## The concept of Flexible Ac Transmission Systems

### BibTeX

@MISC{Magaji_theconcept,

author = {N. Magaji and M. W. Mustafa},

title = {The concept of Flexible Ac Transmission Systems},

year = {}

}

### OpenURL

### Abstract

Abstract: This study applies a neural-network-based optimal TCSC controller for damping oscillations. Optimal neural network controller is related to model-reference adaptive control, the network controller is developed based on the recursive “pseudo-linear regression”. Problem statement: The optimal NN controller is designed to damp out the low frequency local and inter-area oscillations of the large power system. Approach: Two multilayer-perceptron neural networks are used in the design-the identifier/model network to identify the dynamics of the power system and the controller network to provide optimal damping. By applying this controller to the TCSC devices the damping of inter-area modes of oscillations in a multi-machine power system will be handled properly. Results: The effectiveness of the proposed optimal controller is demonstrated on two power system problems. The first case involves TCSC supplementary damping control, which is used to provide a comprehensive evaluation of the learning control performance. The second case aims at addressing a complex system to provide a very good solution to oscillation damping control problem in the Southern Malaysian Peninsular Power Grid. Conclusion: Finally, several fault and load disturbance simulation results are presented to stress the effectiveness of the proposed TCSC controller in a multi-machine power system and show that the proposed intelligent controls improve the dynamic performance of the TCSC devices and the associated power network.

### Citations

28 |
Neural Network Based System Identification Toolbox
- Norgaard
- 1997
(Show Context)
Citation Context ...f the identifier are updated based on the error between the plant output y and its desired output ym. In this research the network is trained in advance with the nnarx function in the NNSYSID toolbox =-=[19]-=- . The variables defining the network includes input and two delayed inputs signals, output and two delayed output of the system as mention already together with initial weights of the network and the... |

14 |
A new approach for placement of FACTS devices in open power markets
- Singh, David
- 2001
(Show Context)
Citation Context ... major thrust of FACTS technology is the development of power electric based systems that provide dynamic control of the power transfer parameters transmission voltage, line impedance and phase angle =-=[1,2]-=- . Power system oscillations occur due to the lack of damping torque at the generators rotors. The oscillation of the generators rotors cause the oscillation of other power system variables (bus volta... |

7 | A radial basis function neural network controller for UPFC
- Dash, Mishra, et al.
- 2000
(Show Context)
Citation Context ...model of Identifier, model of DHP controller and critic. In addition there are too many functions to store and training is base on offline unlike MLP that is straightforward to implement. Dash et al. =-=[16]-=- Presents single-neuron and multineuron Radial Basis Function Controller (RBFNN) for the UPFC control in single machine-infinite-bus and three-machine power systems and claimed to provide the best tra... |

5 |
Neural networks for adaptive control coordination of PSSs and FACTS devices in multimachine power system
- Nguyen, Gianto
- 2008
(Show Context)
Citation Context ...he variables y(t), u(t) and ζ(t) are the system output, system input and white noise respectively. For the purpose of identification, Eq. 7 can be written in the form of: T y(t) = θ (t) ϕ (t) + ζ (t) =-=(10)-=- Where: [ ] θ (t) = a a a b b b (11) 1 2 3 1 2 3 ⎡−y(t −1) −y(t − 2) −y(t − 3) ⎤ ϕ (t) = ⎢ u(t −1) u(t − 2) u(t − 3) ⎥ ⎣ ⎦ T (12) where, θ(t) and ϕ(t) are the parameter vector and the measurement vari... |

4 |
Indirect adaptive external neuro-control for a series capacitive reactance compensator based on a voltage source PWM converter in damping power oscillations
- Qiao, Harley
(Show Context)
Citation Context ...ussed in [8,9] for such Corresponding Author: N. Magaji, Department of Power System, Faculty of Electrical Engineering, University Technology Malaysia, 81310, Skudai, Malaysia 980J. Computer Sci., 5 =-=(12)-=-: 980-987, 2009 systems which relies on continuous online training of the identifier and controller network. The research on the application of neural networks to the FACTS controllers design so far i... |

3 |
On the identification and control of dynamical systems using neural networks
- Rios-Patron, Braatz
- 1997
(Show Context)
Citation Context ...for non-linear control design. They have been successfully applied to the identification and control of dynamical systems especially in the field of adaptive control by making use of on-line training =-=[6,7]-=- . Direct and indirect adaptive control with MLP and RBF neural networks has been discussed in [8,9] for such Corresponding Author: N. Magaji, Department of Power System, Faculty of Electrical Enginee... |

3 |
An adaptive power system stabilizer using on-line trained neural networks
- Shamsollahi, Malik
- 1997
(Show Context)
Citation Context ...for non-linear control design. They have been successfully applied to the identification and control of dynamical systems especially in the field of adaptive control by making use of on-line training =-=[6,7]-=- . Direct and indirect adaptive control with MLP and RBF neural networks has been discussed in [8,9] for such Corresponding Author: N. Magaji, Department of Power System, Faculty of Electrical Enginee... |

