Differential evolution based fault tolerant parallel optimization of reactive power flow

Fault tolerance plays a key role in the success of distributed computing systems. Under widely distributed computation circumstance, the process of evolutionary algorithm based optimization of power systems involves substantial evaluation. Current fault tolerance schemes that do not consider the characteristic of specific applications do not fit the evolutionary algorithms based applications. In evolutionary algorithm, the optimal resolve is searched according to certain probability. Hence failure of separate individuals won't be fatal to the operation of the whole system. A simple and effective approach using differential evolution (DE) is proposed to realize the fault tolerance in parallel optimization of reactive power flow. In the proposed method the filial generation not being able to return is substituted by the corresponding parent generation. Simulation in the IEEE 118 system shows that at the cost of performance deterioration, the system can handle individual failure and realize fault tolerance conveniently and effectively. However, based on the approach proposed, we can use evolutionary algorithm with larger population size to gain better performance even with a very high fault probability.