Abstract
This work presents a neuro-expert system for medium term load forecasting and expansion planning of distribution systems. The planning module uses optimization technique, with heuristic rules to minimize the total loss and total annual cost. The constraints like overloading capacity and thermal ratings of conductors and transformers, voltage regulation, etc are satisfied. Concentric circle relaxation technique is proposed for feeder configuration. The trained network is used to forecast the yearly peak loads for a lead time of ten years. The back propagation algorithm is slightly modified and is used to train the artificial neural network. The modifications are done to reduce the training time and memory requirements. The proposed algorithms were tested on the practical 66/11 kV primary distribution system of Mysore City, Karnataka State, South India. The software was developed using the C++ language.
| Original language | English |
|---|---|
| Pages (from-to) | 309-314 |
| Number of pages | 6 |
| Journal | International Journal of Electrical Power and Energy System |
| Volume | 21 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 1999 |
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All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
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Neuro-expert system for planning and load forecasting of distribution systems. / Chandrashekara, Adiga S.; Ananthapadmanabha, T.; Kulkarni, A. D.
In: International Journal of Electrical Power and Energy System, Vol. 21, No. 5, 1999, p. 309-314.Research output: Contribution to journal › Article
TY - JOUR
T1 - Neuro-expert system for planning and load forecasting of distribution systems
AU - Chandrashekara, Adiga S.
AU - Ananthapadmanabha, T.
AU - Kulkarni, A. D.
PY - 1999
Y1 - 1999
N2 - This work presents a neuro-expert system for medium term load forecasting and expansion planning of distribution systems. The planning module uses optimization technique, with heuristic rules to minimize the total loss and total annual cost. The constraints like overloading capacity and thermal ratings of conductors and transformers, voltage regulation, etc are satisfied. Concentric circle relaxation technique is proposed for feeder configuration. The trained network is used to forecast the yearly peak loads for a lead time of ten years. The back propagation algorithm is slightly modified and is used to train the artificial neural network. The modifications are done to reduce the training time and memory requirements. The proposed algorithms were tested on the practical 66/11 kV primary distribution system of Mysore City, Karnataka State, South India. The software was developed using the C++ language.
AB - This work presents a neuro-expert system for medium term load forecasting and expansion planning of distribution systems. The planning module uses optimization technique, with heuristic rules to minimize the total loss and total annual cost. The constraints like overloading capacity and thermal ratings of conductors and transformers, voltage regulation, etc are satisfied. Concentric circle relaxation technique is proposed for feeder configuration. The trained network is used to forecast the yearly peak loads for a lead time of ten years. The back propagation algorithm is slightly modified and is used to train the artificial neural network. The modifications are done to reduce the training time and memory requirements. The proposed algorithms were tested on the practical 66/11 kV primary distribution system of Mysore City, Karnataka State, South India. The software was developed using the C++ language.
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UR - http://www.scopus.com/inward/citedby.url?scp=0032665001&partnerID=8YFLogxK
U2 - 10.1016/S0142-0615(98)00057-X
DO - 10.1016/S0142-0615(98)00057-X
M3 - Article
VL - 21
SP - 309
EP - 314
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
SN - 0142-0615
IS - 5
ER -