Abstract
Power swing is an undesirable variation in power flow. This can be caused by large disturbances in demand load, switching, disconnection or reclosing lines. This phenomenon may enter the zones of distance relays and cause relay malfunction leading to the disconnection of healthy lines and undermining network reliability. Accordingly, this paper presents a new power swing detection method based on the prediction of current signal with a GMDH (Group Method of Data Handling) artificial neural network. The main advantage of the proposed method over its counterparts is the immunity to noise effect in signals. In addition, the proposed method can detect stable, unstable, and multi-mode power swings and is capable of distinguishing them from the variety of permanent faults occurring simultaneously. The method is tested for different types of power swings and simultaneous faults using DIgSILENT and MATLAB, and compared with some latest power swing detection methods. The results demonstrate the superiority of the proposed method in terms of response time, the ability to detect power swings of different varieties, and the ability to detect different faults that may occur simultaneously with power swings.
Highlights
Distance relays are widely used in power systems to protect transmission lines [1, 2]
Despite their excellent features and contributions to power system protection, these relays may malfunction during power swings, leading to the tripping of healthy lines
This paper presents a new power swing detection method based on the prediction of current signals by the Group Method of Data Handling (GMDH)
Summary
Distance relays are widely used in power systems to protect transmission lines [1, 2]. A detection method based on the rate of changes in the average apparent power is proposed in [22] This can properly detect multi-mode power swings but not the asymmetrical ones. A method based on zero-frequency filtering is presented in [23] and can detect fast three-phase faults that occur at the same time as a power swing [24] It cannot properly detect multi-mode power swings and could malfunction when the signals are noisy. A method based on the prediction of signal samples using phaselet transform is proposed in [30], though it needs a longer time for detecting faults that occur during a power swing and determining the appropriate threshold value is very challenging. Pm−PMax sinδ ð3Þ where Pm is the input of mechanical power (in pu), H is the inertial constant (in MWS/MVA), and t is time (in seconds)
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