Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
| Hi-index | 0.00 |
This paper presents a new optimal adaptive dynamic load-shedding scheme for a large steelmaking industry with cogeneration units. The proposed method is based on the initial rate of a frequency change (df0/dt) and is coordinated with tie-lines frequency protection relays. An adaptive network-based fuzzy inference system (ANFIS) with a new training algorithm is developed in order to increase the speed of the load-shedding scheme and to have an optimum response at different loading conditions. To overcome the ANFIS training difficulties, a new hybrid approach composed of particle swarm optimization and gradient decent algorithms is used. The training data set for the ANFIS is prepared by a transient stability analysis to determine the minimum load shedding for various operation scenarios without causing the tripping problem of cogeneration units. By using an accurate dynamic modeling of the Mobarakeh steelmaking company in Esfahan Regional Electrical Company network, the performance of the proposed method is compared with the traditional ANFIS learning algorithms, adaptive artificial neural network load-shedding scheme and transient stability analysis. Simulation results show the effectiveness of the proposed method.