stability status in post‐fault state during severe contingencies. A supervised learning engine is based on LS‐SVM proposed to rank the generators according to the values of TSIs.…”

Section: Introductionmentioning

confidence: 99%

Transient stability‐oriented assessment and application of preventive control action for power system

Soni

Saxena

Gupta

et al. 2019

J. eng.

Self Cite

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“…Since this involves more significant detection of coherent devices after the occurrence of a fault in real time. The required results will be obtained in order to achieve fast and accurate real-time transient stability assessment (TSA) deploying phasor measuring units (PMUs) at required locations and fast processing algorithms [5]. TSA includes evaluating the rotor swings future behaviour after failure or significant disturbance to accurately predict transient stability [6].…”

Section: Introductionmentioning

confidence: 99%

Development of Back-Propagation Neural Network for Transient Stability Assessment of Power Systems

Anirudh*,

Janibasha,

Chaitanya

et al. 2019

IJRTE

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Transient Stability Assessment (TSA) is aspect of the power system dynamic stability assessment, which includes measuring the capacity of the system to stay synchronized under extreme disturbances. This research work shows the transient stability status of the power system following a major disturbance, such as a faults, line switchings, generator voltages. It can be predicted early based on response trajectories of rotor angle. This early prediction of transient stability is achieved by training a Back Propagation Neural Network (BPNN) taking trajectory of rotor angles as training features. Transient stability index (TSI) proposed in [4] is utilized as a target feature. The proposed methodology is tested with wide range of fault data collected from simulated IEEE 39-Benchmark system. The simulation results shows, utilization of BPNN for transient stability prediction resulted in better performance when compared to Radial Basis Neural Network (RBFNN) [4]..

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Identification of generator criticality and transient instability by supervising real-time rotor angle trajectories employing RBFNN

Cited by 12 publications

References 32 publications

Cropland Use Transitions and Their Driving Factors in Poverty-Stricken Counties of Western Hubei Province, China

Cropland Use Transitions and Their Driving Factors in Poverty-Stricken Counties of Western Hubei Province, China

Transient stability‐oriented assessment and application of preventive control action for power system

Development of Back-Propagation Neural Network for Transient Stability Assessment of Power Systems

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