# A Method of Discriminating Between Power Swings and Faults Based on Principal Component Analysis
## 1 Introduction  
Distance relays are widely applied in AC transmission systems because of their simple operating principles, which utilize the measuring impedance. During power swings, the measured impedance oscillates. It may also enter into the operational zone of the distance relay, leading to undesirable tripping. Therefore, a power swing blocking unit (PSBU) is needed to work with a distance relay [1, 2, 3]. A complete PSBU takes on two tasks: blocking the distance relay after detecting a power swing (PSB), and then unblocking it after detecting a fault during the power swing (PSD). In order to accomplish these tasks, extensive studies have been conducted. In asymmetric fault situations, asymmetric faults and power swings can be readily discriminated, due to the presence of negative- and zero-sequence components. Therefore, many studies have aimed to discriminate between three-phase symmetrical faults and power swings. Conventional methods are based on the concept that there is no sudden change in the voltage or current of a transmission line during a power swing. The authors of [4] utilized the change rate of the measured impedance to discriminate power swings and faults. To accomplish this discrimination, two concentric impedance characteristics,separated by impedance \(\Delta Z\), were placed on the impedance plane, and a timer was used to time the duration of the impedance locus as it traveled between them. However, complex studies are needed to obtained a suitable time set and value of \(\Delta Z\). Tracking of the change rate of the swing center voltage (SCV) was conducted in [5; 6]. However, these methods assumed a 90\({}^{\circ}\) impedance angle for each component in the system during derivation, which typically requires compensation in practical applications, complicating the process. The change rates of active and reactive power were used in [7]. However, this method required an extended period of time to differentiate between power swings and faults, particularly in the case of three-phase symmetrical faults. Apart from conventional methods, applications of novel techniques in PSBUs have also been reported in the literature. Signal processing techniques, such as the wavelet transform [8; 9; 10], wavelet singular entropy [11], the Hilbert-Huang transform [12], the Prony method [13], and the fast Fourier transform [14], have been introduced for identifying power swings. Although the reported results are satisfactory, some of these techniques require high sampling frequencies or complex calculations. Some training-based techniques have also been applied for the identification of power swings. The adaptive fuzzy neural inference system was used in [15; 16], support vector machine (SVM) was applied in [17], and a discrimination scheme based on a decision tree (DT) was shown in [18]. In [19], a method based on a combination of the S-transform and a probabilistic neural network (PNN) was proposed. In [20], another combination of a multi-resolution morphological gradient and SVM was used. Test results were used to validate the effectiveness of the methods mentioned above, but they also required some training procedures to be employed. In addition, some other novel methods have also been proposed for PSBUs. A mathematical morphology (MM) with a low computational burden was applied in [21; 22]. The characteristics of the Lissajous figure were introduced in [23]. In [24], a combination of the Lissajous figure and the auto-regression technique was used. Although the test results indicated that the auto-regression model performed well, the requirements of finding suitable parameters for it and using a relatively high sampling frequency (200 sampling points in one cycle) make the method less attractive. Estimation- and error-calculation-based methods were introduced in [25; 26; 27]. The authors of [25] tracked the difference between the predicted and actual power using the auto-regression technique. In [26], the values of the current dynamic phasor were estimated to discriminate between faults and power swings.