Decision of Maintenance Priority of Power System Using an Improved Risk Priority Number Methodology Based on Fuzzy Theory

2021 ◽  
Vol 70 (2) ◽  
pp. 94-101
Author(s):  
Sung-Hun Lee ◽  
Byeong-Chan Oh ◽  
Sung-Yul Kim
Keyword(s):  
Power System ◽  
Fuzzy Theory ◽  
Risk Priority Number

scholarly journals Development of energy control system for parallel hybrid power system using dynamic equations and fuzzy theory

2020 ◽  
Vol 12 (11) ◽  
pp. 168781402096692
Author(s):  
Po-Tuan Chen ◽  
Cheng-Jung Yang ◽  
K David Huang
Keyword(s):  
Power System ◽  
Control Strategy ◽  
Combustion Engine ◽  
Fuzzy Theory ◽  
Optimal Operation ◽  
Power Sources ◽  
Hybrid Power System ◽  
Hybrid Power ◽  
Parallel Hybrid ◽  
Fuzzy Control Strategy

A fuzzy control strategy is developed in this study to manage the parallel hybrid power system of internal combustion engine (ICE) and electric motor (EM) for hybrid vehicles. The rules established for the fuzzy logic are based on the conditions of vehicle pedal position, vehicle velocity, and the state of charge to control the throttle position of the ICE and the switch position of EM in low-, mid-, and high-power cruising. The optimization of the control strategy can make vehicles achieving ECE 40 driving pattern. In addition, the ICE can work in an optimal operation range, thus reducing carbon emission. The EM may provide power according to the demand, such that the torque output of the output shaft of the power split device is twice of the input of the two power sources separately.

scholarly journals The Risk Priority Number Evaluation of FMEA Analysis Based on Random Uncertainty and Fuzzy Uncertainty

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaojun Wu ◽  
Jing Wu
Keyword(s):  
Evaluation Method ◽  
Fuzzy Theory ◽  
Integrated Model ◽  
Risk Priority Number ◽  
Fuzzy Uncertainty ◽  
Time Dynamic ◽  
Random Uncertainty ◽  
Term Evaluation ◽  
Fmea Analysis ◽  
Uncertainty Environment

The risk priority number (RPN) calculation method is one of the critical subjects of failure mode and effects analysis (FMEA) research. Recently, RPN research under a fuzzy uncertainty environment has become a hot topic. Accordingly, increasing studies have ignored the important impact of the random sampling uncertainty in the FMEA assessment. In this study, a fuzzy beta-binomial RPN evaluation method is proposed by integrating fuzzy theory, Bayesian statistical inference, and the beta-binomial distribution. This model can effectively realize real-time, dynamic, and long-term evaluation of RPN under the condition of continuous knowledge accumulation. The major contribution of the proposed model is to use the random uncertainty and fuzzy uncertainty in an integrated model and provide a Markov Chain Monte Carlo (MCMC) method to solve the complex integrated model. The study presented a case study, which presented how to apply this model in practice and indicated the significant influence on the measurement error caused by ignoring the random uncertainty caused by expert evaluation in RPN calculations.

scholarly journals Application of Artificial Intelligence Techniques in Reactive Power/Voltage Control of Power System

2014 ◽  
Vol 1 (1) ◽  
pp. 5
Author(s):  
Gehao Sheng ◽  
Guangyu Tu ◽  
Yi Luo
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Power System ◽  
Reactive Power ◽  
Fuzzy Theory ◽  
Voltage Control ◽  
Control Methods ◽  
Multi Agent System ◽  
Multi Agent ◽  
Artificial Neural Network Ann

<p>As one of the important constituents of power system automation, reactive power/voltage control possesses inherent characteristics of complexity, nonlinearity, inaccuracy and high requirement for control speed, parts of which are hard to be described by the traditional mathematical models or to be realized by routine control methods. The artificial intelligence (AI) techniques have intelligence feature which traditional method does not bear, so special attentions are paid to the application of AI techniques in reactive voltage control and a lot of results in this field are obtained. In this paper the main results and methods of applying the AI techniques, such as Expert System (ES), Artificial Neural Network (ANN), Fuzzy Theory (FT), Genetic Algorithm (GA) and Multi-Agent System (MAS), etc., to reactive voltage control in power systems are summarized, the respective application features of these techniques are analyzed and compared and some problems to be solved are pointed out.</p>

Failure Mode and Effects Analysis on Control Equipment Using Fuzzy Theory

2013 ◽  
Vol 837 ◽  
pp. 16-21
Author(s):  
Nadia Belu ◽  
Daniel Constantin Anghel ◽  
Nicoleta Rachieru
Keyword(s):  
System Design ◽  
Failure Mode ◽  
Failure Modes ◽  
Fuzzy Theory ◽  
Risk Level ◽  
Risk Priority Number ◽  
Control Equipment ◽  
Wide Range ◽  
Criticality Analysis ◽  
Made In

Failure Mode and Effects Analysis is a methodology to evaluate a system, design, process, machine or service for possible ways in which failures (problems, errors, risks and concerns) can occur and it has been used in a wide range of industries. Traditional method uses a Risk Priority Number to evaluate the risk level of a component or process. This is obtained by finding the multiplication of three factors, which are the severity of the failure (S), the probability/occurrence of the failure (O), and the probability of not detecting the failure (D). There are significant efforts which have been made in FMEA literature to overcome the shortcomings of the crisp RPN calculation. Fuzzy logic appears to be a powerful tool for performing a criticality analysis on a system design and prioritizing failure identified in analisys FMEA for corrective actions. In this paper we present a parallel between the typical and the fuzzy computation of RPNs, in order to assess and rank risks associated to failure modes that could appear in the functioning of control equipment.

Load Interval Prediction of the Power System based on Type-2 Fuzzy Theory

2016 ◽  
Vol 9 (2) ◽  
pp. 73-84
Author(s):  
He Tao ◽  
Liang Zhidong ◽  
Pang Jihong ◽  
Ye Xianquan ◽  
Yuan Jinxin
Keyword(s):  
Power System ◽  
Fuzzy Theory ◽  
Interval Prediction

Risk Assessment of Cascading Failures in Power System Based on Fuzzy Theory

2014 ◽  
Vol 496-500 ◽  
pp. 2733-2736
Author(s):  
Fu Chao Zhang ◽  
Jia Dong Huang
Keyword(s):  
Risk Assessment ◽  
Power System ◽  
Fuzzy Theory ◽  
Power Grid ◽  
Cascading Failures ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Level Of Risk

This paper makes a grading of the probability of cascading failures using the theory of fuzzy C-means clustering to implement the level of risk assessment. Then from the perspective of power system transient security, use the specific severity functions to grade the probability of cascading failures, and the synthetical severity is graded by fuzzy theory. Finally, synthesizing the probability of cascading failures with the severity of failure, and the level of risk assessment is determined according to the principle of the maximum membership. Finally, taking IEEE 39 system for example, to assess the level of the risk of cascading failures in power grid, thus the rationality and effectiveness of the proposed algorithm are verified.

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