Maximization of Loadability of Distributed System by Optimal ...Maximization of Loadability of Distributed System by Optimal placement and sizing of DG using Parallel Particle Swarm

Maximization of Loadability of Distributed System by Optimal placement and sizing of DG using Parallel Particle Swarm

Optimization (PPSO)

A.Marimuthua*,K.Gnanambalb,S.Divyac, T.Jaipooranid,P.Lakshmie a Associate Professor, Department of Electrical and Elecronics Engg, K.L.N.College of Engineering, Pottapalayam-630612, India

b Professor, Department of Electrical and Elecronics Engg, K.L.N.College of Engineering, Pottapalayam-630612, India c,d,e UG Students, Department of Electrical and Elecronics Engg, K.L.N.College of Engineering, Pottapalayam-630612, India

Abstract

Recently, Distributed Generation (DG) is increasing adopted in the distribution systems because of its high penetration levels and role on the voltage stability. In this paper, PSO with parallel mutation namely Parallel Particle Swarm Optimization (PPSO) is proposed to maximize system loadability by optimally placing and sizing of DG units in a radial distribution system The proposed method is to overcome the drawback of Particle swarm Optimization (PSO), which works with less number of iterations, but as the iterations increases, there is a possibility of getting trapped in local optima. The efficiency of proposed method is evaluated using IEEE 33 bus and IEEE 69 bus radial distribution test systems. The results show the integrity and effectiveness of the proposed method.

Keywords: DG; Voltage stability; PSO; PPSO; RDS;

1. Introduction

Distributed Generation, which is placed in distribution systems, has the ability to meet out the demand of growing energy markets. DG is mainly preferred because of its capability to improve maximum loadability, voltage profile and stability [1-5]. To achieve maximum technical and economic benefits, optimum location must be determined. Optimal location has been carried out by using both conventional as well as evolutionary algorithm. Conventional algorithms [6] including numerical method [7], analytical methods [8] and evolutionary algorithms such as bacterial foraging optimization [9], Particle Swarm Optimization [10], Imperialist competition algorithm [11] have been applied to the optimization problem of DG units. The hybrid genetic algorithm have developed for multi-objective location and sizing of DG [12]. To reduce cost, power loss and emission, a study has developed based on hybrid modified shuffled frog algorithm and differential evolutionary algorithm [13]. The result shows that the combined method has a higher probability of finding the optimal solution. However different strategies for installing multiple DGs are not considered. To optimally solve the allocation problem of DG, Artificial Bee Colony algorithm has also been presented [14, 15]. Multiple wind turbines are connected to distribution network using genetic algorithm [16, 17]. Different types of renewable DGs are investigated using modified honey bee mating algorithm [18]. Investigation on impact of loadability in distribution system is proposed [19, 20]. Weakest bus is identified as an optimum location for DG placement based on Continuation Power Flow (CPF) method [21]. Maximum loading up to the voltage constraint for optimal placement of DG is also considered [22]. The study [23] has concluded that the maximum loadability of distribution system has been limited by voltage limit rather than the thermal limit. Overall voltage profile is thus improved by improving maximum loadability. Due to increased loading power systems are heavily stressed which results in voltage stability problem. Several methods have been proposed to calculate maximum loadability limit. To maximize savings, PSO approach has been used to optimally determine the location of capacitors [24]. The optimal size and sites of DGs and capacitor combination are determined by Artificial Bee Colony algorithm [25]. _____________

* E-mail address:[email protected].

This paper presents parallel mutation in Particle Swarm Optimization technique to locate optimally DG

JASC: Journal of Applied Science and Computations

Volume 5, Issue 6, June /2018

ISSN NO: 0076-5131

Page No:258

considering maximum loadability and minimum power losses. In addition, Parallel Particle Swarm Optimization takes the advantage of reduced computation time compared to PSO technique. The proposed algorithm will allocate different part of the task to multiple processors and each processor will execute different part of the program. Furthermore, the proposed algorithm is tested in IEEE 33-bus and 69-bus radial distribution systems. The results obtained by proposed algorithm show that the proposed algorithm is outperformed by other evolutionary optimization methods. This paper is organised as follows: In section 2, the need for DG on voltage stability is discussed. Section 3, will briefly discuss about the Particle Swarm Optimization (PSO) algorithm. In section 4, the proposed PPSO algorithm is presented. Section 5, presents the problem formulation. Discussion about PSO algorithm on 33-bus and 69-bus system is in section 6. In section 7, proposed PPSO algorithm is used to test the system.

