1. Introduction
A power transformer is an essential component used in power systems where voltage conversion is required. To ensure efficient operation in power systems, current differential protection is conventionally adopted as the primary protection, which is based on Kirchoff’s current law. However, it is susceptible to unwanted abnormalities such as magnetizing inrush currents during transformer energization and a parallel connection of transformers under normal operations, as well as CT saturation due to overexcitation. These abnormalities might negatively result in the mis-operation of the current differential protection. An inrush current is a non-sinusoidal and high-magnitude current generated due to flux saturation in the transformer during energization. The magnitude of an inrush current is highly dependent on the switching angle, the amount of residual flux, and the sizes of the transformers. The fundamental principles and derivation of magnetizing an inrush current are presented in [
1].
Since magnetizing inrush currents generally has a large ratio of the second-harmonic component compared to an internal fault and normal conditions, harmonic blocking and restraint have been designed to avoid false operations due to inrush currents [
2] and have been widely employed in commercial relays [
3]. Moreover, with the newly improved material of modern transformers, second-harmonic restraint/blocking faces the downside of lower second-harmonic components during transformer energization [
4]. Therefore, the conventional scheme in transformer protection can be blocked for several cycles due to an indecisive threshold. In addition, the energization of a faulty transformer may reduce the sensitivity of harmonic restraint due to the high ratio of the second harmonic in healthy phases and leads to low reliability during the energization of a faulty transformer. Thus, novel functionalities must be proposed or integrated with the existing differential protection to enhance reliability and security in detecting internal faults during energization. Recently, the Korean Electric Power Company (KEPCO) reported numerous failures of the differential relay in the field when internal faults occurred during an inrush current, leading to a malfunction in the differential relay, as conventional harmonic blocking could not respond to them and continuously blocked the differential relay until the presence of the second harmonic fell under a set value. Therefore, a new scheme for power transformer protection is urgently required to secure the stability of power systems.
1.1. Literature Review and Related Works
Conventionally, the utilization of the second-harmonic principle is widely adopted in power transformer protection against inrush currents, as described in the above section. However, this method has been proven to be ineffective in several circumstances [
5,
6]. During internal faults, there is a large ratio of the second harmonic in a few cycles, which blocks the differential relay from operating, resulting in damage to power transformers. An extensive outage and a blackout were reported in [
7] when the power transformer protection mis-operated under inrush conditions. Moreover, as the power system expands, the second-harmonic components increase on long transmission lines when the transformers are connected to shunt reactors or series capacitors [
8]; as a result, differential protection is bypassed when this scenario occurs.
Several transformer-protection techniques have been actively proposed to identify the inrush condition, such as artificial neural networks, fuzzy logic, wavelet transform, and mathematical-based algorithms. A statistical approach based on Principle Component Analysis (PCA) was described in [
9] to differentiate inrush currents, internal faults, and overexcitation conditions. It captures 2D feature space as a pattern recognition for each abnormal condition. Methods based on fuzzy and artificial neural networks were proposed in [
10,
11], and a correlation-based algorithm was developed for inrush current discrimination [
12]. For a similar purpose, a method combining a support vector machine as the classifier and a wavelet transform for feature extraction was also proposed in [
13]. A deep learning application was proposed in [
14,
15] to address current transformer saturation on transmission lines, and another deep learning-based approach was also proposed in [
16] to remove the decaying DC offset in a power system.
As signal processing techniques based on wavelet transforms have proven to be efficient tools for the analysis, detection, and classification of non-stationary signals at various levels of time–frequency resolution in the literature, they could be applicable in real-time devices. For instance, a wavelet transform has been utilized to address existing issues in power systems such as fault detection, location, and classification [
17,
18], as well as in the differential protection of power transformers [
19,
20,
21,
22,
23]. Although it has good performance without the need of harmonic information, there are some limitations for practical applications in power system protection, such as the strong influence of the mother wavelet and time delay. However, it does not provide an answer for internal fault detection during inrush conditions, which is a significant concern in transformer differential protection. An improved wavelet transformation, namely the Real-Time Boundary Stationary Wavelet Transform (RT-BSWT), was proposed in [
24] to detect internal faults during inrush currents. Despite the improvement made, a high sampling rate is required, and it may be susceptible to noise. A process to identify an inrush current based on the enhanced GSA-BP approach was proposed in [
25] to discriminate inrush currents from fault currents in transformers.
