Utility harmonic impedance is an important parameter for harmonic mitigation. In this paper, a method for utility harmonic impedance estimation method based on constrained independent component analysis is proposed. The conventional impedance estimation method based on ComplexICA has two major problems: the algorithm is not suitable for separating weak and strong source mixed signals, and lots of sample data should be provided to avoid converging on a local optimum. To solve the two problems, the prior information of the utility harmonic source is added to the objective function of ComplexICA; in this paper, the measurement data at PCC when the load is shutdown are chosen as the prior information. Then the utility harmonic source signal can be recovered and the separated matrix can be obtained effectively. The connection between the utility harmonic source, utility harmonic impedance and the data at PCC are established using Norton equivalent circuit, and then the separation matrix is used to calculate utility harmonic impedance. The performance and feasibility of the proposed method are verified by the computer simulation and field test. Compared with the current ComplexICA method, the proposed method is more adaptive to changes in the background harmonic and the calculation result is more stable.
The proliferation of power electronic equipment brings lots of problems to the power system, one of them is harmonic. Harmonic impedance is an essential parameter to evaluate the harmonic emission level and distinguish the harmonic responsibility when the harmonic level exceeds planning levels. At present, there are a lot of methods to estimate utility harmonic impedance, divided into invasive method and non-invasive method. In this paper, some typical non-invasive methods have been compared, and the simulation system is built in the MATLAB. The background harmonic fluctuation and the different values of utility and customer harmonic impedances are considered to evaluate the performance of the typical non-invasive methods. The simulation results proved that the complex ICA method is the most robust to the background harmonic fluctuation than other methods, but in some special cases, the method based on data selection has a better performance.
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