A novel PQM placement method using Cp and Rp statistical indices for power transmission and distribution networks

kazemi, asadollah; Mohamed, Azah; Shareef, Hussain

doi:10.1109/peoco.2012.6230843

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…The application of multiobjective evolutionary algorithms was presented in [6] by Nondominated Sorting Genetic Algorithm (NSGA-II), as an elitist strategy to obtain the Pareto Frontier for the formulated problem. In [7], a method utilizing multivariable regression was introduced, where the objective was to detect similar behaviors among nodes allowing the reduction of measurement equipment, determining their optimal allocation. Regarding optimization tools, Genetic Algorithms (GA) stand out because they are heuristics of evolutionary methods that achieve good results in a known search space.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Coverage Matrix Method for Power Quality Monitor Allocation Utilizing Genetic Algorithm

Gomes¹,

Oleskovicz²,

Kempner³

et al. 2014

REPQJ

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With the increase of sensitive loads and, in many cases, causing disturbances in distribution systems, power quality monitor allocation has become a subject with increasingly importance. Therefore, it becomes necessary to have well-formed strategies to allocate such equipment, since they have high cost that needs to be minimized. With this objective, this research provides a methodology for monitor's allocation that seeks to minimize the amount of equipment needed to observe voltage sags in distribution systems. The developed methodology is based on the fault voltage matrix, to obtain the residual voltage during faults, on the coverage matrix and the application of a genetic algorithm as optimization tool. The research also brings as contribution, changes in traditional methodology to better represent the indicated phenomena in distribution systems and to give better support to fault location studies, justifying a development of a generalized coverage matrix. To test this methodology, the IEEE 34-node test feeder was utilized with promising results reported and commented further.

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Section: Introductionmentioning

confidence: 99%

A Generalized Coverage Matrix Method for Power Quality Monitor Allocation Utilizing Genetic Algorithm

Gomes¹,

Oleskovicz²,

Kempner³

et al. 2014

REPQJ

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“…As a result, a negative severity sag index (NSSI) is proposed to assess the best placement of PQMs. The NSSI can be determined by multiplying of complementary of SSI matrix with transposed MP vector with considering the number of fault types (NFT) as expressed follows [9]. Then a lower NSSI value concludes a better organization of PQMs.…”

Section: Maximizing the Sag Severity Index (Ssi)mentioning

confidence: 99%

“…In 2011, a new method based on the MVR model was presented in the placement of PQMs [8]. A novel PQM placement technique using Cp and Rp statistical indices for power transmission and distribution networks was analyzed [9]. Recently, the MVR method was combined with Cp and Rp statistical indices and used for optimal PQM placement [10].…”

Section: Introductionmentioning

confidence: 99%

Binary Shufﬂed Frog Leaping Algorithm for Optimal Allocation of Power Quality Monitors in Unbalanced Distribution System

Mohammadi,

Mohammadi

2024

JAMRIS

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This paper deals with optimal detection of number and best locations of power quality monitors (PQMs) in an unbalanced distribution network based on the monitor reach area concept. The proposed model uses binary strings which represent the state presence or absence of PQMs in each buses of network. In this paper the binary version of shufﬂed frog-leaping algorithm (BSFLA) because of having ability to improve the search capability with a fast convergence rate is utilized for optimization process. The overall cost function is formulated to optimize the two indices which are monitor overlapping index and sag severity index. The only optimization constraint in this problem is that, the number of monitors that can detect voltage sags due to a fault at a specific bus must not be zero. In this study, DIGSILENT software is utilized for fault analysis while the optimization problem is handled by the BSFLA. To verify the proposed algorithm, the IEEE 34 Bus unbalanced distribution network, is considered as case studies and results are compared to the similar investigations so as to illustrate the effectiveness of proposed algorithm.

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“…The NSSI index is determined through multiplying the SSI matrix in the complementary format by a vector resulted from the transposition of the MP. By considering the NFT which counts the fault types, the NSSI index is made clear as follows [14]. When NSSI meets its minimum value, it means that the best organization of PQMs is obtained.…”

Section: ▪ Maximizing the Sag Severity Index (Ssi)mentioning

confidence: 99%

“…A novel statistical-based PQM placement technique is proposed that employs the statistical indices of Cp and Rp for LV and HV power networks [14]. In the similar research, the MVR method was combined with Cp and Rp statistical indices and was used to optimal PQM placement [15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Power Quality Monitor Placement in Transmission Systems Method Using Binary Teaching Learning-Based Optimization Algorithm Considering Monitoring Redundancy

Mohammadi

2022

IJIE

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This paper deals with optimal employment of power quality monitors (PQMs) in transmission networkson the basis of the idea of monitor reach area. The proposed model uses binary string, representing the installation modeof PQMs (Yes or No) in each bus of the network. In the current article, the binary version ofthe teaching learning-based optimization algorithm (BTLBO) is used. It has the ability to enhance the search potential and provides a rapid and secure convergence rate in the optimization process. The concept of redundancy is considered in this study. Theoverall cost function is formulated to optimize the two indices. The first one is the index of monitoring overlap, and the second one is the index of sag severity. Amongthe solutions whoseobjective function experiments the minimum value, the evaluating the redundancyindexgives the final optimal answer. With an excellent redundancy, the buses of the system faced with faults are monitored more times, on average. In the current research, DIGSILENT software is employed in short circuit analysis, whereas the BTLBO manages the optimization process. In the proposed algorithm, the IEEE 30 Bus transmission network is consideredas a case study, and results are compared to similar investigations to clarify the impressiveness of the proposed algorithm.

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A novel PQM placement method using Cp and Rp statistical indices for power transmission and distribution networks

Cited by 6 publications

References 9 publications

A Generalized Coverage Matrix Method for Power Quality Monitor Allocation Utilizing Genetic Algorithm

A Generalized Coverage Matrix Method for Power Quality Monitor Allocation Utilizing Genetic Algorithm

Binary Shufﬂed Frog Leaping Algorithm for Optimal Allocation of Power Quality Monitors in Unbalanced Distribution System

A Novel Power Quality Monitor Placement in Transmission Systems Method Using Binary Teaching Learning-Based Optimization Algorithm Considering Monitoring Redundancy

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