Abstract
The ever-increasing dependence on electrical power has posed more challenges to power system engineers to deliver secure, stable, and sustained energy to electricity consumers. Due to the increasing occurrence of short- and long-term power interruptions in the power system, the need for a systematic approach to mitigate the negative impacts of such events is further manifested. Self-healing and its control strategies are generally accepted as a solution for this concern. Due to the importance of self-healing subject in power distribution systems, this paper conducts a comprehensive literature review on self-healing from existing published papers. The concept of self-healing is briefly described, and the published papers in this area are categorized based on key factors such as self-healing optimization goals, available control actions, and solution methods. Some proficient techniques adopted for self-healing improvements are also classified to have a better comparison and selection of methods for new investigators. Moreover, future research directions that need to be explored to improve self-healing operations in modern power distribution systems are investigated and described at the end of this paper.
ELECTRICAL power is an inevitable part of our daily lives to the extent that the present world could not be imagined without it. Continuity of electrical power and its secure delivery is a significant concern and challenge for the utilities, as the world faces more unexpected events than before, especially in recent years [
The smart grid concept provides more advanced options to manage and control the power system components and increases power system’s reliability, resiliency, and efficiency. Self-healing is the key characteristic of a smart grid defined as the ability of power distribution systems to automatically restore themselves after faults, by the report from National Energy Technology Laboratory (NETL), USA [
Self-healing in smart distribution systems follows specific algorithms to isolate the faulted area and restore the system entirely or partially during an extreme event for the regular operation [
FLISR provides the utilities with an intelligent automation operation during fault conditions by effective monitoring and decision-making without human intervention [
Self-healing is expected to operate in real-time and quickly locate, isolate, and reconfigure the network to restore the maximum load. Restoration function is achieved by adopting advanced intelligent control systems that incorporate multiple available control actions in smart grids. For instance, service restoration is addressed by the network configuration technique based on the optimal switching actions on the feeders. Reference [
For instance, [
Several research papers that have recently been published further illustrate the significant impact of self-healing on power system reliability and security. In this scenario, a comprehensive review paper is needed for a more in-depth understanding and organization of the existing literature. Some review papers have been published in the literature partially describing the self-healing subject in power distribution systems. Reference [
An overview of distributed multi-agent systems in self-healing is presented in detail in [
Although valuable papers have been published on self-healing reviews so far, the literature still lacks a comprehensive review on the subject of self-healing in power distribution systems, which organizes the research papers considering different aspects of self-healing. To fill the gap, this paper presents a complete up-to-date review on the self-healing research area by focusing on different features such as solution algorithms, optimization objective functions, and proficient techniques and methodologies adopted to address the implementation of self-healing. At the same time, several fundamental limitations and strengths are investigated for the reviewed papers. Finally, the challenges are identified, and future research works are proposed at the end of this paper. Thus, the main contributions of this paper are as follows.
1) A comprehensive and up-to-date literature review is conducted on self-healing in power distribution systems.
2) The self-healing operational procedures are summarized by describing different operating stages, including fault detection, fault understanding, fault isolating, and service restoration.
3) Up-to-date self-healing research papers are organized and key features such as self-healing control actions, objective functions, and solution algorithms are categorized.
4) Fundamental limitations on self-healing are addressed, major challenges in the present paper are identified, and some future research paths are recommended.
The rest of the paper is organized as follows. The self-healing concept is described in Section II. Section III presents motivation and paper analysis. Section IV discusses self-healing control actions while Section V discusses the self-healing goals. The optimization algorithms of self-healing are discussed in Section VI. The future works are presented in Section VII, and finally, conclusions are given in Section VIII.
The primary concept of self-healing comes jointly from the United States Department of Defense (DOD) and the Electric Power Research Institute (EPRI) that enable extensive national infrastructure to self-heal in response to threat [
Fig. 1 Self-healing process and its principle of operation during a fault event.
The first stage of self-healing operations is fault detection. A self-healing system should detect the fault in the distribution system as fast as possible to avoid adversarial impacts of abnormal events and reduce productivity loss.
