Node Voltage Estimation of Distribution System Using Artificial Neural Network Considering Weather Data

2021 ◽  
Author(s):  
Kesh Pun ◽  
Saurav MS Basnet ◽  
Ward Jewell
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distribution System ◽  
Weather Data ◽  
Estimation Of Distribution ◽  
Artificial Neural ◽  
Node Voltage

scholarly journals A new protection scheme for PQ events prediction and classification in distribution system: A SSO-ANN strategy

2019 ◽  
Vol 52 (5-6) ◽  
pp. 449-461 ◽  
Author(s):  
K Karthikumar ◽  
V Senthil Kumar ◽  
M Karuppiah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Quality ◽  
Distribution System ◽  
Swarm Optimization ◽  
Protection Scheme ◽  
Event Prediction ◽  
Power Quality Disturbances ◽  
Artificial Neural

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.

Weather Forecasting Using Artificial Neural Network and Bayesian Network

2014 ◽  
Vol 18 (5) ◽  
pp. 812-817 ◽  
Author(s):  
Klent Gomez Abistado ◽  
◽  
Catherine N. Arellano ◽  
Elmer A. Maravillas ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bayesian Network ◽  
Weather Forecasting ◽  
Weather Conditions ◽  
Dew Point ◽  
Weather Data ◽  
Maximum Temperature ◽  
Prevailing Wind Direction ◽  
Artificial Neural

This paper presents a scheme of weather forecasting using artificial neural network (ANN) and Bayesian network. The study focuses on the data representing central Cebu weather conditions. The parameters used in this study are as follows: mean dew point, minimum temperature, maximum temperature, mean temperature, mean relative humidity, rainfall, average wind speed, prevailing wind direction, and mean cloudiness. The weather data were collected from the PAG-ASA Mactan-Cebu Station located at latitude: 10°19´, longitude: 123°59´ starting from January 2011 to December 2011 and the values available represent daily averages. These data were used for training the multi-layered backpropagation ANN in predicting the weather conditions of the succeeding days. Some outputs from the ANN, such as the humidity, temperature, and amount of rainfall, are fed to the Bayesian network for statistical analysis to forecast the probability of rain. Experiments show that the system achieved 93%–100% accuracy in forecasting weather conditions.

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

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.

scholarly journals Improving prediction of aphid flights by temporal analysis of input data for an artificial neural network

2002 ◽  
Vol 55 ◽  
pp. 312-316 ◽  
Author(s):  
S.P. Worner ◽  
G.O. Lankin ◽  
S. Samarasinghe ◽  
D.A.J. Teulon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Model Development ◽  
Temporal Analysis ◽  
Weather Data ◽  
Flight Period ◽  
Noisy Information ◽  
Artificial Neural ◽  
Trap Catches

Weather data in its raw form frequently contains irrelevant and noisy information Often the hardest task in model development regardless of the technique used is translating independent variables from their raw form into data relevant to a particular model A sequential or cascading temporal correlation analysis was used to identify weather sequences that were strongly correlated with aphid trap catches recorded at Lincoln Canterbury New Zealand over 19822000 Trap catches in the previous year and 13 weather sequences associated with eight climate variables were identified as significant predictors of aphid trap catch during the autumn flight period The variables were used to train artificial neural network (ANN) models to predict the size of autumn aphid migrations into cereal crops in Canterbury Such models would assist cereal growers to make better informed and more timely pest management decisions ANN predictive performance was compared with multiple regression predictions using jackknifed data The ANN gave superior prediction compared with multiple regression over 13 jackknifed years

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