In this paper an Enhanced Bean Optimization Algorithm (EBA) is used to solve optimal reactive power problem. Stimulated by the diffusion of beans in nature, a novel swarm intelligence algorithm-Bean Optimization Algorithm (BOA) has been projected previously. In the domain of incessant optimization problems solving, Bean Optimization Algorithm has exposed a first-class performance. In this paper, an Enhanced Bean Optimization Algorithm is presented for solving optimal reactive power problem. In this algorithm two novel evolution methodologies named population migration and deductive information cross-sharing are proposed to perk up the performance of Bean Optimization Algorithm. The projected Enhanced Bean optimization algorithm (EBA) has been tested in standard IEEE 30 bus test system and simulation results show clearly the enhanced performance of the projected algorithm in tumbling the real power loss.
Bean Optimization Algorithm Optimization Optimal Reactive Power Transmission Loss1. Introduction
Different algorithms are utilized to solve the Reactive Power Dispatch problem. Different types of numerical techniques like the gradient method [1] [2] , Newton method [3] and linear programming [4] - [7] have been already used to solve the optimal reactive power dispatch problem. The voltage stability problem plays an important role in power system planning and operation [8] . Evolutionary algorithms such as genetic algorithm, Hybrid differential evolution algorithm, Biogeography Based algorithm, a fuzzy based approach, an improved evolutionary programming [9] - [15] have been already utilized to solve the reactive power flow problem. In [16] - [18] different methodologies like interior point, upgraded approach are successfully handled the optimal power problem. In [19] [20] , a programming based approach and probabilistic algorithm is used to solve the optimal reactive power dispatch problem. This paper proposes an Enhanced bean optimization algorithm (EBA) to solve reactive power dispatch problem. Inspired by the diffusion mode of seeds, a novel swarm intelligence optimization algorithm named Bean Optimization Algorithm (BOA) has been projected already to solve various problems. Bean Optimization Algorithm is mixture of nature evolutionary approach and narrow arbitrary search. Bean Optimization Algorithm has steady robust behavior on explored tests and stands out as a promising alternative to existing optimization methods for engineering applications [21] - [24] . In this paper, an Enhanced Bean Optimization Algorithm (EBA) is presented for solving optimal reactive power problem. Two novel evolution mechanisms named population migration and deductive information cross-sharing are proposed to perk up the performance of Bean Optimization Algorithm. The proposed EBA has been evaluated in standard IEEE 30 bus test system. The simulation results show that the projected approach outperforms all the entitled reported algorithms in minimization of real power loss.
2. Objective Function2.1. Active Power loss
Main aim of the reactive power dispatch problem is to reduce the active power loss in the transmission network, which can be described as:
where gk: is the conductance of branch between nodes i and j, Nbr: is the total number of transmission lines in power systems.
2.2. Voltage profile Improvement
For minimization of the voltage deviation in PQ buses, the objective function turns into:
where ωv: is a weighting factor of voltage deviation.
VD is the voltage deviation given by:
2.3. Equality Constraint
The equality constraint of the Reactive power problem is represented by the power balance equation, and can be written as, where the total power generation must cover the total power demand and total power loss:
where,―Total Power Generation,―Total Power Demand,―Total Power Loss.
2.4. Inequality Constraints
Inequality constraints define the limitations in power system components and power system security. Upper and lower bounds on the active power of slack bus, and reactive power of generators are written as follows:
Upper and lower bounds on the bus voltage magnitudes are described as follows:
Upper and lower bounds on the transformers tap ratios are given as follows:
Upper and lower bounds on the compensators reactive powers are written as follows:
where N is the total number of buses, NT is the total number of Transformers; Nc is the total number of shunt reactive compensators.
3. Bean Optimization Algorithm
Stimulated by the diffusion mode of beans, Bean Optimization Algorithm (BOA) has been proposed previously to solve the various problems. In BOA, the position of an individual bean is articulated with real number vector and written as
where n is determined by the scale of problem .Bean group is comprises of large number of beans. The size of the bean group can be attuned depending upon realistic problems. In adding to the above, beans are propagated to the region and the area is defined by the type of problem. Father beans are those beans whose fitness value is greater than others. In BOA, the number and distribution of offspring beans will be placed according to their father bean’s fitness value. The fundamental equation of BOA is written as follows,
In the above equation, is the position of bean i. is the position of the father bean. Distribution ( ) is an arbitrary variable with a definite distribution of father bean in order to get the positions of its offspring’s. Parameter D can be set according to the range of the problem to be resolved. In adding to that, the allocation of some beans does not follow the equation Reported above. They select arbitrary positions in order to emphasize the global optimization performance. When the offspring beans finished positioning, their fitness value has to be calculated. The beans with most optimal fitness value will be chosen as the candidates of father beans in the subsequent generation. The candidates of father beans should also gratify the condition that the distance between every two father beans should be greater than the distance threshold. This condition assures that the father beans can have a superior distribution to keep away from early convergence and augment the performance of the BOA. If all the conditions are satisfied, then the candidate can be set as the father bean for subsequent generation.
4. Enhanced Bean Optimization Algorithm
Bean Optimization Algorithm (BOA) utilizes population evolution mechanism for solving optimization problems. Since most of the population evolution methods are continuous, they are complicated to solve discrete optimization problems. In this paper an Enhanced Bean Optimization Algorithm (EBA) is utilized for solving Reactive Power Problem.
