| Reference | Year | Objective | Technique(s) | |
| Distribution system planning | Kahouli et al. [136] | 2021 | An ideal approach to increasing the safety of a distribution system and decrease power loss by optimizing the network reconfiguration problem | Genetic algorithm and particle swarm optimization |
| Žarković et al. [137] | 2019 | Although the primary goal of a DSP is to minimize the total cost of ownership, it also aims to maximize system reliability | Mixed-integer linear programming and genetic algorithm | |
| Ahmetovic et al. [138] | 2021 | It is proposed that the Bellman–Zadeh decision-making process shall use the proposed fuzzy-inference system type Mamdani to assess the Powerline feeder reliability as a planning criterion | Fuzzy logic | |
| Suresh et al. [139] | 2017 | These studies are crucial to establishing the status of each node or bus and conditions in the distribution system and these artificial neural networks are efficient at describing the relationship between the raw data and these neural networks | Artificial neural network | |
| Kumari et al. [140] | 2018 | This article offers the optimal energy distribution system for routes and optimal drives with the lowest energy-loss costs | Particle swarm optimization | |
| Saha et al. [141] | 2021 | Solving the optimal problem of diesel generator allocation | Genetic algorithm | |
| Hosseini et al. [142] | 2021 | Cyber-physical detection, stochastic and cyber security enhancement to detect and estimate damages | Artificial neural network | |
| Lytras et al. [143] | 2019 | There are different methods suggested to optimize distribution system planning | Particle swarm optimization | |
| Gandhar et al. [144] | 2020 | Using a proportional–integral (PI) controller and FACTS, the performance of the test system is assessed by the unified power flow controller (UPFC), which is usually used in traditional energy systems. To investigate the hybrid microgrid test system, this paper uses UPFC | Fuzzy logic | |
| Tang et al. [145] | 2021 | Improving the minimum reactive system based on the harmonic analysis method | Artificial neural network, genetic algorithm | |
| Harrye et al. [146] | 2014 | A new three-phase shift algorithm is presented that reduces the total reactive power of a converter | Artificial neural network | |
| Sharma et al. [147] | 2012 | The method suggested reduces active power loss. All the control variables are bus generator tensions, tap locations and capacitor banks for shunt switchable | Particle swarm optimization | |
| Wang et al. [148] | 2021 | To maximize the population’s ability to exploit a new space, the proposed algorithm employs a sequential optimization strategy | Genetic algorithm | |
| Bhattacharyya et al. [149] | 2014 | FACTS devices, such as static var compensator and thyristor-controlled series compensator (TCSC), are placed at weak nodes in the power system by using fuzzy membership functions, while the TCSC is placed according to reactive power flow in lines in this proposed approach to FACTS | Fuzzy logic | |
| Capacitor placement | Bharti et al. [150] | 2020 | A strategy to optimize the location of shunt capacitor banks in electricity distribution systems | Ant colony optimization, genetic algorithm |
| Roy et al. [151] | 2020 | Reduced power loss through the optimal location of the condenser using AI techniques | Artificial neural network, fuzzy logic | |
| Pimentel Filho et al. [152] | 2009 | The aim is to decrease overall losses by placing capacitor banks in distribution networks | Ant colony optimization | |
| Isac et al. [153] | 2013 | The target function comprises energy loss, energy loss and condenser banks. The placement of the condenser sites is selected based on loss sensitivity factors | Fuzzy logic | |
| Reddy et al. [154] | 2008 | A fuzzy and PSO method for placing condensers in the primary suppliers of the radial distribution systems was developed to reduce power losses and enhance the voltage profile | Fuzzy logic and particle swarm optimization | |
| Shwehdi et al. [155] | 2018 | The article focuses on the performance between the stable and the transient states in the 380-kV transmission line West–East. To dynamically handle the condenser placement problem, the GA technique is explained and implemented | Genetic algorithm | |
| Mahdavian et al. [156] | 2017 | The research aims to enhance the voltage profile and activity loss. Loss sensitivity and GA are utilized for the condenser placement and sizing | Fuzzy logic |
