State variable technique islanding detection using time-frequency energy analysis for DFIG wind turbine in microgrid system

Guo, Wenzhong; Wang, Jiuhui; Zhou, Zi-Xuan; Wang, Qiantao; Wu, Qiong; Jiang, Xingyu; Santana, Efstathios

doi:10.1016/j.isatra.2018.07.017

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…Wang et al 38 used a hybrid method that includes injecting perturbation and measuring the rates of change in signals of frequency, angle, active power, reactive power, voltage harmonics, current harmonic and d-and q-axis voltages for islanding detection. A hybrid method proposed by Seyedi et al 39…”

Section: Local Methods For Islanding Detectionmentioning

confidence: 99%

Detection of islanding, operation and reconnection of microgrids to utility grid using local information

Sadoughi

Hojjat

Abardeh

2020

Int Trans Electr Energ Syst

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For proper performance of protection and control systems in microgrids, an islanding event must be detected as soon as possible. In this paper, a novel algorithm is proposed to detect the microgrid operation mode using only the voltage and current samples at the relay location. This approach can be used in all microgrids with any type of source such as synchronous or inverterbased distributed generators. An optimal method is proposed to determine the input parameters of a classification method by using local information. The goal of the optimization is to minimize the time and maximize the accuracy of microgrid connection state detection. Moreover, in the classification method, three states, that is, islanding, reconnection to the utility grid and other events are considered. The objective function of this problem is defined as the summation of the weighted time factor and detection error. The inputs of the islanding detection problem can be a large number of network parameters, such as frequency, voltage, current, power, their sequence components or rates of changes. Consequently, the variables of the classification problem are the number and the type of input parameters of the classification problem. The optimization problem is solved by using a genetic algorithm-based, and a support vector machine is used for the classification process. In order to evaluate

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Section: Local Methods For Islanding Detectionmentioning

confidence: 99%

Detection of islanding, operation and reconnection of microgrids to utility grid using local information

Sadoughi

Hojjat

Abardeh

2020

Int Trans Electr Energ Syst

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“…voltage amplitude [46][47][48][49][50][51][52][53][54][55][56] current [57] rotor angle [44,45] frequency [58] active power [59] voltage and current amplitude [45,[60][61][62][63][64][65] voltage, current and frequency [66] voltage, current, frequency and power factor [67] voltage, frequency and active power [68] voltage, current and rotor angle [45] voltage amplitude and frequency [69] voltage, frequency, current, active and reactive power [70][71][72][73] voltage, frequency, active and reactive power [74] frequency and active power [75] voltage, active and reactive power [76] active and reactive power [77,78] The sources of input data used for training and testing the IDMs are extracted into data obtained by measurement and downloaded from relays that can record data or fault recorders, or they are extracted in dynamic analysis real-time simulation using programs such as PSCAD-EMTDC, DIgSILENT, ATP, PSIM, and MATLAB. Table 2 shows the sources of input data, and most papers use measurements as a source of the input data.…”

Section: Input Data Referencementioning

confidence: 99%

Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review

Kaluđer,

Fekete,

Čvek

et al. 2023

Applied Sciences

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Active distribution grids that contain energy sources (so-called distributed generation or DG) are nowadays a reality. Besides the many benefits DGs bring to the distribution grid, some challenges are associated with their integration. Since there are DGs now in the distribution grid, the occurrence of islanding operation is possible. Since an islanding operation can be dangerous, it is necessary to have an effective method to detect it. In the last decade, scientists have made a great effort to develop and test various islanding detection methods (IDMs). Many approaches have been tested, and the methods based on computational intelligence (CI) have shown great potential. Among them, artificial neural networks (ANNs) gained most of the research attention. This paper focuses on ANN application for islanding detection. It gives an exhaustive review of the ANN types used for islanding detection, the types of input data, and their transformation to fit the ANNs. Furthermore, various applications based on specific input data, preprocessing types, different learning algorithms, real-time implementation, and various distribution models used for ANN are reviewed. This paper investigates the potential of ANNs to enhance islanding detection accuracy, reduce non-detection zone (NDZ), and contribute to an overall efficient detection method.

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“…All the WT based method depended on selected signal for detection and this make a strong challenge because if the threshold of the signal is high, the detection may not be occurred and if it is very small, a wrong tripping may be occurring. In [20], the neuro-fuzzy algorithm was used to avoid the selection of signal threshold and it was applied on multi-DGs microgrid (MG) and succeeded to detect the islanding of whole MG but it couldn't detect the disconnection of single DG in the MG.…”

Section: Nomenclaturementioning

confidence: 99%

Islanding Detection of Microgrid Incorporating Inverter Based DGs Using Long Short-Term Memory Network

Abdelsalam¹,

Salem²,

Oda³

et al. 2020

IEEE Access

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This paper proposes a new approach for rapid detection of islanding events in a microgrid (MG). The proposed approach is a two-step procedure in which the first step is to extract some valuable features from the voltage and current signals. Such signals are analyzed for finding the second harmonic by the discrete Fourier transform (DFT). Then, the symmetrical components of this second harmonic are calculated for voltage and current, resulting in six features; positive, negative and zero sequence components. In the second step, a novel deep learning classifier based on long short-term memory (LSTM) network to identify the islanding decision is applied. The LSTM is a new artificial intelligence technique which is a distinctive pattern of recurrent neural networks. To evaluate the performance of the proposed approach, simulated and practical voltage and current signals are used. The simulated signals are generated by simulating a MG consisting of inverter based wind DGs using Matlab Simulink, while the practical data are collected from an experimental model consisting of wind and PV DGs. Different intentional and unintentional islanding events are conducted and processed using the proposed approach. The results show that in comparison with other artificial intelligence algorithms such as decision tree (DT), support vector machine (SVM) and artificial neural network (ANN), the proposed approach is efficient and reliable in detecting the islanding with high accuracy, high dependability and small detection time.

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State variable technique islanding detection using time-frequency energy analysis for DFIG wind turbine in microgrid system

Cited by 13 publications

References 28 publications

Detection of islanding, operation and reconnection of microgrids to utility grid using local information

Detection of islanding, operation and reconnection of microgrids to utility grid using local information

Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review

Islanding Detection of Microgrid Incorporating Inverter Based DGs Using Long Short-Term Memory Network

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