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Annual availability assessment of a gas turbine power plant using Monte Carlo simulation based on fuzzy logic and an adaptive neuro-fuzzy repair time prediction system

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Abstract

Maintenance Scheduling and repair time of equipment play a significant role in the availability and reliability of power plants. This paper focuses on repair time estimation and time-dependent availability of the main components of a gas turbine power plant and their constituent sub-systems based on the direct use of human experience. This approach covers all different repair scenarios and estimates their unsteady-state annual availability and repair rates for the sub-components and the entire power plant. For this purpose, a human knowledge database, training, and simulating algorithm were used to obtain the desired results. Fuzzy logic and an adaptive neuro-fuzzy inference system were employed for simulating and predicting repair time. The Monte Carlo simulation method was also utilized to estimate the annual time-dependent availability for 20 years. The approach was used for predicting the availability of a power plant during its final five years of operation. The results revealed the critical components in the power plant units and indicated that the fuel system and lubrication system had lower availability than other units in the power plant, with availability averages of 95.5% and 96.4%, respectively. Therefore, these units had more susceptibility to failure than others. For the total system, the highest availability, 95%, was estimated to occur in the 16th year of the power plant operation. However, the lowest availability, 87% was predicted for the 19th year of the plant’s operation period.

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Abbreviations

FIS:

Fuzzy inference system

ANFIS:

Adaptive neuro-fuzzy system

MF:

Membership function

A:

Fuzzy set

\(\mu_{{\text{A}}} \left( x \right)\) :

Membership function for the fuzzy set A

a:

Year

d:

Day

h:

Hour

r:

Number of rules in fuzzy logic

\(\tilde{A}_{1}^{\text{r}} ,\tilde{A}_{2}^{\text{r}} , \ldots ,\tilde{A}_{\text{n}}^{\text{r}}\) :

Linguistic values of the repair factors or fuzzy sets

\(\tilde{b}^{\text{r}}\) :

Output linguistic value of the repair time

b:

Fuzzy output

\({\text{FP}}_{{{\text{nr}}}} \;{\text{and}}\;{\text{FP}}_{{{\text{br}}}}\) :

Prepositions in the sense of n-valued logic

\(\mu_{{\tilde{A}_{\text{i}}^{\text{r}} }} (x_{\text{i}} )\;{\text{or}}\;\mu_{{{\text{FP}}_{{{\text{ir}}}} }} (x_{\text{i}} )\) :

Strength of the membership function (MF) prepositions in fuzzy logic

\(\mu_{{\tilde{b}^{\text{r}} }} \;{\text{or}}\;\mu_{{{\text{FP}}_{{{\text{br}}}} }} (b)\) :

Strength of MF consequent in fuzzy logic

\(y_{\text{i}} ,\;x_{\text{i}}\) :

Inputs of ANFIS

\(w_{\text{i}}\) :

Fuzzy ANFIS output

\(\overline{w}_{\text{i}}\) :

Normalized fuzzy ANFIS output

Y:

Final output of ANFIS

\(p_{\text{i}} ,q_{\text{i}} ,r_{\text{i}}\) :

Consequent parameters of output node i in ANFIS

v1:

Training inputs

v2:

Training target

RMSE:

Root-mean-squared error

m(t)::

Repair probability distribution function

ɛ(t)::

Repair rate

M(t)::

Maintainability

R(t)::

Reliability

λ:

Failure ratetOperating time

t :

Repair time

CDF:

Cumulative distribution function

PDF:

Probability distribution function

A(t)::

Availability

n :

Number of iterations in Monte Carlo simulation

m :

Number of the year under consideration in Monte Carlo simulation

RCM:

Reliability centered maintenance

RCA:

Root cause analysis

RBI:

Risk based inspection

FFS:

Fitness for service

MTBF:

Mean time between failures

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Authors and Affiliations

  1. Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Danesh Mirzaei, Ashkan Abdalisousan & Seyed Mohammadreza Miri Lavasani

  2. Department of Energy Systems Engineering, Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Ali Behbahaninia

  3. Department of Engineering and Technology, Astara Branch, Islamic Azad University, Astara, Iran

    Ashkan Abdalisousan

Authors
  1. Danesh Mirzaei
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  2. Ali Behbahaninia
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  4. Seyed Mohammadreza Miri Lavasani
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Correspondence to Ali Behbahaninia.

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Mirzaei, D., Behbahaninia, A., Abdalisousan, A. et al. Annual availability assessment of a gas turbine power plant using Monte Carlo simulation based on fuzzy logic and an adaptive neuro-fuzzy repair time prediction system. J Therm Anal Calorim 148, 8675–8696 (2023). https://doi.org/10.1007/s10973-023-12091-7

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