Comparison of Three Electromechanical Oscillation Damping Estimation Methods

Two improvements of power system electromechanical oscillation estimation via subspace identification are proposed in this paper. Singular value-based criteria are introduced not only to improve the computation efficiency for determining the model order of the estimated system but also to improve the accuracy of the estimates. A mode matching method based on the characteristics of accurate mode shape estimates is proposed to ensure the correctness of the estimates. Numerical results from three different scale power systems illustrate the feasibility of the proposed methods.

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“…Load variations can be seen following Gaussian distribution [22]. Based on this assumption, the accuracy of the estimates has been evaluated.…”

Section: Accuracy Evaluationmentioning

confidence: 99%

“…Note that IM 7 and IM 8 have similar frequencies and one cannot tell the estimates apart only by the frequencies. Take voltage angles of buses 13,22,25,92,97,120,122,133, and 139 as system outputs at a length of N(=3,000). The model order is chosen to be 26 by SVC.…”

Section: Npcc 48-machine Systemmentioning

confidence: 99%

Improvements for electromechanical oscillation mode estimation via subspace identification methods

Ding

Huang

2015

EURASIP J. Adv. Signal Process.

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“…In electrical engineering, there are vast numbers of papers in the field of power system measurement, partial discharges, and power system transients, protection, or quality. There are fewer papers related to the WT analysis of electromechanical dynamic phenomena in power systems and they are mainly related to disturbance identification and the analysis and identification of low-frequency electromechanical oscillations (LFEOs) in power systems, as well as the identification and estimation of an active power unbalance in the power system [16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Applications of wavelets and neural networks for classification of power system dynamics events

Avdaković¹,

Nuhanovic²,

Kusljugic³

2014

Turk J Elec Eng & Comp Sci

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This paper investigates the possibility of classifying power system dynamics events using discrete wavelet transform (DWT) and a neural network (NN) by analyzing one variable at a single network bus. Following a disturbance in the power system, it will propagate through the system in the form of low-frequency electromechanical oscillations (LFEOs) in a frequency range of up to 5 Hz. DWT allows the identification of components of the LFEO, their frequencies, and magnitudes. After determining the energy components' share of the analyzed signal using DWT and Parseval's theorem, the input data for the classification process using a NN are obtained. A total of 5 classes of disturbances, 3 different wavelet functions, and 2 different variables are tested. Simulation results show that the proposed approach can classify different power disturbance types efficiently, regardless of the choice of variable or wavelet function.

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“…Em vista disso, dados obtidos de PMUs têm sido utilizados de forma complementar para estimação de modos de oscilação eletromecânica em sistemas elétricos, possibilitando a análise quase em tempo real da estabilidade dos SEP a 2 pequenas perturbações [2,5,7,8,9,20,21,22,23].…”

Section: Motivaçãounclassified

“…Tendo em vista a complexidade dos modelos matemáticos necessários para a avaliação da estabilidade de um SEP a partir da análise dos autovalores da matriz de transição de estados e que os parâmetros do sistema variam a todo instante, os dados de medição fasorial provenientes de PMUs têm sido empregados de forma complementar na identificação dos modos de oscilação, possibilitando uma análise quase em tempo real da estabilidade dos SEPs a pequenas perturbações [2,20,21,22,23]. Além disso, a análise de dados de PMU, por fornecer informações importantes do estado dinâmico do sistema em tempo real [7,35,36,37], têm sido utilizada também em malhas de controle de PSS [38,39].…”

Section: Avaliação Da Estabilidade De Seps Utilizando Medições Fasoriunclassified

Identificação de modos de oscilação utilizando Filtro de Kalman Estendido

Amaral¹

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Este trabalhoé dedicadò a minha esposa Patrícia, ao meu filho João Pedro, aos meus pais Izabel e Nelson e aos meus irmãos Lívia e Gustavo.iv AGRADECIMENTOS A minha esposa Patrícia pelo amor, companheirismo, dedicação, compreensão, paciência, garra para cuidar do nosso príncipe em minhas ausências, por entender minhas ausências diárias, pelo apoio e carinho essenciaisà minha tranquilidade para mais essa vitória.Ao meu príncipe João Pedro por receber o papai todos os dias com um sorriso lindo e reconfortante, por dar sentido a vida do papai e da mamãe, e por compreender, ainda que com poucos meses de vida, que minhas ausências diárias tinham oúnico propósito de trazer conforto e aprendizadoà nossa família. The proposed methodology was evaluated by two kinds of data. In a first kind, the signals were simulated. For this purpose, it was used the Power System Toolbox software (PST). In the second kind, it was considered field data obtained from PMUs installed at low voltage in Brazilian universities in several states. Considering the stochastic nature of the data, the proposed method identified the dominant modes present in the simulated ambient data of the test system accordingly, both in situations in which the frequency and damping of the mode were fixed, as in situations in which the mode parameters varied throughout the period of simulation. In the case of the field data, the results identified the presence of dominant oscillation mode in the frequency range between 0,2 Hz and 0,4 Hz. This is an inter-area mode, characteristic of the Brazilian National Grid. Therefore, in both kinds of data, the proposed methodology was appropriate to identify dominant modes from ambient data.vii

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Comparison of Three Electromechanical Oscillation Damping Estimation Methods

Cited by 88 publications

References 31 publications

Improvements for electromechanical oscillation mode estimation via subspace identification methods

Improvements for electromechanical oscillation mode estimation via subspace identification methods

Applications of wavelets and neural networks for classification of power system dynamics events

Identificação de modos de oscilação utilizando Filtro de Kalman Estendido

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