Feedforward neural network-based transient stability analysis of electric power systems

Abdallah, H. Hadj; Chtourou, Mohamed; Guesmi, Tawfik; Ouali, A.

doi:10.1002/etep.105

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…Previous studies [19] have shown that the CCT can be approximated with high accuracy, based on the load flow data, using a polynomial function. For the purpose of CCT approximation, feed-forward ANNs have been used in many studies [20][21][22][23]. The structure of a feedforward neural network has been thoroughly described in the literature, and is not shown here for brevity.…”

Section: Using the Ann To Approximate The Stability Boundarymentioning

confidence: 99%

“…This paper proposes an alternative approach to solve the TSC-OPF problem, where the transient stability constraints are approximated using artificial neural networks (ANNs). The study is motivated by previous research, which showed that the CCT can be approximated with very high accuracy from prefault load flow data using various artificial intelligence networks [19][20][21][22]. Hence, the ANNs can provide analytical expressions of the stability boundaries and their sensitivity with respect to optimization variables.…”

Section: Introductionmentioning

confidence: 99%

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A novel approach to solve transient stability constrained optimal power flow problems

Nguyen-Duc¹,

Tran-Hoai²,

Vo³

2017

Turk J Elec Eng & Comp Sci

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Section: Using the Ann To Approximate The Stability Boundarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel approach to solve transient stability constrained optimal power flow problems

Nguyen-Duc¹,

Tran-Hoai²,

Vo³

2017

Turk J Elec Eng & Comp Sci

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“…Destaca-se que as RNA são aceitas cientificamente na área de Sistemas Elétricos de Potência para diversas aplicações: estimação da velocidade de máquinas elétricas (Goedtel et al, 2013), estabilidade transitória (Abdallah et al, 2006) e classificação de cargas não lineares (Saraiva et al, 2015).…”

Section: Introductionunclassified

Forecasting Electric Power Generation of Grid-Connected Solar Photovoltaic Systems by Using Artificial Neural Networks

Bernardes

Vasconcelos

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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The adoption of photovoltaic systems installed in homes and small/medium commercial and industrial buildings has grown considerably in the last decades in Brazil and worldwide. These systems, which are typically linked to primary (medium-voltage) or secondary (low-voltage) distribution networks, are called grid-connected power photovoltaic (PV) systems. Evaluating the return on investment of these systems requires a robust modelling and tools to prognosticate the amount of generated energy. This work, therefore, is based on the application of Artificial Neural Networks (ANN) to predict the daily-produced energy from a PV system installed at the University of São Paulo (USP). Meteorological and performance data were collected from this PV system for one month. This methodology may allow carrying out the efficiency analysis of the device, to plan the system operation and commercialization of the energy obtained strategically. We present several graphs which estimate the quality of the PV system, along with performance metrics, such as the number of epochs, mean squared errors and variances achieved in the simulations. Finally, the validation studies were performed by comparing the error between the experimentally measured data and the estimated data from ANN. The computational tests show that the purpose is interesting to solve the problem within an acceptable percentage error. Resumo: A adoção de sistemas fotovoltaicos instalados em casas e pequenas e médias instalações comerciais e industriais tem crescido consideravelmente nas últimas décadas no Brasil e também internacionalmente. Esses sistemas, que são tipicamente ligados a redes primária (média tensão) ou secundária (baixa tensão) de redes de distribuição, são também chamados de sistemas fotovoltaicos de energia conectados à rede elétrica (SFCR's). Avaliar o retorno de investimento desses sistemas requerem um modelo robusto e ferramentas para diagnosticar a quantidade de energia gerada. Esse trabalho, portanto, baseado na aplicação de Redes Neurais Artificiais (RNAs) prediz a energia gerada no dia de um SFCR instalado na Universidade de São Paulo. Dados de desempenho e meteorológico foram coletados durante um mês. Essa metodologia pode permitir analisar a eficiência desse dispositivo, planejar a operação e comercialização da energia obtida estrategicamente. Diversos gráficos são apresentados que estimam a qualidade do SFCR, junto com métricas de desempenho, tais como número de épocas, erros quadráticos médios e variâncias obtidas nas simulações. Finalmente, os estudos de validação foram executados comparando os erros entre os dados medidos experimentalmente e os estimados pela RNA. Os testes computacionais mostram que o propósito é interessante para resolver o problema dentro de um erro percentual aceitável.

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“…Many researchers have suggested the application of machine learning methods for on-line predication of CCT and therefore, transient stability status. The ANNs are the most popular method among machine learning methods and have been proposed for the transient stability assessment [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…The transient stability can be assessed by different techniques such as time domain integration [1][2][3], direct methods based on energy function and extended equal area criterion [4][5][6], pattern recognition [7,8], decision trees (DTs) [9,10], and artificial neural networks (ANNs) [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A new BVM based approach to transient security assessment

Mohammadi

Gharehpetian

Raoofat

2010

Int Trans Elec Energy Syst

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SUMMARYMachine Learning techniques have been extensively used in Power Systems Analysis during the last years. This paper describes a ball vector machine based algorithm for on-line transient security assessment of large-scale power systems. The proposed ball vector machine based security assessment reduces the training time and space complexities in comparison with support vector machines, artificial neural networks, and other machine learning based algorithms. In addition, the proposed algorithm has less support vectors and therefore is faster than existing algorithms for on-line applications. A new decision tree based feature selection technique has been presented, too. The proposed ball vector machine based algorithm has been applied to New England 39-bus test system. The simulation results show the effectiveness of the proposed algorithm for on-line transient security assessment procedure.

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Feedforward neural network-based transient stability analysis of electric power systems

Cited by 6 publications

References 22 publications

A novel approach to solve transient stability constrained optimal power flow problems

A novel approach to solve transient stability constrained optimal power flow problems

Forecasting Electric Power Generation of Grid-Connected Solar Photovoltaic Systems by Using Artificial Neural Networks

A new BVM based approach to transient security assessment

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