Study on Fault Line Detection Based on Genetic Artificial Neural Network in Compensated Distribution System

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.

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Adaptive DOCR Coordination in Loop Electrical Distribution System With DG Using Artificial Neural Network LMBP

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) ◽

10.1109/isriti.2018.8864433 ◽

2018 ◽

Author(s):

Daeng Rahmatullah ◽

Belly Yan Dewantara ◽

D.P.K. Iradiratu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Distribution System ◽

Electrical Distribution ◽

Artificial Neural

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Optimization and Assessment of Residual Chlorine using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Modeling

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4122.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 258-263

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Distribution System ◽

Water Distribution ◽

Residual Chlorine ◽

Urban Water ◽

Ann Model ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Rsm Model

Water polluted with microorganisms and pathogens is one of the most significant hazards to public health. Potential microorganisms unsafe to human health can be destroyed through effective disinfection. To stop the re-growth of microorganisms, it is also advisable to take care of the residual disinfectant in the water distribution networks. The most frequently used cleanser material is chlorine. When the chlorine dosage is too low, there will be a deficiency of enough residues at the end of the water network system, leading to re-growth of microorganisms. Addition of an excessive amount of chlorine will lead to corrosion of the pipeline network and also the development of disinfection by-products (DBPs) including carcinogens. Thus, to determine the best rate of chlorine dosage, it is essential to model the system to forecast chlorine decay within the network. In this research study, two major modeling and optimization strategies were employed to assess the optimum dosage of chlorine for municipal water disinfection and also to predict residual chlorine at any predetermined node within the water distribution network. Artificial neural network (ANN) modeling techniques were used to forecast chlorine concentrations in different nodes in the urban water distribution system in Muscat, the capital of the Sultanate of Oman. One-year dataset from one of the distribution system was used for conducting network modeling in this study. The input factors to RSM model considered were pH, chlorine dosage and time. Response variables for RSM model were fixed as total organic carbon (TOC), Biological oxygen demand (BOD) and residual chlorine An Artificial neural network (ANN) model for residual chlorine was created with pH, inlet-concentration of chlorine and initial temperature as input parameters and residual chlorine in the piping network as an output parameter. The ANN model created using these data can be employed to forecast the residual chlorine value in the urban water network at any given specific location. The results from this study utilizing the uniqueness of an ANN model to predict residual chlorine and water quality parameters have the potential to detect complex, higher-order behavior between input and output parameters exist in urban water distribution system.

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