2. Role of Distributed Generation on voltage stability To analyse the potential effect of DG on distribution system following definitions are used: Active line loss reduction:

�� = {��}�� {��}��

{��}�� × 100 (1)

Reactive line loss reduction:

�� = {��}�� {��}��

{��}�� × 100 (2)

DG penetration level:

�� = ��

�� × 100 (3)

3. Overview of Particle Swarm Optimization PSO is an optimization technique based on the movement of swarms. It was developed by James Kennedy and Russell Eberhart. Each particle keeps track of its position in the solution space which is associated with best solution that is achieved so far by that particle. The value is called as personal best, pbest. Another best value is tracked by PSO is the best value obtained so far by any particle in the neighbourhood of that particle. This value is called gbest. Velocity of particle i at time k+1 is given by Eqn.4

�� = ��

� + �� − �� + �� − ��

� � (4)

Fig. 1 Depiction of velocity and position updates in PSO

Where

�� : Current position;

�� : Modified new position

�� : Current velocity �� : Modified velocity ��

: Personal best g� : Global best 3.1 Parameter selection

Vki Xk

i+1

gb

pb

Vk+1

Xki



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The performance of the PSO is greatly affected by its parameter values. In this case, the selection of the PSO parameters follows the strategy of considering different values for each particular parameter and evaluating its effect on the PSO performance. Each particle fitness value has to be evaluated using a power flow solution at each iteration; the number of particles must be small to reduce the particles are chosen as an appropriate population sizes. 3.2 Inertia weight The inertia weight is linearly decreased. The purpose is to improve the speed of convergence of the results by reducing the inertia weight from an initial value of 0.9 to 0.1 in even steps over the maximum number of iterations.

1max

18.09.0

iteration

iterationWk

Where �� : Inertia weight at iteration k�� : Iteration number �� : Maximum number of iterations

3.3 Acceleration constant A set of three values for the individual acceleration constants are evimportance to the individual’s best or the swarm’s best: C1 = {1.5, 2 and 2.5}. The value for the social acceleration constant is defined as: C2 = 4.5

4. Overview of Parallel Particle Swarm Optimization Parallel processing is concerned with producing the same results using multiple processors with the goal of reducing the running time. The two basic parallel procesparallelism. The principle of pipeline processing is to separate the problem into a cascade of tasks where each of the tasks is executed by an individual processor. Data are transmitted through each processor wdifferent part of the process on each of the data elements. Since the program is distributed over the processors in the pipeline and the data moves from one processor to the next, no processor can proceed until the previous processor has finished its task. Data parallelism is an alternative approach which involves distributing the data to be processed amongst all processors which then executes the same procedure on each subset of the data. Data parallelism has been applied fairly widely incl(communication), each subpopulation will receive some new and promising chromosomes to replace the worst chromosomes in a subpopulation. This helps to avoid premature convergence. The parallel genetic algoribeen applied to vector quantization based communication via noisy channels successfully (Pan, McInnes & Jack 1996). Fig. 3 shows the flow chart for PPSO algorithm.

The performance of the PSO is greatly affected by its parameter values. In this case, the selection of the PSO considering different values for each particular parameter and evaluating its

effect on the PSO performance. Each particle fitness value has to be evaluated using a power flow solution at each iteration; the number of particles must be small to reduce the computational effort. Swarms of 5 and 20 particles are chosen as an appropriate population sizes.

The inertia weight is linearly decreased. The purpose is to improve the speed of convergence of the results by eight from an initial value of 0.9 to 0.1 in even steps over the maximum number of

: Inertia weight at iteration k

: Maximum number of iterations

A set of three values for the individual acceleration constants are evaluated to study the effect of giving more importance to the individual’s best or the swarm’s best: C1 = {1.5, 2 and 2.5}. The value for the social

C2 = 4.5 – C1. Flow chart of PSO algorithm is as shown in Fig. 2

Fig. 2 Flow chart for PSO algorithm

4. Overview of Parallel Particle Swarm Optimization

Parallel processing is concerned with producing the same results using multiple processors with the goal of reducing the running time. The two basic parallel processing methods are pipeline processing and data parallelism. The principle of pipeline processing is to separate the problem into a cascade of tasks where each of the tasks is executed by an individual processor. Data are transmitted through each processor wdifferent part of the process on each of the data elements. Since the program is distributed over the processors in the pipeline and the data moves from one processor to the next, no processor can proceed until the previous

nished its task. Data parallelism is an alternative approach which involves distributing the data to be processed amongst all processors which then executes the same procedure on each subset of the data. Data parallelism has been applied fairly widely including to genetic algorithms. (communication), each subpopulation will receive some new and promising chromosomes to replace the worst chromosomes in a subpopulation. This helps to avoid premature convergence. The parallel genetic algoribeen applied to vector quantization based communication via noisy channels successfully (Pan, McInnes & Jack 1996). Fig. 3 shows the flow chart for PPSO algorithm.

