Adaptive Weighted Improved Discrete Particle Swarm Optimization for Optimal Distribution Network Reconfiguration

Subramaniyan, Manikandan; Sasidharan, S.; Viswanatharao, J.; Veerasamy, Moorthy

doi:10.1166/jctn.2017.6633

Cited by 3 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In an attempt to expose the predominance of mGWO algorithm in tackling DNR problem, the viable indices that have obtained by FWA [16], CSA [17], RRA [18], IAICA [21], MBFOA [22] and AWIDPSO [25] techniques for test systems-I, and CSA [17], IAICA [21], MBFOA [22] and AWIDPSO [25] for test systems-II including mGWO were contrasted in Tables 3 and 4 respectively. The improvement in PLRI of mGWO over FWA [16], CSA [17], RRA [18], IAICA [21], MBFOA [22] and AWIDPSO [25] is 0.0375, 00286, 0.0353, 0.0357, 0.0104 and 0.0065 respectively in case of test system-I. Similarly, the PLRI has minimized by mGWO over CSA [17], IAICA [21], MBFOA [22], AWIDPSO [25] is 0.0074, 0.008, 0.0072 and 0.0048 respectively in case of test system-II.…”

Section: Comparison Of Viable Solutionmentioning

confidence: 99%

“…Likewise, enhancement in NVDI of mGWO over FWA [16], CSA [17], RRA [18], IAICA [21] and AWIDPSO [25] is 0.0035, 0.0024, 0.0036, 0.007 and 0.0006 respectively in case of test system-I. At the same time, the NVDI has improved by mGWO is 0.0104, 0.0099 and 0.0077 higher than CSA [17], IAICA [21] and AWIDPSO [25] respectively in case of test system-II. Further, mGWO has minimized the power loss of 34.68%, 56.92% of test system-I and test system-II, respectively therefore, the proposed method offered the least network losses.…”

Section: Comparison Of Viable Solutionmentioning

confidence: 99%

“…The power loss reduction rate is 7.56kW, 6.45kW, 7.13kW, 7.09kW, 2.1kW and 1.31kW greater than FWA [16], CSA [17], RRA [18], IAICA [21], MBFOA [22] and AWIDPSO [25] respectively, of test system-I. As well, the power loss reduction rate is 1.4483kW, 1.45kW, 1.44kW and 1.08kW greater than CSA [17], IAICA [21], MBFOA [22] and AWIDPSO [25] respectively, of test system-II.…”

Section: Solution Quality Improvementmentioning

confidence: 99%

“…Likewise, a mapping strategy is incorporated with the imperialist competitive algorithm and it becomes an improved adaptive imperialist competitive algorithm (IAICA) which adapts ICA into discrete nonlinear optimization problem [21], the step size of the E-coli of modified bacterial foraging optimization (MBFOA) has varied in each iteration [22], in case of theta-modified bat algorithm (T-BA) the Cartesian form is transformed into polar form results the velocity and position of each bat is updated using the phase angle vectors thus increases the convergence speed [23], as the genetic operator has augmented in an enhanced genetic algorithm (EGA), generating superior solutions for electrical distribution reconfiguration problem [24] and the inertia weight is adaptively varied in adaptive weighted improved discrete particle swarm optimization (AWIDPSO) for minimizing power loss [25] and load balancing index [26].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, modified algorithms such as AACO [19], APSO [20], IAICA [21], MBFOA [22] T-BA [23] and AWIDPSO [25][26], hybrid algorithms MIHDE [27] and HPSO [28] have applied to ascertain optimal distribution reconfiguration under steady state condition.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Nataraj¹,

