This paper introduces a new approach based on a chaotic strategy and a neural network algorithm (NNA), called chaotic-based NNA (CNNA), to solve the optimal distributed generation allocation (ODGA), in the radial distribution system (RDS). This consists of determining the optimal locations and sizes of one or several distributed generations (DGs) to be inserted into the RDS to minimize one or multiple objectives while meeting a set of security limits. The robustness of the proposed method is demonstrated by applying it to two different typical RDSs, namely IEEE 33bus and 69-bus. In this regard, simulations are performed for three DGs in the cases of unity power factor (UPF) and optimal power factor (OPF), considering single and multi-objective optimization, by minimizing the total active losses and improving the voltage profile, voltage deviation (VD) and voltage stability index (VSI). Compared to its original version and recently reported methods, the CNNA solutions are more competitive without increasing the complexity of the optimization algorithm, especially when the RDS size and problem dimension are extended.
Two-way Load Flow Analysis using Newton-Raphson and Neural Network MethodsIRJET Journal
The document presents a study comparing two-way load flow analysis using the Newton-Raphson method and a neural network method for networked microgrids. The optimal power flow problem is solved using both a conventional Newton-Raphson method and an artificial intelligence neural network method. Results show that the neural network method achieves minimum losses and higher efficiency compared to the Newton-Raphson method, with efficiencies of 99.3% and 97% respectively for the test networked microgrid system.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
New typical power curves generation approach for accurate renewable distribut...IJECEIAES
This paper investigates, for the first time, the accuracy of normalized power curves (NPCs), often used to incorporate uncertainties related to wind and solar power generation, when integrating renewable distributed generation (RDG), in the radial distribution system (RDS). In this regard, the present study proposes a comprehensive, simple, and more accurate model, for estimating the expected hourly solar and wind power generation, by adopting a purely probabilistic approach. Actually, in the case of solar RDG, the proposed model allows the calculation of the expected power, without going through a specific probability density function (PDF). The validation of this model is performed through a case study comparing between the classical and the proposed model. The results show that the proposed model generates seasonal NPCs in a less complex and more relevant way compared to the discrete classical model. Furthermore, the margin of error of the classical model for estimating the expected supplied energy is about 12.6% for the photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This introduces an offset of about 10% when calculating the total active losses of the RDS after two RDGs integration.
Simultaneous network reconfiguration and capacitor allocations using a novel ...IJECEIAES
Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Combining two of the numerous strategies for solving these problems and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies, proposing a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA’s results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The network voltage profile was significantly improved as the least voltage at bus 18 was increased to 0.9530 p.u. Furthermore, the overall real power loss was significantly mitigated by 48.87%, which, when compared to the results of other methods, was superior.
Improvement of voltage profile for large scale power system using soft comput...TELKOMNIKA JOURNAL
In modern power system operation, control, and planning, reactive power as part of power system component is very important in order to supply electrical load such as an electric motor. However, the reactive current that flows from the generator to load demand can cause voltage drop and active power loss. Hence, it is essential to install a compensating device such as a shunt capacitor close to the load bus to improve the voltage profile and decrease the total power loss of transmission line system. This paper presents the application of a genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC)) to obtain the optimal size of the shunt capacitor where those capacitors are located on the critical bus. The effectiveness of the proposed technique is examined by utilizing Java-Madura-Bali (JAMALI) 500 kV power system grid as the test system. From the simulation results, the PSO and ABC algorithms are providing satisfactory results in obtaining the capacitor size and can reduce the total power loss of around 15.873 MW. Moreover, a different result is showed by the GA approach where the power loss in the JAMALI 500kV power grid can be compressed only up to 15.54 MW or 11.38% from the power system operation without a shunt capacitor. The three soft computing techniques could also maintain the voltage profile within 1.05 p.u and 0.95 p.u.
A hybrid algorithm for voltage stability enhancement of distribution systems IJECEIAES
This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.
Optimal planning of RDGs in electrical distribution networks using hybrid SAP...IJECEIAES
The impact of the renewable distributed generations (RDGs), such as photovoltaic (PV) and wind turbine (WT) systems can be positive or negative on the system, based on the location and size of the DG. So, the correct location and size of DG in the distribution network remain an obstacle to achieving their full possible benefits. Therefore, the future distribution networks with the high penetration of DG power must be planned and operated to improve their efficiency. Thus, this paper presents a new methodology for integrated of renewable energy-based DG units with electrical distribution network. Since the main objective of the proposed methodology is to reduce the power losses and improve the voltage profile of the radial distribution system (RDS). In this regard, the optimization problem was formulated using loss sensitivity factor (LSF), simulated annealing (SA), particle swarm optimization (PSO) and a combination of loss sensitivity index (LSI) with SA and PSO (LSISA, LSIPSO) respectively. This paper contributes a new methodology SAPSO, which prevents the defects of SA and PSO. Optimal placement and sizing of renewable energy-based DG tested on 33-bus system. The results demonstrate the reliability and robustness of the proposed SAPSO algorithm to find the near-optimal position and size of the DG units to mitigate the power losses and improve the radial distribution system's voltage profile.
IRJET- Comparative Study of Radial and Ring Type Distribution SystemIRJET Journal
The document presents a comparative study of radial and ring type electrical power distribution systems. It discusses how a radial distribution system has only one path of power flow, while a ring system has one or more alternate paths, improving reliability. A 5-bus distribution system is modeled in MATLAB/Simulink to compare the two system types. Simulation results show the ring type distribution system provides more reliable power supply with better voltage profile and quality compared to the radial system.
Two-way Load Flow Analysis using Newton-Raphson and Neural Network MethodsIRJET Journal
The document presents a study comparing two-way load flow analysis using the Newton-Raphson method and a neural network method for networked microgrids. The optimal power flow problem is solved using both a conventional Newton-Raphson method and an artificial intelligence neural network method. Results show that the neural network method achieves minimum losses and higher efficiency compared to the Newton-Raphson method, with efficiencies of 99.3% and 97% respectively for the test networked microgrid system.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
New typical power curves generation approach for accurate renewable distribut...IJECEIAES
This paper investigates, for the first time, the accuracy of normalized power curves (NPCs), often used to incorporate uncertainties related to wind and solar power generation, when integrating renewable distributed generation (RDG), in the radial distribution system (RDS). In this regard, the present study proposes a comprehensive, simple, and more accurate model, for estimating the expected hourly solar and wind power generation, by adopting a purely probabilistic approach. Actually, in the case of solar RDG, the proposed model allows the calculation of the expected power, without going through a specific probability density function (PDF). The validation of this model is performed through a case study comparing between the classical and the proposed model. The results show that the proposed model generates seasonal NPCs in a less complex and more relevant way compared to the discrete classical model. Furthermore, the margin of error of the classical model for estimating the expected supplied energy is about 12.6% for the photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This introduces an offset of about 10% when calculating the total active losses of the RDS after two RDGs integration.
Simultaneous network reconfiguration and capacitor allocations using a novel ...IJECEIAES
Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Combining two of the numerous strategies for solving these problems and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies, proposing a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA’s results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The network voltage profile was significantly improved as the least voltage at bus 18 was increased to 0.9530 p.u. Furthermore, the overall real power loss was significantly mitigated by 48.87%, which, when compared to the results of other methods, was superior.
Improvement of voltage profile for large scale power system using soft comput...TELKOMNIKA JOURNAL
In modern power system operation, control, and planning, reactive power as part of power system component is very important in order to supply electrical load such as an electric motor. However, the reactive current that flows from the generator to load demand can cause voltage drop and active power loss. Hence, it is essential to install a compensating device such as a shunt capacitor close to the load bus to improve the voltage profile and decrease the total power loss of transmission line system. This paper presents the application of a genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC)) to obtain the optimal size of the shunt capacitor where those capacitors are located on the critical bus. The effectiveness of the proposed technique is examined by utilizing Java-Madura-Bali (JAMALI) 500 kV power system grid as the test system. From the simulation results, the PSO and ABC algorithms are providing satisfactory results in obtaining the capacitor size and can reduce the total power loss of around 15.873 MW. Moreover, a different result is showed by the GA approach where the power loss in the JAMALI 500kV power grid can be compressed only up to 15.54 MW or 11.38% from the power system operation without a shunt capacitor. The three soft computing techniques could also maintain the voltage profile within 1.05 p.u and 0.95 p.u.