The performance of the PSO is greatly affected by its parameter values. In this case, the selection of the PSO considering different values for each particular parameter and evaluating its

effect on the PSO performance. Each particle fitness value has to be evaluated using a power flow solution at computational effort. Swarms of 5 and 20

The inertia weight is linearly decreased. The purpose is to improve the speed of convergence of the results by eight from an initial value of 0.9 to 0.1 in even steps over the maximum number of

(5)

aluated to study the effect of giving more importance to the individual’s best or the swarm’s best: C1 = {1.5, 2 and 2.5}. The value for the social

. Flow chart of PSO algorithm is as shown in Fig. 2

Parallel processing is concerned with producing the same results using multiple processors with the goal of sing methods are pipeline processing and data

parallelism. The principle of pipeline processing is to separate the problem into a cascade of tasks where each of the tasks is executed by an individual processor. Data are transmitted through each processor which executes a different part of the process on each of the data elements. Since the program is distributed over the processors in the pipeline and the data moves from one processor to the next, no processor can proceed until the previous

nished its task. Data parallelism is an alternative approach which involves distributing the data to be processed amongst all processors which then executes the same procedure on each subset of the data. Data

With this migration (communication), each subpopulation will receive some new and promising chromosomes to replace the worst chromosomes in a subpopulation. This helps to avoid premature convergence. The parallel genetic algorithm has been applied to vector quantization based communication via noisy channels successfully (Pan, McInnes & Jack



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The performance of the PPSO is also highly dependent on the level of correlation between parameters and the nature of the communication strategy. Three communication strategies have been developed for parallel particle swarm optimization algorithm. The firobservation that if the parameters are independent or are only loosely correlated, then the better particles may get good results quite quickly. Thus multiple copies of the best particles imutated particles migrate and replace the worst particles in the other groups for every R1 iterations.

However, if the parameters of the solution are loosely correlated the better particles in each group may not get optimum results particularly quickly. In this case, a second communication strategy may be applied as depicted in Fig 5. This strategy is based on selfits neighbour groups to replace some of the more poorly performing particles for R2 iterations.an associated position vector xi (t) and velocity vector vupdate of each particle should be done by taking into account coordinates personal best and global best. This is done by calculating the signed distance between these coordinates and the current posIteration ‘t’ and integrating these computations into the calculation of the updated velocity vector. The velocity of particle ‘i’ at iteration‘t’ is determined by taking into account the previous velocity of the particle ‘i’, vi (t − 1) weighed by a inertia factor w (t). The current position of the particle, weighed by R1 , a random value within the range (0, 1) generated by a uniform distribution at each iteration, and by C1 , a positive valued constant named the cognitive acceleration constant, with a suggested value of 2.0. This second component represents the linear attraction towards the best position ever found by particles personal best and therefore is named “memory” or “self-knowledge”best value namely global best, and the current position of the particle, weighed by R2, a random value within the range (0, 1) generated by a uniform distribution at each iteration, and by C2 , a positive valuethe social acceleration constant, with a suggested value of 2.0. This last component is the linear attraction towards the best position ever found by any particle in the group, thus receiving the name

Fig. 3 Flow chart of proposed PPSO algorithm

The performance of the PPSO is also highly dependent on the level of correlation between parameters and the

nature of the communication strategy. Three communication strategies have been developed for parallel particle swarm optimization algorithm. The first communication strategy shown in Fig. 4 is designed based on the observation that if the parameters are independent or are only loosely correlated, then the better particles may get good results quite quickly. Thus multiple copies of the best particles in each group are mutated and those mutated particles migrate and replace the worst particles in the other groups for every R1 iterations.

Fig. 4 Loosely correlated parameter

However, if the parameters of the solution are loosely correlated the better particles in each group may not get optimum results particularly quickly. In this case, a second communication strategy may be applied as depicted

on self-adjustment in each group. The best particle in each group is migrated to groups to replace some of the more poorly performing particles for R2 iterations.

