Seamless Switching and Grid Reconnection of Microgrid Using Petri Recurrent Wavelet Fuzzy Neural Network

Tan, Kean Ming; Tseng, Tzu-Yu

doi:10.1109/tpel.2021.3066986

Cited by 26 publications

(15 citation statements)

References 31 publications

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“…Hence, the product of the value 3/2, the q-axis current command I * sq and the q-axis voltage V q obtained by the three-phase output voltages v a , v b , v c with the abc/dq coordinate transformation is equal to the real power error P error [1,2]. Since the real power error P error is also equivalent to the value P sm − P sout , the estimated virtual inertia in Equation ( 7) can be rewritten based on the real power error P error as follows:…”

Section: Proposed Virtual Inertia Estimator For Vsgmentioning

confidence: 99%

“…where σ 2 ij and m 2 ij are the standard deviation and the mean of the Gaussian function, respectively, in the jth term input linguistic variable y 1 i (N) to the node of membership layer. Layer 3 (Petri Layer): The main purpose of the Petri layer is to take advantage of the competition law to choose the proper fired nodes [2] for the generation of tokens. When the tokens are generated in input position, the transitions are in enable state.…”

Section: Network Structurementioning

confidence: 99%

“…where t 3 p (N) is the transition; α and β are positive constants and are set as 1.3 and 0.06; d th is the dynamic threshold value and is modulated by the function as follows [2,23]:…”

Section: Network Structurementioning

confidence: 99%

“…In recent years, the percentage of distributed generators (DGs) based on renewable energy sources has been significantly increasing [1,2] due to the increased awareness of environmental pollution. However, a serious issue arises with respect to the stability of the power system.…”

Section: Introductionmentioning

confidence: 99%

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Virtual Synchronous Generator Using an Intelligent Controller for Virtual Inertia Estimation

et al. 2021

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Virtual synchronous generators (VSGs) with inertia characteristics are generally adopted for the control of distributed generators (DGs) in order to mimic a synchronous generator. However, since the amount of virtual inertia in VSG control is usually constant and given by trial and error, the real power and frequency oscillations of a battery energy storage system (BESS) occurring under load variation result in the degradation of the control performance of the DG. Thus, in this study, a novel virtual inertia estimation methodology is proposed to estimate suitable values of virtual inertia for VSGs and to suppress the real power output and frequency oscillations of the DG under load variation. In addition, to improve the function of the proposed virtual inertia estimator and the transient responses of the real power output and frequency of the DG, an online-trained Petri probabilistic wavelet fuzzy neural network (PPWFNN) controller is proposed to replace the proportional integral (PI) controller. The network structure and the online learning algorithm using backpropagation (BP) of the proposed PPWFNN are represented in detail. Finally, on the basis of the experimental results, it can be concluded that superior performance in terms of real power output and frequency response under load variation can be achieved by using the proposed virtual inertia estimator and the intelligent PPWFNN controller.

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Section: Proposed Virtual Inertia Estimator For Vsgmentioning

confidence: 99%

Section: Network Structurementioning

confidence: 99%

“…where t 3 p (N) is the transition; α and β are positive constants and are set as 1.3 and 0.06; d th is the dynamic threshold value and is modulated by the function as follows [2,23]:…”

Section: Network Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Virtual Synchronous Generator Using an Intelligent Controller for Virtual Inertia Estimation

et al. 2021

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“…And then, a method of generalized fuzzy wavelet neural network was proposed in [15], which consists of polynomial neural network and fuzzy wavelet neurons. Further, by constructing feedback nodes in the second layer, a wavelet-based recurrent fuzzy neural network with memory capability was developed in [16]. It should be pointed out that these fuzzy wavelet networks methods were computationally complex.…”

Section: Introductionmentioning

confidence: 99%

Distributed wavelet neural networks

Yang

Chen

2021

Appl Intell

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Utilizing the wavelet theory, the wavelet coefficients with respect to translation and scaling factors can be obtained through the iteration of neural network, which effectively solves the window immobility problem of short time Fourier transform. Notice that, the centralized wavelet neural network is weak in the representation of signal integrity as localized information is lost in the case of the signal properties are large span time-domain and high frequency. To solve this problem, the learning algorithm of distributed wavelet neural networks (DWNNs) based on domain segment is proposed in this paper, where the time frequency characteristic of objective function is set as a constraint condition, while the time width, frequency, center of time domain and band center of the wavelet are employed to determine the translation and scaling parameters. Especially, wavelet networks are distributed into several orthogonal subnet by partitioning the information of input data, and the complexity of calculating for each nodes is thus decreased. Moreover, simulations are presented to demonstrate the advantages and efficiency of the proposed DWNNs.

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Control Method for Grid-Connected/Islanding Switching of Hybrid AC/DC Microgrid

Chong²,

Wang³

et al. 2022

J. Electr. Eng. Technol.

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Seamless Switching and Grid Reconnection of Microgrid Using Petri Recurrent Wavelet Fuzzy Neural Network

Cited by 26 publications

References 31 publications

Virtual Synchronous Generator Using an Intelligent Controller for Virtual Inertia Estimation

Virtual Synchronous Generator Using an Intelligent Controller for Virtual Inertia Estimation

Distributed wavelet neural networks

Control Method for Grid-Connected/Islanding Switching of Hybrid AC/DC Microgrid

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