Dec 11, · Manual on pumps used as turbines by Jean-Marc Chapallaz, , Vieweg edition, in EnglishPages: Open Library is an initiative of the Internet Archive, a (c)(3) non-profit, building a digital library of Internet sites and other cultural artifacts in digital lai.filesetup.pw projects include the Wayback Machine, lai.filesetup.pw and lai.filesetup.pw 1 PERFORMANCE ANALYSIS OF PUMP AS TURBINE USING ARITIFICIAL NEURAL NETWORK TECHNIQUES Rahul Pandey, lai.filesetup.pw, Student, IITRoorkee Associate Professor,A.H.E.C.,IITRoorkee Abstract— Small hydropower generation system is one of the I. INTRODUCTION economical and environmental friendly renewable energy Use of centrifugal pump in turbine mode is systems. A centrifugal pump can be used as a turbine to gen- erate electrical power in the dam across a river by using water potential or to drive a machine by making use of the residual liquid pressurized. PUMPS USED AS TURBINES Power Recovery, Energy Efficiency, CFD Analysis by Jasmina B. BOGDANOVI]-JOVANOVI]a*, Dragica R. MILENKOVI]a, Dragan M. SVRKOTAb, Boidar BOGDANOVI] a, and ivan T. SPASI] a a Faculty of Mechanical Engineering, University of Nis, Nis, Serbia, bJaroslav Cerni Institute for the Development of Water Resources, Belgrade, Serbia Original scientific paper.
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There is large amount of hydro potential available in the hilly areas in small streams of micro hydro range. Development beneficial where the required power is small and low initial of micro hydropower plants has a direct effect on sustainable cost of plant is more important then the high efficiency.
Micro hydropower plants have Efficiency of pump in reverse mode may decrease because, the problem of non availability of standard hydro turbines. Losses increased not be economical by standard turbine manufacturers as micro in reverse running of pump because it is designed as a fluid hydro sites are not similar in nature. It is therefore, centrifugal mover. Use of PAT Pump as Turbine for micro hydro range pump can be used in reverse mode as turbine for these plants, is economical and its installation, maintenance and repair are which are readily available in the market in large range of head easy then conventional turbine.
Manual on pumps used as turbines
Pumps are readily available in and flow. Before using pump in turbine mode, it is necessary to find out the performance of pump in turbine mode. Performance the local market but turbine is required to order for of pump as turbine can be found by actual testing of pump in manufacture for micro hydro range.
Centrifugal pump is turbine mode. However testing of pump as turbine will also not similar as Francis turbine in construction except flow control be economical feasible solution. Large amount of work has been done in U.
In the present study, testing of centrifugal pump and other countries to determine the pump performance in NWE in the turbine mode has been conducted for the reverse running in turbine mode.
Stapnoff has performance evaluation. Using the experimental data an ANN developed a method of prediction turbine performance form Artificial Neural Network based model has been developed to the pump performance. Ken Grover has provided plots calculate the power output and efficiency of the pump as turbine. A comparison between the then 70 metric.
Fernandez, J. The developed methodology rotating speeds, comparing the respective head and flows and may be helpful for evaluating the performance of pump in observed the influence of rotational speed on efficiency as turbine mode at different head and flow, and thereby, ANN well as obtained the characteristics at constant head and model may be considered a good tool for selection of pump in runaway speed.
Network Model. This model is used to determine the efficiency of PAT at different head and flow ranging m and For this purpose testing data of pump type NW10E have been taken. Manuscript submitted on July5, Rahul Pandey, student M. Knowledge about the learning task is , E-mail: rajsafah iitr. ANN is specified by architecture, a set of neurons and links connecting neurons. A learning algorithm is used for IV.
First, The aim is to obtain a NN that generalizes well, that behaves the synapses of the neuron are modeled as weights. The correctly on new instances of the learning task. Negative weight values that has a natural propensity to store experimental knowledge reflect inhibitory connections, while positive values designate and make it available for use.
The name neural network is excitatory connections .
The next two components model derived from the fact that it resembles human brain. An adder sums up all the inputs modified by their respective weights. This activity A is referred to as linear combination. An acceptable range of output is usually between 0 and 1, or - y y D D 1 and 1. Mathematically, this process is described in the fig 3. Fig : Neural Computation Fig : Mathematical model An input is presented to the neural network and a corresponding desired or target response set at the output B.
This error information is fed network. Training of the created network is to be done for the back to the system and adjusts the system parameters in a given input and target data. Once the network is perfectly systematic fashion the learning rule. The process is repeated trained the response of network can be simulated to the new until the performance is acceptable.
It is clear from this input to get the required output. These neurons were i Training of model presented as models of biological neurons and as conceptual In the training the network has trained for the given components for circuits that could perform computational set of data by defining weights and computing the gradient.
The basic model of the neuron is founded upon the Parameters for training have decided on the basis of the functionality of a biological neuron.
Parameters related to ANN training signaling units of the nervous system" and "each neuron is a algorithm are given in table 1. To generate the ANN model input data shows the Input layers, hidden layers and output layers of the set is given in table 2.
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In this study two models are generated for efficiency Table : Data obtained from testing of pump NW10E in and power output of PAT because efficiency of PAT is turbine mode dependent on the power output. These function of both Input data set to generate ANN network models are shown in fig 6.
Head, Flow and power input is taken as input data and power out put and efficiency of PAT taken as target data for the model.
Manual on Pumps used as Turbines (Volume 11)
This data has imported in Fig : Network shows input layer, hidden layer and output the NNtool for training and parameters of network as shown layer in Fig 4. Target data is considered as the output of PAT. Generated network should be perfectly trend. For the training of this network epochs are used.
Fig 7 shows the testing of network and trend of performance curve shows that error reduced sharply till the 0.
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Fig : Importing the input and target data for training and parameters of network After importing the input and target data in the form of matrix form a network creation is started as shown in fig 5.
Hence the generated network is perfectly trend.
The maximum error in the network is obtained 0. Output is compared with the actual data taken from the performance testing pump NW10E in turbine mode error of found during testing is also listed below.
The developed methodology may be helpful for evaluating the performance of pump in turbine mode at different head and flow. January Jaico Publishing House, Private Ltd.
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