A hybrid algorithm for voltage stability enhancement of distribution systems IJECEIAES
This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.
Optimal planning of RDGs in electrical distribution networks using hybrid SAP...IJECEIAES
The impact of the renewable distributed generations (RDGs), such as photovoltaic (PV) and wind turbine (WT) systems can be positive or negative on the system, based on the location and size of the DG. So, the correct location and size of DG in the distribution network remain an obstacle to achieving their full possible benefits. Therefore, the future distribution networks with the high penetration of DG power must be planned and operated to improve their efficiency. Thus, this paper presents a new methodology for integrated of renewable energy-based DG units with electrical distribution network. Since the main objective of the proposed methodology is to reduce the power losses and improve the voltage profile of the radial distribution system (RDS). In this regard, the optimization problem was formulated using loss sensitivity factor (LSF), simulated annealing (SA), particle swarm optimization (PSO) and a combination of loss sensitivity index (LSI) with SA and PSO (LSISA, LSIPSO) respectively. This paper contributes a new methodology SAPSO, which prevents the defects of SA and PSO. Optimal placement and sizing of renewable energy-based DG tested on 33-bus system. The results demonstrate the reliability and robustness of the proposed SAPSO algorithm to find the near-optimal position and size of the DG units to mitigate the power losses and improve the radial distribution system's voltage profile.
IRJET- Comparative Study of Radial and Ring Type Distribution SystemIRJET Journal
The document presents a comparative study of radial and ring type electrical power distribution systems. It discusses how a radial distribution system has only one path of power flow, while a ring system has one or more alternate paths, improving reliability. A 5-bus distribution system is modeled in MATLAB/Simulink to compare the two system types. Simulation results show the ring type distribution system provides more reliable power supply with better voltage profile and quality compared to the radial system.
EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wirel...IJECEIAES
This document presents a new energy-efficient clustering protocol called EEIT2-F LEACH for wireless sensor networks. It uses an interval type-2 fuzzy inference system to select cluster heads, taking into account the residual energy, distance to the base station, and centrality of each node. The proposed protocol runs in set-up and steady-state phases similar to the LEACH protocol. In the set-up phase, cluster heads are selected based on the interval type-2 fuzzy logic system. Simulation results show that the new protocol outperforms existing approaches in terms of energy consumption and extending the network lifetime.
A new simplified approach for optimum allocation of a distributed generationIAEME Publication
The document describes a new methodology for optimal placement and sizing of distributed generation (DG) units in distribution networks. It involves:
1) Calculating the Tail End Nodes Voltage Deviation Index (TENVDI) by placing DG at each node to determine the optimal location with the minimum TENVDI.
2) Determining the optimal size of DG placed at the optimal location by varying the DG size and finding the size that results in minimum complex power losses.
3) The methodology is tested on IEEE 33-bus and 69-bus test systems in MATLAB. The results show reductions in losses and improvements in voltage profiles with optimal DG placement and sizing.
IMPROVEMENT of MULTIPLE ROUTING BASED on FUZZY CLUSTERING and PSO ALGORITHM I...IJCNCJournal
One of the most important issues discussed in Wireless Sensor Networks (WSNs) is how to transfer information from nodes within the network to the base station and select the best possible route for transmission of this information, taking into account energy consumption for the network lifetime with
maximum reliability and security. Hence, it would be useful to provide a suitable method that would have the features mentioned. This paper uses an Ad-hoc On-demand Multipath Distance Vector (AOMDV) as a routing protocol. This protocol has high energy consumption due to its multipath. However, it is a big challenge if it can reduce AOMDV energy consumption. Therefore, clustering operations for nodes are of high priority to determine the head of clusters which LEACH protocol and fuzzy logic and Particle Swarm Optimization (PSO) algorithm are used for this purpose. Simulation results represent 5% improvement in energy consumption in a WSN compared to AOMDV method.
The document presents a multi-objective optimization model for allocating thyristor-controlled series capacitors (TCSCs) in power systems with wind power and load randomness. It first describes existing literature on modeling renewable energy and load uncertainty and optimizing power system objectives. It then introduces a method to generate and reduce scenarios for wind power and load. Next, it establishes a multi-objective optimization model with available transmission capacity and voltage stability as objectives. Finally, it proposes an improved multi-objective particle swarm optimization algorithm to solve the established model.
Enhancing radial distribution system performance by optimal placement of DST...IJECEIAES
In this paper, A novel modified optimization method was used to find the optimal location and size for placing distribution Static Compensator in the radial distribution test feeder in order to improve its performance by minimizing the total power losses of the test feeder, enhancing the voltage profile and reducing the costs. The modified grey wolf optimization algorithm is used for the first time to solve this kind of optimization problem. An objective function was developed to study the radial distribution system included total power loss of the system and costs due to power loss in system. The proposed method is applied to two different test distribution feeders (33 bus and 69 bus test systems) using different Dstatcom sizes and the acquired results were analyzed and compared to other recent optimization methods applied to the same test feeders to ensure the effectiveness of the used method and its superiority over other recent optimization mehods. The major findings from obtained results that the applied technique found the most minimized total power loss in system, the best improved voltage profile and most reduction in costs due power loss compared to other methods.
Performance based Comparison of Wind and Solar Distributed Generators using E...Editor IJLRES
Distributed Generation (DG) technologies have become more and more important in power systems. The objective of the paper is to optimize the distributed energy resource type and size based on uncertainties in the distribution network. The three things are considered in stand point of uncertainties are listed as, (i) Future load growth, (ii) Variation in the solar radiation, (iii) Wind output variation. The challenge in Optimal DG Placement (ODGP) needs to be solved with optimization problem with many objectives and constraints. The ODGP is going to be done here, by using Non-dominated Sorting Genetic Algorithm II (NSGA II). NSGA II is one among the available multi objective optimization algorithms with reduced computational complexity (O=MN2). Because of this prominent feature of NSGA II, it is widely applicable in all the multi objective optimization problems irrespective of disciplines. Hence it is selected to be employed here in order to obtain the reduced cost associated with the DG units. The proposed NSGA II is going to be applied on the IEEE 33-bus and the different performance characteristics were compared for both wind and solar type DG units.
Review on Optimal Allocation of Capacitor in Radial Distribution SystemIRJET Journal
This document discusses optimizing the allocation of capacitors in a radial distribution system to minimize power losses and improve voltage profiles. It first reviews previous work on using techniques like loss sensitivity factor (LSF) analysis and algorithms like particle swarm optimization (PSO) and genetic algorithms (GA) to determine the optimal location and sizing of capacitors. It then outlines the objectives of applying these methods simultaneously to the test 69-bus radial distribution system, noting it leads to better optimization results than separate solutions. The conclusion reaffirms the proposed approach will minimize losses and test on the 69-bus system to develop an intelligent model for accurate capacitor placement.
Network Reconfiguration in Distribution Systems Using Harmony Search AlgorithmIOSRJEEE
This manuscript explores feeder reconfiguration in distribution networks and presents an efficient method to optimize the radial distribution system by means of simultaneous reconfiguration. Network Reconfiguration of radial distribution system is a significant way of altering the power flow through the lines. This assessment presents a modern method to solve the network reconfiguration problem with an objective of minimizing real power loss and improving the voltage profile in radial distribution system (RDS). A precise and load flow algorithm is applied and the objective function is formulated to solve the problem which includes power loss minimization. HSA Algorithm is utilized to restructure and identify the optimal strap switches for minimization of real power loss in a distribution network.. The strategy has been tested on IEEE 33-bus and 69- bus systems to show the accomplishment and the adequacy of the proposed technique. The results demonstrate that a significant reduction in real power losses and improvement of voltage profiles.
