scholarly journals A novel hybrid approach based artificial bee colony and salp swarm algorithms for solving ORPD problem

2021 ◽  
Vol 23 (3) ◽  
pp. 1825
Author(s):  
Souheil Salhi ◽  
Djemai Naimi ◽  
Ahmed Salhi ◽  
Saleh Abujarad ◽  
Abdelouahab Necira
Keyword(s):  
Artificial Bee Colony ◽  
Reactive Power ◽  
Electrical Power ◽  
Hybrid Approach ◽  
Stable State ◽  
Search Space ◽  
Electrical Power System ◽  
Solution Quality ◽  
Abc Algorithm ◽  
Bee Colony

Optimal reactive power dispatch (ORPD) is an important task for achieving more economical, secure and stable state of the electrical power system. It is expressed as a complex optimization problem where many meta-heuristic techniques have been proposed to overcome various complexities in solving ORPD problem. A meta-heuristic search mechanism is characterized by exploration and exploitation of the search space. The balance between these two characteristics is a challenging problem to attain the best solution quality. The artificial bee colony (ABC) algorithm as a reputed meta-heuristic has proved its goodness at exploration and weakness at exploitation where the enhancement of the basic ABC version becomes necessary. Salp swarm algorithm (SSA) is a newly developed swarm-based meta-heuristic, which has the best local search capability by using the best global solution in each iteration to discover promising solutions. In this paper, a novel hybrid approach-based ABC and SSA algorithms (ABC-SSA) is that developed to enhance the exploitation capability of the ABC algorithm using SSA and applied for solving ORPD problem. The efficiency of ABC-SSA is investigated using two standard test systems IEEE-30 and IEEE-300 buses, and that by considering the famous objective functions in ORPD problem.

scholarly journals Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Edgar J. Amaya ◽  
Alberto J. Alvares
Keyword(s):  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Remaining Useful Life ◽  
Data Repository ◽  
Echo State Network ◽  
Health State ◽  
Future Health ◽  
Large Space ◽  
Abc Algorithm ◽  
Bee Colony

Prognostic is an engineering technique used to predict the future health state or behavior of an equipment or system. In this work, a data-driven hybrid approach for prognostic is presented. The approach based on Echo State Network (ESN) and Artificial Bee Colony (ABC) algorithm is used to predict machine’s Remaining Useful Life (RUL). ESN is a new paradigm that establishes a large space dynamic reservoir to replace the hidden layer of Recurrent Neural Network (RNN). Through the application of ESN is possible to overcome the shortcomings of complicated computing and difficulties in determining the network topology of traditional RNN. This approach describes the ABC algorithm as a tool to set the ESN with optimal parameters. Historical data collected from sensors are used to train and test the proposed hybrid approach in order to estimate the RUL. To evaluate the proposed approach, a case study was carried out using turbofan engine signals show that the proposed method can achieve a good collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). The experimental results using the engine data from NASA Ames Prognostics Data Repository RUL estimation precision. The performance of this model was compared using prognostic metrics with the approaches that use the same dataset. Therefore, the ESN-ABC approach is very promising in the field of prognostics of the RUL.

Steady State Load Shedding to Prevent Blackout in the Power System using Artificial Bee Colony Algorithm

2015 ◽  
Vol 74 (1) ◽  
Author(s):  
R. Mageshvaran ◽  
T. Jayabarathi
Keyword(s):  
Steady State ◽  
Power System ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Optimal Solution ◽  
Load Shedding ◽  
Solution Quality ◽  
Bee Colony ◽  
The Difference

Real and reactive power deficiencies due to generation and overload contingencies in a power system may decline the system frequency and the system voltage. During these contingencies cascaded failures may occur which will lead to complete blackout of certain parts of the power system. Under such situations load shedding is considered as an emergency control action that is necessary to prevent a blackout in the power system by relieving overload in some parts of the system. The aim of this paper is to minimize the amount of load shed during generation and overload contingencies using a new meta-heuristic optimization algorithm known as artificial bee colony algorithm (ABC). The optimal solution for the problem of steady state load shedding is done by taking squares of the difference between the connected and supplied real and reactive power. The supplied active and reactive powers are treated as dependent variables modeled as functions of bus voltages only. The proposed algorithm is tested on IEEE 14, 30, 57, and 118 bus test systems. The applicability of the proposed method is demonstrated by comparison with the other conventional methods reported earlier in terms of solution quality and convergence properties. The comparison shows that the proposed algorithm gives better solutions and can be recommended as one of the optimization algorithms that can be used for optimal load shedding.

scholarly journals A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Zhendong Yin ◽  
Xiaohui Liu ◽  
Zhilu Wu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Additive White Gaussian Noise ◽  
Ultra Wideband ◽  
System Capacity ◽  
Food Sources ◽  
Solution Quality ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Multiuser Detector

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) is proposed and implemented in direct-sequence ultra-wideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD) while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity.

scholarly journals Minimizing Power Loss Using Modified Artificial Bee Colony Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 111-118
Author(s):  
Nur Azlin Ashiqin Mohd Amin ◽  
Siti Hafawati Jamaluddin ◽  
Nur Syuhada Muhammat Pazil ◽  
Norwaziah Mahmud ◽  
Norhanisa Kimpol
Keyword(s):  
Power System ◽  
Power Loss ◽  
Power Flow ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Control Variable ◽  
Test System ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Reactive Power Flow

Electrical energy losses are found in any part of the power system. In the power system, it is essential to minimize the real power loss in transmission lines. The voltage deviation at the load buses through controlling the reactive power flow is very important. This ensures the secured operation of power systems regarding voltage stability and the economics of the process due to loss minimization. In this paper, the Modified Artificial Bee Colony (MABC) algorithm is implemented to solve the power system's optimal reactive power flow problem. Generator bus voltages, transformer tap positions, and settings of switched shunt of compensators are used as decision variables to control the reactive power flow. These control variable values are adjusted for loss reduction. MABC algorithm is tested on the standard IEEE-30 bus test system. The results are compared with Firefly algorithm (FA) and Artificial Bee Colony (ABC) algorithm method to prove the effectiveness of the newest algorithm. The power loss results are quite productive, and the algorithm is the most efficient than the other methods such as ABC algorithm and FA algorithm. These results are produced by Matlab 2017b.

Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network

2022 ◽  
pp. 728-748
Author(s):  
Gummadi Srinivasa Rao ◽  
Y. P. Obulesh ◽  
B. Venkateswara Rao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Load Flow ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this chapter, an amalgamation of artificial bee colony (ABC) algorithm and artificial neural network (ANN) approach is recommended for optimizing the location and capacity of distribution generations (DGs) in distribution network. The best doable place in the network has been approximated using ABC algorithm by means of the voltage deviation, power loss, and real power deviation of load buses and the DG capacity is approximated by using ANN. In this, single DG and two DGs have been considered for calculation of doable place in the network and capacity of the DGs to progress the voltage stability and reduce the power loss of the system. The power flow of the system is analyzed using iterative method (The Newton-Raphson load flow study) from which the bus voltages, active power, reactive power, power loss, and voltage deviations of the system have been achieved. The proposed method is tested in MATLAB, and the results are compared with particle swarm optimization (PSO) algorithm, ANN, and hybrid PSO and ANN methods for effectiveness of the proposed system.

scholarly journals A Modified Artificial Bee Colony Algorithm Based on Search Space Division and Disruptive Selection Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Zhen-an He ◽  
Caiwen Ma ◽  
Xianhong Wang ◽  
Lei Li ◽  
Ying Wang ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Search Space ◽  
Poor Quality ◽  
Population Diversity ◽  
Disruptive Selection ◽  
Selection Strategy ◽  
Abc Algorithm ◽  
Space Division ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has attracted much attention and has been applied to many scientific and engineering applications in recent years. However, there are still some insufficiencies in ABC algorithm such as poor quality of initial solution, slow convergence, premature, and low precision, which hamper the further development and application of ABC. In order to further improve the performance of ABC, we first proposed a novel initialization method called search space division (SSD), which provided high quality of initial solutions. And then, a disruptive selection strategy was used to improve population diversity. Moreover, in order to accelerate convergence rate, we changed the definition of the scout bee phase. In addition, we designed two types of experiments to testify our proposed algorithm. On the one hand, we conducted experiments to make sure how much each modification makes contribution to improving the performance of ABC. On the other hand, comprehensive experiments were performed to prove the superiority of our proposed algorithm. The experimental results indicate that SDABC significantly outperforms other ABCs, contributing to higher solution accuracy, faster convergence speed, and stronger algorithm stability.

scholarly journals DECREASING ACTUAL POWER LOSS BY REFINED ABC ALGORITHM

2017 ◽  
Vol 5 (10) ◽  
pp. 63-71
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Real Power ◽  
Abc Algorithm ◽  
The Real ◽  
Bee Colony ◽  
Search Speed ◽  
Convergence Performance ◽  
Power Problem

Refined ABC algorithm (RABC) proposed in this paper to solve the optimal reactive power problem. An artificial bee colony (ABC) algorithm is one of copious swarm intelligence algorithms that employ the foraging behavior of honeybee colonies. To progress the convergence performance and search speed of finding the best solution RABC algorithm has been developed. The main objective in this problem is to minimize the real power loss and also to keep the variables within the specified limits. Proposed Refined ABC (RABC) algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulations results reveal about the better performance of the proposed Refined ABC algorithm (RABC) algorithm in reducing the real power loss and the voltage profiles within the limits.

Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network

2019 ◽  
pp. 35-55
Author(s):  
Gummadi Srinivasa Rao ◽  
Y. P. Obulesh ◽  
B. Venkateswara Rao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Load Flow ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this chapter, an amalgamation of artificial bee colony (ABC) algorithm and artificial neural network (ANN) approach is recommended for optimizing the location and capacity of distribution generations (DGs) in distribution network. The best doable place in the network has been approximated using ABC algorithm by means of the voltage deviation, power loss, and real power deviation of load buses and the DG capacity is approximated by using ANN. In this, single DG and two DGs have been considered for calculation of doable place in the network and capacity of the DGs to progress the voltage stability and reduce the power loss of the system. The power flow of the system is analyzed using iterative method (The Newton-Raphson load flow study) from which the bus voltages, active power, reactive power, power loss, and voltage deviations of the system have been achieved. The proposed method is tested in MATLAB, and the results are compared with particle swarm optimization (PSO) algorithm, ANN, and hybrid PSO and ANN methods for effectiveness of the proposed system.

scholarly journals REAL POWER LOSS REDUCTION ENHANCED ARTIFICIAL BEE COLONY ALGORITHM

2018 ◽  
Vol 6 (3) ◽  
pp. 203-213
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Search Space ◽  
Loss Reduction ◽  
Real Power ◽  
Bee Colony ◽  
Power Loss Reduction ◽  
Simulation Results ◽  
Power Problem

In this paper, Enhanced Artificial Bee Colony (EABC) algorithm is proposed for solving optimal reactive power problem. The projected method assimilates crossover operation from Genetic Algorithm (GA) with artificial bee colony (ABC) algorithm. The EABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space.  Projected EABC algorithm has been tested on has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly about the premium performance of the proposed algorithm in reducing the real power loss.

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