(t) and velocity vector vi (t) in the hyperspace. As referred earlier, the position update of each particle should be done by taking into account coordinates personal best and global best. This is done by calculating the signed distance between these coordinates and the current position of each particle at Iteration ‘t’ and integrating these computations into the calculation of the updated velocity vector. The velocity

is determined by taking into account the previous velocity of the particle ‘i’, − 1) weighed by a inertia factor w (t). The current position of the particle, weighed by R1 , a random value

within the range (0, 1) generated by a uniform distribution at each iteration, and by C1 , a positive valued tive acceleration constant, with a suggested value of 2.0. This second component

represents the linear attraction towards the best position ever found by particles personal best and therefore is knowledge”. The signed distance between the coordinates associated with the global

best value namely global best, and the current position of the particle, weighed by R2, a random value within the range (0, 1) generated by a uniform distribution at each iteration, and by C2 , a positive valuethe social acceleration constant, with a suggested value of 2.0. This last component is the linear attraction towards the best position ever found by any particle in the group, thus receiving the name

t=R1+1

MUTATE AND UPDATE

J=00 J=01 J=10

t=1

t=2

t=R1

The performance of the PPSO is also highly dependent on the level of correlation between parameters and the nature of the communication strategy. Three communication strategies have been developed for parallel particle

st communication strategy shown in Fig. 4 is designed based on the observation that if the parameters are independent or are only loosely correlated, then the better particles may

n each group are mutated and those mutated particles migrate and replace the worst particles in the other groups for every R1 iterations.

However, if the parameters of the solution are loosely correlated the better particles in each group may not get optimum results particularly quickly. In this case, a second communication strategy may be applied as depicted

adjustment in each group. The best particle in each group is migrated to groups to replace some of the more poorly performing particles for R2 iterations. Each particle has

(t) in the hyperspace. As referred earlier, the position update of each particle should be done by taking into account coordinates personal best and global best. This is

ition of each particle at Iteration ‘t’ and integrating these computations into the calculation of the updated velocity vector. The velocity

is determined by taking into account the previous velocity of the particle ‘i’, − 1) weighed by a inertia factor w (t). The current position of the particle, weighed by R1 , a random value

within the range (0, 1) generated by a uniform distribution at each iteration, and by C1 , a positive valued tive acceleration constant, with a suggested value of 2.0. This second component

represents the linear attraction towards the best position ever found by particles personal best and therefore is en the coordinates associated with the global

best value namely global best, and the current position of the particle, weighed by R2, a random value within the range (0, 1) generated by a uniform distribution at each iteration, and by C2 , a positive valued constant named the social acceleration constant, with a suggested value of 2.0. This last component is the linear attraction towards the best position ever found by any particle in the group, thus receiving the name “cooperation” or



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“social knowledge”. When the correlation property of the solution space is known the first and second communication strategies work well. However, they can perform poorly if applied in the wrong situation. In the cases where the correlation property is unknown, a hybrid communication strategy can be applied. The hybrid communication strategy separates the groups into two subgroups with the first subgroup applying the first communication strategy for R1 iterations and all groups applying the second communication strategy for R2 iterations.

Fig. 5 Strongly correlated parameter

Parallel mutation uses multiple processors to yield the same results but with reduced run time. Pipeline processing and data parallelism are the two basic methods used for parallel processing. Pipeline processing is aimed to segregate the problem into a series of tasks each task is evaluated by an individual processor. The main disadvantage of this method is that a processor can proceed only if the previous processor has completed its work. Data parallelism overcomes the disadvantage of pipeline processing. In this work, data parallelism is deployed for creating a parallel mutation PSO (PPSO) algorithm. 5. Problem Formulation PSO and PPSO techniques are used to find the maximum loadability of the system, real and reactive power losses and also to observe the increase in active and reactive load using Eqn. (6, 7) till divergence is attained. P�� = P�� × � (6) Q�� = Q�� × � (7) Where � : Loading factor P�� : Initial active power Q�� : Initial reactive power P�� : Final active power Q�� : Final reactive power Size of DG: 0 ≤ ∑ ��

�� ≤ �� (8)

Position of DG: 2 ≤ �� ≤ �� (9) The amount of active and reactive power are calculated using Eqn. (7, 8, 9)

J=00 J=01 J=10 J=11

t=1

t=2

t=R2

t=R2+1



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�� = �� × �� (10)

�� = �� − ��

� (11)

�� = �∑ ��

��+ ∑ ��

��

(12)

