Research on the influence factors of ubiquitous power Internet of things for promoting consumption of wind power based on fuzzy G1-ISM in China

Liu, Jicheng; Li, Yinghuan; Lu, Yunyuan; Fu, Xuelong; Song, Yanlong

doi:10.1016/j.ijepes.2020.106124

Cited by 24 publications

(11 citation statements)

References 46 publications

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“…( 25)-( 27) as follows: According to the index weight sets B 1 B 2 B 3 between Categories 1, 2 and 3 and the fmatrix above, the F comprehensive evaluation model M (•, +) is adopted, and the vector C 1 C 2 C 3 of the F comprehensive evaluation results of factors of categories 1, 2 and 3 can be obtained as shown in Eqs. ( 28)- (30):…”

Section: Index Comprehensive Evaluation Results and Analysismentioning

confidence: 99%

“…Wu et al [29] determined the development level by the comprehensive score of adaptive neurofuzzy reasoning system (ANFIS) based on principal component analysis. Liu et al [30] adopted an improved ISM model to select influencing factors, and adopted fuzzy order relation analysis method (G1 method) to make up for the shortcomings of traditional methods. Wu et al [31] used TiFN-Fuzzy DEMATEL to determine and evaluate weights, and used mixed evaluation method to avoid the uncertainty of decision making.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Green Development Level of Electric Energy in Distribution Network Based on Multilevel Fuzzy Comprehensive Evaluation

Tan¹,

Wang²,

Tan³

et al. 2022

Energy Engineering

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Section: Index Comprehensive Evaluation Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Green Development Level of Electric Energy in Distribution Network Based on Multilevel Fuzzy Comprehensive Evaluation

Tan¹,

Wang²,

Tan³

et al. 2022

Energy Engineering

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“…where SI is the safety index of the intersection; TC P is the hourly conflict of p level; MPCU is the mixed passenger car unit of the hourly entering volumes; and W Gp is the weight of p level, which can be determined using order relation analysis (G1 method) [46,47] through the scores provided by 15 experts in this study. e higher the SI value is, the lower the safety conditions are.…”

Section: Correlation Coefficient Calculationmentioning

confidence: 99%

A Safety Evaluation Model of Intersections under Mixed Traffic Conditions Using Traffic Conflicts and Cloud Model

Vuong

Mou

et al. 2021

Journal of Advanced Transportation

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Safety evaluation of traffic conflict is a very important and challenging issue in evaluating intersection safety under incomplete traffic accident data conditions and is also one of the main safety surrogate measures of analyzing accident data recently. It helps to analyze and solve intersection problems comprehensively and deeply. From there, it helps to improve traffic safety as well as reduce the risk of traffic accidents at intersections. Various evaluation methods based on traffic conflict have been proposed to make conflict safety levels at intersections more consistent and objective. However, a major concern is that many existing measurements are still subjective and are not easy to obtain uniformly. This study aimed to develop a model for safety evaluation at intersections in a comprehensive way that may be expected to directly link to the severity of the accident from different evaluation indicators. First, the three factors, including time to collision (TTC), conflicting speed (CS), and deceleration rate (DR) to avoid a crash, are introduced into safety evaluation of conflicts as the indicators. And then, as regards the fuzziness and randomness of the evaluation indicators, the qualitative concept has to be converted into a quantitative one utilizing cloud model, which implements the natural transformation between the qualitative concept of the safety level of traffic conflict and the membership degree of the evaluation indicators corresponding to the different safety levels. Finally, an indicator weight model is built based on the information entropy and the AHP method to determine the safety level. We illustrate the practical implementation of the proposed method using actual data of a typical signalized intersection from Hanoi City of Vietnam. The results indicate that traffic conflict analyzed by the proposed method was appropriate with actual state of the intersection, and the proposed method is simple, effective, and feasible, so it has a certain application value.

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“…First, combining the value and the weight of the secondary index in Tables 6 and 7, respectively, the value of the technical index and economic index are calculated by using formula (2). Next, combining the weight and the value of the technical index and economic index, the assessment value of the PIoT is calculated by using formula (3).…”

Section: Case Studymentioning

confidence: 99%

“…As being a hub of the energy system, smart grid (abbreviated as SG) has the important mission that it not only unblocks the energy industry chain but also promotes the achievement of carbon peaks and carbon neutrality goals [1]. Consequently, it is necessary to continuously strengthen the integration of power grid (abbreviated as PG), information network, and energy network to form a new energy ecosystem with SG as the main body [2,3]. Meanwhile, with the continuous upgrading and transformation of the power system, the Internet of ings (abbreviated as IoT) plays an increasingly important role in it [4].…”

Section: Introductionmentioning

confidence: 99%

Technoeconomic Assessment Based on Active Context-Knowledge Orchestration for Power Internet of Things

Zheng

Sun

et al. 2021

Security and Communication Networks

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Power Internet of Things (abbreviated as PIoT) is the information infrastructure to provide ubiquitous perception ability for smart grid (abbreviated as SG). To better deploy and utilize PIoT, its perception ability must be comprehensively assessed in terms of technical performance and economic benefits. However, at present, there is no assessment framework for PIoT due to the high diversity and heterogeneousness of SG scenarios. Additionally, there is information overlap between metrics in the assessment framework. The assessment model which could remove redundant information between metrics and simplify the assessment framework is an urgent demand to improve the effectiveness and timeliness of assessment. Consequently, first, aiming at the power system requirements of complex and diverse, a general assessment framework is put forward to assess the ability of PIoT in terms of technology and economy. Next, the requirement characteristics of power distribution scenario (abbreviated as PDS) are precisely analyzed with active context-knowledge orchestration technology. The general assessment framework is instantiated to build an instantiation assessment scheme in PDS. Moreover, an assessment model is established based on the instantiation assessment scheme to assess the efficiency of PIoT in Beijing. Finally, the assessment model is further refined with the machine learning technology to improve the efficiency of assessment. This refinement model achieves the extraction of 4-dimensional metrics from 23-dimensional metrics for assessment and finally improves assessment efficiency by 82.6%.

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Research on the influence factors of ubiquitous power Internet of things for promoting consumption of wind power based on fuzzy G1-ISM in China

Cited by 24 publications

References 46 publications

Evaluation of Green Development Level of Electric Energy in Distribution Network Based on Multilevel Fuzzy Comprehensive Evaluation

Evaluation of Green Development Level of Electric Energy in Distribution Network Based on Multilevel Fuzzy Comprehensive Evaluation

A Safety Evaluation Model of Intersections under Mixed Traffic Conditions Using Traffic Conflicts and Cloud Model

Technoeconomic Assessment Based on Active Context-Knowledge Orchestration for Power Internet of Things

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