A low-computation method based on a fault component network was developed in [
26] to enhance the accuracy of transformer protection, regardless of magnetizing inrush conditions. A method based on the current and voltage ratio was demonstrated in [
27], where it deployed the absolute difference of the current and voltage to differentiate inrush currents from internal faults. A unidirectional index was utilized to detect the direction of magnetizing inrush currents in power transformers [
28]. The detection of inrush currents based on the dead angle was introduced in [
29]. If the waveform distortion is so severe that the wave width is less than 140°, it will cause a delay in protection or even a wrong judgment; therefore, the efficacy of this method presents a drawback. A new adaptive coordination approach between generator and transformer was proposed to enhance the abnormal operating conditions [
30].
1.2. Key Contributions and Organization
Motivated by the above-mentioned problems with the conventional approach, this paper presents a protection scheme to discriminate internal faults and inrush currents by combining a data window with deep neural networks (DNNs). In recent years, new techniques based on intelligent methods have demonstrated a robust distinction between inrush currents and internal faults for power transformer protection, overcoming the drawbacks of traditional differential protection. To detect inrush currents and internal faults, the proposed scheme first utilizes the data window to obtain the distinctive feature signal that separates the region of internal faults and inrush currents. The proposed scheme can identify internal faults during inrush currents. It not only provides stability when these two abnormal conditions occur simultaneously but also improves the response time compared to conventional harmonic-blocking methods. Furthermore, the proposed scheme is applicable to inrush currents and internal faults of various magnitudes due to its normalization quantity during the preprocessing prior to deep-learning training. Then, a DNN is employed to discriminate internal faults from inrush currents. The key contributions of the proposed work can be highlighted as follows.
A wide range of applicability, regardless of inrush current magnitude, the residual flux in power transformers, internal fault magnitude, and fault angles;
An improved discrimination of internal faults, considering winding-ground faults during inrush currents;
A universal application for other power transformers with different characteristics;
A data window-based operation without the need for a threshold.
The rest of this paper is organized as follows.
Section 2 highlights the literature review of the behavior of inrush currents using a data window and addresses issues related to the second-harmonic-blocking method. This section also includes information on data acquisition and dataset preparation for training, along with a detailed description of inrush current features.
Section 3 presents the proposed deep neural network (DNN) method and its structure. The simulation setup, implemented in both Python and PSCAD/EMTDC, is detailed in
Section 4.
Section 5 addresses the results of the proposed method for inrush current and internal fault detection and provides a comparative analysis using the conventional approach. A discussion of the performance evaluation based on statistical percentages is demonstrated in
Section 6. Lastly,
Section 7 includes concluding remarks and information regarding potential future works.
5. Simulation Results
In this section, the efficiency of the proposed DNN is verified and compared to the unidirectional index method in [
28], the conventional harmonic-blocking scheme [
34], and the Extended Kalman filter in [
35]. Graphical illustration and evaluation metrics make it abundantly evident that the proposed method is effective against inrush currents and internal faults. In
Figure 6,
Figure 7,
Figure 8,
Figure 9,
Figure 10 and
Figure 11, DNN, UNI, and HAR denote the proposed DNN method, the unidirectional index in [
28], and the second-harmonic-blocking approach [
34], respectively. The Extended Kalman filter in [
35] is used for comparison when internal faults are present, because EKF only detects the instance of internal faults. It is generally known that protection relays in power system protections operate after one cycle. Therefore, the evaluation of the proposed DNN and alternative methods will be discussed based on the 58th (=
N–6) and 61st (=
N–3) samples from each abnormality.