| Reference | Year | Objective | Technique(s) | |
| Distribution system planning | Kahouli et al. [136] | 2021 | An ideal approach to increasing the safety of a distribution system and decrease power loss by optimizing the network reconfiguration problem | Genetic algorithm and particle swarm optimization |
| Žarković et al. [137] | 2019 | Although the primary goal of a DSP is to minimize the total cost of ownership, it also aims to maximize system reliability | Mixed-integer linear programming and genetic algorithm | |
| Ahmetovic et al. [138] | 2021 | It is proposed that the Bellman–Zadeh decision-making process shall use the proposed fuzzy-inference system type Mamdani to assess the Powerline feeder reliability as a planning criterion | Fuzzy logic | |
| Suresh et al. [139] | 2017 | These studies are crucial to establishing the status of each node or bus and conditions in the distribution system and these artificial neural networks are efficient at describing the relationship between the raw data and these neural networks | Artificial neural network | |
| Kumari et al. [140] | 2018 | This article offers the optimal energy distribution system for routes and optimal drives with the lowest energy-loss costs | Particle swarm optimization | |
| Saha et al. [141] | 2021 | Solving the optimal problem of diesel generator allocation | Genetic algorithm | |
| Hosseini et al. [142] | 2021 | Cyber-physical detection, stochastic and cyber security enhancement to detect and estimate damages | Artificial neural network | |
| Lytras et al. [143] | 2019 | There are different methods suggested to optimize distribution system planning | Particle swarm optimization | |
| Gandhar et al. [144] | 2020 | Using a proportional–integral (PI) controller and FACTS, the performance of the test system is assessed by the unified power flow controller (UPFC), which is usually used in traditional energy systems. To investigate the hybrid microgrid test system, this paper uses UPFC | Fuzzy logic | |
| Tang et al. [145] | 2021 | Improving the minimum reactive system based on the harmonic analysis method | Artificial neural network, genetic algorithm | |
| Harrye et al. [146] | 2014 | A new three-phase shift algorithm is presented that reduces the total reactive power of a converter | Artificial neural network | |
| Sharma et al. [147] | 2012 | The method suggested reduces active power loss. All the control variables are bus generator tensions, tap locations and capacitor banks for shunt switchable | Particle swarm optimization | |
| Wang et al. [148] | 2021 | To maximize the population’s ability to exploit a new space, the proposed algorithm employs a sequential optimization strategy | Genetic algorithm | |
| Bhattacharyya et al. [149] | 2014 | FACTS devices, such as static var compensator and thyristor-controlled series compensator (TCSC), are placed at weak nodes in the power system by using fuzzy membership functions, while the TCSC is placed according to reactive power flow in lines in this proposed approach to FACTS | Fuzzy logic | |
| Capacitor placement | Bharti et al. [150] | 2020 | A strategy to optimize the location of shunt capacitor banks in electricity distribution systems | Ant colony optimization, genetic algorithm |
| Roy et al. [151] | 2020 | Reduced power loss through the optimal location of the condenser using AI techniques | Artificial neural network, fuzzy logic | |
| Pimentel Filho et al. [152] | 2009 | The aim is to decrease overall losses by placing capacitor banks in distribution networks | Ant colony optimization | |
| Isac et al. [153] | 2013 | The target function comprises energy loss, energy loss and condenser banks. The placement of the condenser sites is selected based on loss sensitivity factors | Fuzzy logic | |
| Reddy et al. [154] | 2008 | A fuzzy and PSO method for placing condensers in the primary suppliers of the radial distribution systems was developed to reduce power losses and enhance the voltage profile | Fuzzy logic and particle swarm optimization | |
| Shwehdi et al. [155] | 2018 | The article focuses on the performance between the stable and the transient states in the 380-kV transmission line West–East. To dynamically handle the condenser placement problem, the GA technique is explained and implemented | Genetic algorithm | |
| Mahdavian et al. [156] | 2017 | The research aims to enhance the voltage profile and activity loss. Loss sensitivity and GA are utilized for the condenser placement and sizing | Fuzzy logic |
| Reference | Year | Objective | Technique(s) | |