In a simple protection system, the circuit breaker trips the equipment to prevent the fault from damaging the equipment and creating further losses. For a centralized supervisory and control approach, the supervisory control and data acquisition (SCADA) system executes the communication, collects information and measurements, and sends them to the control center. For a decentralized and distributed approach, the communication system implemented in a peer-to-peer format collects the information and measurements required for decision-making. Collecting the information about fault severity, fault locations, and fault types is the first step to perform the subsequent operations discussed in the following text. Although it may seem straightforward, there are always challenges for accurately and adequately detecting the faults in a power distribution system. Several research papers have been published on fault detection with different methods in [
One challenging task in the self-healing process is to analyze the type of fault. The faults in distribution systems are categorized as symmetrical and unsymmetrical. Symmetrical faults occur irregularly and cause extreme damage to the equipment. In unsymmetrical faults, the possibility of damage is less than that in symmetrical cases. This type of fault can be in line-to-ground and line-line-to-ground. Reference [
Fault isolation is the next stage of the self-healing process after a fault is detected. Fault isolation is executed by tripping the circuit breakers and disconnecting the faulted area from the rest of power system. During the isolation operation, it is necessary to identify the root cause of the fault and the location of the fault. An important task at this stage is to estimate the capability of the neighboring feeder to be selected for isolation. The fault should be cleared, load shedding should be performed if needed, and the grid should be divided into subsections, if possible. All these actions should be taken immediately to avoid significant impacts on power systems due to the fault. Some research related to fault isolation exists in [
The self-healing process starts with fault detection and ends with the restoration process after a fault occurrence. When the power supply is interrupted, it is critical to rapidly restore power to the affected areas to avoid further customer interruptions. Therefore, the restoration strategy changes the power system outage state to normal stage using available energy resources. A self-healing system performs restoration process primarily using line switching operations within the faulty area. Restoration strategy needs an appropriate capable backup feeder and sources to transfer the load to another feeder. After restoring the power to the out of service areas, the system needs to perform corrective actions. There are more publications available in literature that address service restoration, including [
Self-healing is one of the essential techniques in power systems to improve their reliability, efficiency, and quality of service. Due to its importance, researchers worldwide are working on enhancing the self-healing performance. Although there are review papers in other areas of power systems, to the authors’ best knowledge, no complete review paper summarizes self-healing applications in power distribution systems. Thus, this paper will motivate the researchers to work in this area, especially the beginner researchers. This section describes the motivation behind the self-healing review papers and presents the paper selection strategy, frequency, and venue of publications.
Significant research on self-healing in power distribution systems has been conducted since a couple of decades ago, which is also flourishing nowadays. The first research paper with the term self-healing in power distribution systems was published in 2004; however, the power restoration and restoring service in power systems have been in the literature since 1981 [
Fig. 2 Ratio of different publications on self-healing subjects in power distribution systems.
After detecting a fault in a power distribution system, self-healing is usually performed through a set of control actions. This section discusses such control strategies and explains how the self-healing process is performed in a faulted system.
In a self-healing power distribution system, the first control action could be grid reconfiguration after detecting contingencies. When a fault occurs in a power distribution system, grid reconfiguration is performed for two reasons. First, isolate the fault or faulted area to avoid supporting the fault current and minimize the fault impacts on other loads in the system. Second, reroute the power from available sources to the loads located downstream of the fault location. If operated instantly, efficiently, and strategically, the grid reconfiguration could reduce restoration time significantly. Reference [
Another control action as part of the self-healing process is the control of DG output power. If appropriately controlled, utility-owned or customer-owned DGs could partially or fully supply the existing loads in a distribution system during contingencies. For an efficient self-healing process, the available output power of all DGs and existing loads should be known to the self-healing decision-making system before performing any restorative actions. Therefore, forecasted load and generation data and potential fault impacts in different locations are crucial for optimum system restorative actions.
References [
When disconnected from the grid, the power generated by the DGs may be insufficient to support all the loads in the system. In such cases, they should be disconnected from the grid, or their consumption should be adjusted to match the available supply. In this scenario, the priority of loads should be considered based on the customer’s reliability requirements. A load management strategy has been presented in [
Energy storage systems (ESSs) are integrated into modern power distribution systems to improve the system performance. As part of self-healing restorative actions, while the DG support is not sufficient to supply all existing loads, the energy stored in such storage units could support the distribution system and energize the remaining loads. Since the capacity and output power of ESSs are limited, their available power and energy should be known to the self-healing decision-making system for performing an optimal task. Reference [
Reactive power sources exist in power distribution systems as fixed or controllable capacitors/reactors or provided by DGs. During the self-healing process in power distribution systems, controlling reactive power sources can improve the quality of restored service in terms of power factor, voltage profile and power losses. Reference [
The energy storage units and reactive power sources are modeled similarly to the DGs, where the former has only active power and the latter only reactive power.
