RAM analysis on Power systems using Monte Carlo & Matlab

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Fernando Alberto Tellez Miotta

4:36:58

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1 - Introduction

1 - Course scope and objectives.mp4

09:11

1 - matlab from absolute scratch to the top.zip

1 - monte carlo simulations using matlab.zip

2 - Basic concepts

2 - Why Monte Carlo.mp4

04:36

2 - Why-Monte-Carlo-Lecture-2.pdf

3 - Mathematical concepts Probability density function PDF.mp4

06:14

3 - PDF-2.mp4

06:14

4 - Mathematical concepts CPF and ICPF.mp4

08:39

4 - Mathematical-concepts-CPF-and-ICPF-Lecture-4.pdf

5 - Practical examples Random sample generation through ICPFs.mp4

10:55

6 - Mathematical concepts STD SEM and CI.mp4

07:49

6 - Mathematical-concepts-STD-J-SEM-and-CI-Lecture-6.pdf

7 - Practical examples Commands for acquiring STD SEM and CI.mp4

03:38

7 - ci.zip

8 - Reliability Availability and Mantainability definitions TTF TTR MTTF MTTR.mp4

08:23

8 - Reliability-Availability-and-Mantainability-concepts-Lecture-7.pdf

9 - Practical examples Adjusting a sample to a probability density function.mp4

07:18

9 - fitmethis.zip

9 - fitmethis file exchange matlab central francisco de castro 2022 fitmethis httpswwwmathworkscommatlabcentralfileexchange40167fitmethis matlab central file exchange recuperado september 22 2022.zip

9 - plotfitdist.zip

9 - samples.zip

10 - Simulating an individual components on and off status switches over tmission 1.mp4

04:45

10 - Simulating-an-individual-component-Lecture-10.pdf

11 - Simulating an individual components on and off status switches over tmission 2.mp4

08:42

12 - Creating our first Monte Carlo RAM analysis code for an individual component.mp4

12:55

13 - How many simulations are enough.mp4

06:59

14 - Exponential probability density function memoryless function.mp4

03:44

14 - The-memoryless-function.pdf

3 - How do systems fail

15 - What is a reliability block diagram.mp4

08:04

15 - What-is-a-Reliability-block-diagram-Lecture-15.pdf

16 - Simulating series and parallel RBDs.mp4

03:31

16 - What-is-a-Reliability-block-diagram-Lecture-15.pdf

16 - parallel-status.zip

16 - series-status.zip

17 - Minimum cut sets applied to electrical power systems for RBD construction.mp4

06:24

17 - Minimum-cut-sets-Lecture-17.pdf

18 - The adjecency matrix of a graph.mp4

03:57

18 - The-adjacency-matrix-of-a-graph-Lecture-17.pdf

19 - Generating the adjacency matrix of a power system using Matlab.mp4

06:34

20 - Practical case applying the adjacency matrix function to our big case of study.mp4

04:20

20 - Resources-for-lesson-20.rar

21 - Example of elaboration of an RBD for a power system.mp4

08:17

21 - Resources.rar

22 - How do systems fail and how to simulate this failings.mp4

04:57

22 - How-do-systems-fail-Lecture-21.pdf

4 - Building our Monte Carlo RAM analysis software

23 - Resources-for-series-system-simulation-lesson.rar

23 - Simulating a series system.mp4

26:16

24 - Resources-for-parallel-system-simulation-lesson.rar

24 - Simulating a parallel system.mp4

13:49

25 - Making sure our Monte Carlo RAM Analysis software works.mp4

03:20

25 - Making-sure-our-software-works-properly.rar

26 - Finding the weak links within the RBD.mp4

11:23

26 - Input.xlsx

27 - 10-bus-system.zip

27 - Final project of section 4 part I.mp4

06:19

27 - times2fail.zip

27 - times2repair.zip

28 - Final project of section 4 part II.mp4

10:36

28 - Input-3.xlsx

28 - random-gen.rar

5 - LOLP and LOLE

29 - Loss of load probability Loss of load expectation and how to calculate them.mp4