| Distribution system planning | Kahouli et al. [136] | 2021 | An ideal approach to increasing the safety of a distribution system and decrease power loss by optimizing the network reconfiguration problem | Genetic algorithm and particle swarm optimization |
| Žarković et al. [137] | 2019 | Although the primary goal of a DSP is to minimize the total cost of ownership, it also aims to maximize system reliability | Mixed-integer linear programming and genetic algorithm | |
| Ahmetovic et al. [138] | 2021 | It is proposed that the Bellman–Zadeh decision-making process shall use the proposed fuzzy-inference system type Mamdani to assess the Powerline feeder reliability as a planning criterion | Fuzzy logic | |
| Suresh et al. [139] | 2017 | These studies are crucial to establishing the status of each node or bus and conditions in the distribution system and these artificial neural networks are efficient at describing the relationship between the raw data and these neural networks | Artificial neural network | |
| Kumari et al. [140] | 2018 | This article offers the optimal energy distribution system for routes and optimal drives with the lowest energy-loss costs | Particle swarm optimization | |
| Saha et al. [141] | 2021 | Solving the optimal problem of diesel generator allocation | Genetic algorithm | |
| Hosseini et al. [142] | 2021 | Cyber-physical detection, stochastic and cyber security enhancement to detect and estimate damages | Artificial neural network | |
| Lytras et al. [143] | 2019 | There are different methods suggested to optimize distribution system planning | Particle swarm optimization | |
| Gandhar et al. [144] | 2020 | Using a proportional–integral (PI) controller and FACTS, the performance of the test system is assessed by the unified power flow controller (UPFC), which is usually used in traditional energy systems. To investigate the hybrid microgrid test system, this paper uses UPFC | Fuzzy logic | |
| Tang et al. [145] | 2021 | Improving the minimum reactive system based on the harmonic analysis method | Artificial neural network, genetic algorithm | |
| Harrye et al. [146] | 2014 | A new three-phase shift algorithm is presented that reduces the total reactive power of a converter | Artificial neural network | |
| Sharma et al. [147] | 2012 | The method suggested reduces active power loss. All the control variables are bus generator tensions, tap locations and capacitor banks for shunt switchable | Particle swarm optimization | |
| Wang et al. [148] | 2021 | To maximize the population’s ability to exploit a new space, the proposed algorithm employs a sequential optimization strategy | Genetic algorithm | |
| Bhattacharyya et al. [149] | 2014 | FACTS devices, such as static var compensator and thyristor-controlled series compensator (TCSC), are placed at weak nodes in the power system by using fuzzy membership functions, while the TCSC is placed according to reactive power flow in lines in this proposed approach to FACTS | Fuzzy logic | |
| Capacitor placement | Bharti et al. [150] | 2020 | A strategy to optimize the location of shunt capacitor banks in electricity distribution systems | Ant colony optimization, genetic algorithm |
| Roy et al. [151] | 2020 | Reduced power loss through the optimal location of the condenser using AI techniques | Artificial neural network, fuzzy logic | |
| Pimentel Filho et al. [152] | 2009 | The aim is to decrease overall losses by placing capacitor banks in distribution networks | Ant colony optimization | |
| Isac et al. [153] | 2013 | The target function comprises energy loss, energy loss and condenser banks. The placement of the condenser sites is selected based on loss sensitivity factors | Fuzzy logic | |
| Reddy et al. [154] | 2008 | A fuzzy and PSO method for placing condensers in the primary suppliers of the radial distribution systems was developed to reduce power losses and enhance the voltage profile | Fuzzy logic and particle swarm optimization | |
| Shwehdi et al. [155] | 2018 | The article focuses on the performance between the stable and the transient states in the 380-kV transmission line West–East. To dynamically handle the condenser placement problem, the GA technique is explained and implemented | Genetic algorithm | |
| Mahdavian et al. [156] | 2017 | The research aims to enhance the voltage profile and activity loss. Loss sensitivity and GA are utilized for the condenser placement and sizing | Fuzzy logic |
| Reference | Year | Objective | Technique(s) | |