Self-healing in power distribution systems is performed by detecting, locating, and isolating faulty parts and restoring power as quickly as possible. An effective self-healing system performs such actions through an optimized strategy. An optimization problem is defined for this purpose, where different researchers have used different goals or objective functions to formulate the self-healing problem. The classifications of self-healing control actions, objective functions of self-healing, and optimization algorithms used for self-healing are summarized in
Fig. 3 Classifications of self-healing control actions, objective functions of self-healing, and optimization algorithms used for self-healing.
Reducing power losses is one of the goals for performing efficient self-healing in power distribution systems. Power losses have always been a concern in power distribution systems, and engineers have always tried to minimize them. High power losses in a system can cause operational issues, e.g., power quality issues, and increase the customer’s electricity costs. Although it cannot be eliminated, the loss in a system can be controlled and minimized to improve the efficiency of the system’s operation. An optimized self-healing strategy planner will try to minimize the losses during this process. Reference [
One important aspect for performing an efficient self-healing is to speed up the process and minimize the affected consumers. Self-healing systems usually use advanced communication technologies such as software-defined networks, to locate and recover from faults. Reference [
Minimizing operational costs has always been a challenge for power system engineers and yet is for performing self-healing.
The operational cost for a system during self-healing can be minimized in different aspects. For instance, a cost-efficient self-healing system could be achieved by using the minimum cost generators, minimizing the costly load shedding, minimizing power losses, and shortening the distance of power transfer in feeders. Reference [
Minimizing the number of switching operations during self-healing can speed up the process and minimize the affected consumers. Using advanced communication and control technologies to locate the faulted area, the number of switches to recover the loads can be reduced. A service restoration strategy is presented in [
Improving voltage profile in distribution systems is another consideration while performing self-healing. Different self-healing strategies have different impacts on the voltage profile of the system. Therefore, among the possible options for self-healing, the one that, besides addressing other concerns, improves the voltage profile should be selected. Volt-var functions of inverters to mitigate voltage violations and improve the voltage stability in distribution systems have been discussed by case studies in [
Observability restoration during the self-healing process is another concern for distribution system operators. A series of synchronized phasor measurement units are usually required to achieve global observability in a power distribution system. Therefore, it is critical to consider the minimum number of phasor measurement units to complete the observation restoration while performing self-healing in a power distribution system. Reference [
Climate changes could directly affect the modern world and cause many intensive unwanted natural disasters such as hurricanes and floods. The power system is one of the areas which is recurrently affected by such events. Almost 80% of all major power outages happen due to natural disasters in the United States and are expected to increase continuously [
To fill up the ever-increasing energy demand, the power system needs to accommodate large-scale DERs. This will create a new paradigm for the existing power system to face challenges during operation, especially for renewable energy sources (RESs). Although RESs can support the grid by providing economically and environmentally friendly clean energy, there are issues with managing their uncertain characteristics. In these scenarios, service interruption may be unavoidable, especially for large-scale integration of RESs. Self-healing can substantially improve the reliability of the power systems. Reference [
This section classifies the publications based on the optimization algorithms used for self-healing. A summary of papers for each algorithm is presented in each subsection. Moreover,
| Type of test feeder | Optimization algorithm | Reference | Goal | Strength | Limitation | Year |
|---|---|---|---|---|---|---|
| 16-, 33-, and 69-bus | MILP |
[ | Minimize restoration cost and maximize the number of supplied customers based on priority ratings | Optimal solution based on DG output and load shedding is obtained | Fault detection and clearing are not considered | 2018 |
| A 53-node case | MINLP |
[ | Maximize load restoration, minimize the number of switching operations, and prioritize special loads | Optimal solution with high quality is provided | Processing time is very long | 2016 |
| IEEE 13-node and 37-node | MINLP |
