Research On The Parameter Estimation Method Of A Single Line Based On Multi-period Measurement Information

With the development of the power system, the structure of the network is getting more and more complex. In order to ensure the power grid operating safely and stably, the demand of monitoring, analyzing, determining, controlling is more insistent, which proposes the higher request to precision of state estimate, power flow. The accuracy of the parameter in transmission network is the base of the power system analyzing and computing software. But the parameter has certain errors due to various reasons. Using simulated and actual PMU measurement data, the parameter estimation model and algorithm of independent line is deeply researched in this paper. Major contents are as follows:①Resistance, reactance and susceptance to earth in high voltage lines which have a great difference in value are estimated respectively as independent variables in the traditional parameter estimation methods, which results in the inconsistency of resistance, reactance and susceptance to earth estimation error, and the large error of small impedance. A new line parameter estimation model based on the parameter per unit length and length is proposed concerning above problems. In this model, the parameters per unit length and length of line are given constant, state variables are multi-period voltage amplitude and phase angle of line's two ends. Because the influence of line parameter on power flow state is highlighted by introducing the length as common variable of resistance, reactance and susceptance to earth, the consistency of line parameter estimation error can be guaranteed, and large estimation error of small impedance which appears in traditional model can be voided in the new model. Besides, the chaos quantum immune algorithm to solve the proposed parameter estimation is presented, which has the advantages of global optimality and stability. The validity of the proposed model and algorithm is verified by the simulation test.②The parameter estimate would be effected by the measure rand errors, which could be reduced by calculate the average value of several estimate results. The present methods don't propose the appropriate sample number, the small sample number will make the precision low, while the big sample number will make the data large. The Monte Carlo simulation method of parameter average estimate is proposed by introducing the Monte Carlo to the parameter estimate. The parameters'approximate values are ensured by the average value of several estimate, and the sample number is ensured by the variance coefficient. The high precision and reasonable sample number are verified by the simulation. The method in paper can clear the influence of rand measure errors, and has the advantages of high precision in parameter estimate.