An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

Ahmadipour, Masoud; Hizam, Hashim; Othman, Mohammad Lutfi; Radzi, Mohd Amran Mohd

doi:10.3390/en11102701

Cited by 26 publications

(20 citation statements)

References 51 publications

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“…Recently, some computational intelligent methods in combination with modern signal processing techniques have been introduced to get rapid detection of islanding condition, such as artificial neural network, [11][12][13][14][15][16][27][28][29][30][31] fuzzy logic, [15][16][17][18]28 and wavelet. [15][16][17][24][25][26][27][28] In spite of the outstanding features and the capability of these intelligent methods to determine the most proper combination of features, as well as thresholds, that can reduce the NDZ, such intelligent methods need additional software and hardware support and are difficult to implement in practice. 1,19 In addition, intelligent methods need higher execution time due to the high level of complexity.…”

Section: Passive Methodsmentioning

confidence: 99%

Zero non‐detection zone assessment for anti‐islanding protection in rotating machines based distributed generation system

Abd-Elkader

Saleh

2020

Int J Energy Res

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The challenges for a reliable operation of electrical power system have increased due to the presence of multi-distributed generation units (DGs) in the distribution systems in order to meet the increase of the load demand. Detection of unintentional islanding situation is very important as nondetection of islanding situation could result in a cascaded failure of the system. If the islanding situation remains undetected, the instability in the islanded part can lead to a complete failure of the electrical power system. This paper introduces a new passive scheme for islanding detection, which is suitable for multi-distributed generation units based on rotating machines. The proposed method is based on the measurements of the system voltage and frequency to compute two indices called the islanding index and harmonics index. The islanding index is the main index used to discriminate and identify the islanding situation. However, the harmonics index in conjunction with a strategy called speed reduction strategy assists the islanding index to discriminate between islanding situation in case of a close power match and system disturbances. The simulation studies were conducted in MATLAB/SIMULINK environment, and various cases have been considered, such as normal operation, islanding operation, sudden load change, DG tripping, separation of some DG units, faults, etc. The novelty of the proposed strategy is that it provides fast detection and has zero nondetection zone compared with the existing detection methods. Moreover, the proposed strategy has no effect on the power quality, and the maximum detection time is almost 350 ms at a close power match. The results indicate that the proposed scheme is successful in discrimination of the islanding conditions from other grid disturbances, revealing its great potential to be able to detect islanding events. Finally, the proposed method is applied only for rotating machine based DGs, such as wind turbines. Wind farms'

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Section: Passive Methodsmentioning

confidence: 99%

Zero non‐detection zone assessment for anti‐islanding protection in rotating machines based distributed generation system

Abd-Elkader

Saleh

2020

Int J Energy Res

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“…The proposed fault detection technique transfers a "trip signal is set to 1" command if fault cases are predicted; otherwise, for normal conditions a "trip signal is set to 0" command is set. Figure 2 shows a 250 kW grid-connected PV system, the details for which are given in [19]. The studied system was modeled using the MATLAB simulation tool.…”

Section: Proposed Protection Schemementioning

confidence: 99%

“…Wavelet singular entropy theory in combination with fuzzy logic was proposed to detect faults in a multiple distributed generation (DG) system [18]. In [19], a methodology based on probabilistic neural network and wavelet packet transform was presented to predict and classify grid faults in a PV system. Various intelligent fault detection methods in PV systems have been proposed based on computational intelligent methods and modern signal processing techniques to detect grid faults correctly and to improve the performance of PV systems [20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

et al. 2019

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A new protection scheme based on applying a combination of wavelet multi-resolution singular spectrum entropy and support vector machine is proposed to identify different types of grid faults in a three-phase grid-tied photovoltaic system. In this technique, discrete wavelet transform with multi-resolution singular spectrum entropy is utilized to extract the unique features of three-phase voltage signals at the point of common coupling. The three-phase voltage signals are decomposed to provide detail and approximation coefficients of wavelet transform. Then, various features between different types of grid faults can be extracted by a combination of multi resolution analysis and spectrum analysis with entropy as the output. The constructed features vector is utilized as input data of a support vector machine classifier to identify and classify various types of faults. The results illustrate that the proposed intelligent technique not only recognizes different types of grid faults correctly, but also performs quickly in identifying grid faults in a grid-connected photovoltaic system. Apart from this, a graphical investigation is executed to observe the effects of different types of grid faults in photovoltaic (PV) operation which highlight the necessity of intelligent protection methods to protect PV systems.

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“…It is a time-frequency method, along with empirical mode decomposition, which makes it suitable for islanding detection; therefore, HHT is a very efficient approach for the removal and detection of islanding characteristics [33]. An islanding detection method based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) was presented [34]. The voltage obtained at the point of common coupling (PCC) is measured and decomposed by the WPT.…”

Section: Introductionmentioning

confidence: 99%

A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network

et al. 2020

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Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation (PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue.

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An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

Cited by 26 publications

References 51 publications

Zero non‐detection zone assessment for anti‐islanding protection in rotating machines based distributed generation system

Zero non‐detection zone assessment for anti‐islanding protection in rotating machines based distributed generation system

A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network

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