Performance comparison of distributed generation installation arrangement in ...journalBEEI
Placing Distributed Generation (DG) into a power network should be planned wisely. In this paper, the comparison of having different installation arrangement of real-power DGs in transmission system for loss control is presented. Immune-brainstorm-evolutionary programme (IBSEP) was chosen as the optimization technique. It is found that optimizing fixed-size DGs locations gives the highest loss reduction percentage. Apart from that, scattered small-sized DGs throughout a network minimizes transmission loss more than allocating one biger-sized DG at a location.
Optimal distributed generation in green building assessment towards line loss...journalBEEI
This paper presents an optimization approach for criteria setting of Renewable Distributed Generation (DG) in the Green Building Rating System (GBRS). In this study, the total line loss reduction is analyzed and set as the main objective function in the optimization process which then a reassessment of existing criteria setting for renewable energy (RE) is proposed towards lower loss outcome. Solar photovoltaic (PV)-type DG unit (PV-DG) is identified as the type of DG used in this paper. The proposed PV-DG optimization will improve the sustainable energy performance of the green building by total line losses reduction within accepted lower losses region using Artificial bee colony (ABC) algorithm. The distribution network uses bus and line data setup from selected one of each three levels of Malaysian public hospital. MATLAB simulation result shows that the PV-DG expanding capacity towards optimal scale and location provides a better outcome in minimizing total line losses within an appropriate voltage profile as compared to the current setting of PV-DG imposed in selected GBRS. Thus, reassessment of RE parameter setting and the proposed five rankings with new PV-DG setting for public hospital provides technical justification and give the best option to the green building developer for more
effective RE integration.
Lifetime enhanced energy efficient wireless sensor networks using renewable e...IJECEIAES
In this paper, we consider a remote environment with randomly deployed sensor nodes, with an initial energy of E0 (J) and a solar panel. A hierarchical clustering technique is implemented. At each round, the normal nodes send the sensed data to the nearest cluster head (CH) which is chosen on the probability value. Data after aggregation at CHs is sent to the base station (BS). CH requires more energy than normal nodes. Here, we energize only CHs if their energy is less than 5% of its initial value with the use of solar energy. We evaluate parameters like energy consumption, the lifetime of the network, and data packets sent to CH and BS. The obtained results are compared with existing techniques. The proposed protocol provides better energy efficiency and network lifetime. The results show increased stability with delayed death of the first node. The network lifetime of the proposed protocol is compared to the multi-level hybrid energy efficient distributed (MLHEED) technique and low-energy adaptive clustering hierarchy (LEACH) variants. Network lifetime is enhanced by 13.35%. Energy consumption is reduced with respect to MLHEED-4, 5, and 6 by 7.15%, 12.10%, and 14.975% respectively. The no. of packets transferred to the BS is greater than the MLHEED protocol by 39.03%.
Comparative analysis of optimal power flow in renewable energy sources based...IJECEIAES
Adaptation of renewable energy is inevitable. The idea of microgrid offers integration of renewable energy sources with conventional power generation sources. In this research, an operative approach was proposed for microgrids comprising of four different power generation sources. The microgrid is a way that mixes energy locally and empowers the end-users to add useful power to the network. IEEE-14 bus system-based microgrid was developed in MATLAB/Simulink to demonstrate the optimal power flow. Two cases of battery charging and discharging were also simulated to evaluate its realization. The solution of power flow analysis was obtained from the Newton–Raphson method and particle swarm optimization method. A comparison was drawn between these methods for the proposed model of the microgrid on the basis of transmission line losses and voltage profile. Transmission line losses are reduced to about 17% in the case of battery charging and 19 to 20% in the case of battery discharging when system was analyzed with the particle swarm optimization. Particle swarm optimization was found more promising for the deliverance of optimal power flow in the renewable energy sources-based microgrid.
AN OPTIMUM ENERGY CONSUMPTION HYBRID ALGORITHM FOR XLN STRATEGIC DESIGN IN WSN’SIJCNCJournal
In this paper, X-Layer protocol is originated which executes mobility error prediction (MEP) algorithm to calculate the remaining energy level of each node. This X-Layer protocol structure employs the mobility aware protocol that senses the mobility concerned to each node with the utilization of Ad-hoc On-Demand Distance Vector (AODV), which shares the information or data specific to the distance among individual nodes. With the help of this theory, the neighbour list will be updated only to those nodes which are mobile resulting in less energy consumption when compared to all (static/mobile) other nodes in the network. Apart from the MEP algorithm, clustering head (CH) election algorithm has also been specified to identify the relevant clusters whether they exists within the network region or not. Also clustering multi-hop routing (CMHR) algorithm was implemented in which the node can identify the cluster to which it belongs depending upon the distance from each cluster surrounding the node. Finally comprising the AODV routing protocol with the Two-Ray Ground method, we implement X-Layer protocol structure by considering MAC protocol in accordance to IEEE 802.15.4 to obtain the best results in energy consumption and also by reducing the energy wastage with respect to each node. The effective results had been illustrated through Network Simulator-II platform.
Network Reconfiguration of Distribution System for Loss Reduction Using GWO A...IJECEIAES
This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This paper proposes a Grey Wolf Optimization (GWO) algorithm to solve the feeder reconfiguration problem through fitness function corresponding to optimum combination of switches in power distribution systems. The objective function is formulated to solve the reconfiguration problem which includes minimization of real power loss. A nature inspired Grey Wolf Optimization Algorithm is utilized to restructure the power distribution system and identify the optimal switches corresponding minimum power loss in the distribution network. The GWO technique has tested on standard IEEE 33-bus and 69-bus systems and the results are presented.
Optimum Network Reconfiguration using Grey Wolf OptimizerTELKOMNIKA JOURNAL
Distribution system Reconfiguration is the process of changing the topology of the distribution
network by opening and closing switches to satisfy a specific objective. It is a complex, combinatorial
optimization problem involving a nonlinear objective function and constraints. Grey Wolf Optimizer (GWO)
is a recently developed metaheuristic search algorithm inspired by the leadership hierarchy and hunting
strategy of grey wolves in nature. The objective of this paper is to determine an optimal network
reconfiguration that presents the minimum power losses, considering network constraints, and using GWO
algorithm. The proposed algorithm was tested using some standard networks (33 bus, 69 bus, 84 bus and
118 bus), and the obtained results reveal the efficiency and effectiveness of the proposed approach.
An Energy Efficient Mobile Sink Based Mechanism for WSNs.pdfMohammad Siraj
Network lifetime and energy efficiency are crucial performance metrics used to evaluate
wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be
employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering
is to decrease the energy consumption of the network. In fact, the clustering technique will be
considered effective if the energy consumed by sensor nodes decreases after applying clustering,
however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this
paper, the energy consumption of nodes, before clustering, is considered to determine the optimal
cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of
cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent
problem which leads to a remarkable decrease in the network’s lifespan. This problem stems from
the asynchronous energy depletion of nodes located in different layers of the network.
Network loss reduction and voltage improvement by optimal placement and sizin...nooriasukmaningtyas
This document presents a study on optimizing the placement and sizing of distributed generators (DGs) in radial distribution systems using a fine-tuned particle swarm optimization approach. Simulation results on the IEEE 33 bus, IEEE 69 bus, and a 54 bus Malaysian network show that integrating both active and reactive power injection (type II DGs) achieves greater reductions in network power losses and improvements in voltage profiles compared to only active power injection (type I DGs). The maximum power loss reductions achieved with three type II DGs are 89.54% for IEEE 33 bus, 94.95% for IEEE 69 bus, and 95.23% for the 54 bus Malaysian network.
