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  1. (Dept. of Electrical and Computer Engineering, Sungkyunkwan University, Korea.)

Neural network, Fault contribution, Intelligent Protective Method, Microgrid, Signal processing

1. Introduction

The conventional power system has a radial structure, owing to which, the impedance increases with the distance between the point at which the fault occurs and the main power source; consequently, the fault current decreases. Accordingly, depending on the difference in the magnitude of the fault current, the operating time of the protective relay can be set; as a result, the main protective device, which is the closest protective device where the fault has occurred, can be set to operate first. If the operation of the main protective devices fails, then the backup protective device, which is the second closest protective device to the fault point, is set for operation. This series of protective processes is called protective coordination, which can be performed smoothly because the fault current decreases as the fault point moves away from the main power source.

However, this approach is not suitable in microgrids (MGs) owing to the presence of distributed generation (DG) systems such as RESs and energy storage systems (ESSs). MGs, which can be self-sufficient regarding generation-load balance in small areas, are useful as they are capable of integrating the RESs into the power system. In other words, an MG is independent and a next-generation power grid that combines RESs such as photovoltaics (PVs), wind power and ESSs. Furthermore, owing to the inclusion of various power sources in the MGs, the fault current is not reduced even if the distance between fault point and main power source is farther away. On the contrary, various problems are caused by contribution of individual power sources to the fault current. Owing to these structural problems in MGs, the conventional protective system becomes ineffective (1).

Therefore, numerous studies are aimed at investigating solutions to these problems of MG protection. In particular, several studies have been conducted to prevent problems caused by fault contribution and islanding operation of RESs. Recently, methods to protect MGs through pattern analysis using machine learning have been proposed (3). However, there are few review papers regarding each of these topics and how the studies are being conducted. This paper describes the challenges related to protection in MGs, which are different from those in the existing power system, and discusses the recent research trends and different approaches available for solving the related problems. The following are the main contributions of this paper:

(1) Comprehensive review of various challenging issues and methods of conventional MG protection.

(2) Comprehensive review of intelligent protective method (IPM) using machine learning to protect MG.

(3) Comparison of various IPMs based on their principles of operation, advantages, and disadvantages.

2. Challenging issues of Microgrid Protection

The challenging issues concerning protection of the MG can be classified. Problems arise due to the fault contribution of RESs and the reverse current when RESs are installed in the middle or at the end of the feeder. Moreover, the change in the structure of the MG and islanding operation of RESs also cause some issues. This section identifies those issues that are related to the MG protection and studies the recent research trends and possible solutions (2).

2.1 Fault contribution of DG

When some of the RESs are connected to the power system in parallel, the total system impedance decreases, causing an increase in the total fault current. In addition, fault current is generated from various power sources because RESs are distributed throughout power system. Therefore, the fault characteristics of MGs are different from those of the existing power systems, and as a result, several problems may occur; these are discussed as follows.

2.1.1 Blinding protection

When a fault occurs in a feeder in which an RES is installed, the fault contribution of the main source is reduced owing to the fault contribution of the RESs. As a result, the overcurrent relay that was previously functional may not operate. There are two ways to solve the problem of blinding protection. First approach is to reset the methods of protective relay using optimization algorithms considering various scenarios (3). The second is to reduce the fault contribution of RESs by using fault current limiter so that the operation of the protective relay is untroubled (4).

2.1.2 Sympathetic tripping

The blinding protection is caused by fault contribution of RESs in the fault phase, whereas sympathetic tripping a result of the fault contribution of RESs in the healthy feeder. When a fault occurs, a fault contribution from RESs flows through the healthy feeder. As a result, an overcurrent relay in the healthy feeder, which should not operate, can operate (5). Various solutions have been proposed to prevent the occurrence of this phenomenon; correspondingly, there exist relay resetting methods such as counter measurement of blinding protection (6), and methods for analyzing fault characteristics of healthy and faulty signals through signal processing techniques (7). In addition, there is also an approach that does not block the reverse fault current by including a direction detection method in an existing overcurrent relay (8).

2.1.3 Problems of auto-reclosing

80% of faults in a power system are temporary faults with a lifetime of several milliseconds. In case of such a temporary fault, effective protection can be performed through a recloser. In the conventional power system, after the recloser is opened, the part of power system blocked by the recloser is in a no-voltage state. However, in the case where the RESs are injecting power into blocked part of the power system, the blocked part is not in a no-voltage state even when the recloser opens. Therefore, during reclosing, an asynchronous situation may occur at both ends of the recloser, which may cause transients, such as overcurrent and overvoltage surges (9). This situation should be prevented, and an algorithm for determining a temporary fault and a permanent fault is currently being developed in several studies (10). In addition, studies on performing synchronization naturally are also being conducted (11).