5.1. Case Study 1: Inrush Current at a Switching Angle of 0°
Magnetizing inrush currents are generated due to the remanent magnetism and no-load closing of a power transformer. The closing instance significantly influences the waveform characteristics of the inrush current, while its remanent magnetism mainly affects its amplitude.
Transformer energization cases without and with residual flux are studied in this section.
Figure 6 shows the results when a transformer without residual flux was energized at a switching instance of 0°, corresponding to 0.2 s. As the ratio of the second harmonic sharply increased at the closing instance, the HAR was theoretically effective in quickly detecting the inrush current. The UNI detected the inrush current after a timing delay due to the data window, while the proposed DNN detected it at 0.231 s, with a slightly quicker response than the DNN reference. Based on
Figure 6, it is evident that the proposed DNN presented a promising output in noticing the inrush current after sample
N–6, which was comparable to the HAR and UNI.
The performance of the proposed DNN was also evaluated considering transformer energization with the maximum residual flux, which was approximately 80%. The amount of residual flux heavily influenced the magnitude of the inrush current; as a result, the magnitude of the inrush current nearly doubled in this case, as demonstrated in
Figure 7. It can be seen that the HAR yielded the best output among the three approaches in this case. Considering a time delay, the UNI responded to the inrush current at 0.234 s, whereas the DNN demonstrated a quicker detection instance than the UNI. For instance, the DNN detected inrush currents faster (one sample) and more accurately than the UNI.
5.2. Case Study 2: Inrush Current at a Switching Angle of 90°
Switching a power transformer at 90° with no residual flux does not impact the operation of conventional differential relays and produces the least inrush currents. However, the maximum flux in the power transformer strongly influences the nonlinear nature of the magnetizing inrush current, as depicted in
Figure 8. The magnitude of the inrush current in this case is similar to that depicted in
Figure 6. Therefore, the detection of the inrush current was examined at the maximum switching angle and with residual flux. As displayed in
Figure 8, the HAR showed the most promising outcome, as it reacted to the first instance of an inrush current due to the presence of the second harmonic ratio. Due to the data window used in the UNI and DNN, their detections showed a timing delay of less than 1 cycle. In particularly, the DNN yielded a more promising outcome than the UNI, as it was 8 samples quicker. That is, the DNN faultlessly detected the inrush current after the 61st (=
N–3) sample from the switching instance.
5.3. Case Study 3: Energization of a Power Transformer in the Presence of an Internal Fault
Energizing a power transformer in the presence of an internal fault is a challenging task for conventional protections, as the ratio of the second harmonic may cause the differential relay to be blocked, potentially leading to severe damage to the power transformer. In this case, we consider a–g faults for internal faults.
Figure 9 shows the results of internal-fault detection when a power transformer was energized in the presence of an internal fault. The evaluation was conducted in two different scenarios at fault inception angles of 0° and 90°.
As shown in
Figure 9a, the conventional HAR method detected the inrush current rather than the internal fault due to the presence of the second harmonic in the decaying DC component generated during the internal fault. Consequently, it prevented the internal fault from being detected, resulting in the blocking of the differential relay operation. In contrast, the UNI detected the differential current as an inrush current instead of an internal fault. The EKF could not discriminate the internal fault from the inrush current. Moreover, the inaccuracy increased as the EKF estimated differential currents with noise. Unlike the conventional HAR and UNI methods, the proposed DNN demonstrated an impressive success rate in discriminating the internal fault from the inrush current after the 58th sample from the abnormality. In this manner, the DNN exhibited high sensitivity to internal faults, even though the HAR and UNI failed to detect them. As shown in
Figure 9a, for the fault inception angle of 90°, the HAR failed to detect the internal fault for several cycles, highlighting a drawback of using HARs in modern transformers. In contrast, the proposed DNN successfully detected the internal fault, starting from just one sample later than the DNN reference. Similarly, as illustrated in
Figure 9b, the DNN exhibited a promising output in discriminating between inrush currents and internal faults at a fault inception angle of 0°.
