Jim, Yours is the prevailing view in the industry. However, it doesn't seem to work. Even given months of time to analyze past failures, they are often unable to divine rules that would have reliably avoided the problems. In short, until you adequately understand the system that your sensors are sensing, all the readings in the world won't help. Further, when a system is fundamentally unstable, you must have a control system that completely deals with the instability, or it absolutely will fail. The present system meets neither of these criteria.
There is another MAJOR issue. Presuming a power control center in the middle of the U.S., the round-trip time at the speed of light to each coast is ~16ms, or two half-cycles at 60Hz. In control terms, that is an eternity. Distributed control requires fundamental stability to function reliably. Times can be improved by having separate control systems for each coast, but the interface would still have to meet fundamental stability criteria (like limiting the rates of change), and our long coasts would still require a full half-cycle of time to respond. Note that faults must be responded to QUICKLY to save the equipment, and so cannot be left to central control systems to operate. So, we end up with the system we now have, that does NOT meet reasonable stability criteria. Hence, we may forever have occasional outages until the system is radically re-conceived. Steve ========== On Mon, Jun 21, 2010 at 9:17 AM, Jim Bromer <jimbro...@gmail.com> wrote: > I think a real world solution to grid stability would require greater use > of sensory devices (and a some sensory-feedback devices). I really don't > know for sure, but my assumption is that electrical grid management has > relied mostly on the electrical reactions of the grid itself, and here you > are saying that is just not good enough for critical fluctuations in 2010. > So while software is also necessary of course, the first change in how grid > management should be done is through greater reliance on off-the-grid (or at > minimal backup on-grid) sensory devices. I am quite confident, without > knowing anything about the subject, that that is what needs to be done > because I understand a little about how different groups of people work and > I have seen how sensory devices like gps and lidar have fundamentally > changed AI projects because they allowed time sensitive critical analysis > that was too slow and for contemporary AI to solve. 100 years from now, > electrical grid management won't require another layer of sensors because > the software analysis of grid fluctuations will be sufficient. On the other > hand, grid managers will not remove these additional layers of sensors from > the grid a hundred years from now anymore than we telephone engineers would > suggest that maybe they should stop using fiber optics because they could > get back to 1990 fiber optic capacity and reliability using copper wire with > today's switching and software devices. > Jim Bromer > On Mon, Jun 21, 2010 at 11:19 AM, Steve Richfield < > steve.richfi...@gmail.com> wrote: > >> There has been an ongoing presumption that more "brain" (or computer) >> means more intelligence. I would like to question that underlying >> presumption. >> >> That being the case, why don't elephants and other large creatures have >> really gigantic brains? This seems to be SUCH an obvious evolutionary step. >> >> There are all sorts of network-destroying phenomena that rise from complex >> networks, e.g. phase shift oscillators there circular analysis paths enforce >> themselves, computational noise is endlessly analyzed, etc. We know that our >> own brains are just barely stable, as flashing lights throw some people into >> epileptic attacks, etc. Perhaps network stability is the intelligence >> limiter? If so, then we aren't going to get anywhere without first fully >> understanding it. >> >> Suppose for a moment that theoretically perfect neurons could work in a >> brain of limitless size, but their imperfections accumulate (or multiply) to >> destroy network operation when you get enough of them together. Brains have >> grown larger because neurons have evolved to become more nearly perfect, >> without having yet (or ever) reaching perfection. Hence, evolution may have >> struck a "balance", where less intelligence directly impairs survivability, >> and greater intelligence impairs network stability, and hence indirectly >> impairs survivability. >> >> If the above is indeed the case, then AGI and related efforts don't stand >> a snowball's chance in hell of ever outperforming humans, UNTIL the >> underlying network stability theory is well enough understood to perform >> perfectly to digital precision. This wouldn't necessarily have to address >> all aspects of intelligence, but would at minimum have to address >> large-scale network stability. >> >> One possibility is chopping large networks into pieces, e.g. the >> hemispheres of our own brains. However, like multi-core CPUs, there is work >> for only so many CPUs/hemispheres. >> >> There are some medium-scale network similes in the world, e.g. the power >> grid. However, there they have high-level central control and lots of >> crashes, so there may not be much to learn from them. >> >> Note in passing that I am working with some non-AGIers on power grid >> stability issues. While not fully understood, the primary challenge appears >> (to me) to be that the various control mechanisms (that includes humans in >> the loop) violate a basic requirement for feedback stability, namely, that >> the frequency response not drop off faster then 12db/octave at any >> frequency. Present control systems make binary all-or-nothing decisions that >> produce astronomical high-frequency components (edges and glitches) related >> to much lower-frequency phenomena (like overall demand). Other systems then >> attempt to deal with these edges and glitches, with predictable poor >> results. Like the stock market crash of May 6, there is a list of dates of >> major outages and near-outages, where the failures are poorly understood. In >> some cases, the lights stayed on, but for a few seconds came ever SO close >> to a widespread outage that dozens of articles were written about them, with >> apparently no one understanding things even to the basic level that I am >> explaining here. >> >> Hence, a single theoretical insight might guide both power grid >> development and AGI development. For example, perhaps there is a necessary >> capability of components in large networks, to be able to custom tailor >> their frequency response curves to not participate on unstable operation? >> >> I wonder, does the very-large-scale network problem even have a >> prospective solution? Is there any sort of existence proof of this? >> >> My underlying thought here is that we may all be working on the wrong >> problems. Instead of working on the particular analysis methods (AGI) or >> self-organization theory (NN), perhaps if someone found a solution to >> large-network stability, then THAT would show everyone the ways to their >> respective goals. >> >> Does anyone here know of a good starting point to understanding >> large-scale network stability? >> >> Any thoughts? >> >> Steve >> >> *agi* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/> | >> Modify<https://www.listbox.com/member/?&;>Your Subscription >> <http://www.listbox.com/> >> > > *agi* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
