Novel method for power system synthesis load identification

Abstract

Power load parameters are one of the most important parameters for power system model. The load has the characteristics of time‐varying, diversity and non‐linearity. Therefore, obtaining the load parameters accurately is crucial to the safe and stable operation of the power system. The research in this paper is based on the synthesis load model which takes into account the impedance of distribution network. The multi‐fault curves identification method and chaos‐whale algorithm are adopted to improve the accuracy and practicability of load parameter identification. First, the algorithm proposed in this paper applies chaos algorithm to whale algorithm, and uses the randomness and ergodicity of chaos algorithm to make up for the shortcomings of the whale algorithm that the solution accuracy is not high and it is easy to fall into local optimum. Secondly, the identification method of multi‐fault curves is to select several different faults for parameter identification, and the average value of each group of results obtained is regarded as the final result. Then compared with single curve identification, the multi‐fault curves method is more superior. Finally, the accuracy of the results is verified by other faults. Simulation results show that the method is reliable to a certain extent.

Load component decomposition and online modelling based on small disturbance response characteristic matching

“…For a mathematical analysis, we need to establish at least N C equations if we want to solve a unique set of proportions for FIGURE 6 The PDF and CDF of the decomposition error for the active power N C loads. Two power equations were established for each set of measurement data.…”

“…First, a MC algorithm was adopted to randomly generate multiple load proportional combinations satisfying the constraint condition (6), including all the possible proportional combinations. N W was the number of typical load proportional combinations.…”

“…), the load rated unit power p i is 1.0 p.u. 𝜔 i is the proportion of the load category i in the total load after normalization, satisfying the constraint condition Equation (6).…”

“…Based on the global sensitivity analysis, the load model parameters with little influence on the system response are determined [5], and an improved genetic algorithm is applied to identify the other model parameters. Parameter identification is carried out in multiple fault scenarios [6], and chaos algorithm with global search advantage is integrated into whale algorithm to solve the optimal parameters, which improves the accuracy and generalization ability of load model parameters. The faults rarely occur in the actual system and the models obtained by the measurement-based method are usually posterior results with poor timeliness.…”