[ | Maximize load restoration | Optimal solution for voltages/currents in a system is provided | Optimal solution for a large-scale system is not provided | 2007 |
| 69-bus | MILP |
[ | Minimize costs of switching actions and minimize load shedding | Fast response time is obtained with local control actions and supported with microgrids | No load shedding is considered | 2017 |
| 38- and 119-bus | Multi-agent |
[ | Maximize a restoration index which considers the priority of loads and number of switching operations | It is capable of working with different type of test systems | Active power and reactive power are ignored | 2018 |
| IEEE 34- and 118-bus | MILP |
[ | Maximize load restoration | Recover a large scale of outage area | Fault detection stage is not analyzed | 2018 |
| 53-bus | Heuristics |
[ | Maximize load restoration, minimize power loss, topology variation and power flow changes | Availability of DG is considered. The restoration is performed in parallel in multiple simultaneous faults | The priority of critical loads is not considered | 2018 |
| IEEE 34- and 8500-bus | MILP |
[ | Maximize restored power considering repair crew routing | Large scale problems can be solved | Limited type of complicated optimization problem is solved | 2018 |
| 16 switches, 2 reclosers | Multi-agent |
[ | Maximize restored load considering efficient fault location and isolation | The number of messages is lower since each agent only sends single messages | Power losses need to be improved in distribution network | 2016 |
| IEEE 33-bus test | Fuzzy logic |
[ | Perform grid reconfiguration to eliminate feeders’ congestion, correct voltage violations, and coordinate reactive power devices | Overstress of substation voltage regulator tap changer is avoided | The minimum number of switching operation needs to be considered to minimize the system operational cost | 2015 |
| 44-node, radial | MINLP |
[ | Minimize the cost of de-energized zones, load-shedding in nodes, active power losses, after reconfiguring the system | High-quality optimal solutions are found | Computing time is high | 2016 |
| 1069-bus unbalanced | MILP |
[ | Maximize the cumulative service time of microgrids to loads weighted by their priority | Microgrids are utilized to restore critical loads | The variety of DGs for restoring the power system efficiently needs to be focused on | 2018 |
| IEEE 123 | MINLP |
[ | Maximize the restored energy over the time horizon considering weight factors for each load | Generate a sequence of control actions assigned to multiple time steps | Power losses and the number of switching operation are not considered | 2018 |
| IEEE 39- and 118-bus | MILP |
[ | Minimize the outage period | It is very simple and flexible | System parameters are very limited | 2018 |
| Roy Billinton test system (RBTS4) | MINLP |
[ | Minimize a combination of system average interruption duration index (SAIDI) and total cost of reliability | Parking lots participate in service restoration as backup units and storage units | Power flow analysis is not considered | 2016 |
| Type of test feeder | Optimization algorithm | Reference | Goal | Strength | Limitation | Year |
|---|---|---|---|---|---|---|
| IEEE 13- and 123-node | Graph theory |
[ | Determine the optimal restoration strategy, coordinate multiple sources to serve critical loads | Multiple sources are integrated to obtain a better solution | The decision-making does not focus on critical load in an unbalanced distribution system | 2019 |
| IEEE 33- and 123-node | MILP |
[ | Maximize the restored load and minimize the voltage unbalance and power losses | The optimization algorithm is capable of avoiding a major fault | ESSs are not considered | 2020 |
| 6-bus | Multi-agent |
[ | Efficient fault location and isolation | It is combined DG restoration with topology reconfiguration to restore the maximum load | The modeling analysis is complex | 2019 |
| 70-bus | Fuzzy logic |
[ | Realize fast load restoration, predict the distributed generation, and consider different load scenarios | Power losses are reduced in emergency state | The data are locally collected; thus, the problem is not globally solved | 2019 |
| IEEE 2- and 90-bus | MINLP |
[ | Minimize investment costs and maximize system reliability | Reliability indexes are in a multi-objective and multi-period optimization | Initial investment cost of the capacitor’s installation is not considered | 2019 |
| IEEE 123- and 8500-bus | MILP |
[ | Minimize unserved energy considering crew dispatch | An optimal solution based on switching sequence is obtained | Power flow is ignored | 2019 |
| IEEE 123-node | MILP |
[ | Maximize restored power and minimize restoration time | Dynamic modeling is considered | Operation costs are not considered | 2020 |
| IEEE 123-node | MILP |
[ | Maximize the restored energy over the time horizon, considering weight factors for each load | It is applicable to large-scale systems without computation complexity | Network reconfiguration is not considered | 2019 |
| IEEE 118- and 30-node | Heuristics |