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
Optimal power flow with distributed energy sources using whale optimization a...IJECEIAES
Renewable energy generation is increasingly attractive since it is non-polluting and viable. Recently, the technical and economic performance of power system networks has been enhanced by integrating renewable energy sources (RES). This work focuses on the size of solar and wind production by replacing the thermal generation to decrease cost and losses on a big electrical power system. The Weibull and Lognormal probability density functions are used to calculate the deliverable power of wind and solar energy, to be integrated into the power system. Due to the uncertain and intermittent conditions of these sources, their integration complicates the optimal power flow problem. This paper proposes an optimal power flow (OPF) using the whale optimization algorithm (WOA), to solve for the stochastic wind and solar power integrated power system. In this paper, the ideal capacity of RES along with thermal generators has been determined by considering total generation cost as an objective function. The proposed methodology is tested on the IEEE-30 system to ensure its usefulness. Obtained results show the effectiveness of WOA when compared with other algorithms like non-dominated sorting genetic algorithm (NSGA-II), grey wolf optimization (GWO) and particle swarm optimization-GWO (PSOGWO).
Optimal Placement of D-STATCOM Using Hybrid Genetic and Ant Colony Algorithm ...IJAPEJOURNAL
In this work, a modern algorithm by hybrid genetic algorithm and ant colony algorithm is designed to placement and then simulated to determine the amount of reactive power by D-STATCOM. Also this method will be able to minimize the power system losses that contain power loss in transmission lines. Furthermore, in this design a IEEE 30-bus model depicted and three D-STATCOM are located in this system according to Economic Considerations. The optimal placement of each D-STATCOM is computed by the ant colony algorithm. In order to optimize placement for each D-STATCOM, two groups of ant are selected, which respectively located in near nest and far from the nest. Moreover, for every output simulation of D-STATCOM that is used to produce or absorb of reactive power, a genetic algorithm to minimizing the total network losses is applied. Finally, the result of this simulation shows net losses reduction about 150% that it verifies the new algorithm performance.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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In this work, a modern algorithm by hybrid genetic algorithm and ant colony algorithm is designed to placement and then simulated to determine the amount of reactive power by D-STATCOM. Also this method will be able to minimize the power system losses that contain power loss in transmission lines. Furthermore, in this design a IEEE 30-bus model depicted and three D-STATCOM are located in this system according to Economic Considerations. The optimal placement of each D-STATCOM is computed by the ant colony algorithm. In order to optimize placement for each D-STATCOM, two groups of ant are selected, which respectively located in near nest and far from the nest. Moreover, for every output simulation of D-STATCOM that is used to produce or absorb of reactive power, a genetic algorithm to minimizing the total network losses is applied. Finally, the result of this simulation shows net losses reduction about 150% that it verifies the new algorithm performance.
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the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
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including local and remote approaches, and modern techniques, including
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aforementioned factors. Using Expert Choice software, the proposed
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Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
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Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
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An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
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Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
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Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
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Multi-objective distributed generation integration in radial distribution system using modified neural network algorithm
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 5, October 2023, pp. 4810~4823
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp4810-4823 4810
Journal homepage: http://ijece.iaescore.com
Multi-objective distributed generation integration in radial
distribution system using modified neural network algorithm
Ali Tarraq, Faissal El Mariami, Abdelaziz Belfqih
Laboratory of Energy and Electrical Systems, Department of Electrical Engineering, Ecole Nationale Supérieure d’Electricité et de
Mécanique, Hassan II University of Casablanca, Casablanca, Morocco
Article Info ABSTRACT
Article history:
Received Oct 3, 2022
Revised Dec 25, 2022
Accepted Feb 4, 2023
This paper introduces a new approach based on a chaotic strategy and a
neural network algorithm (NNA), called chaotic-based NNA (CNNA), to
solve the optimal distributed generation allocation (ODGA), in the radial
distribution system (RDS). This consists of determining the optimal
locations and sizes of one or several distributed generations (DGs) to be
inserted into the RDS to minimize one or multiple objectives while meeting
a set of security limits. The robustness of the proposed method is
demonstrated by applying it to two different typical RDSs, namely IEEE 33-
bus and 69-bus. In this regard, simulations are performed for three DGs in
the cases of unity power factor (UPF) and optimal power factor (OPF),
considering single and multi-objective optimization, by minimizing the total
active losses and improving the voltage profile, voltage deviation (VD) and
voltage stability index (VSI). Compared to its original version and recently
reported methods, the CNNA solutions are more competitive without
increasing the complexity of the optimization algorithm, especially when the
RDS size and problem dimension are extended.
Keywords:
Chaotic-map
Distributed generation
Metaheuristics
Neural network algorithm
Radial distribution system
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ali Tarraq
Laboratory of Energy and Electrical Systems, Department of Electrical Engineering, Ecole Nationale
Supérieure d’Electricité et de Mécanique, Hassan II University of Casablanca
B.P 8118, Oasis, Casablanca, Morocco
Email: ali.tarraq@gmail.com
1. INTRODUCTION
During this period of rising prices, mainly caused by the severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) pandemic and the conflict between Russia and Ukraine, the grid integration of
renewable distributed generation (RDG) in the radial distribution system (RDS), is one of the most
sustainable alternatives considered by the majority of countries in the world [1]. For developing countries
such as Morocco, this type of source allows, for the best, to reach energy independence and transition [2].
RDG’s integration allows for achieving the desired development and mitigating the economic damage
resulting from this global crisis.
According to the literature, distributed generation (DG) based on renewable sources is a small-scale
production unit exploiting renewable energy resources (such as solar, wind, water, biomass, or geothermal
energy), close to the point of use, where the users are the producers-whether they are individuals, small businesses
and/or a local community. If DGs are also connected (to share surplus power), they become a local renewable
energy network, also called a microgrid. Which in turn can be connected to similar networks nearby [3], [4].
Depending on the nature of the power supplied, DGs are classified into three categories [5]:
− Type 1: Generates active power only unity power factor distributed generation (UPF-DG).
− Type 2: Generates reactive power only (zero power factor (PF)).
2. Int J Elec & Comp Eng ISSN: 2088-8708
Multi-objective distributed generation integration in radial distribution system using … (Ali Tarraq)
4811
− Type 3: Generates active power and reactive power (lagging PF).
In addition to helping reduce greenhouse gas emissions, the integration of RDG into the RDS
improves the controllability and overall efficiency of the system and increases the rate of benefit [6].
However, due to the complexity of the distribution system, the location and/or random sizing of these sources
can harm the overall system performance and parameters [7]. For this reason, the insertion of DGs with
renewable sources requires an optimization study, often referred to as the optimal DG allocation (ODGA) in
the RDS. Typically, ODGA is used to find the optimal location and size of one or several DGs to be
integrated, to improve the overall performance of the RDS, by subjecting to a set of constraints related to
voltage, current, and power [8].
However, the interdependence between the parameters of the RDS as well as its size makes the
ODGA a combinatorial, and multimodal problem, whose solution is too hard to be done using conventional
and analytical methods [8]. In this context, most researchers have proven the effectiveness of metaheuristic
methods for solving ODGA. These methods are inspired by the innate behavior developed in living beings, or
by physical and natural phenomena such as gravitation [9]. Due to their random nature, this type of method
does not depend on the initial solution, as are conventional methods, e.g., linear or non-linear programming.
Therefore, these methods are quite robust, as they can generate good-quality solutions with reduced
algorithmic complexity.
During the last seven years, metaheuristic optimization methods have attracted more attention from
researchers and investors for solving the ODGA problem. Muthukumar and Jayalalitha [10] proposed the
hybridization of the harmonic search algorithm (HSA) and the particle artificial bee colony algorithm
(PABCA), to find the sizes and locations of two different DGs simultaneously with capacitor banks in the
typical IEEE 33-bus and 119-bus RDSs, to reduce the power losses and improve the voltage profile.
