Classification of disturbances in hybrid DG system using modular PNN and SVM

Mohanty, Soumya R.; Ray, Pravat Kumar; Kishor, Nand; Panigrahi, B. K.

doi:10.1016/j.ijepes.2012.08.020

Cited by 65 publications

(43 citation statements)

References 33 publications

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“…The simulation results prove the feasibility of the method. MPNN is used in [108] for islanding and power quality disturbance classification for a hybrid DG system. [109] combines chaos synchronization and type 2 ENN for ID for a grid connected Chua's circuit based PV system.…”

Section: Annmentioning

confidence: 99%

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Shifting of research trends in islanding detection method - a comprehensive survey

Dutta

Sadhu

Reddy

et al. 2018

Prot Control Mod Power Syst

117

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The augmentation in electricity demand, power system privatization as well as efficacy of renewable resources has paved the way for power system companies and researchers to exploit the field of grid connected distributed generation (DG) and its issues, islanding being a dominant one. Several research works have been conducted to mitigate the issues of islanding detection (ID). In context of this, the paper gives a comprehensive review of islanding issues, standard test systems, criteria and shifting of research trends in islanding detection methods (IDMs). The significant contributions pertain to categorization of IDMs, evaluation of non-detection zone (NDZ) for each test system, disquisition on evolution and advancement of IDMs and its comparisons based on criteria such as NDZ, run on time, nuisance tripping percentage, applicability in multi DG system and implementation cost to draw out the strength and shortcomings of individual methods that will come to aid to the companies or researchers for establishing the applicability and appropriateness of such method for their concerned domain.

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Section: Annmentioning

confidence: 99%

“…The results were also compared with DT and PNN which proved its inferiority. The application of SVM in ID for a hybrid DG system is developed in [108]. S transform is used to construct a matrix containing important information like magnitude, frequency and phase.…”

Section: Svm Ann Has Certain Disadvantagesmentioning

confidence: 99%

Shifting of research trends in islanding detection method - a comprehensive survey

Dutta

Sadhu

Reddy

et al. 2018

Prot Control Mod Power Syst

117

View full text Add to dashboard Cite

show abstract

“…Due to its concise training and strong classification ability, the PNN has widely applications for fault diagnosis in practical applications [18]. Compared with BP neural network, the advantage of a PNN is that the topology, connection weights, and thresholds can be set immediately when training samples are attainable [19]. The general structure of a PNN model is shown in Figure 5.…”

Section: Pnn Based Fault Diagnosismentioning

confidence: 99%

A General Purpose Adaptive Fault Detection and Diagnosis Scheme for Information Systems with Superheterodyne Receivers

Song

et al. 2018

Complexity

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A superheterodyne receiver is a type of device universally used in a variety of electronics and information systems. Fault detection and diagnosis for superheterodyne receivers are therefore of critical importance, especially in noise environments. A general purpose fault detection and diagnosis scheme based on observers and residual error analysis was proposed in this study. In the scheme, two generalized regression neural networks (GRNNs) are utilized for fault detection, with one as an observer and the other as an adaptive threshold generator; faults are detected by comparing the residual error and the threshold. Then, time and frequency domain features are extracted from the residual error for diagnosis. A probabilistic neural network (PNN) acts as a classifier to realize the fault diagnosis. Finally, to mimic electromagnetic environments with noise interference, simulation model under different fault conditions with noise interferences is established to test the effectiveness and robustness of the proposed fault detection and diagnosis scheme. Results of the simulation experiments proved that the presented method is effective and robust in simulated electromagnetic environments.

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“…It is a feedforward network derived from radial basis function (RBF) neural network. Compared with other neural networks, PNN is faster in convergence and higher in accuracy, which is suited to pattern classification and fault diagnosis [8,9]. PNN is a four-layer network which consists of input layer, pattern layer, summation layer and output layer, as shown in Figure 1.…”

Section: Probabilistic Neural Networkmentioning

confidence: 99%

“…Hereby, the combination of two or three methods may be utilized. A fault detection method that combines Hilbert transform and wavelet packet transform was proposed to extract modulating signal and help detect the early gear fault [8]. Wu etc.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Machinery based on Feature Selection and Probabilistic Neural Network

Li¹

2017

IJPE

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Fault diagnosis for the maintenance of machinery is more difficult since it becomes more precise, automatic and efficient. To tackle this problem, a feature selection and probabilistic neural network-based method is presented in this paper. Firstly, feature parameters are extracted and selected after obtaining the raw signal. Then, the selected feature parameters are preprocessed according to the faulted characteristic frequencies of components. Finally, the diagnosis results are outputted with the decision method of PNN. Experimental data is utilized to demonstrate the effectiveness of this methodology.

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Classification of disturbances in hybrid DG system using modular PNN and SVM

Cited by 65 publications

References 33 publications

Shifting of research trends in islanding detection method - a comprehensive survey

Shifting of research trends in islanding detection method - a comprehensive survey

A General Purpose Adaptive Fault Detection and Diagnosis Scheme for Information Systems with Superheterodyne Receivers

Fault Diagnosis for Machinery based on Feature Selection and Probabilistic Neural Network

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