Table 1 shows the system details of 33-bus and 69-bus radial distribution system

Table 1. Test details of 33-bus and 69-bus radial distribution system

Test system Active Power in KW

Reactive Power in KW

Apparent Power in KVA

Power Factor

33-bus system 3715 2300 4369.35 0.8502

69-bus system 3801.9 2694.1 4659.67 0.8159

6. Implementation PSO in radial distribution system

In this section, optimum multi – DG unit placement with PSO technique will be applied on 3 phase, 12.66 KV standard 33-bus and 69-bus test system. DG will provide active and reactive power hence DG is considered as a PQ load. 6.1. IEEE 33-bus radial distribution system The base load of 33-bus radial distribution system is 4369.35 KVA. For DG placement, when PSO algorithm is applied on 33-bus radial distribution system, the obtained results are presented in Table 2.

Table 2. Application of PSO algorithm for optimum single and multi-DG unit’s placement on 33-bus radial distribution system

Parameters Without DG PSO technique for DG allocation in 33 - bus system

No. of DG units

--

Single DG unit

Two DG units

Three DG units

DG size ( Position)

--

1258.2500 (30)

12.0000 (22)

463.7316 (30)

24.0000 (10)

402.8337 (30) 1112.2106 (33)

Active power losses (KW)

210.9991

151.3733

141.8485

138.8979

Reactive power losses (KW)

143.1775

103.8981

96.5329

94.4287

Maximum loadability

3.41

3.9827

4.4320

4.6821

From Table 2, It can be found that for base case the active and reactive power losses were found to be 210.9991 KW and 143.1775 KW. For single DG unit the losses are found to be reduced. When multi-DGs are included the losses are reduced further than the previous cases. Maximum loadability is improved with addition of DG. The variation of real power losses with the number of DG unit is as shown in Fig. 6.

Fig. 6 Real power loss for different DG units in 33-bus system with PSO technique

With PSO algorithm the reactive power losses were found to be reduced with optimal DG placement as shown

0 10 20 30 40 50 60 70 80 90 100135

140

145

150

155

160

165

Iteration

Plo

ss (

KW

)

Single DG (30)

Two DG (22 & 30)

Three DG (10,30 &33 )



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in Fig. 7

Fig. 7 Reactive power loss for different DG units in 33-bus system with PSO technique

6.2. IEEE 69-bus radial distribution system The base load of 69-bus radial distribution system is 4659.67 KVA. For DG placement, when PSO algorithm is applied on 69-bus radial distribution system, the obtained results are presented in Table 3.

Table 3. Application of PSO algorithm for optimum single and multi-DG unit’s placement on 69-bus radial distribution system

Parameters Without DG PSO technique for DG allocation in 69 - bus system

No. of DG units

--

Single DG unit

Two DG units

Three DG units

DG size ( Position)

--

1329.8991 (61)

16.0000 (61)

334.6632 (26)

20.0000 (20)

252.1558 (61) 1142.4090 (26)


224.9521

152.0051

146.4946

145.6454


102.3115

70.6369

68.3470

66.8185

Maximum loadability

3.32

4.0179

4.3578

4.6801

From Table 3, It can be found that for base case of 69-bus system the active and reactive power losses were found to be 224.9521 KW and 102.3115 KW. The losses are reduced with single DG unit. When multi-DGs are included the losses are reduced further than the previous cases. Maximum loadability can also be improved with number of DG unit’s placement. The variation of real power losses with the number of DG unit is as shown in Fig. 8.

Fig. 8 Real power loss for different DG units in 69-bus system with PSO technique

The reactive power losses were found to be reduced with optimal placement of DG units as shown in Fig. 9. 7. Implementation Proposed PPSO in radial distribution system Optimum multi-DG unit placement with Proposed PPSO technique will be applied on 3 phase, 12.66 KV standard 33-bus and 69-bus test system. As the DG unit produces active and reactive power, it is considered as PQ load. The power factor of 33-bus system is 0.85 and for 69-bus system is 0.8159.

