Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
A neo-fuzzy approach for bottom parameters estimation in oil wells
WSEAS Transactions on Systems and Control
A modified gradient-based neuro-fuzzy learning algorithm and its convergence
Information Sciences: an International Journal
Particle swarm optimization for solving combined economic and emission dispatch problems
EE'10 Proceedings of the 5th IASME/WSEAS international conference on Energy & environment
Solving economic emission load dispatch problems using hybrid differential evolution
Applied Soft Computing
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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At the central energy management center in a power system, the real time controls continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is minimized while all the operating constraints are satisfied. However, due to the strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained economic dispatch formulation is to estimate the optimal generation schedule of generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. Conventional optimization techniques become very time consuming and computationally extensive for such complex optimization tasks. These methods are hence not suitable for on-line use. Neural networks and fuzzy systems can be trained to generate accurate relations among variables in complex non-linear dynamical environment, as both are model-free estimators. The existing synergy between these two fields has been exploited in this paper for solving the economic and environmental dispatch problem on-line. A multi-output modified neo-fuzzy neuron (NFN), capable of real time training is proposed for economic and environmental power generation allocation. This model is found to achieve accurate results and the training is observed to be faster than other popular neural networks. The proposed method has been tested on medium-sized sample power systems with three and six generating units and found to be suitable for on-line combined environmental economic dispatch (CEED).