The algorithm model can be described as follows,
1) Individual beans
The position vector of an individual bean is located as
The above indicates that there is a route as
2) Population progress
In the procedure of population migration, minimum two populations should be initialized. The father bean in each population will be mixed up in cross-species process through the interaction between populations in order to endorse the affluence of populations.
3) Cross-sharing of deductive information
In order to keep the deductive information of the father beans, there are cross operations between the father beans and the individual beans to create new offspring’s.
The explicit operation is shown as follows.
1) Pick an arbitrary position in the vectors of a father bean f and an individual bean s separately as a cross-re- gion.
2) Swap cross-region between f and s. Then remove the duplicate elements in f and s separately. Two new offspring individuals’ g and h will be produced.
In EBA, the first step is population has to be initiated (let the size of population be n). According to the fitness values of individual beans, choose the father beans (let the number of father beans be three): R1, R2, R3. (n − 3)/3 individuals will be displayed as sub-populations “1” according to the Euclidean distance between individual beans and R1. By using same method, sub-populations 2 and sub-population 3 will be produced. Then let R2 be the cross father bean of sub-population 3 and cross operations will be carried out between R2 and individual beans in sub-population 3. Choose the offspring with the most excellent fitness value to shift the previous individual bean in sub-population 3. Let R3 be the cross father bean of sub-population 1 and cross operations will be carried out between R3 and individual beans in sub-population 1. Pick the offspring with the most excellent fitness value to shift the former individual bean in sub-population 1. Let R1 be the cross father bean of sub-popu- lation 2 and cross operations will be carried out between R1 and individual beans in sub-population 2. Choose the offspring with the most excellent fitness value to relocate the previous individual bean in sub-population 2.
Reiterate the above procedure until the termination condition is met.
EBA for solving Optimal Reactive Power problem
Set the number of iterations be S.
Arbitrarily produce n initial beans.
Compute the fitness value of the preliminary beans and Select S father beans.
Create z sub-populations by using clustering algorithm.
While (the number of iterations < S)
For i = 1:S
For j = 1:n
Cross operations are carried out between Yj and R(i+1);
The bean with the best fitness value is recorded as Yj1;
Yi = Yj1;
End
Modernize the Father beans;
End
End
Output the finest solution.
5. Simulation Results
Enhanced Bean Algorithm has been tested in IEEE 30-bus, 41 branch system. The system has 6 generator-bus voltage magnitudes, 4 transformer-tap settings, and 2 bus shunt reactive compensators. Bus 1 is considered as slack bus and 2, 5, 8, 11 and 13 are considered as PV generator buses and the other buses are taken as PQ load buses. Generators buses (PV) are 2, 5, 8, 11, 13 and slack bus is 1. Control variables limits are listed in Table 1. The power limits generators buses are displayed in Table 2. Table 3 shows the projected approach succeeded in keeping the control variables within limits.
Table 4 narrates about the performance of the proposed EBA algorithm. Figure 1 explains about the convergence characteristics of the proposed EBA where it took 25 iterations to converge. Figure 2 shows about the voltage deviations during the iterations in the low, medium and high level through EBA method. Table 5 summarizes the results of the optimal solution obtained by various standard methods.
BASIC variable limits (PU)
List of Variables
Min. Value
Max. Value
Category
Generator Bus
0.90
1.08
Continuous
Load Bus
0.90
1.01
Continuous
Transformer-Tap
0.91
1.00
Discrete
Shunt Reactive Compensator
−0.10
0.30
Discrete
list of generators power limits
Bus
Pg.
Pgmin
Pgmax
Qgmin
1
90.00
47
121
−20
2
82.00
18
75
−20
5
50.00
10
41
−11
8
20.00
10
32
−13
11
20.00
10
19
−10
13
20.00
11
35
−13
Control variables values after optimization
Control Variables
EBA
V1
1.0612
V2
1.0503
V5
1.0312
V8
1.0417
V11
1.0814
V13
1.0601
T4, 12
0.00
T6, 9
0.01
T6, 10
0.90
T28, 27
0.90
Q10
0.11
Q24
0.11
Real power loss
4.2781
Voltage deviation
0.9057
Performance of EBA algorithm
Iterations
25
Time taken (secs)
4.32
Real power loss
4.2781
Convergence characteristics
Voltage deviation (VD) characteristics
Comparison of real power loss
Methods
Real power loss (MW)
SGA [25]
4.98
PSO [26]
4.9262
LP [27]
5.988
EP [27]
4.963
CGA [27]
4.980
AGA [27]
4.926
CLPSO [27]
4.7208
HSA [28]
4.7624
BB-BC [29]
4.690
EBA
4.2781
6. Conclusion
In this paper, Enhanced Bean Optimization Algorithm (EBA) has been efficiently solved the Optimal Reactive Power Dispatch problem. The projected algorithm has been tested in standard IEEE 30 bus system. Simulation study shows the robustness of projected Enhanced Bean Optimization Algorithm (EBA) method in providing improved optimal solution by decreasing the real power loss. The control variables values obtained after the optimization by Enhanced Bean Optimization Algorithm (EBA) are well within the limits.
Cite this paper
Kanagasabai Lenin,Bhumanapally Ravindhranath Reddy,Munagala Suryakalavathi, (2016) Enhanced Bean Optimization Algorithm for Solving Reactive Power Problem. Open Access Library Journal,03,1-8. doi: 10.4236/oalib.1102464
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