scholarly journals Islanding Detection Method of a Photovoltaic Power Generation System Based on a CMAC Neural Network

Energies ◽  
2013 ◽  
Vol 6 (8) ◽  
pp. 4152-4169 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Min-Sen Yang ◽  
Chin-Pao Hung
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Detection Method ◽  
Islanding Detection ◽  
Generation System ◽  
Cmac Neural Network ◽  
Photovoltaic Power ◽  
Power Generation System

An Islanding Detection Method for Photovoltaic Power Generation System Using Fluctuation Characteristic of PCC Harmonic Voltage

2014 ◽  
Vol 998-999 ◽  
pp. 574-577
Author(s):  
Luo Yun
Keyword(s):  
Power Generation ◽  
Detection Method ◽  
Islanding Detection ◽  
Generation System ◽  
Detection Zone ◽  
Photovoltaic Power ◽  
Point Of Common Coupling ◽  
Harmonic Voltage ◽  
Power Generation System ◽  
Common Coupling

In the photovoltaic power generation system, the harmonic voltage of the point of common coupling (PCC) is determined by system disturbance, local load and photovoltaic power generation system itself together. Based on the fluctuations of this harmonic voltage, a new islanding detection method for the PV system is proposed. By extracting the higher harmonic components from the wavelet transformation of point of common coupling (PCC) voltage, and then taking its fluctuations as the islanding identification and detection index, non-detection zone (NDZ) can be eliminated, without a plus disturbance. According to IEEE Std.1547, simulated verification of the proposed method is accomplished in the ideal photovoltaic grid system. When the PCC voltage and frequency are within the normal range, it can not only give a fast and accurate detection to the occurrence of islanding, but also identify the false islanding, such as voltage dips and switching loads, effectively avoiding false detection caused by the false islanding.

scholarly journals Analysis of Different Meta Heuristics Method in Intelligent Fault Detection of Multilevel Inverter with Photovoltaic Power Generation Source

2018 ◽  
Vol 9 (3) ◽  
pp. 1214
Author(s):  
T. G. Manjunath ◽  
Ashok Kusagur
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Fault Detection ◽  
Search Algorithm ◽  
Multilevel Inverter ◽  
Heuristic Methods ◽  
Generation System ◽  
Photovoltaic Power ◽  
The Neural Network ◽  
Power Generation System

<p>Meta Heuristic methods have made a deep impact in the area of optimization in different streams of engineering. The performance of these algorithms is of importance because the hardware implementation of these algorithms is to be carried out for different engineering applications. As an important application in High Voltage DC (HVDC) transmission and Industrial Drives the multilevel inverter fault diagnosis is carried out using the different meta-heuristic methods with Neural Network as the decision making algorithm. The optimization of the weight and the bias values in the neural network diagnosis system is carried out in order to analyze the performance by means of comparing the Mean Square Error (MSE) while the Neural Network is getting trained for different fault conditions in the multilevel inverter. Matlab based implementation is carried out and the results are tabulated and inferred for a Multilevel Inverter fed from the Photovoltaic power generation system. In order to increase the robustness of the fault detection, with renewable energy based power generation system as the source for the Multilevel Inverter, the feature extracted from the multilevel inverter are positive, negative and zero sequence voltage along with the THD of the output voltage. The optimization algorithm used is Particle Swarm Optimization (PSO), Cuckoo Search Algorithm(CSA), Genetic Algorithm(GA) and Tabu Search Algorithm (TSA).</p>

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