### Abstract

This paper proposes a new method to identify the real power transfer between generators and load using modified nodal equations. Based on solved load flow results, the method partitions the Y-bus matrix to decompose the current of the load buses as a function of the generators' current and load voltages. Then it uses the modified admittance matrix to decompose the load voltage dependent term into components of generator dependent terms. Finally using these two decompositions of current and voltage terms, the real power transfer between loads and generators are obtained. Next part of this paper focuses on creating an appropriate Artificial Neural Network (ANN) to solve the same problem in a simpler and faster manner. For this purpose, supervised learning paradigm and feedforward architecture have been chosen for the proposed ANN power transfer allocation technique. Almost all system variables obtained from load flow solutions are utilised as inputs to the neural network. Moreover, tan-sigmoid activation functions are incorporated in the hidden layer to realise the non-linear nature of the power transfer allocation. The modified IEEE 30-bus system is utilised as a test system to illustrate the effectiveness of the ANN technique compared to that of the modified nodal equations method.

Original language | English |
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Title of host publication | Proceedings of the 4th IASTED Asian Conference on Power and Energy Systems, AsiaPES 2008 |

Pages | 135-142 |

Number of pages | 8 |

Publication status | Published - 2008 |

Externally published | Yes |

Event | 4th IASTED Asian Conference on Power and Energy Systems, AsiaPES 2008 - Langkawi Duration: 2 Apr 2008 → 4 Apr 2008 |

### Other

Other | 4th IASTED Asian Conference on Power and Energy Systems, AsiaPES 2008 |
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City | Langkawi |

Period | 2/4/08 → 4/4/08 |

### Fingerprint

### Keywords

- Artificial neural network
- Load flow
- Modified nodal equations method and real power

### ASJC Scopus subject areas

- Energy Engineering and Power Technology

### Cite this

*Proceedings of the 4th IASTED Asian Conference on Power and Energy Systems, AsiaPES 2008*(pp. 135-142)

**Real power transfer allocation method with the application of artificial neural network.** / Mustafa, M. W.; Khalid, S. N.; Shareef, H.; Khairuddin, A.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the 4th IASTED Asian Conference on Power and Energy Systems, AsiaPES 2008.*pp. 135-142, 4th IASTED Asian Conference on Power and Energy Systems, AsiaPES 2008, Langkawi, 2/4/08.

}

TY - GEN

T1 - Real power transfer allocation method with the application of artificial neural network

AU - Mustafa, M. W.

AU - Khalid, S. N.

AU - Shareef, H.

AU - Khairuddin, A.

PY - 2008

Y1 - 2008

N2 - This paper proposes a new method to identify the real power transfer between generators and load using modified nodal equations. Based on solved load flow results, the method partitions the Y-bus matrix to decompose the current of the load buses as a function of the generators' current and load voltages. Then it uses the modified admittance matrix to decompose the load voltage dependent term into components of generator dependent terms. Finally using these two decompositions of current and voltage terms, the real power transfer between loads and generators are obtained. Next part of this paper focuses on creating an appropriate Artificial Neural Network (ANN) to solve the same problem in a simpler and faster manner. For this purpose, supervised learning paradigm and feedforward architecture have been chosen for the proposed ANN power transfer allocation technique. Almost all system variables obtained from load flow solutions are utilised as inputs to the neural network. Moreover, tan-sigmoid activation functions are incorporated in the hidden layer to realise the non-linear nature of the power transfer allocation. The modified IEEE 30-bus system is utilised as a test system to illustrate the effectiveness of the ANN technique compared to that of the modified nodal equations method.

AB - This paper proposes a new method to identify the real power transfer between generators and load using modified nodal equations. Based on solved load flow results, the method partitions the Y-bus matrix to decompose the current of the load buses as a function of the generators' current and load voltages. Then it uses the modified admittance matrix to decompose the load voltage dependent term into components of generator dependent terms. Finally using these two decompositions of current and voltage terms, the real power transfer between loads and generators are obtained. Next part of this paper focuses on creating an appropriate Artificial Neural Network (ANN) to solve the same problem in a simpler and faster manner. For this purpose, supervised learning paradigm and feedforward architecture have been chosen for the proposed ANN power transfer allocation technique. Almost all system variables obtained from load flow solutions are utilised as inputs to the neural network. Moreover, tan-sigmoid activation functions are incorporated in the hidden layer to realise the non-linear nature of the power transfer allocation. The modified IEEE 30-bus system is utilised as a test system to illustrate the effectiveness of the ANN technique compared to that of the modified nodal equations method.

KW - Artificial neural network

KW - Load flow

KW - Modified nodal equations method and real power

UR - http://www.scopus.com/inward/record.url?scp=62449247040&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=62449247040&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:62449247040

SN - 9780889867321

SP - 135

EP - 142

BT - Proceedings of the 4th IASTED Asian Conference on Power and Energy Systems, AsiaPES 2008

ER -