| Distribution system planning | Kahouli et al. [136] | 2021 | An ideal approach to increasing the safety of a distribution system and decrease power loss by optimizing the network reconfiguration problem | Genetic algorithm and particle swarm optimization |
| Žarković et al. [137] | 2019 | Although the primary goal of a DSP is to minimize the total cost of ownership, it also aims to maximize system reliability | Mixed-integer linear programming and genetic algorithm | |
| Ahmetovic et al. [138] | 2021 | It is proposed that the Bellman–Zadeh decision-making process shall use the proposed fuzzy-inference system type Mamdani to assess the Powerline feeder reliability as a planning criterion | Fuzzy logic | |
| Suresh et al. [139] | 2017 | These studies are crucial to establishing the status of each node or bus and conditions in the distribution system and these artificial neural networks are efficient at describing the relationship between the raw data and these neural networks | Artificial neural network | |
| Kumari et al. [140] | 2018 | This article offers the optimal energy distribution system for routes and optimal drives with the lowest energy-loss costs | Particle swarm optimization | |
| Saha et al. [141] | 2021 | Solving the optimal problem of diesel generator allocation | Genetic algorithm | |
| Hosseini et al. [142] | 2021 | Cyber-physical detection, stochastic and cyber security enhancement to detect and estimate damages | Artificial neural network | |
| Lytras et al. [143] | 2019 | There are different methods suggested to optimize distribution system planning | Particle swarm optimization | |
| Gandhar et al. [144] | 2020 | Using a proportional–integral (PI) controller and FACTS, the performance of the test system is assessed by the unified power flow controller (UPFC), which is usually used in traditional energy systems. To investigate the hybrid microgrid test system, this paper uses UPFC | Fuzzy logic | |
| Tang et al. [145] | 2021 | Improving the minimum reactive system based on the harmonic analysis method | Artificial neural network, genetic algorithm | |
| Harrye et al. [146] | 2014 | A new three-phase shift algorithm is presented that reduces the total reactive power of a converter | Artificial neural network | |
| Sharma et al. [147] | 2012 | The method suggested reduces active power loss. All the control variables are bus generator tensions, tap locations and capacitor banks for shunt switchable | Particle swarm optimization | |
| Wang et al. [148] | 2021 | To maximize the population’s ability to exploit a new space, the proposed algorithm employs a sequential optimization strategy | Genetic algorithm | |
| Bhattacharyya et al. [149] | 2014 | FACTS devices, such as static var compensator and thyristor-controlled series compensator (TCSC), are placed at weak nodes in the power system by using fuzzy membership functions, while the TCSC is placed according to reactive power flow in lines in this proposed approach to FACTS | Fuzzy logic | |
| Capacitor placement | Bharti et al. [150] | 2020 | A strategy to optimize the location of shunt capacitor banks in electricity distribution systems | Ant colony optimization, genetic algorithm |
| Roy et al. [151] | 2020 | Reduced power loss through the optimal location of the condenser using AI techniques | Artificial neural network, fuzzy logic | |
| Pimentel Filho et al. [152] | 2009 | The aim is to decrease overall losses by placing capacitor banks in distribution networks | Ant colony optimization | |
| Isac et al. [153] | 2013 | The target function comprises energy loss, energy loss and condenser banks. The placement of the condenser sites is selected based on loss sensitivity factors | Fuzzy logic | |
| Reddy et al. [154] | 2008 | A fuzzy and PSO method for placing condensers in the primary suppliers of the radial distribution systems was developed to reduce power losses and enhance the voltage profile | Fuzzy logic and particle swarm optimization | |
| Shwehdi et al. [155] | 2018 | The article focuses on the performance between the stable and the transient states in the 380-kV transmission line West–East. To dynamically handle the condenser placement problem, the GA technique is explained and implemented | Genetic algorithm | |
| Mahdavian et al. [156] | 2017 | The research aims to enhance the voltage profile and activity loss. Loss sensitivity and GA are utilized for the condenser placement and sizing | Fuzzy logic |
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