This study shows how to calculate the minimum load that needs to be reduced to restore the frequency to the specified threshold. To implement this problem, the actual operation of the electricity system in the event of a generator outage is considered. The main idea of this method is to use the power balance equation between the generation and the load with different frequency levels. In all cases of operating the electrical system before and after the generator outage, the reserve capacity of other generators is considered in each generator outage situation. The reduced load capacity is calculated based on the reciprocal phase angle sensitivity or phase distance. This makes the voltage phase angle and voltage value quality of recovery nodes better. The standard IEEE 9-generator 37-bus test scheme was simulated to show the result of the proposed technique.
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.
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