10:30

29 - Loss-of-load-expectation-and-Loss-of-load-probability.pdf

30 - Applying LOLP and LOLE to build a deterministic MATLAB simulation.mp4

02:38

30 - Loss-of-load-expectation-and-Loss-of-load-probability.pdf

31 - Why Monte Carlo Part I.mp4

01:48

32 - Why Monte Carlo Part II.mp4

01:30

33 - Explaining a matlab function to calculate LOLE and LOLP.mp4

04:57

33 - LOLE-and-LOLP-calculation-function.rar

34 - Creating a Monte Carlo software to calculate LOLP and LOLE.mp4

15:22

34 - Resources-for-lesson-34.rar

35 - LOLE and LOLP as a function of the target load.mp4

04:59

36 - Evolving LOLP and LOLE for a big generation system.mp4

00:32

36 - input-file2.zip

37 - Wrapping up LOLE and LOLP.mp4

03:43

37 - monte-carlo-LOLE-and-LOLP-calculator-software.rar

6 - Wrapping up all

38 - Wrapping up all.mp4

01:29

7 - Congratulations and course closing

39 - bye bye.mp4

00:27

40 - BONUS.mp4

01:14

Description

Reliability, Availability & Mantainability analysis on electrical power systems using Monte Carlo simulations and MATLAB

What You'll Learn?

Learn reliability, availability and mantainaility fundamental concepts
Learn how to detect the failing modes responsible for having a complete system shutdown
Build individual components failure and reparing time period modeling, from historical data and/or other references using random number generation
Learn to model a whole system's failures and reparing times from its individual building blocs modeling
Learn to estimate realiability, availability and mantainability of a system using Matlab and monte carlo
Create software capable of simulating any kind of system from scratch

Who is this for?

Engineers and engineering students

Matlab practitioners

Reliability centered maintenance engineers

Reliability engineers

Maintenance engineers

Maintenance managers

More details

Description
Reliability centered maintenance has become a common practice on maintenance departments over all kinds of fields, from pretoleum fields to power system, passing through naval and aeronautical industry. Reliability centered maintenance allows for a cost effective maintenance policy that is focused on a system's different modes of failure and consecuences.

Most of the times, the failures of a system are fundamentaly random in their behaviour, thus, having a tool capable of simulating this random behaviour thousands or even millions of times in order to get a statistical trend is extemely valuable so that we can plan maintenance policies that tackle the most likely failing modes and the most catastrophic ones.

Monte Carlo methods is an umbrella terms that covers all the studies that rely on many similations of random systems in order to get their most likely behaviour over the span of several tries.

I had the opportunity to work this specific topic on my undergraduate thesis ''RAM analysis of electrical power system on the operational context using sequential Monte Carlo'' back in 2016 and got awarded with honors upon my disertation. I'll thrive myself to pour everything I learned into this course. I'm looking forward for your questions and feedback!
Who this course is for:
Engineers and engineering students
Matlab practitioners
Reliability centered maintenance engineers
Reliability engineers
Maintenance engineers
Maintenance managers

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Hi! My name is Fernando Téllez, I'm an Electrical Engineer at Universidad Simón Bolivar, the most prestigious University in my country Venezuela, and one of the top universities in Latin America (Universidad Simón Bolívar).My undergraduate thesis: ´´RAM analysis of Electric Power Systems via Sequential Monte Carlo Methods´´ was entirely programmed using Matlab and its results were compared against those of a commercial software yielding negligible differences, thus, I feel Monte Carlo is one of my best fields of expertise.Moreover, I'm currently working at a renewable energy company in Chile as a software engineer, a position in which I have to deal with monitoring APIs in order to gather the information of our several solar power plants so that we can bill the solar production. In this position I also have had the opportunity to apply machine learning, PSQL, mongoDB and angular.

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