2.1.4 Fault characteristics of inverter-based DG

Inverter-based RESs and synchronous-based RESs have different fault characteristics. In general, inverter-based RESs have different fault response times compared to those of a synchronous machine; therefore, it is necessary to consider these characteristics when analyzing the fault before establishing the protective system. Therefore, in the virtual inertia analysis (12), the fault characteristics of the inverter based RESs are analyzed by dividing them into a voltage source or current source (13).

2.2 Reverse power flow

In the conventional power system, the load current flows only in one direction. However, when the RESs are connected to the MG, the load current can exhibit bidirectionality (14). Therefore, the current flowing opposite to the direction of the existing load current is called reverse current; this may cause the following problems.

2.2.1 Misoperation of non-directional devices

Algorithms for determining the directionality are not basically included in existing protective devices, and these existing protective devices that determine only the current magnitude to perform protection may cause various malfunctions when reverse current flows through them. In particular, it may cause malfunction of the sectionalizer, which separates power systems. This is because a sectionalizer can only separate power systems in the event of no-voltage state (15). This problem is solved by introducing an algorithm for determining current directionality of existing protective relay (16). In addition, by controlling the output of the RESs, it is possible to prevent reverse current flow (17); moreover, the output of the RESs may not exceed the load owing to the maintenance of supply and demand balance by controlling the demand response (18).

2.2.2 Overvoltage and over-ampacity in feeder

In the conventional power system, power is supplied from the substation, and the voltage drop due to line impedance increases the direction towards the end the feeder. However, as RESs are installed in the middle or at the end of the feeder line, overvoltage may occur near the installation location of RESs (19). In MGs, there are various voltage regulation devices to adjust the voltage within the allowable range. Furthermore, the connection of RESs through smart inverter has its own voltage regulating function such as Volt-Var function. A typical method for voltage regulation, uses the line drop compensation (LDC) of on load tap changer (OLTC) installed in a substation. OLTC predicts the voltage drop based on the amount of load current from the substation and decides whether to compensate for the voltage. However, as the RESs are connected to the power system, the value of the load current from the main source decreases; consequently, the OLTC may compensate the voltage incorrectly. Therefore, coordination algorithms for voltage regulation among OLTC, shunt capacitor and smart inverters are being studied (20). In addition, studies on hosting capacity, i.e., the maximum number of RESs that will not cause an overvoltage, are being actively conducted (21).

2.3 Changing of microgrid configuration

The advantage of an MG is that it can flexibly change the system structure according to various situations. This advantage helps to maintain reliability of power system and minimizes damage in case a fault occurs. However, changes in the structure of the MG from the perspective of the protective system will lead to new protective coordination concerns. The following sections describe the various problems in the implementation of protective systems caused by changes in grid structure.

2.3.1 Frequent change in configuration

Depending on the structure of the specified MG, the maximum load and fault currents at each protective device are calculated by the line impedance (22). However, if the structure of the MG is changed, the magnitude of these currents will vary as well; moreover, frequent structural changes of the MG due to the automatic power system would make it impossible to perform protection using a uniform protective system. In order to solve these problems, research is underway to set relay setting values that can be commonly used even when the system configuration is changed (23). Moreover, research on an automatic resetting method of protective relays when the system configuration is changed, is actively progressing (24). Finally, a method of constructing a protective system regardless of the structure of an MG, using a traveling wave is also being studied (25).

2.3.2 Varied fault level in dual mode

MGs can be divided based on modes of operation: islanding mode MG and grid-connected mode MG. Naturally, magnitude of the fault current varies depending on the presence or absence of the main power (26). Therefore, in each case, it is necessary to set the protective relays’ setting values. In this case, there is a method of setting the relay using an optimization algorithm in a method similar to that discussed earlier in Section 2.3.1 (27). Moreover, it is also possible to determine the current mode of the MG through signal processing techniques to apply the corresponding protective relay setting values (28).

2.4 Islanding

An islanding operation refers to a phenomenon in which RESs supply power to a power system grid while being separated from the main power source. Unintentional islanding operation may cause system instability and lead to accidents to humans; therefore, its detection and resolution is paramount (29). Methods for islanding detection can be classified into three types: passive methods, active methods, and methods using communication.

