scholarly journals Virtual Visualization of Generator Operation Condition through Generator Capability Curve

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 185
Author(s):  
Chun-Yao Lee ◽  
Maickel Tuegeh
Keyword(s):  
Neural Network ◽  
Optimal Scheduling ◽  
Operation Condition ◽  
Operating Condition ◽  
Generator Operation ◽  
Virtual Visualization

Besides achieving an optimal scheduling generator, the operation safety of the generator itself needs to be focused on. The development of the virtual visualization of a generator capability curve simulation to visualize the operation condition of a generator is proposed in this paper. In this paper, a neural network is applied to redraw the original generator’s capability curve. The virtual visualization of a generator’s capability curve can simulate the generator’s operating condition considering the limitation of the constraints on the various elements of the generator. Furthermore, it is able to show the various possibilities that occur in the operation of a generator in reality, and it can even simulate special conditions which are based on various conditions.

scholarly journals ANALYSIS OF RADIATION SAFETY IN THE NPP SITE IN NORMAL OPERATION CONDITION

2017 ◽  
Vol 18 (2) ◽  
pp. 73 ◽  
Author(s):  
Pande Made Udiyani ◽  
Ihda Husnayani
Keyword(s):  
Radiation Safety ◽  
Population Distribution ◽  
Meteorological Data ◽  
Meteorological Condition ◽  
Normal Operation ◽  
Weather Data ◽  
Normal Operating Condition ◽  
Operation Condition ◽  
Operating Condition ◽  
Normal Operating

ANALYSIS OF RADIATION SAFETY IN THE NPP SITE IN NORMAL OPERATION CONDITION. Construction of nuclear power plant (NPP) requires an evaluation of radiation safety which proves that operation of the NPP under normal operating condition and postulated abnormal conditions is safe. Analysis of radiation safety at the NPP site under normal operating condition is required to complete the documents of site analysis and safety analysis. This study is aimed to obtain radiation dose in the environment of the NPPs at Sebagin site in province of Bangka Belitung. The doses were calculated using PC-Cream code. It is assumed that there are three 1000-MWe PWR operating in Sebagin site. Input data required for PC-Cream simulation are routine sourceterm of three 1000MWe-PWRs, meteorological data, and agricultural and animal production, and population distribution. The meteorological data consist of stability frequency of weather for 16 sectors (wind direction) taken from local weather data for 1 year. The data of agricultural and livestock production and population distribution are also taken for 1 year for 16 sectors and 20 radial directions. The results show that the maximum dose from all types of radionuclides and all pathways accepted by adult public around Sebagin site is approximately 0.053 mSv/year to the north direction in the radius of 1 km. This dose is far below the dose limit value of 1 mSv/year or dose constraint of 0.3 mSv/year as public acceptance criteria (BAPETEN). It can be concluded that radiation doses are influenced by activity and type of nuclides sourceterm, reactor layout, meteorological condition, and environmental condition.

The Fuzzy Comprehensive Evaluation of Hydropower Plant Production and Operating Conditions Based on the BP Neural Network Improved

2011 ◽  
Vol 71-78 ◽  
pp. 4170-4173 ◽  
Author(s):  
Shu Liang Liu ◽  
Yun Xia Song ◽  
Peng Liang Hao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Indicator System ◽  
Operating Conditions ◽  
Plant Production ◽  
Quality Analysis ◽  
Production Operation ◽  
Operation Condition ◽  
Hydropower Plants ◽  
Hydropower Production

With the rapid economic growth, the resources and environment condition gradually tightens. We need to accelerate the development of hydropower resources. The electric power produced by the hydropower plants is a kind of green power energy. The quality analysis of hydropower production operation condition will become the focus of our researching. In paper we construct a reasonable set of evaluating indicator system of hydropower production operation and have used the BP neural network and fuzzy quality synthetic to analyze the production and operation condition of several hydropower plants. Help hydropower to summarize experience.

scholarly journals Multi-Factor Operating Condition Recognition Using 1D Convolutional Long Short-Term Network

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5488 ◽  
Author(s):  
Zhinong Jiang ◽  
Yuehua Lai ◽  
Jinjie Zhang ◽  
Haipeng Zhao ◽  
Zhiwei Mao
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Mean Value ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Short Term ◽  
Vibration Signals ◽  
Operating Condition ◽  
Model Training ◽  
Condition Recognition