Ali et al. [11] have successfully proved the effectiveness of hybridizing the ant-lion optimizer (ALO) and a
loss sensitivity factor (LSF)-based method, to solve the ODGA. The study focuses on the insertion of a
hybrid photovoltaic (PV)-wind-DG in 34-bus and 69-bus networks, to improve the voltage profile and
stability, and to reduce the active power losses with variable load models. Literature [12] demonstrates the
superiority of comprehensive teaching and learning-based optimization (CTLBO) over quasi-oppositional
TLBO (QOTLBO). This method, implemented for the IEEE 33-bus, 69-bus, and 118-bus networks, is used to
reduce losses, improve voltage profile, and save annual consumption in the cases of variable and constant
load, with three UPF-DGs. The research paper [13] proposes a comparative study between tabu search
algorithm (TSA), scatter search algorithm (SSA), and ant colony optimization (ACO). These three algorithms
are applied for the same network (IEEE 13-bus) to minimize the active losses through the optimal integration
of three DGs at different PFs. The study shows that the SSA gives the best results, while the worst are
provided by the ACO. In [14], a Chao and quasi-opposition (QOC) strategy are adopted to improve the
exploration and exploitation capability of the symbiotic organism search (SOS) algorithm, to be able to solve
the ODGA problem. The proposed algorithm aims to reduce the active losses and improve the profile and
stability of the voltage by optimally integrating three DGs with fixed and optimal PF, in the typical 33-bus,
69-bus and 118-bus networks. In [15], the same authors merged SOS and neural network algorithm (NNA) to
allocate, simultaneously, three DGs and three capacitor banks (CBs) within the two IEEE 33-bus and IEEE
69-bus networks, adopting a constant and a variable load models. The study focuses on minimizing a multi-
objective function with five weighted objectives, namely, the index of active losses, voltage deviation,
voltage stability, power supply reliability, and load balance.
According to the latest literature, the NNA in its original version can provide competitive results,
since it has a good operating capability. However, it is still limited in terms of exploration [15]. It is true that
mixing the NNA with another method with good exploration capacity, such as the SOS, improves the results
obtained, but this considerably increases the complexity of the algorithm and the number of tuning
parameters. In this context, the present study proposes the improvement of the global search capacity of the
NNA by adopting a Chao strategy, based on the logistic map. As such, the proposed method is referred to as
the chaotic-based neural network algorithm (CNNA) and is used to solve the ODGA problem for the optimal
insertion of three DGs at different PFs. The effectiveness of CNNA is tested for two different networks,
namely IEEE 33-bus and IEEE 69-bus, in single-objective, and multi-objective contexts, by minimizing three
indices such as active loss index (ALI), voltage deviation index (VDI), and global voltage stability index
(GVSI). The results obtained by CNNA are compared with those obtained by SOS-NNA in [15], and other
existing methods such as quasi-oppositional chaotic symbiotic organisms search (QOCSOS) [14], and sine
cosine algorithm (SCA) [16].
This paper comprises three other sections. The next one describes the adopted formulation of the
ODGA problem, namely the details of the multi-objective function and the constraints involved. The third
section presents a description of the CNNA framework. Section 4 includes a discussion and a comprehensive
analysis of the obtained results. Then, the different limitations and perspectives of the study are presented in
the conclusion section.
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 5, October 2023: 4810-4823
4812
2. POBLEM FORMULATION
2.1. Active loss index
The calculation of total active losses, noted Pl, is a crucial step in determining the efficiency and
performance of the RDS. This is done through a well-established formula that considers various electrical
parameters such as current, voltage, and resistance in the RDS. In (1) is specifically designed to provide an
accurate estimate of the energy losses due to resistive heating within the system. This information is valuable
for optimizing the RDS and ensuring that it operates at peak performance.
𝑃𝑙 = ∑ 𝑅𝑖𝐼𝑖
2
𝑁𝐿
𝑖=1 (1)
where NL presents the total number of branches, Ii and Ri denote respectively the branch current and
resistance of line number i. Subsequently, the ALI can be expressed as (2):
𝐴𝐿𝐼 =
𝑃𝑙𝑤𝐷𝐺
𝑃𝑙𝑤𝑜𝐷𝐺
(2)
where PlwoDG and PlwDG represent the total real losses without and with DG insertion.
2.2. Voltage deviation index
The voltage deviation (VD) presents the total offset between the nodal voltages and the base voltage,
which is also the voltage at the reference node of the network. It is expressed as (3):
𝑉𝐷 = ∑ (𝑉𝑖 − 𝑉𝑟𝑒𝑓)2
𝑁𝑏
𝑖=1 (3)
where Nb shows the total number of nodes in the network. While Vref indicates the reference voltage, which is
equal to 1 per unit (pu), and Vi presents the voltage at node number i. Accordingly, the VDI can have the
following formula:
𝑉𝐷𝐼 =
𝑉𝐷𝑤𝐷𝐺
𝑉𝐷𝑤𝑜𝐷𝐺
(4)
where VDwoDG and VDwDG represent the VD before and after DG insertion.
The reduction of the VDI significantly affects the voltage profile of the RDS. The process involves
bringing the set of nodal voltages closer to the base voltage, which acts as a reference point at the slack bus.
This results in a more uniform voltage profile, with the nodal voltages being closer to the reference value.
The improvement in the voltage profile leads to a more stable and efficient RDS, which is essential for
maintaining reliable power supply. The base voltage acts as a benchmark, ensuring that the voltage
deviations are within acceptable limits and reducing the risk of voltage-related problems such as power
outages, surges, or drops. Overall, the reduction of VDI is a critical step in ensuring the reliability and
stability of RDS.
2.3. Global voltage stability index
The voltage stability index VSI quantifies the ability of a node to resist against strong voltage drops.
For load flow to be possible, the VSI of each node in the network must be strictly positive [17]. The VSI of a
node n of the RDS depends on the voltage at node m upstream n, such that [18]:
𝑉𝑆𝐼(𝑛) = |𝑉
𝑚|4
− 4[𝑃𝑛𝑋𝑚,𝑛 − 𝑄𝑛𝑅𝑚,𝑛]
2
− 4[𝑃𝑛𝑅𝑚,𝑛 + 𝑄𝑖+1𝑋𝑚,𝑛]|𝑉
𝑛|2
(5)
where Rm,n and X m,n are the line resistance and the line reactance of the branch linked between nodes m and n,
Pn and Qn are respectively, the active and reactive power exiting from node n, such that:
𝑃𝑛 + 𝑗 × 𝑄𝑛 = (𝑉
𝑛 × 𝐼𝑚,𝑛)
∗
(6)
where j is an imaginary number such that j² = -1 and Im,n is the current flowing through the line (m,n). It has
the following expression:
𝐼𝑚,𝑛 =
𝑉𝑚−𝑉𝑛
𝑅𝑚,𝑛+𝑗𝑋𝑚,𝑛
(7)
4. Int J Elec & Comp Eng ISSN: 2088-8708
Multi-objective distributed generation integration in radial distribution system using … (Ali Tarraq)
4813
where Vm and Vn present the voltage at node m and node n respectively.
The objective of maximizing the minimum VSI is to improve the overall stability of a power
distribution network. To achieve a more stable network, the minimum VSI must be maximized, which
requires finding the optimal operating point that balances network stability and power flow. Herein, the
GVSI can be used to quantify the stability of the entire network and can be expressed according to (8). By
maximizing the GVSI, the stability and reliability of the power distribution network can be improved,
ensuring a consistent and uninterrupted power supply to customers.
𝐺𝑉𝑆𝐼 =
min (𝑉𝑆𝐼𝑤𝑜𝐷𝐺)
min (𝑉𝑆𝐼𝑤𝐷𝐺)
(8)
where VSIwoDG and VSIwDG indicate the VSI without and with the three DGs respectively.