5.4. Case Study 4: Phase-A-to-Ground Internal Faults Occurring during the Energization of a Power Transformer
The proposed DNN was validated during an internal fault occurring a few cycles after the switching of a power transformer. The harmonic-blocking scheme blocked the operation of the differential relay due to the large ratio of the second harmonic at the onset of an internal fault. This could lead to damage to the power transformer and should be avoided.
A power transformer was switched on for energization at 0.22 s, and the internal fault occurred at 0.32 s, as demonstrated in
Figure 10. With the interference of the internal fault, the HAR showed unsatisfactory results as soon as the internal fault occurred. The HAR blocked the differential relay from operating for around two cycles, which could negatively affect the power transformer. The UNI showed the worst results among the three methods, as it did not respond to the internal fault in this case. UNI is only applicable when there is a direction of the waveform on the positive or negative side, as its bidirectional index makes it vulnerable to internal faults. The proposed DNN could detect the internal fault with a time delay of less than one cycle from the fault inception. The evaluation was performed on internal faults at fault inception angles of 0° and 90°, as illustrated in
Figure 10a,b, respectively. The results show that the proposed DNN can detect internal faults after a time delay of less than one cycle, regardless of the fault inception angle.
The influence of external faults on the proposed DNN can be ignored since the differential current will be zero during an external fault. Therefore, the DNN bypasses external faults and allows relevant protection schemes outside the protection zone to operate based on disturbance criteria.
5.5. Case Study 5: Phase-B–C-to-Ground Internal Faults Occurring during the Energization of a Power Transformer
To demonstrate the capability of the proposed DNN across different fault types, phase-B–C-to-ground internal faults are considered in this case.
Figure 11 presents a case of a phase-B–C-to-ground internal fault at a different time node considering a fault inception angle of 0°. The internal fault depicted in
Figure 11 occurs three cycles after the inrush current takes place. Similar to Case Study 4, the UNI successfully detects the instance of the inrush current; however, the operation of the differential protection is continually blocked for almost one cycle after an internal fault occurs. On the other hand, the UNI proves to be effective in responding to the inrush current but fails to detect the internal fault for several cycles. The EKF exhibits low sensitivity to the internal fault because the estimated current from the EKF produces noise. Unlike these three methods, the proposed DNN demonstrates an accurate and reliable output in discriminating internal faults with a given time delay.
6. Discussion on the Performance Evaluation Metrics
To effectively evaluate the performance of the proposed DNN, three indicators were selected as evaluation metrics: accuracy, sensitivity, and precision. Traditionally, accuracy alone is insufficient to determine whether the proposed DNN yields a promising outcome. To visualize the stability of the proposed DNN method, a confusion matrix was used, summarizing the classification performance and providing a visual representation of the actual and predicted classes. The evaluation matrix was assessed using the following four performance indices:
TP (true positive),
TN (true negative),
FP (false positive), and
FN (false negative).
Conventionally, accuracy (
ACC) shows the authenticity of a detection method, defining the correct detections over the total numbers of detections, including correct and false ones. Sensitivity (
SEN) measures the proportion of inrush and internal faults that were correctly identified among the actual labels. It is a crucial metric in discrimination, because it influences the decision to allow the differential relay to operate when an internal fault occurs during inrush currents. A high percentage of
SENs is essential to determine the stability of the proposed DNN. Precision (
PRE) is another important metric required to affirm the correctness of the proposed DNN. For instance, it demonstrates the capability of the proposed DNN to isolate internal faults from inrush currents when both abnormalities occur simultaneously. In other words, it demonstrates the ability of internal-fault detection without mistakenly identifying it as an inrush current. A comparative analysis was conducted, and the evaluation metrics are presented in
Table 6. The effectiveness of these metrics was assessed at the 58th and 61st samples from the beginning of each abnormality.