[ | Find the optimal repair and activation schedule for damaged components | The solution method is computationally efficient | Restoration stages as a part of the system are not considered | 2020 |
| IEEE 33-bus and PE&G 69-bus | MINLP |
[ | Maximize restore load in microgrid | Load restoration is obtained with lower power loss than traditional methods | Priority of loads is not considered | 2020 |
| PG&E 69-node and IEEE 123-node | Graph theory |
[ | Maximize restored load | Emergency power supplies are considered to restore critical and non-critical loads | Mobile energy storage supports service restoration and performs a better solution | 2019 |
| 11-bus and 39-bus | Graph theory |
[ | Maximize generating power and minimize restoration time | ESSs and microgrid are integrated for service restoration | Backup techniques is not considered | 2019 |
| IEEE 33-bus | Graph theory |
[ | Perform service restoration without violating system operation constraints | The system is efficiently and quickly restored | Unbalanced critical loads are not considered | 2020 |
The out-of-service area is usually energized by changing the distribution system configuration utilizing switching actions on the feeders. The multi-agent optimization is considered in [
The MILP algorithm has been used in literature to optimize the service restoration in a self-healing system. MILP is a more direct approach to generating optimal solutions with high-speed processors. Most research papers using MILP algorithm have considered minimizing the number of switching operations and load shedding, maximizing load restoration, and minimizing the outage period. Reference [
MINLP has been widely employed in solving service restoration problems in self-healing systems, as it can obtain globally optimal solutions. This trend has inspired researchers to apply MINLP to solve optimization problems in self-healing problems. The adopted new healer reinforcement approach in [
Many research papers have been presented to address distributed service restoration problems using heuristics algorithms. Reference [
In [
Graph theory is widely used in self-healing optimization algorithms. Meanwhile, a set of studies are published for power restoration strategy considering the graph theory algorithms for the optimization approach.
Reference [
Genetic algorithms are being widely used to solve restoration problems in power distribution systems with the possibility of reaching the optimal global solution. Reference [
The fuzzy logic optimization algorithm involves finding an optimal solution for service restoration with a minimum time and supplying maximum loads to the out-of-service areas after a fault. Reference [
This algorithm is applied for solving the power restoration problem with iterative schemes for finding the best solution, which is close to the global optimum solution. Reference [
Tabu search is a popular algorithm applied to solve several optimization problems as well as self-healing in power distribution systems. Reference [
The strengths and limitations of the research papers presented in this section are also illustrated in Tables
This paper reviews the up-to-date research in self-healing in power distribution systems. The published papers are analyzed and categorized based on different factors, and their limitations and strengths are investigated. Based on the presented literature review, some research questions arise that need to be explored in further detail. This section categorizes such research paths and briefly explains them as future works.
The phasor measure units are used for complete observability and measurement of the voltage and current phasors of all the nodes located in the power distribution system. The strategic allocation of the phasor measurement units is significant to achieve complete observability. Much research has been conducted regarding the optimal placement of phasor measurement units in power networks. Previous research can be extended in the reconfigurable power distribution system to consider enhancing of the self-healing operation in the network. More specifically, researchers can focus on new methodical developments that will effectively increase the observability in the power distribution system using phasor measurement units during the self-healing process and if the topology of the reconfigurable network is changed.
Smart technology has provided the economic scheduling of DGs in power distribution systems and has responded with an expanded self-healing capability to restore an efficient real-time service during the network’s outage. A large proportion of these DGs are renewable types, and the output power of these non-dispatchable DGs is not always of a deterministic nature. The intermittency of non-dispatchable DGs, besides the probabilistic nature loads, introduces new challenges to the self-healing control actions. In this circumstance, accurate forecasting techniques are vital to predict RESs’ generation and probabilistic loads to create the supply-demand balance during the self-healing process. Therefore, proper forecasting techniques are needed for optimal performance during system disturbances and the self-healing process.