Loganathan²,

Veerasamy³

et al. 2018

IJIES

Self Cite

View full text Add to dashboard Cite

A novel metaheuristic optimization procedure for optimizing electrical distribution system is proposed that may help the system planners to know the minimum and maximum objective values. In which the optimal distribution network reconfiguration (DNR) framework and implementation of modified grey wolf optimization (mGWO) algorithm to minimize power loss reduction and node voltage deviation indices have delineated. The DNR involves nonlinear and multimodal function that has been optimized under practical constraints. For the purpose, mGWO algorithm is employed for ascertaining optimum switching position while reconfiguring the distribution system at a minimum fitness value. In fact grey wolf's update its position linearly from a higher value to zero in the search vicinity that provides perfect balance among intensification and diversification to ascertain the best fitness function and exhibits quick and steady convergence. Moreover, the proposed method seems to be a promising optimization tool for the electrical utilities, thereby modifying their operating strategy of distribution system under steady state conditions. This study is conducted on standard IEEE 33-bus and 69-bus distribution systems, the simulated results have analyzed and compared with several recent methods. It shows that the numerical results have provided, by mGWO is superior among the contestant algorithms.

show abstract

Section: Comparison Of Viable Solutionmentioning

confidence: 99%

Section: Comparison Of Viable Solutionmentioning

confidence: 99%

Section: Solution Quality Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Nataraj¹,

Loganathan²,

Veerasamy³

et al. 2018

IJIES

Self Cite

View full text Add to dashboard Cite

show abstract

Optimal reconfiguration/distributed generation integration in distribution system using adaptive weighted improved discrete particle swarm optimization

Subramaniyan

Subramaniyan²,

Veeraswamy³

et al. 2019

COMPEL

Self Cite

View full text Add to dashboard Cite

Purpose This paper aims to address not only technical and economic challenges in electrical distribution system but also environmental impact and the depletion of conventional energy resources due to rapidly growing economic development, results rising energy consumption. Design/methodology/approach Generally, the network reconfiguration (NR) problem is designed for minimizing power loss. Particularly, it is devised for maximizing power loss reduction by simultaneous NR and distributed generation (DG) placement. A loss sensitivity factor procedure is incorporated in the problem formulation that has identified sensitivity nodes for DG optimally. An adaptive weighted improved discrete particle swarm optimization (AWIDPSO) is proposed for ascertaining a feasible solution. Findings In AWIDPSO, the adaptively varying inertia weight increases the possible solution in the global search space and it has obtained the optimum solution within lesser iteration. Moreover, it has provided a solution for integrating more amount of DG optimally in the existing distribution network (DN). Practical implications The AWIDPSO seems to be a promising optimization tool for optimal DG placement in the existing DN, DG placement after NR and simultaneous NR and DG sizing and placement. Thus, a strategic balance is derived among economic development, energy consumption, environmental impact and depletion of conventional energy resources. Originality/value In this study, a standard 33-bus distribution system has been analyzed for optimal NR in the presence of DG using the developed framework. The power loss in the DN has reduced considerably by indulging a new and innovative approaches and technologies.

show abstract

An Improved Adaptive Weighted Mean Filtering Approach for Metallographic Image Processing

Shao

Kaur

Kumar

2021

Journal of Intelligent Systems

View full text Add to dashboard Cite

Background As noise brings great error in the analysis of metallographic images, an adaptive weighted mean filtering method proposed to overcome the shortcomings of the standard mean filtering method. Methods The method used to detect the pulse noise points in the image, and then the modified mean method used to filter out the detected noise points. Patents on metallographic image processing have discussed for the development of the proposed methodology. Results It is shown that filter window can be filtered in comparison with the conventional 3×3, 5×5 and 7×7 filt window to reduce noise detection and reduce the complexity of the weight calculation. Conclusion It can be concluded that this method can better protect the details of the image, has better filtering effect than the standard mean filtering, and its processing speed is faster than the median filtering of the large window, which has profound significance for the edge detection and processing of the metallographic image.

show abstract

Adaptive Weighted Improved Discrete Particle Swarm Optimization for Optimal Distribution Network Reconfiguration

Cited by 3 publications

References 0 publications

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Optimal reconfiguration/distributed generation integration in distribution system using adaptive weighted improved discrete particle swarm optimization

An Improved Adaptive Weighted Mean Filtering Approach for Metallographic Image Processing

Contact Info

Product

Resources

About