2.4. Multi-objective function (MOF)
Generally, the MOF is formulated to address multiple objectives simultaneously. As a weighted
sum, the MOF represents the optimization problem's objectives and the trade-off between them. The weights
assigned to each objective represent their relative importance, and the MOF can be optimized to find the best
solution that balances these objectives. This approach provides a more comprehensive and flexible way to
address complex optimization problems and can lead to better decision-making and more efficient solutions.
In this study, the MOF can be expressed as (9):
Min 𝑀𝑂𝐹 = 𝑤1𝐴𝐿𝐼 + 𝑤2𝑉𝐷𝐼 + 𝑤3𝐺𝑉𝑆𝐼 + ∑ 𝑝𝑖 (9)
where w1, w2, and w3 are weighting coefficients such that w1 + w2 + w3 =1. In a single-objective context, the
only function chosen is the PLI since the power losses are the most important parameter in the case of RDS.
Herein, the weighting coefficients are respectively equal to 1, 0, and 0. In the multi-objective context, these
coefficients are chosen to be 0.3, 0.5, and 0.2 respectively. This choice is due to the huge number of
executions realized. In (9), pi coefficients present the penalty factors that serve as a support mechanism for
the considered constraints.
For each constraint, a pi factor is associated. If the chosen solution does not respect one of the
constraints, then the corresponding pi takes the value 100. Thus, the optimization program automatically
rejects the corresponding solution, and the search space is limited.
2.5. Constraints
In order to ensure that the solutions reached for the RDS comply with normal operation, a set of
security limits are established. These limits are in place to guarantee that the solutions do not compromise the
stability or performance of the RDS. These security limits include voltage limits, thermal limits, DG capacity
limits, PF limits, and DG location constraints, and are defined as (10):
− Voltage limits
𝑉𝑚𝑖𝑛 ≤ |𝑉𝑖| ≤ 𝑉
𝑚𝑎𝑥 (10)
− Thermal limits
𝐼𝑙 ≤ 𝐼𝑙𝑖𝑚𝑖𝑡 (11)
− DG capacity limits
𝑃𝐷𝐺𝑚𝑖𝑛
≤ 𝑃𝐷𝐺𝑖
≤ 𝑃𝐷𝐺𝑚𝑎𝑥
(12)
0.1 𝑃𝐿 ≤ ∑ 𝑃𝐷𝐺𝑖
≤ 0.75
𝑁𝑑𝑔
𝑖=1
𝑃𝐿 (13)
− PF limits
0.8 ≤ 𝑃𝐹
𝑔 ≤ 1 (14)
− DG location constraint
2 ≤ 𝐷𝐺𝑙𝑜𝑐 ≤ 𝑁𝑏 (15)
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where Vmin and Vmax are the limit values of the nodal voltage and Vi is the voltage at the ith
node, Il is the line
current at the branch l, and Ilimit its maximum value. Powers PDGi and QDGi are respectively active and reactive
power of the DG number i, and PL denotes the total power demand. Moreover, PFg presents the power factor
of DG number g, and Ndg is the total number of DGs.
3. CNNA FRAMEWORK
The NNA is inspired by the biological way in which the nervous system and neural networks
function in living beings [19]. It is similar to the artificial neural network (ANN) method that is often used in
prediction studies. Typically, ANN aims to reduce the difference between the given target value and the
predicted value by adjusting weight factors. The only difference between the two methods is that for the
NNA the target value is considered as an output, while for the ANN it is part of the inputs of the algorithm.
To improve the ability of the exploration in the NNA, a chaotic strategy is joined to it. As a result,
the CNNA framework consists of five components. The first one consists in updating the N positions of an Xt
matrix in each iteration t, according to the following formula:
𝑋𝑡+1 = 𝑋𝑡 ∗ [1 + 𝑊𝑡] (16)
where Wt presents the N×N matrix of weights such that:
∑ 𝑊𝑡(𝑘, 𝑙) = 1
𝑁
𝑖=1 0 < 𝑊𝑡(𝑘, 𝑙) < 1, 𝑘 = 1,2, . . 𝑁, 𝑙 = 1,2. . . 𝑁 (17)
The second step of the NNA is the update of Wt according to (17):
𝑊𝑡+1(𝑘) = 𝑊𝑡(𝑘) + 2𝛾. [𝑊𝑡𝑎𝑟𝑔𝑒𝑡 − 𝑊𝑡(𝑘)] 𝑘 = 1,2, … , 𝑁 (18)
where γ is a random number uniformly chosen between 0 and 1, and Wtarget presents the vector of weights
corresponding to the best position.
The third operator aims to improve the capacity of the global search of the NNA. It is called the bias
operator, which depends on a modification factor β. In each iteration, β is updated according to the following
formula:
𝛽𝑡+1 = 0.99𝛽𝑡 (19)
Once this operator is executed, the bias of Xt and Wt is done according to (20) and (21):
𝑋𝑞,𝑃 = 𝑙𝑏𝑖,𝑃 + (𝑢𝑏𝑞,𝑃 − 𝑙𝑏𝑞,𝑃) ⊗ 𝑟𝑎𝑛𝑑𝑞,𝑃 𝑞 = 1,2, . . , 𝑁 (20)
𝑊
𝑤 = 𝑟𝑎𝑛𝑑𝑤 (21)
where ub and lb present, respectively the upper and lower bounds of the decision variables, rand is a random
number uniformly chosen between 0 and 1 and ⊗ symbolizes the element-wise product. While, P is the
vector of indices of the Np decision variables that must be biased, and w presents the vector of indices of Nw
number of weights to be biased such that:
𝑁𝑝 = 𝑟𝑜𝑢𝑛𝑑(𝐷 × 𝛽𝑡) (22)
𝑁𝑤 = 𝑟𝑜𝑢𝑛𝑑(𝑁 × 𝛽𝑡) (23)
where D presents the problem dimension or the total number of decision variables.
To improve the local search capability, a fourth component is incorporated to the NNA, which is
called the transfer operator. If the bias operator is not executed, then this operator is performed to change the
positions Xt according to the following:
𝑁𝑤 = [𝑋𝑡𝑎𝑟𝑔𝑒𝑡 − 𝑋𝑡(𝑘)] 𝑘 = 1,2, … , 𝑁 (24)
𝑋𝑡+1(𝑘) = 𝑋𝑡(𝑘) + 2𝛾′[𝑋𝑡𝑎𝑟𝑔𝑒𝑡 − 𝑋𝑡(𝑘)] 𝑘 = 1,2, … , 𝑁 (25)
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where γ' is a random number between 0 and 1, Xtarget denotes the best position found so far, and Xt(k) is the kth
position at the tth
iteration.
According to (19), βt decreases with the number of iterations, which decreases the chance of
performing the bias operator. This significantly decreases the exploration capability of the NNA. To cope
with this problem, in this study, a fifth component is added to the NNA. It is a Chao strategy based on the
logistic map, which is the most adopted in the literature. It consists of generating a set of β values according
to (24):
𝛽𝑡+1 = 𝜆𝛽𝑡(1 − 𝛽𝑡) 𝑡 = 1,2, … . , 𝑡𝑚𝑎𝑥 (26)
where tmax the maximum number of iterations and λ is the Chao factor, considered equal to 4. The initial
value of βt is randomly chosen according to the uniform distribution between 0 and 1.
Thus, (19) is not adopted for the modification of the bias factor in the main CNNA framework.
Rather, a number Np of β factors are generated in the initialization step according to (24). Obviously, this
does not affect the time and space complexity of the optimization program. The flowchart of the CNNA is
shown in Figure 1.
Figure 1. Flowchart of CNNA
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4. SIMULATION RESULTS AND DISCUSSION
4.1. Assumptions
In this study, tests are performed for two cases:
a. #Case 1: Three DGs with unity PF (UPF-DG), in single-objective (minimization of ALI only), and in
multi-objective (minimization of MOF).
b. #Case 2: Three DGs with optimal PF (OPF-DG), in single-objective (minimization of ALI only), and
multi-objective (MOF minimization).
