A new method for dynamic reduction of power system using PAM algorithm

Pyo, Gi-Chan; Park, J.W.; Moon, Seung-Il

doi:10.1109/pes.2010.5589996

Cited by 20 publications

(16 citation statements)

References 4 publications

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“…11 is 0.4476 and , both representing the performance of the coherency aggregation method. Therefore, it can be concluded that the proposed method performs better under the metric defined in (23), even under a slightly higher reduction ratio.…”

Section: Casementioning

confidence: 83%

“…Using the metric in (23), the mismatch between the black dotted line and the blue solid line in Fig. 11 is 0.1630, and the reduction ratio defined by (24) is , both of which represent the performance of the proposed method.…”

Section: Casementioning

confidence: 99%

“…Pyo et al [23] present a coherency aggregation method that can handle higher-order models with excitation systems, and apply the method using the IEEE 39-bus system. Here the DEAR method is applied on the same system to compare the performance.…”

Section: Casementioning

confidence: 99%

“…In the IEEE 39-bus system [23] shown in Fig. 14, there are four and six generators in the internal and the external area, respectively.…”

Section: Casementioning

confidence: 99%

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Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction

Wang

Zhou

et al. 2014

IEEE Trans. Power Syst.

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In interconnected power systems, dynamic model reduction can be applied to generators outside the area of interest (i.e., study area) to reduce the computational cost associated with transient stability studies. This paper presents a method of deriving the reduced dynamic model of the external area based on dynamic response measurements. The method consists of three steps, namely dynamic-feature extraction, attribution, and reconstruction (DEAR). In this method, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highest similarity, forming a suboptimal "basis" of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original system. The network model is unchanged in the DEAR method. Tests on several IEEE standard systems show that the proposed method yields better reduction ratio and response errors than the traditional coherency based reduction methods.Index Terms-Dynamic response, feature extraction, model reduction, orthogonal decomposition, power systems. NOMENCLATURETime.Rotor angle of synchronous machine.Rotor speed of synchronous machine.Mechanical power.Electrical power.Inertia constant of synchronous machine.

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Section: Casementioning

confidence: 83%

Section: Casementioning

confidence: 99%

Section: Casementioning

confidence: 99%

“…In the IEEE 39-bus system [23] shown in Fig. 14, there are four and six generators in the internal and the external area, respectively.…”

Section: Casementioning

confidence: 99%

See 2 more Smart Citations

Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction

Wang

Zhou

et al. 2014

IEEE Trans. Power Syst.

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“…The methodologies underlying these approaches are diverse, e.g. artificial neural network (ANN) [11], particle swarm optimization and k-means (PSO-KM) algorithm [12], graph theory [13], partitioning around medoids (PAM) [14], hierarchical clustering [15], self-organizing feature maps [16], Fast-Fourier transform (FFT) [17], [18], Hilbert-Huang transform (HHT) [19] and Principal Component Analysis (PCA) [20], [21]. Despite having the advantages over model-based approaches, these techniques have some limitations, for instance, the ANN algorithm requires excessive off-line training data to train the neural network in order to identify the coherent generators.…”

Section: State Of the Artmentioning

confidence: 99%

Coherency Identification in Interconnected Power System—An Independent Component Analysis Approach

Ariff

Pal

2013

IEEE Trans. Power Syst.

132

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Abstract-This paper presents a novel approach to the coherency identification technique in interconnected power system using independent component analysis (ICA). The ICA is applied to the generator speed and bus angle data to identify the coherent areas of the system. The results of the application of ICA using simulated data from 16-machine 68-bus system model and on data gathered through UK University-based WideArea Measurement System are presented. The approach is able to identify the cluster of generators and buses following a disturbance in the system. It is also demonstrated that the approach is robust in the presence of noise in measured signal, which is an important factor to be considered for assessing the effectiveness of any measurement-based technique.Index Terms-Coherency identification, wide-area measurement, measurement-based, independent component analysis, power system

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Clustering approach to model order reduction of power networks with distributed controllers

Cheng

Scherpen

2018

Adv Comput Math

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This paper considers the network structure preserving model reduction of power networks with distributed controllers. The studied system and controller are modeled as second-order and first-order ordinary differential equations, which are coupled to a closed-loop model for analyzing the dissimilarities of the power units. By transfer functions, we characterize the behavior of each node (generator or load) in the power network and define a novel notion of dissimilarity between two nodes by the H 2-norm of the transfer function deviation. Then, the reduction methodology is developed based on separately clustering the generators and loads according to their behavior dissimilarities. The characteristic matrix of the resulting clustering is adopted for the Galerkin projection to derive explicit reduced-order power models and controllers. Finally, we illustrate the proposed method by the IEEE 30-bus system example.

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A new method for dynamic reduction of power system using PAM algorithm

Cited by 20 publications

References 4 publications

Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction

Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction

Coherency Identification in Interconnected Power System—An Independent Component Analysis Approach

Clustering approach to model order reduction of power networks with distributed controllers

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