Self-healing control actions are scheduled based on the various information received from the system. The security and accuracy of such information will affect the accuracy of the self-healing process. In this scenario, any cyberattack on the self-healing software frameworks could impact the whole system reliability. Therefore, this paper encourages developing advanced cyber security techniques specifically for self-healing control actions as a future research direction. The secure operation of smart grids and their management require conducting various legitimate control actions quickly, and this would not be possible without adequate security of information.
Several factors must be considered during fault conditions in power distribution systems, including equipment’s status and the affected areas. The self-healing prediction model performs accurate data analysis using advanced technology to predict critical events in real-time with locations. The event-based models can be developed based on several factors such as cascading technical failure, extreme natural events, cyber-physical attacks, and space weather events. The event-based models will give ideas to prevent or improve self-healing operations before any power failure incidents. This will also help planners know the limitations of understanding the system for the specific event with the corresponding event locations and the inadequate level of situational awareness. An advanced optimization algorithm can be implemented to analyze these data and predict the outage events to perform a better operation. The prediction model uses forecasted data for future events based on the available current data obtained from the system to formulate the model parameters. The model can be further implemented in future studies, increasing the reliability during the service restoration process.
Centralized approaches for controlling power distribution systems are gaining the attention of power engineering researchers. In a centralized approach, intelligent electronic devices send all the system information through the SCADA system to the control center, where the self-healing strategy is planned. When a fault occurs in the system, the self-healing system starts operating to restore the loads as early as possible. Recently, researchers have focused on overcoming the limitations of this centralized approach to improve the performance and reliability of self-healing. In a centralized approach, the DGs support the grid by providing economical and environmentally friendly electrical energy, increasing the reliability and resiliency of the distribution system. In sharing electrical energy between utilities and DGs, there is a need for an optimization process to minimize power losses, environmental emissions, and system operational costs. These objective functions can be considered individually or collectively based on the design approach and the power requirements of DG owners or utilities.
Centralized approaches that are widely used for automated relay protection systems increase the reliability of the self-healing operation. The operational time delay measurements during protection relay operation are essential for self-healing performance improvements. In a centralized approach, the protection of the backup feeder is vital for the efficient operation of power networks. An extra line can be added to back up the existing feeder and controlled by a smart controller. In these scenarios, extensive and elaborative studies are needed to focus on the relay’s integration with the terminal unit for processing multiple tasks. These tasks may include controlling and monitoring the system network, identifying the system state, and finding relevant data in the system.
In self-healing operations, real-time service restoration is a key step to run the system facility at any point. Service restoration performance can be improved during the self-healing operation by using advanced computational tools. For instance, HPC is being widely used for faster data processing and complex calculations in many applications that may be used in self-healing. HPC-applicable hardware can provide supercomputing functionality with high performance to accelerate the self-healing algorithms during the service restoration operation stage. An example tool used in this regard is general-purpose graphics processing unit (GPGPU) computing, commonly used for crunching big data. GPGPU can be applied to perform a faster self-healing operation by processing the data elements simultaneously during service restoration. HPC hardware computational tools can be extensively used in self-healing for optimal scheduling of the DGs during the restoration process, and thus, further extensive research is required in this area.
One goal of performing self-healing is to minimize the number of switching operations within the shortest time interval. The fewer switching operations ensure restoring the maximum affected loads by a fault during a disturbance in the system, and preserve the limit of the operation at the same time. Moreover, the minimization of the switching operations reduces the risk related to the equipment and increases the switch’s lifespan [
Self-healing is potentially the most important feature of smart power distribution systems, which helps minimize the extreme events’ impacts and automatically and intelligently restores the affected loads. However, a comprehensive study on the self-healing subjects in power distribution operations has not been carried out to this date. This review paper attempts to rectify this issue through a complete literature review of self-healing in distribution systems, which summarizes notes by using self-healing research papers available in the literature.
This paper presents and analyzes different self-healing optimization objective functions, control strategies, and proficient algorithms to improve the self-healing operations employed in the reviewed studies. It also surveys reviewed papers by their publication site and year of publication. Tables are provided by summarizing some research papers with proficient algorithms and applied bus-wise distribution systems for self-healing operations. The future research that needs to be accomplished is also described in detail at the end of this paper. The classified and organized research papers in the self-healing area presented here would help the researchers get familiar with the up-to-date research and make their research efforts more efficient and intelligent.
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