The main assumptions of this study are as: i) the load is considered constant and fixed at its nominal
value; ii) the output power of the DGs is considered constant. The uncertainties related to solar irradiance or
wind speed are not considered. Therefore, the characteristics of the DG system are not required in the main
optimization program; iii) the insertion of DGs is considered without impact on the total harmonic distortion
rate; iv) each node supports no more than one DG; and v) the inserted DGs are all equipped with an inverter,
allowing them to operate in any PF. For simplicity, the present study considers only the case of DGs
operating in type 1 or type 3.
The simulations in this study are conducted using MATLAB software version 2021a on a PC with
an Intel(R) Core (TM) i5-3320M CPU running at 2.60 GHz and equipped with 4 CPUs, and 6GB RAM. The
simulations are repeated 5 times with a population size of 40 and a maximum number of iterations set to 150.
These settings are used consistently across all simulations to ensure accurate and reliable results. The load
flow calculation program is based on the direct approach introduced by Teng [20].
4.2. IEEE 33-bus system
The IEEE 33-bus is a typical 32-branch RDS, introduced by Baran and Wu in [21]. The voltage at
its reference node is equal to 12.66 kV with a base power of 100 MVA. The power rating of the total load is
equal to 3,715 kW and 2,300 kVar. In the base case, this RDS has no devices. Herein, the load flow program
estimates the active and reactive losses at 210.98 kW and 143 kVar.
4.2.1. Case 1
Table 1 summarizes all the results obtained for the 33-bus system in case 1. Herein, the total losses
are reduced up to 65.5% in single-objective and 58.7% in multi-objective, which is respectively equivalent
and superior to the results obtained by the recent hybrid algorithm SOS-NNA. In single-objective, the
SOS-NNA proves a good local search capacity. Therefore, the results for the indices VD and VSI are slightly
more refined compared to the CNNA. Besides, the choice of the weighting coefficients wi in (9), can also
increase the discrepancy between the obtained indices, but this always remains marginal. In addition, the
minimum voltage of the network was increased from 0.9038 pu at bus 18 to 0.9809 pu at bus 25 in multi-objective
optimization.
Table 1. Results for 33-bus system for three UPF-DGs in single-objective and in multi-objective (case 1)
Method Locations Sizes (kW) Power loss (kW)/
Loss reduction (%)
VD Minimum VSI Minimum voltage
(pu)/@ bus
Single-objective
Base case - - 210.98/0% 0.1337 0.6671 0.9038/18
Proposed 13
30
24
797.5
1036.1
1089.7
72.80/65.50% 0.0156 0.8782 0.9680/33
SCA [23] 13
30
24
827.3
1082.15
1022.4
72.83/65.48% - - 0.9680/33
NNA [15] 14
29
24
929.4
887.9
1009.2
75.76/64.09% 0.01505 0.8804 -
SOS-NNA [15] 13
30
24
801.8
1053.6
1091.3
72.78/65.50% 0.015113 0.88043 -
Multi-objective
Base case - - 210.98/0% 0.1337 0.6671 0.9038/18
Proposed 31
14
6
997.8
937.2
1027.8
87.13/58.7% 0.00391 0.9258 0.9809/25
SOS-NNA [15] 16
24
33
1123.4
1453.2
1138.6
95.79/54.6% 0.00339 0.9267 -
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The results are also presented in Figure 2. The figure displays the convergence curves of both the
single-objective and multi-objective scenarios, with the former displayed in Figure 2(a) and the latter
depicted in Figure 2(b). The CNNA's ability to converge rapidly to a near-optimal solution is evident from
the convergence curves, which highlights the algorithm's exploration capability.
In addition to the convergence curves, Figure 2 also displays the voltage profiles for both the single-
objective and multi-objective scenarios. A comparison of Figures 2(a) and 2(b) shows a significant
improvement in the voltage profile in the multi-objective case compared to the single-objective case. This is
a further testament to the effectiveness of the CNNA in multi-objective integration of DGs into the RDS.
(a) (b)
Figure 2. Convergence curve and voltage profile for 33-bus in case 1: three UPF-DGs (a) with single-
objective and (b) with multi-objective
4.2.2. Case 2
Table 2 presents the outcomes acquired for case 2 of the 33-bus system. The results demonstrate a
remarkable reduction in total losses, decreasing up to 93.66% in multi-objective optimization. Moreover, the
VD is decreased to 0.000289 pu and the minimum voltage stability index (VSI) is enhanced to 0.9763 pu.
These findings show that the proposed algorithm is highly effective in enhancing the problem dimension,
particularly in multi-objective optimization. Notably, the results achieved through the proposed method
exceed those attained by recent optimization methods, indicating the superior performance of the proposed
algorithm.
The convergence curves in Figures 3(a) and 3(b) are used to assess the global search capability of
the CNNA. These curves reveal that although the convergence rate is slow, the solutions generated by the
algorithm are competitive with recent optimization methods. A comparison between the voltage profiles of
the single-objective and multi-objective scenarios in Figures 3(a) and 3(b) indicates a slight improvement in
the latter. Furthermore, the voltage profile in case 2 in Figure 3 exhibits a marked enhancement in
comparison to case 1 in Figure 2, underscoring the significance of the DG's power factor variation in
achieving satisfactory outcomes.
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Table 2. Results for 33-bus system for three OPF-DGs in single-objective and
in multi-objective (case 2)
Method Locations Sizes (kW) Power loss (kW)/
Loss reduction (%)
VD Minimum VSI Minimum voltage
(pu)/@ bus
Single-objective
Base case - - 210.98/0% 0.1337 0.6671 0.9038/18
Proposed 30
24
14
1,167 (0.800)
712.4 (0.873)
906.1 (0.814)
13.15/93.7% 0.00076 0.9637 0.9908/8
NNA [15] 12
25
30
809.1 (0.824)
497.8 (0.536)
1,275.1 (0.873)
21.1/89.99% 0.00081 0.9532 -
SOS-NNA [15] 13
24
30
793.9 (0.904)
1,070 (0.900)
1,029.7 (0.713)
11.74/94.44% 0.00063 0.9688 -
Multi-objective
Base case - - 210.98/0% 0.1337 0.6671 0.9038/18
Proposed 13
30
24
807 (0.878)
1,173.1 (0.800)
975 (0.805)
13.37/93.66% 0.000289 0.9763 0.9940/22
NNA [15] 13
24
30
711.6 (0.884)
536 (0.532)
1,660 (0.867)
25.38/87.97% 0.001078 0.9763 -
SOS-NNA [15] 13
24
30
817 (0.888)
1,433.2 (0.905)
1,116.5 (0.725)
14.554/93.1% 0.000315 0.9775 -
(a) (b)
Figure 3. Convergence curve and voltage profile for 33-bus in case 2: three OPF-DGs (a) with single-
objective and (b) with multi-objective
voltage
magnitude
(pu)
ALI
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4.3. IEEE 69-bus system
The typical IEEE 69-bus RDS is introduced by the same authors of the IEEE 33-bus [22]. It has 68
branches with base values equal to 12.66 kV and 100 MVA. The nominal power injected is equal to
3,800 kW and 2,690 kVar. In the base case, the active and reactive losses are estimated at 225 kW and
102.16 kVar.
4.3.1. Case 1
The effectiveness of the CNNA algorithm in the 69-bus system is demonstrated through Table 3,
which reveals favorable outcomes. Specifically, in the single-objective optimization scenario, the active
losses are lowered by 69.08%. In the multi-objective optimization case, the minimum VSI is increased to
0.9605 pu, and the active losses and VD are reduced to 67.28% and 0.00133 pu, respectively. These results
are notably competitive and have not been achieved in existing literature, indicating the strength and efficacy
of the CNNA algorithm, particularly as the RDS size is increased.
