Techniques for Measuring Supply System Impedances

• Author / Creator

  Zhang, Shijia

• The Thevenin circuit impedance of the supply system is an important data for both utility companies and their customers. The data has many uses, such as calculating short-circuit currents, verifying models of power system networks, and establishing protective relay settings. The impedance data is typically obtained using model-based short-circuit calculations. The accuracy of the results highly depends on the quality of input data and the status of equipment used in the model. This limitation has resulted in a need to develop methods that can measure supply system impedances. In this thesis, a set of novel and practical methods has been developed for impedance measurement using the waveform data collected at the customer-utility interface point.

  The first technique is to use natural disturbances produced by customer loads for impedance estimation. It consists of two methods: one is the large-disturbance-based method (LD method) and the other is the small-disturbance-based method (SD method). A detection scheme was proposed for the combined and individual use of the two methods.

  For sites that lack natural disturbances, a method that injects intentional disturbances for impedance estimation is proposed. This technique consists of two components: one component is a portable thyristor-based device, which is used for generating active disturbances; and the other component is the active-disturbance-based method (AD method) for impedance estimation. Statistical theories and signal processing techniques are utilized to develop the method.

  Finally, the above passive and active methods are combined to form an integrated impedance measurement tool. The methods proposed in this thesis have been verified using simulation studies and extensive field data. The results show that the proposed methods can serve as useful tools to support the supply system impedance measurement task

• Subjects / Keywords

  • System impedance

• Graduation date

  Spring 2020

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-dq04-2c50

• License

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