Abstract
Evaluating the harmonic contributions of each nonlinear customer is important for harmonic mitigation in a power system with diverse and complex harmonic sources. The existing evaluation methods have two shortcomings: (1) the calculation accuracy is easily affected by background harmonics fluctuation; and (2) they rely on Global Positioning System (GPS) measurements, which is not economic when widely applied. In this paper, based on the properties of asynchronous measurements, we propose a model for evaluating harmonic contributions without GPS technology. In addition, based on the Gaussianity of the measured harmonic data, a mixed entropy screening mechanism is proposed to assess the fluctuation degree of the background harmonics for each data segment. Only the segments with relatively stable background harmonics are chosen for calculation, which reduces the impacts of the background harmonics in a certain degree. Additionally, complex independent component analysis, as a potential method to this field, is improved in this paper. During the calculation process, the sparseness of the mixed matrix in this method is used to reduce the optimization dimension and enhance the evaluation accuracy. The validity and the effectiveness of the proposed methods are verified through simulations and field case studies.
Highlights
Harmonic sources in a power system become complex and diverse as more nonlinear customers connected to the power grid [1,2]
In the single point model, the power grid is divided into the utility and the customer sides at the point of common coupling (PCC), indicating that there is only one suspicious harmonic source
To ensure evaluation accuracy even when background harmonics fluctuate, Wang and co-workers [21] expanded the application of the complex independent component analysis method (CICA) from the single point model to the decentralized multipoint model
Summary
Harmonic sources in a power system become complex and diverse as more nonlinear customers connected to the power grid [1,2]. To ensure evaluation accuracy even when background harmonics fluctuate, Wang and co-workers [21] expanded the application of the complex independent component analysis method (CICA) from the single point model to the decentralized multipoint model. Compared with the former two methods, CICA has a superior ability to resist the impacts from the background harmonics. As shown, the improved method has three advantages compared with the traditional CICA: (1) it does not rely on GPS-based measurements; (2) it is able to select data segments with stable background harmonics; and (3) it ensures a high calculation accuracy even when the background harmonics fluctuate and/or the number of suspicious harmonics sources is large. X the fast-varying components of a signal the calculated results the Hermitian transpose the observed signals in the blind source separation model
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