scholarly journals Multiple-deme parallel genetic algorithm based on modular neural network for effective load shedding

2021 ◽  
Author(s):  
Ali Gholami-Rahimabadi ◽  
Hadi Razmi ◽  
Hasan Doagou-Mojarrad
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Load Shedding ◽  
Parallel Genetic Algorithm ◽  
Modular Neural Network

scholarly journals Multiple-deme parallel genetic algorithm based on modular neural network for effective load shedding

2021 ◽  
Author(s):  
Ali Gholami-Rahimabadi ◽  
Hadi Razmi ◽  
Hasan Doagou-Mojarrad
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Voltage Stability ◽  
Stability Margin ◽  
Test System ◽  
Load Shedding ◽  
Parallel Genetic Algorithm ◽  
Detailed Model ◽  
Modular Neural Network ◽  
Voltage Stability Margin

Abstract One of the most effective corrective control strategies to prevent voltage collapse and instability is load shedding. In this paper, a multiple-deme parallel genetic algorithm (MDPGA) is used for a suitable design of load shedding. The load shedding algorithm is implemented when the voltage stability margin index of the power system is lower than a predefined value. In order to increase the computational speed, the voltage stability margin index is estimated by a modular neural network method in a fraction of a second. In addition, in order to use the exact values of the voltage stability margin index for neural network training, a simultaneous equilibrium tracing technique has been employed considering the detailed model of the components of the generating units such as the governor and the excitation system. In the proposed algorithm, the entire population is partitioned into several isolated subpopulations (demes) in which demes distributed in different processors and individuals may migrate occasionally from one subpopulation to another. The proposed technique has been tested on New England-39 bus test system and the obtained results indicate the efficiency of the proposed method.

scholarly journals ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION

SINERGI ◽  
2020 ◽  
Vol 24 (1) ◽  
pp. 29
Author(s):  
Widi Aribowo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Power System ◽  
Recurrent Neural Networks ◽  
Load Shedding ◽  
Feed Forward Neural Network ◽  
Linear Problems ◽  
Elman Recurrent Neural Network ◽  
And Control

Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility.  In recent years, Neural networks have been very victorious in several signal processing and control applications.  Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods. 

scholarly journals A hybrid artificial neural network - genetic algorithm for load shedding

2020 ◽  
Vol 10 (3) ◽  
pp. 2250 ◽  
Author(s):  
Le Trong Nghia ◽  
Quyen Huy Anh ◽  
Phung Trieu Tan ◽  
N Thai An
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Load Shedding ◽  
Software Systems ◽  
Training Time ◽  
Power Load ◽  
Artificial Neural ◽  
Input Variables

This paper proposes the method of applying Artificial Neural Network (ANN) with Back Propagation (BP) algorithm in combination or hybrid with Genetic Algorithm (GA) to propose load shedding strategies in the power system. The Genetic Algorithm is used to support the training of Back Propagation Neural Networks (BPNN) to improve regression ability, minimize errors and reduce the training time. Besides, the Relief algorithm is used to reduce the number of input variables of the neural network. The minimum load shedding with consideration of the primary and secondary control is calculated to restore the frequency of the electrical system. The distribution of power load shedding at each load bus of the system based on the phase electrical distance between the outage generator and the load buses. The simulation results have been verified through using MATLAB and PowerWorld software systems. The results show that the Hybrid Gen-Bayesian algorithm (GA-Trainbr) has a remarkable superiority in accuracy as well as training time. The effectiveness of the proposed method is tested on the IEEE 37 bus 9 generators standard system diagram showing the effectiveness of the proposed method.

Evaluation of Chess Position by Modular Neural Network Generated by Genetic Algorithm

2004 ◽  
pp. 1-10 ◽  
Author(s):  
Mathieu Autonès ◽  
Ariel Beck ◽  
Philippe Camacho ◽  
Nicolas Lassabe ◽  
Hervé Luga ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Modular Neural Network
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