0 10 20 30 40 50 60 70 80 90 10094

96

98

100

102

104

106

108

110

112

Iteration

Qlo

ss (

KW

)

Single DG (30)

Two DG (22 & 30)

Three DG (10,30 &33)

0 10 20 30 40 50 60 70 80 90 100145

150

155

160

165

170

175

180

185

190

195

Iteration

Plo

ss (

KW

)

Single DG (61)

Two DG (26 & 61)

Three DG (20,26 & 61)



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Fig. 9 Reactive power loss for different DG units in 69-bus system with PSO technique

7.1. 33-bus radial distribution system The base load of 33-bus radial distribution system is 4369.35 KVA. For DG placement, when PPSO algorithm is applied on 69-bus radial distribution system, the obtained results are presented in Table 4. Table 4. Application of proposed PPSO algorithm for optimum single and multi-DG unit’s placement on 33-bus radial distribution system

Parameters

Without DG

PPSO technique for DG allocation in 33 - bus system

No. of DG units

--

Single DG unit

Two DG units

Three DG units

DG size ( Position)

--

1137.9077 (32)

13.0000 (30)

813.2147 (24)

24.0000 (13)

895.3480 (30) 911.1455 (21)

Active power losses (KW) 210.9991 111.0285 87.2528 73.5895 Reactive power losses (KW)

143.1775

81.7274

59.7651

50.9988

Maximum loadability

3.41

4.1241

4.5107

4.8107

From Table 4, it can be found that for base case the active and reactive power losses were found to be 210.9991 KW and 143.1775 KW. The losses are reduced and maximum loadability is improved with addition of DG unit. The variation of real power losses with the number of DG unit is as shown in Fig. 11.

Fig. 10 Real power loss for different DG units in 33-bus system with proposed PPSO technique

Fig.11 Reactive power loss for different DG units in 33-bus system with Proposed PPSO technique

0 10 20 30 40 50 60 70 80 90 10066

68

70

72

74

76

78

80

82

Iteartion

Qlo

ss (

KW

)

Single DG (61)

Two DG (26 & 61)

Three DG (20,26 & 61)

0 10 20 30 40 50 60 70 80 90 10070

80

90

100

110

120

130

140

150

160

170

Iteration

Plo

ss (

KW

)

Single DG (32)

Two DG (24 &30)

Three DG (13,21 & 30)

0 10 20 30 40 50 60 70 80 90 10050

60

70

80

90

100

110

120

130

140

150

Iteration

Qlo

ss (

KW

)

Single DG (32)

Double DG (24 & 30)

Three DG (13,21,30)



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The reactive power loss of the system can be reduced with multi-DG unit rather than a system with no DG placement as shown in Fig.11. 7.2 69-bus radial distribution system The base load of 69-bus radial distribution system is 4659.67 KVA. When PPSO algorithm is applied on 69-bus radial distribution system, the results obtained are as shown in Table 5. Table 5. Application of proposed PPSO algorithm for optimum single and multi-DG unit’s placement on 69-bus radial distribution system

Parameters Without DG PPSO technique for DG allocation in 69-bus system

No. of DG units

--

Single DG unit

Two DG units

Three DG units

DG size ( Position)

--

1872.6293 (61)

17.0000 (61)

568.5644 (36)

49.0000 (61)

589.4083 (22) 1761.6589 (18)


224.9521

83.1894

71.7659

70.5379


102.3115

40.6482

36.0753

33.3004

Maximum loadability

3.32

4.1107

4.3591

4.7347

From Table 5, the following results are observed for base case of 69-bus system, the active and reactive power losses were found to be 224.9521 KW and 102.3115 KW. When DG units are included both active and reactive power losses are reduced. Maximum loadability can also be improved with number of DG unit’s placement. The variation of real power losses with optimal DG unit placement is as shown in Fig. 12.

Fig. 12 Real power loss for different DG units in 69-bus system with Proposed PPSO technique

Fig. 13 shows the reduction in reactive power losses in 69-bus system with DG placement using PPSO technique.

Fig. 13 Reactive power loss for different DG units in 69-bus system with proposed PPSO technique

8. Conclusion This paper has presented an effective new approach for optimal placement of DG with Particle Swarm Optimization involving Parallel mutation. The DG placement and sizing are based on maximization of loadability and to minimize active and reactive power losses. PPSO will help in getting the best result considering the objective function. The proposed technique is applied on 33-bus and 69-bus radial distribution system and it was found that PPSO technique works better than simple PSO technique.

0 10 20 30 40 50 60 70 80 90 10065

70

75

80

85

90

95

100

Iteration

Plo

ss

(K

W)

Single DG (61)

Two DG (36 & 61)

Three DG (18,22 & 61)

0 10 20 30 40 50 60 70 80 90 10032

34

36

38

40

42

44

46

48

50

Iteration

Qlo

ss (

KW

)

Single DG (61)

Two DG (36 & 61)

Three DG (18,22 & 61)



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Maximization of Loadability of Distributed System by Optimal ...Maximization of Loadability of Distributed System by Optimal placement and sizing of DG using Parallel Particle Swarm