A comparative analysis of intelligent classifiers for passive islanding detection in microgrids

Azim, Riyasat; Sun, Kai; Li, Fangxing; Zhu, Yongli; Saleem, Hira; Shi, Di; Sharma, Ratnesh

doi:10.1109/ptc.2015.7232369

Cited by 9 publications

(8 citation statements)

References 16 publications

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“…Naïve Bayes is a statistical method from a family of probabilistic classifiers exhibiting strong (naïve) independence assumptions. This independence assumption is very useful because Naïve Bayes classifier is widely used in classification application with high input data dimensionality 76 and this makes it very difficult to calculate the joint probability p ( bold-italicX = { x i } ∣ bold-italicC = bold-italicc ) . 77 In such scenarios, this type of classifier is coupled with kernel density estimation to increase its level of accuracy.…”

Section: Classification Methodsmentioning

confidence: 99%

Wide area monitoring, protection, and control application in islanding detection for grid integrated distributed generation: A review

Tshenyego

Samikannu

Mtengi

2021

Measurement and Control

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The assimilation of Distributed Generation (DG) into the electric power system (EPS) has become more attractive as the world is following a trend to reduce greenhouse gas emissions by introducing more renewable energy forms resulting in high penetration scenarios. This high penetration of DGs brings several challenges to the protection philosophy of the EPS which compromises its reliability, availability, and efficiency. Under high DG penetration scenarios, conventional islanding detection methods (Idms) fail to detect an island as the grid loses its inertia to leverage a significant frequency and voltage mismatch necessary for Idms to effectively detect an islanding event. This has given rise to the birth of Artificial Intelligent (AI) methods that are found to perform better in islanding detection. AI Idms are computationally intensive and require a lot of data to operate accurately. Because the computational burden of these methods requires fast computing hardware, the current trend of AI Idms are integrated with Wide Area Monitoring, Protection, and Control (WAMPAC) system. This paper aims at reviewing all these Idms and the WAMPAC’s system latency when hosting AI Idms which are currently the best in islanding detection. This is done to determine if the WAMPAC system latency plus Idms computational time meet the islanding detection time specified by the IEEE Standard 1547 framework.

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Section: Classification Methodsmentioning

confidence: 99%

Wide area monitoring, protection, and control application in islanding detection for grid integrated distributed generation: A review

Tshenyego

Samikannu

Mtengi

2021

Measurement and Control

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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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“…In this context, grid-connected MGs must be capable of detecting grid faults, ceasing the operation of power converters, and finally operating in island mode [10]. Consequently, gridconnected MGs are also designed with either passive [104] or active islanded detection systems [105] as well as dedicated control algorithms for regulating the electrical transients when commuting between operation modes [97]. However, some efforts have been made to propose a single controller for both modes of operation, such as the fuzzy logic controller proposed in [106] to predict increases in current and voltage and limiting the power supply of DERs.…”

Section: Grid Faults and Island Detection [1ms -~1s]mentioning

confidence: 99%

A review of hierarchical control for building microgrids

Yamashita

Vechiu

Gaubert

2020

Renewable and Sustainable Energy Reviews

125

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A comparative analysis of intelligent classifiers for passive islanding detection in microgrids

Cited by 9 publications

References 16 publications

Wide area monitoring, protection, and control application in islanding detection for grid integrated distributed generation: A review

Wide area monitoring, protection, and control application in islanding detection for grid integrated distributed generation: A review

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

A review of hierarchical control for building microgrids

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