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 Practical GERI-Based Method for Identifying Multiple Erroneous Parameters and Measurements Simultaneously

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3390
Author(s):  
Ruipeng Guo ◽  
Lilan Dong ◽  
Hao Wu ◽  
Fangdi Hou ◽  
Chen Fang
Keyword(s):  
Power System ◽  
Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Least Square ◽  
Least Square Estimator ◽  
Calculation Technique ◽  
Reactive Power Flow ◽  
Active Power Flow ◽  
Bulk Power

Even with modern smart metering systems, erroneous measurements of the real and reactive power in the power system are unavoidable. Multiple erroneous parameters and measurements may occur simultaneously in the state estimation of a bulk power system. This paper proposes a gross error reduction index (GERI)-based method as an additional module for existing state estimators in order to identify multiple erroneous parameters and measurements simultaneously. The measurements are acquired from a supervisory control and data acquisition system and mainly include voltage amplitudes, branch current amplitudes, active power flow, and reactive power flow. This method uses a structure consisting of nested two loops. First, gross errors and the GERI indexes are calculated in the inner loop. Second, the GERI indexes are compared and the maximum GERI in each inner loop is associated with the most suspicious parameter or measurement. Third, when the maximum GERI is less than a given threshold in the outer loop, its corresponding erroneous parameter or measurement is identified. Multiple measurement scans are also adopted in order to increase the redundancy of measurements and the observability of parameters. It should be noted that the proposed algorithm can be directly integrated into the Weighted Least Square estimator. Furthermore, using a faster simplified calculation technique with Givens rotations reduces the required computer memory and increases the computation speed. This method has been demonstrated in the IEEE 14-bus test system and several matpower cases. Due to its outstanding practical performance, it is now used at six provincial power control centers in the Eastern Grid of China.

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 ENHANCED SEEKER OPTIMIZATION ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS

2017 ◽  
Vol 5 (10) ◽  
pp. 336-347
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Food Sources ◽  
Uncertainty Reasoning ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Power Problem ◽  
Seeker Optimization Algorithm

This paper projects Enhanced Seeker Optimization (ESO) algorithm for solving optimal reactive power problem. Seeker optimization algorithm (SOA) models the deeds of human search population based on their memory, experience, uncertainty reasoning and communication with each other. In Artificial Bee Colony (ABC) algorithm the colony consists of three groups of bees: employed bees, onlookers and scouts. All bees that are presently exploiting a food source are known as employed bees. The number of the employed bees is equal to the number of food sources and an employed bee is allocated to one of the sources. In this paper hybridization of the seeker optimization algorithm with artificial bee colony (ABC) algorithm has been done to solve the optimal reactive power problem. Enhanced Seeker Optimization (ESO) algorithm combines two different solution exploration equations of the ABC algorithm and solution exploration equation of the SOA in order to progress the performance of SOA and ABC algorithms.  At certain period’s seeker’s location are modified by search principles obtained from the ABC algorithm, also it adjust the inter-subpopulation learning phase by using the binomial crossover operator. In order to evaluate the efficiency of proposed Enhanced Seeker Optimization (ESO) algorithm it has been tested in standard IEEE 57,118 bus systems and compared to other specified algorithms. Simulation results clearly indicate the best performance of the proposed Enhanced Seeker Optimization (ESO) algorithm in reducing the real power loss and voltage profiles are within the limits.

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