Abstract
This paper presents an improved technique for optimal power generation using ensemble artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor computing rather than traditional serial computation to reduce bias and variance in machine learning. The load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN) with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power generation. The results of MATLAB simulations are analyzed and compared along with computational complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed to LR.
Highlights
Interconnected power systems are foundations on which modern civilization rests
A research objective is to overcome the mentioned shortcomings and to explore economic-dispatch optimally using Lagrange relaxation with an enhanced neural network (NN) by adopting a bootstrap aggregation algorithm for energy deficient scenarios. Such an enhanced neural network is termed an ensemble of artificial neural networks (EANN)
The input to the neural network is power demand which is divided among 20 load busses while the output of the neural network is the power generated on 6 busses, obtained from the Lagrange relaxation method
Summary
Interconnected power systems are foundations on which modern civilization rests. The economy of any developing country, like Pakistan, is based on the provision of cheap and abundant sources of electrical energy. K. Mehmood et al.: Optimal Power Generation in Energy-Deficient Scenarios Using Bagging Ensembles. In [19], a dynamic neural network is employed to solve the combined economic and emission dispatch problem with fast convergence. Chan et al presented an artificial neural network and genetic algorithm to optimize the load distribution for a chiller plant [20]. A research objective is to overcome the mentioned shortcomings and to explore economic-dispatch optimally using Lagrange relaxation with an enhanced neural network (NN) by adopting a bootstrap aggregation algorithm for energy deficient scenarios. Such an enhanced neural network is termed an ensemble of artificial neural networks (EANN).
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