For a diesel engine, operating conditions have extreme importance in fault detection and diagnosis. Limited to various special circumstances, the multi-factor operating conditions of a diesel engine are difficult to measure, and the demand of automatic condition recognition based on vibration signals is urgent. In this paper, multi-factor operating condition recognition using a one-dimensional (1D) convolutional long short-term network (1D-CLSTM) is proposed. Firstly, a deep neural network framework is proposed based on a 1D convolutional neural network (CNN) and long short-Term network (LSTM). According to the characteristics of vibration signals of a diesel engine, batch normalization is introduced to regulate the input of each convolutional layer by fixing the mean value and variance. Subsequently, adaptive dropout is proposed to improve the model sparsity and prevent overfitting in model training. Moreover, the vibration signals measured under 12 operating conditions were used to verify the performance of the trained 1D-CLSTM classifier. Lastly, the vibration signals measured from another kind of diesel engine were applied to verify the generalizability of the proposed approach. Experimental results show that the proposed method is an effective approach for multi-factor operating condition recognition. In addition, the adaptive dropout can achieve better training performance than the constant dropout ratio. Compared with some state-of-the-art methods, the trained 1D-CLSTM classifier can predict new data with higher generalization accuracy.

Numerical Simulation and Analysis of Energy Saving Operation of Chillers in an Air Conditioning System

2015 ◽  
Vol 744-746 ◽  
pp. 2293-2296
Author(s):  
Xiao Yu Sun ◽  
Rui Li
Keyword(s):  
Energy Consumption ◽  
Air Conditioning ◽  
Water System ◽  
Operating Conditions ◽  
Load Control ◽  
Operation Condition ◽  
Total Energy Consumption ◽  
Air Conditioning System ◽  
Operating Condition ◽  
Starting Point

The chiller’s operating energy consumption accounts for large proportion in energy consumption of air conditioning system. There are kinds of strategies can be used in the multiple chillers system. When running according to the load control of the chiller, different starting point and operating condition will affect the total energy consumption. Different operating conditions are put forward for a building chilled water system. According to simulate and analysis different operating condition, the optimum operation condition is concluded.

scholarly journals Radial Basis Function Neural Network Model for Optimizing Thermal Annealing Process Operating Condition

Nano Hybrids ◽  
2013 ◽  
Vol 4 ◽  
pp. 21-31 ◽  
Author(s):  
Mehdi Qasim ◽  
Jinan B. Al-Dabbagh ◽  
Ahmed N. Abdalla ◽  
M.M. Yusoff ◽  
Gurumurthy Hegde
Keyword(s):  
Neural Network ◽  
Porous Silicon ◽  
Radial Basis Function ◽  
Thermal Annealing ◽  
Basis Function ◽  
Electrochemical Etching ◽  
Annealing Process ◽  
Operating Condition ◽  
Radial Basis ◽  
Thermal Annealing Process

Optimum thermal annealing process operating condition for nanostructured porous silicon (nPSi) by using radial basis function neural network (RBFNN) was proposed. The nanostructured porous silicon (nPSi) layer samples prepared by electrochemical etching process (EC) of p-type silicon wafers under different operatingconditions, such as varyingetchingtime (Et), annealing temperature (AT), and annealing time (At). The electrical properties of nPSi show an enhancement with thermal treatment.Simulation result shows that the proposed model can be used in the experimental results in this operating condition with acceptable small error. This model can be used in nanotechnology based photonic devices and gas sensors.

Study on Steady Condition Control of Hybrid Turbocharging System

2010 ◽  
Vol 139-141 ◽  
pp. 1941-1944
Author(s):  
Fu Zhou Zhao ◽  
Rong Liang ◽  
Xiao Ping Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Speed ◽  
Linear Interpolation ◽  
Control Model ◽  
Steady Condition ◽  
Operation Condition ◽  
Engine Operation ◽  
Mapping Algorithm ◽  
Turbocharging System

This paper analyzes the principle of hybrid turbocharging system in a vehicle diesel engine, and proposes motor control model about hybrid turbocharging system in steady engine operation condition according to energy imbalance of the exhaust gas. The high-speed motor can work as a motor or a generator in this control model of different engine condition. Then mapping algorithms about n-dimensional linear interpolation and BP neural network are presented to solve steady condition control problem of the hybrid turbocharging system. Each algorithm is applied to map same sample data, the simulation results reveal that BP neural network mapping algorithm is more suitable for the mapping control of hybrid turbocharging system because BP neural network has better generalization ability and faster processing speed.

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