A Fault Analysis Method for Three‐Phase Induction Motors Based on Spiking Neural P Systems

Huang, Zhu; Wang, Tao; Liu, Wei; Valencia-Cabrera, Luis; Pérez–Jiménez, Mario J.; Li, Pengpeng

doi:10.1155/2021/2087027

Cited by 38 publications

(24 citation statements)

References 42 publications

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“…Among the efficient DMUs, the lowest contribution in paying the fixed costs is related to DMU5, because it has the lowest share in the input resources. Among the inefficient DMUs, the largest contribution belongs to DMU6, as it has the largest share in the input resources [36][37][38][39]. DMUs 1 and 7 have the same share in the inputs resources; however, DMU 7 is more efficient than DMU 1, and as seen in column one of Table 2, DMU 7 has a larger contribution in paying the fixed costs.…”

Section: Numerical Examplementioning

confidence: 99%

New Approach in Fixed Resource Allocation and Target Setting Using Data Envelopment Analysis with Common Set of Weights

et al. 2022

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One of the mathematical programming techniques is data envelopment analysis (DEA), which is used for evaluating the efficiency of a set of similar decision-making units (DMUs). Fixed resource allocation and target setting with the help of DEA is a subject that has gained much attention from researchers. A new model was proposed by determining a common set of weights (CSW). All DMUs were involved with the aim of achieving higher efficiency in every DMU after the procedure. The minimum resources and targets allocated to each DMU were commensurate to the efficiency of that DMU and the share of DMU in the input resources and the output productions. To examine the proposed method, other methods in the DEA literature were examined as well, and then, the efficiency of the method was demonstrated through a numerical example.

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Section: Numerical Examplementioning

confidence: 99%

New Approach in Fixed Resource Allocation and Target Setting Using Data Envelopment Analysis with Common Set of Weights

et al. 2022

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“…Consequently, optimal non-prevail solutions are collected in a Pareto frontier to impersonate a curve containing optimal spots. The most powerful decision parameters are picked as the optimization input [105][106][107][108]. Optimization may be carried by employing different algorithms.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

“…In the current work, exergy efficiency and product cost are considered as objective functions and EES software has been linked with MATLAB software to carry the optimization through the genetic algorithm toolbox of MATLAB. The genetic algorithm is used for system optimization and a similar approach is taken into account as described in Ref [100][101][102][103][104][105][106][107][108][109][110]. The population size of 100 besides 10 generations, Crossover, and mutation of 85% and 1% have been deemed to carry the optimization.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

A Comprehensive Thermoeconomic Evaluation and Multi-Criteria Optimization of a Combined MCFC/TEG System

et al. 2021

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In this study, an integrated molten carbonate fuel cell (MCFC), thermoelectric generator (TEG), and regenerator energy system has been introduced and evaluated. MCFC generates power and heating load. The exit fuel gases of the MCFC is separated into three sections: the first section is transferred to the TEG to generate more electricity, the next chunk is conducted to a regenerator to boost the productivity of the suggested plant and compensate for the regenerative destructions, and the last section enters the surrounding. Computational simulation and thermodynamic evaluation of the hybrid plant are carried out utilizing MATLAB and HYSYS software, respectively. Furthermore, a thermoeconomic analysis is performed to estimate the total cost of the product and the system cost rate. The offered system is also optimized using multi-criteria genetic algorithm optimization to enhance the exergetic efficiency while reducing the total cost of the product. The power generated by MCFC and TEG is 1247.3 W and 8.37 W, respectively. The result explicates that the provided electricity and provided efficiency of the suggested plant is 1255.67 W and 38%, respectively. Exergy inquiry outcomes betokened that, exergy destruction of the MCFC and TEG is 13,945.9 kW and 262.75 kW, respectively. Furthermore, their exergy efficiency is 68.22% and 97.31%, respectively. The impacts of other parameters like working temperature and pressure, thermal conductance, the configuration of the advantage of the materials, etc., on the thermal and exergetic performance of the suggested system are also evaluated. The optimization outcomes reveal that in the final optimum solution point, the exergetic efficiency and total cost of the product s determined at 70% and 30 USD/GJ.

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“…Therefore, several fault diagnosis approaches of power systems for the aided decision making have been developed, such as expert systems [3], Bayesian networks [4], the rough set theory [5], artificial neural networks [6], Petri nets [7][8][9], cause-effect networks [10,11], the fuzzy theory [12], and spiking neural P systems (SNPSs) [13][14][15][16][17]. Among above methods, the SNPS is a class of distributed parallel computing models based on structures and functions of nerve cells.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method Considering Meteorological Factors for Transmission Networks Based on P Systems

Chen

Wang

Ying

et al. 2021

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Bad meteorological conditions may reduce the reliability of power communication equipment, which can increase the distortion possibility of fault information in the communication process, hence raising its uncertainty and incompleteness. To address the issue, this paper proposes a fault diagnosis method for transmission networks considering meteorological factors. Firstly, a spiking neural P system considering a meteorological living environment and its matrix reasoning algorithm are designed. Secondly, based on the topology structure of the target power transmission network and the action logic of its protection devices, a diagnosis model based on the spiking neural P system considering the meteorological living environment is built for each suspicious fault transmission line. Following this, the action messages of protection devices and corresponding temporal order information are used to obtain initial pulse values of input neurons of the diagnosis model, which are then modified with the gray fuzzy theory. Finally, the matrix reasoning algorithm of each model is executed in a parallel manner to obtain diagnosis results. Experiment results achieved out on IEEE 39-bus system show the feasibility and effectiveness of the proposed method.

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A Fault Analysis Method for Three‐Phase Induction Motors Based on Spiking Neural P Systems

Cited by 38 publications

References 42 publications

New Approach in Fixed Resource Allocation and Target Setting Using Data Envelopment Analysis with Common Set of Weights

New Approach in Fixed Resource Allocation and Target Setting Using Data Envelopment Analysis with Common Set of Weights

A Comprehensive Thermoeconomic Evaluation and Multi-Criteria Optimization of a Combined MCFC/TEG System

A Fault Diagnosis Method Considering Meteorological Factors for Transmission Networks Based on P Systems

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