Evaluation Of Distribution Network Reliability Considering Network Configuration

Distribution network reconfiguration was a practical method which could effectively improve the reliability of power supply and not require large investments.It had a direct impact on the results of reliability evaluation and traditional reliability evaluation did not fully take into account this.During the operation of the grid,many factors made the reliability evaluation data had uncertainties.If still using the traditional reliability evaluation method without processing these data, it would have difference between the computation results and the actual situation.Aiming at above problems,in the paper some investigations were made as follows:on the basis of full studying and understanding of the meaning,principle,model and the method of distribution network reliability evaluation,the paper provided a reliability evaluation method which had considering distribution network reconfiguration.The power flow calculation was embedded into Bayesian network which improved and made up the shortage of traditional reliability evaluation methods.In order to account for the uncertainties in distribution network reliability evaluation,a Blind number model which applicable to the distribution reliability evaluation was presented.The Blind number theory was used to tackle the uncertainty of reliable parameters:using the improved Breakpoint method figuring out reliability parameters’ Blind number interval,and using judgment matrix method evaluating the degree of reliability.A method based on Blind number combined with Bayesian Network for distribution network reliability evaluation was proposed.And in the case of simplifying the calculation of blind number,this method obtained range and the corresponding degree of reliability of load and system reliability index.Using mean value to dealing with the blind reliability evaluation results,then the overall expected value of reliability index. which could reflect the impact of uncertainties was obtained.The impact of the components on the network were quantitatively analyzed and the weak links of the system were identified based on the bi-direction reasoning model of the Bayesian Network.