Basic power analysis confusion

In summary, the above conversation discusses the roles of two machines, one acting as a motor and the other as a generator. The question arises as to why both machines absorb negative Real and Reactive power, leading to confusion about their designated roles. However, it is clarified that the current convention and the assumption of one machine being turned by an external energy source helps to differentiate between the generator and motor. It is also noted that while theoretically any generator can be used as a motor and vice versa, practical considerations lead to optimization for one specific role.
  • #1
Ian_Brooks
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http://img528.imageshack.us/img528/6983/energytut1rc0.jpg
http://img300.imageshack.us/img300/8168/energytut1suppou2.jpg
Now why does machine 1 act as a motor and 2 as a generator?

The above question shows that both have current leaving the positive terminal
- so the generator convention is assumed

but they both absorb negative Real and Reactive power. Then why is either a generator and a motor?

Do we consider the other a load once one is assumed to be a generator? Then what happens when we have multiple generators in a network?
 
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  • #2
I think you may just be reading too much into the figure.

In the absence of anything else, I would assume that machine 2 ("generator") is being turned by a herd of elephants (grad students?--more plentiful) or a waterfall, or a boiler, something which is putting energy into the system. Machine 1 ("load") then spins because of the current output by Machine 2, using up the energy put into the system.

The current convention just says "measure current going this way", but says nothing about whether or not this is negative (which, in this case, it better be!)--or the complex equivalent of this when dealing with AC stuff.

In theory, any generator can be used as a motor and vice versa. In practice, they're optimized one way or the other.
 
  • #3
AHhh

so once we choose one convention (the generator side in this case), we assume the other device - the receiving end of the current ( whether positive or negative ) to be at the load side.

Its makes sense now - as device 1 is considered to be on the generator side, its absorbing real and reactive power and hence is a motor

Then the other device is considered to be on the load side and is delivering Real and reactive power and is hence a generator.

I see now.

Thanks
 

1. What is basic power analysis confusion?

Basic power analysis confusion refers to a common misunderstanding of the concept of power analysis in the field of research. It involves confusion about the purpose, interpretation, and calculation of power analysis, which can lead to incorrect conclusions and decisions in research studies.

2. Why is power analysis important in research?

Power analysis is important in research because it helps researchers determine the appropriate sample size needed to detect a significant effect or relationship in their study. It also helps in planning and designing studies by providing information on the statistical power of the study, which is the probability of detecting a true effect if it exists.

3. What factors influence power analysis?

The factors that influence power analysis include the desired level of statistical power, the effect size of the variable being studied, the alpha level (significance level), and the sample size. Other factors such as the type of statistical test used, the study design, and the variability of the data can also affect power analysis.

4. How is power analysis calculated?

Power analysis is typically calculated using statistical software programs, such as G*Power or R. The calculation involves inputting information on the desired level of statistical power, effect size, alpha level, and sample size. The software then calculates the necessary sample size needed to achieve the desired power level.

5. What are some common misconceptions about power analysis?

Some common misconceptions about power analysis include: 1) that a study must have high power to be valid, 2) that a larger sample size always leads to greater power, 3) that non-significant results mean the study was underpowered, and 4) that power analysis can only be used in quantitative research. It is important to have a clear understanding of power analysis to avoid these misconceptions and correctly interpret research findings.

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