In cases where a power transformer is energized in the presence of an internal fault, the aim is to avoid a situation where the DNN mistakenly detects it as an inrush current instead of an internal fault. Therefore, the DNN places emphasis on minimizing FNs; otherwise, incorrect detections could lead to damage to the power transformer. The DNN detects the internal fault at the 61st sample, which is three samples later than the DNN reference; therefore, the DNN experienced three FNs in this case. The performance of the proposed DNN and the other methods was evaluated at the 58th (=N–6) and 61st (=N–3) samples from the beginning of each abnormality. It is noted that detection with a time delay of 61 samples will be sufficient to protect the power transformer, as the protection decision will be made after 64 samples.
According to the percentages presented in
Table 6, it is evident that all four methods correctly classified the normal condition from the other two abnormalities without any defects. For inrush conditions, the HAR was undoubtedly proven to be effective, achieving the highest metrics at the 58th and 61st samples. The UNI exhibited good performance in detecting inrush currents, with
ACC,
SEN, and
PRE values of 99.852%, 93.814%, and 95.724%, respectively. The UNI is unable to achieve the highest metric at the 61st sample, as inrush currents were detected at the 62nd sample in some cases. On the other hand, the UNI performed poorly when experiencing internal faults, as it was more sensitive to inrush currents. The DNN displayed a promising evaluation index in detecting the inrush duration at the 58th sample, yielding the highest
ACC,
SEN, and
PRE values of 99.526%, 100%, and 99.523%, respectively. At the 61st sample, the DNN could accurately classify between inrush currents and internal faults, achieving 100% for all three metrics.
Furthermore, the DNN demonstrates excellent performance in detecting internal faults during inrush currents. The evaluation index produced by the DNN outperformed the other three methods at sample N–6, achieving ACC, SEN, and PRE values of 99.651%, 99.642%, and 100%, respectively. At sample N–3, the DNN achieved the best metrics (ACC, SEN, and PRE), all at 100%. In contrast, the EKF showed worse performance compared to the DNN in this study, as it mis-detected the internal faults due to the difference between the measured and estimated currents. Moreover, EKF is inapplicable to other systems and significantly relies on a threshold to detect internal faults, presenting a less favorable discrimination between inrush currents and internal faults. At sample N–3, it yielded ACC, SEN, and PRE values of 92.136%, 71.369%, and 70.364%, respectively.
7. Conclusions
This paper proposes a DNN-based method to discriminate between inrush currents and internal faults utilizing a data window. The effectiveness of the proposed DNN was assessed through numerical simulations, including inrush currents, internal faults, and cases where the inrush current coincided with internal faults. Despite achieving less accurate results during inrush currents, compared to HAR, DNN performs better in detecting internal faults, even during inrush conditions. Based on graphical illustrations and evaluation metrics, DNN successfully detects internal faults during inrush conditions, enabling the differential relay to operate without delay, regardless of the fault inception angle and residual flux. As DNN does not require a specific threshold to perform the discrimination, it can be applied to different systems to discriminate inrush currents from internal faults.
HAR and UNI are insufficient to deal with both inrush currents and internal faults occurring together. Although EKF can detect internal faults, the effectiveness of EKF is reduced in other systems due to an indecisive threshold. The deficiencies of the prevailing methods, such as reliance on physical parameters and indecisive predefined thresholds, decrease their reliability and generality. In comparison to prevailing methods (HAR, UNI, and EKF), the proposed DNN shows promising results from sample N–3, achieving accuracy, sensitivity, and precision values of 100%. It is considered to be one of the promising solutions for discriminating between inrush currents and internal faults. The proposed DNN may produce errors in the presence of CT saturation. Our future work involves developing a discrimination model for the main and backup protections that considers CT saturation and implementing the proposed DNN to discriminate internal faults from inrush currents in real time. The experiment will be based on hardware implementation, which consists of RTDS and EVM boards.