3. Comprehensive Review of Intelligent Protective Method

Most recently, MG protection using machine learning became a trending research topic. When a fault occurs, the magnitude of the voltage, current, and other power quality fluctuates from the normal state, and the pattern thus obtained is different depending on the type of the fault. The method of protecting MG through each machine learning is similar. Each fault characteristic (voltage, current, energy, ntropy, etc.) shows different characteristics according to the fault location and fault type. Each machine learning method learns the corresponding characteristics and when an actual fault occurs, analyzes the learned characteristics to classify the location and type of the fault. However, there are advantages and disadvantages to each machine learning method, and the comparison has proceeded in Section 4.

3.1 ANN-SVM method

An artificial neural network (ANN) is a statistical learning algorithm inspired by neural networks of biology that are applied in machine learning. It refers to the entire model with problem solving ability to perform machine learning. In general, it receives the voltage and current data as input for each fault situation and learns which voltage and current occur when any fault situation occurs. Moreover, support vector machine (SVM) is one of the areas of machine learning for pattern recognition and data analysis. Given a set of data belonging to either category, the SVM algorithm creates a non-stochastic binary linear classification model that determines which category the new data belong to; this classification is based on the given data set. The created classification model is expressed as a boundary in the space where the data are mapped. The SVM algorithm finds the boundary with the largest width. Moreover, SVM can be used in both linear and nonlinear classification. In order to perform non-linear classification, it is necessary to map the given data into a high-dimensional feature space; kernel tricks are used to do this efficiently. Using this method, the input data for the fault are learned, and when a fault occurs, the situation determined with the highest probability is recognized as the current situation (30).

3.2 Sparse autoencoder and deep neural network

Neural networks have two types learning methods. One is called supervised learning, where learning is performed is a state where both the input value and target value of the data are given. In contrast, learning that finds the characteristics of data in a state where only the input value of data is given is called non-supervised learning. The SVM method belongs to supervised learning, whereas the sparse autoencoder (SAE) belongs to unsupervised learning. A proposed SAE based deep neural network scheme has the ability to automatically learn features from the unlabeled dataset consisting of instantaneous values of voltage and current signals without specifically extracting attributes for different fault cases. SAE is a neural network that simply copies inputs to outputs. It appears to be a simple neural network, but it is made into a complicated neural network by constraining the network in various ways. For example, the number of neurons in the hidden layer is smaller than the number of neurons in the input layer to compress the data or add noise to the input data to restore the original input. Owing to the effective performance of SAE in discovering the system structure information from input dataset with reduced computation effort, it has been successfully implemented in various classification applications (31).

3.3 Hilbert transform and machine learning techniques

Hilbert transform (HT) is utilized to calculate various functions for the MG fault classification process. Through HT, it is possible to derive various functions (energy, entropy, etc.) as required in various conditions in the power system, which can be learned to determine the fault situations. Input data are required to use HT. In general, voltage or current is used to obtain the input data through decomposition into a mono component signal called intrinsic mode function (IMF) through empirical mode decomposition (EMD); finally, this IMF value is used as input data of HT. Various features that are obtained through HT are learned through various machine learning techniques such as SVM, which further become indicators for determining a fault situation. The features that can be obtained through HT, include maximum and minimum values related to the size of HT, root-mean square, energy, standard deviation, skewness, kurtosis, and entropy (32).

3.4 Convolutional neural Network

The input data of a general ANN is limited to a one-dimensional (array) form. However, if multiple items of input data are required, multiple dimensions must be compressed into single dimension; information could be lost during this compression process. As a result, ANN has limitations in extracting and learning features and increasing accuracy owing to lack of information. Therefore, convolutional neural network (CNN) is proposed as a model that can be trained to wolve this problem while maintaining information. CNN can be divided into the parts that extract the features and the parts that classify. The feature extraction area is composed of multiple layers of the convolution layers and pooling layers. A convolution layer is an essential element that reflects the activation function after applying a filter to the input data. In contrast, the pooling layer is applied to the feature produced by the convolutional layer, and is an optional layer. Using the CNN as described above, it is possible to perform more effective fault diagnosis by receiving individual data on three-phase current or voltage (33).

Table 1 Comparison of various IPMs (Merits)






1) Data analysis is considerable fast.

2) It is also applicable when it is difficult to classify data through a linear model.