Table 3. Results for 69-bus system for three UPF-DGs in single-objective and in multi-objective (case 1)
Method Locations Sizes (kW) Power loss (kW)/ Loss
reduction (%)
VD Minimum
VSI
Minimum voltage
(pu)/@ bus
Single-objective
Base case - - 225/0% 0.13370 0.6671 0.9091/65
Proposed 11
61
19
434
1723.5 381.5
69.55/69.08% 0.00567 0.9171 0.9785/65
SCA [23] 15
27
61
567.6 49.08
1749.33
71.77/68.11% - - 0.9783/65
MGSA [24] 15
61
63
562.65 1190.1
523.3
71.90/- - - -
SOS-NNA [15] 11
18
61
526.8
380.3
1719
69.43/69.14% 0.005201 0.9185 -
Multi-objective
Base case - - 225/0% 0.13370 0.6671 0.9091/65
Proposed 61
66
21
2024.7 607.3
397.5
73.62/67.28% 0.00133 0.9605 0.9899/65
MOSCA [25] 10
61
63
1155.7 205.74
1322.8
157.64/- - 0.7764 0.9384/-
SOS-NNA [15] 20
44
63
102.5 849.5
1813.1
91.37/59.38% 0.00400 0.9430 -
Figure 4 offers additional validation for the effectiveness of the multi-objective DG placement in the
RDS. A comparison of the voltage profile between the single-objective scenario (presented in Figure 4(a))
and the multi-objective scenario (depicted in Figure 4(b)) reveals a significant improvement in the latter,
further emphasizing the advantages of the multi-objective approach. Additionally, the convergence curves in
both Figures 4(a) and 4(b) exhibit the strong exploration capability of the CNNA algorithm, demonstrating its
ability to provide accurate and efficient results. These results provide persuasive evidence that the integration
of DG with a multi-objective framework leads to optimal solutions for DG placement in the RDS.
4.3.2. Case 2
The convergence curves and voltage profiles obtained by of the CNNA algorithm in case 2 for the
69-bus system are presented in Figure 5. The curves provide insight into the exploration and convergence
capabilities of the algorithm and its ability to generate high-quality solutions. Figures 5(a) and 5(b)
demonstrate that CNNA displays a strong exploration capability, albeit at the cost of a slower convergence
rate, as the chaotic strategy helps avoid premature convergence and enhances the quality of the obtained
solutions. Notably, an improvement in voltage profile was observed (Figures 5(a) and 5(b)), with the best
profiles generated in case 2, highlighting the suitability of the OPF-DG for compensating for instabilities and
power losses. These results provide compelling evidence of the effectiveness of the proposed algorithm in
tackling multi-objective optimization problems and dealing with problems of higher dimensionality.
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(a) (b)
Figure 4. Convergence curve and voltage profile for 69-bus in case 1: three UPF-DGs (a) with single-
objective and (b) with multi-objective
(a) (b)
Figure 5. Convergence curve and voltage profile for 69-bus in case 2: three OPF-DGs (a) with single-
objective and (b) with multi-objective
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From Table 4, the results obtained by the CNNA are very impressive and show its robustness
compared to other recent algorithms. The single-objective optimization showed a decrease in total losses to
98% while the multi-objective optimization showed a reduction in total losses to 97.5% and a decrease in
voltage deviation to 0.000128 pu and an improvement in minimum VSI to 0.9772 pu. The results clearly
demonstrate the superiority of the CNNA algorithm over SOS-NNA, especially with respect to the multi-
objective resolution of the ODGA.
These results are a first in the literature and leave room for further improvements. The search space
can be expanded by decreasing the severity of the constraints without compromising the safety and proper
operation of the system. This can be tested taking into account load and generation uncertainties.
Table 4. Results for 69-bus system for three OPF-DGs in single-objective and in multi-objective (case 2)
Method Locations Sizes (kW) (PF) Power loss (kW)/ Loss
reduction (%)
VD Minimum
VSI
Minimum voltage
(pu)/@ bus
Single-objective
Base case - - 225/0% 0.13370 0.6671 0.9091/65
Proposed 66
61
21
459.5 (0.800)
1,672.3 (0.812)
380.2 (0.851)
4.68/98% 0.000135 0.9772 0.9942/50
NNA [15] 9
37
62
1,092.1 (0.792)
582.3 (0.668)
1,592.1 (0.839)
17.63/92.2% 0.005974 0.9197 -
HHO [26] 17
61
66
270.8 (0.570)
1,541.4 (0.760)
696.8 (0.970)
6.58/97.1% - - -
SOS-NNA
[15]
11
18
61
493.5 (0.812)
380 (0.833)
1,674.3 (0.813)
4.27/98.1% 0.000127 0.9772 -
Multi-objective
Base case - - 225/0% 0.13370 0.6671 0.9091/65
Proposed 18
67
61
470.8 (0.947)
480.6 (0.894)
1,664.2 (0.808)
5.6/97.5% 0.000128 0.9772 0.9942/50
MOHHO
[26]
15
60
61
332 (0.370)
314 (0.35)
1,784 (0.980)
21.8/90.3% 0.0008 0.980 -
MOIHHO
[26]
13
49
62
1,064 (0.810)
1,235 (0.950)
1,610 (0.810)
13.9/93.83% 0.0005 0.9910 -
SOS-NNA
[15]
16
49
61
608.3 (0.827)
1,192.8 (0.814)
1,835.9 (0.812)
6.59/97.06% 0.000297 0.9879 -
5. CONCLUSION
In this study, the solution to the ODGA problem is done through a chaotic neural networks
algorithm CNNA. The efficiency of the CNNA is proved through its application on two typical systems,
namely the IEEE 33-bus and the IEEE 69-bus. The simulations are done with three UPF-DGs for single and
multi-objective optimization, as well as for three OPF-DGs. The results obtained show that the exploration
capacity of the NNA has improved thanks to the Chao strategy. The results are quite competitive and superior
to those obtained by existing methods in the literature, especially when the problem dimension is increased
with a system having a large number of nodes such as the IEEE-69 bus. Hence, thanks to the CNNA a strong
and optimal insertion of the DG can be ensured, without increasing the complexity of the optimization
algorithm in space and time. Despite its remarkable performance, there are still areas for improvement. The
CNNA shows a slight delay in convergence. Moreover, incorporating the generation and demand
uncertainties, as well as expanding the system size, could lead to further optimization by the CNNA. Further
research and improvement of the CNNA can enhance its capabilities and lead to solutions that are even more
efficient.
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BIOGRAPHIES OF AUTHORS
Ali Tarraq was born in Safi, Morocco. He received the M. Eng. degree in
industrial engineering from the University of Cadi Ayyad (UCA), National School of
Applied Sciences, ENSA of Safi, Morocco, in 2010. Currently he is a Ph.D. student in
electrical engineering at the National School of Electricity and Mechanics, ENSEM of
Casablanca, Morocco. His current research interests include distributed generation based
renewable energy, and smart grids applications. He can be contacted at email:
ali.tarraq@gmail.com.
14. Int J Elec & Comp Eng ISSN: 2088-8708
Multi-objective distributed generation integration in radial distribution system using … (Ali Tarraq)
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Faissal El Mariami is an engineer, Ph.D. and holder of university HDR
accreditation. He is a professor qualified to direct research and member of the RECS
research team at the National School of Electricity and Mechanics ENSEM (Hassan II
University). Electrical networks stability and protection coordination are the center of his
interest. He can be contacted at email: f_elmariami@yahoo.fr.
Abdelaziz Belfqih is an engineer, Ph.D. and holder of university HDR
accreditation. He is a professor qualified to direct research and member of the RECS
research team at the National School of Electricity and Mechanics ENSEM (Hassan II
University). His research subjects focus on electrical networks and smart grids. He can be
contacted at email: a-belfqih@hotmail.com.