2 |
Comparison of adaptive critic-based and classical wide-area controllers for power systems
- Ray, Venayagamoorthy, et al.
- 2008
(Show Context)
Citation Context ... of dynamical systems especially in the field of adaptive control by making use of on-line training [6,7] . Direct and indirect adaptive control with MLP and RBF neural networks has been discussed in =-=[8,9]-=- for such Corresponding Author: N. Magaji, Department of Power System, Faculty of Electrical Engineering, University Technology Malaysia, 81310, Skudai, Malaysia 980J. Computer Sci., 5 (12): 980-987,... |

2 | Using Artificial Neural Networks for the Modeling of a Distillation Column
- Chetouani
(Show Context)
Citation Context ...ler design: For engineering purposes, the neural network can be thought of as a black box model which accepts inputs, processes them and produces outputs according to some nonlinear transfer function =-=[17]-=- . MATERIALS AND METHODS TCSC model: A typical TCSC module consists of a Fixed series Capacitor (FC) in parallel with a Thyristor Fig. 1: TCSC model 981J. Computer Sci., 5 (12): 980-987, 2009 A(z ) =... |

2 | Modified Recursive Prediction Error Algorithm for Training Layered Neural Network
- Mashor
- 2003
(Show Context)
Citation Context ...timation. They include: Back propagation by gradient descent, Recursive Prediction Error algorithm (RPE), BFGS, CG. In this project a Modified Recursive Prediction Error Algorithm (MRPE) explained by =-=[20]-=- is adapted here. Modified recursive prediction error algorithm: Recursive Prediction Error algorithm (RPE) was originally derived by Ljung and Soderstrom [20,21] and modified by [22] to train MLP net... |

1 | Placement of FACTS controllers using modal controllability indices to damp out power system oscillations - Chaudhuri - 2005 |

1 |
Mohd Wazir bin Mustafa, 2008. Application of SVC device for damping oscillations based on eigenvalue techniques
- Magaji
(Show Context)
Citation Context ... (bus voltage, bus frequency, transmission lines active and reactive powers). Power system oscillations are usually in the range between 0.1 and 2 Hz depending on the number of generators involved in =-=[3,4]-=- . Local oscillations lie in the upper part of that range and consist of the oscillation of a single generator or a group of generators against the rest of the system. In contrast, inter-area oscillat... |

1 |
RBFN based Static Synchronous Series Compensator (SSSC) for Transient Stability improvement
- Chandrakar, Kothari
- 2006
(Show Context)
Citation Context ... of dynamical systems especially in the field of adaptive control by making use of on-line training [6,7] . Direct and indirect adaptive control with MLP and RBF neural networks has been discussed in =-=[8,9]-=- for such Corresponding Author: N. Magaji, Department of Power System, Faculty of Electrical Engineering, University Technology Malaysia, 81310, Skudai, Malaysia 980J. Computer Sci., 5 (12): 980-987,... |

1 |
Stable adaptive control using fuzzy system and neural networks
- Jeffrey, Pooner, et al.
- 1996
(Show Context)
Citation Context ...meters: ⎡dy(t, ˆ θ) ⎤ ψ(t, θ ) = ⎢ dθ ⎥ ⎣ ⎦ P(t) in Eq. 19 is updated recursively according to: ˆ ⎡ P(t) = ⎢P(t −1) − dθ ⎣ T dy(t, θ) P(t −1) ψ(t) ψ (t)P(t −1) γ ⎤ ⎥ ⎦ (18) (19) ( ) 1 V ∑ (t, ) (t, ) =-=(13)-=- N ˆ T ˆ −1 N θ = ε θ Λ ε θˆ 2N t= 1 By updating the estimated parameter vector, (consists of w’s and b’s), recursively using GaussNewton algorithm: Where ε(t) and Λ are the prediction error and m×m s... |

1 |
Direct heuristic dynamic programming for damping oscillations in a large power system
- Lu, Si, et al.
- 2008
(Show Context)
Citation Context ... (TCR) as shown in Fig. 1. The TCR is formed by a reactor in series with a bi-directional thyristor valve that is fired with an angle ranging between 90 and 180° with respect to the capacitor voltage =-=[14]-=- . Consider a line l, having line reactance XL, connected between buses k and m. If the reactance of TCSC placed in the line l is Xc, the percentage of compensation of TCSC (kc) is given by: kc X X C ... |

1 | internal optimal neurocontrol for a series FACTS device in a power transmission line - New |

1 |
Theory and Practice of Recursive Identification. 4th Edn
- Ljung, Soderstrom
- 1983
(Show Context)
Citation Context ...tion Error Algorithm (MRPE) explained by [20] is adapted here. Modified recursive prediction error algorithm: Recursive Prediction Error algorithm (RPE) was originally derived by Ljung and Soderstrom =-=[20,21]-=- and modified by [22] to train MLP networks. RPE algorithm is a Gauss-Newton type algorithm that will generally give better performance than a steepest descent type algorithm such as back propagation ... |