1) The more the number of samples, the slower the speed and the larger the memory allocation; this ultimately decreases the performance.

2) It is difficult to understand how predictions were decided and how the models were analyzed.


1) Enables efficient data representation.

2) Excellent effect on data compression and noise rejection.

1) Increased the number of parameters in proportion to the size of the data.

2) Taking advantage of data-specific attributes is difficult.


1) Owing to the convolution characteristics, it is easier to input and learn more than two dimensions of data compared to a normal neural network.

2) Multi-dimensional analysis can be performed better than other algorithms.

1) Requires innumerable computations.

2) Continuous re-learning is required as the environment varies.



1) It works well with noisy signals.

2) It has an ability to process non-stationary and non-linear data.

1) The performance of composite signals is low.

2) It is limited to interpreting a narrowband signal.


1) It is simple for frequency analysis.

2) It is effective for analysis of discontinuous signals.

1) In case of detailed analysis, it becomes computationally intensive.

2) It is less efficient.

3.5 Wavelet-based deep neural network

Numerous wavelet transform (WT) techniques are already being applied to detect the fault in the power system. The method of determining the type of fault situation using the wavelet transform is not significantly different from the HT method discussed earlier in Section 3.3. This method also requires the process of extracting features such as maximum and minimum values related to the size of HT, root-mean square, energy, standard deviation, skewness, kurtosis, and entropy using signal processing and learns them through the neural network (34).

4. Comparison of Various IPMs

After a comprehensive review and in-depth analysis, a comparison of various IPMs considering different capability parameters is presented in Table 1 and 2. These IPMs are primarily used to detect faults and determine the location of faults. Each protective method can be classified according to the a) structure of neural network and type of b) signal processing technique the method applies (30-34).

Table 2 Comparison of various IPMs (Characteristic)


















5. Conclusion

This paper presents a comprehensive review of challenging issues of MGs, and various IPMs. Most protection-related issues that can occur in MGs are caused by the presence of RESs, especially in the event of a fault, owing to the fault contribution of the RESs. Furthermore, the fault characteristics of the power system are changed owing to the fault contribution of the RESs; consequently, the reliability of the existing protective system decreases. Therefore, it is necessary to establish a protective system that takes into account the fault contribution of the RESs. In addition, IPMs use machine learning to learn the fault characteristics, detect the type of fault, and determine the location of occurrence of the fault. Currently, in the topic of MGs, IPMs will become the future trend in MG protection. In the case of studying IPMs, research into new neural network structures or research into the appropriate type of signal processing method to extract the features, which are to be used as input data for machine learning, are being conducted extensively.

This review paper is believed to be useful for development of MG protection systems in the future. In addition, this paper presents the classification of IPMs for MGs that were not previously classified, thus helping in the construction of a more systematic MG protection system.


This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2018R1A2A1A05078680).


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Ji-Soo Kim

He received a B.S degree from the College of Information and Communication Engineering, Sungkyunkwan University, Korea, in 2016. At present, he is enrolled in the combined master’s and doctorate program. His research interests include power system transients, wind power generation and distributed energy resource.

Jin-Sol Song

He received a B.S degree from the College of Information and Communication Engineering, Sungkyunkwan University, Korea, in 2017.

At present, he is enrolled in the combined master’s and doctorate program.

His research interests include distributed generation and power system protection.

Gwang-Su Shin

He received a B.S, degrees in electrical engineering from Kangwon national University, in 2019.

At present, he is enrolled in the combined master’s and doctorate program of the College of Information and Communication, Sungkyunkwan University.

His research interests include power system protection and power system transients.

Ho-Young Kim

He received a B.S degree from College of Information and Communication Engineering, Sungkyunkwan University, Korea, in 2020.

At present, he is enrolled in the master program.

His research interests include power system transients, distributed energy resource.

Chul-Hwan Kim

He received the B.S., M.S., and Ph.D. degrees in electrical engineering from Sungkyunkwan University, Suwon, Korea, in 1982, 1984, and 1990, respectively.

In 1990, he joined Jeju National University, Jeju, Korea, as a Full-Time Lecturer.

He was a Visiting Academic with the University of Bath, Bath, U.K., in 1996, 1998, and 1999.

He has been a Professor with the College of Information and Communication Engineering, Sungkyunkwan University, since 1992, where he is currently the Director of the Center for Power Information Technology.

His current research interests include power system protection, artificial intelligence applications for protection and control, modeling/protection of underground cable, and electromagnetic transients program software.