Power quality disturbance classification employing S-transform and three-module artificial neural network

Increased utilization of nonlinear loads and fault event on the power system have resulted in a decline in the quality of power provided to the customers. It is fundamental to recognize and distinguish the power quality disturbances in the distribution system. To recognize and distinguish the power quality disturbances, the development of high protection schemes is required. This paper presents an optimal protection scheme for power quality event prediction and classification in the distribution system. The proposed protection scheme combines the performance of both the salp swarm optimization and artificial neural network. Here, artificial neural network is utilized in two phases with the objective function of prediction and classification of the power quality events. The first phase is utilized for recognizing the healthy or unhealthy condition of the system under various situations. Artificial neural network is utilized to perceive the system signal’s healthy or unhealthy condition under different circumstances. In the second phase, artificial neural network performs the classification of the unhealthy signals to recognize the right power quality event for assurance. In this phase, the artificial neural network learning method is enhanced by utilizing salp swarm optimization based on the minimum error objective function. The proposed method performs an assessment procedure to secure the system and classify the optimal power quality event which occurs in the distribution system. At that point, the proposed work is executed in the MATLAB/Simulink platform and the performance of the proposed system is compared with different existing techniques like Multiple Signal Classification-Artificial Neural Network (MUSIC-ANN), and Genetic Algorithm - Artificial Neural Network (GA-ANN). The comparison results demonstrate the superiority of the SSO-ANN technique and confirm its potential to power quality event prediction and classification.

Download Full-text

Power Quality Enhancement using Dynamic Voltage Restorer (DVR) by Artificial Neural Network and HysteresisVoltage Control Techniques

2019 Global Conference for Advancement in Technology (GCAT) ◽

10.1109/gcat47503.2019.8978333 ◽

2019 ◽

Author(s):

Arpitha M J ◽

Sowmyashree N ◽

M S Shashikala

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Quality ◽

Quality Enhancement ◽

Dynamic Voltage Restorer ◽

Control Techniques ◽

Voltage Restorer ◽

Dynamic Voltage ◽

Artificial Neural

Download Full-text

Harmonic Distortion Prediction Model of a Grid-Tie Photovoltaic Inverter Using an Artificial Neural Network

Energies ◽

10.3390/en12050790 ◽

2019 ◽

Vol 12 (5) ◽

pp. 790 ◽

Cited By ~ 1

Author(s):

Matej Žnidarec ◽

Zvonimir Klaić ◽

Damir Šljivac ◽

Boris Dumnić

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Quality ◽

Prediction Models ◽

Harmonic Distortion ◽

Pv System ◽

Current Harmonics ◽

Pv Systems ◽

Input Parameters ◽

Artificial Neural

Expanding the number of photovoltaic (PV) systems integrated into a grid raises many concerns regarding protection, system safety, and power quality. In order to monitor the effects of the current harmonics generated by PV systems, this paper presents long-term current harmonic distortion prediction models. The proposed models use a multilayer perceptron neural network, a type of artificial neural network (ANN), with input parameters that are easy to measure in order to predict current harmonics. The models were trained with one-year worth of measurements of power quality at the point of common coupling of the PV system with the distribution network and the meteorological parameters measured at the test site. A total of six different models were developed, tested, and validated regarding a number of hidden layers and input parameters. The results show that the model with three input parameters and two hidden layers generates the best prediction performance.

Download Full-text