An Ultra-fast Magic Sinewave Calculator

An Ultra-fast Magic Sinewave Calculator

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An Ultra Fast Magic Sinewave Calculator
Don Lancaster Synergetics, Box 809, Thatcher, AZ 85552 copyright c2007 as GuruGram #73-R (928) 428-4073

agic Sinewaves are a newly discovered class of mathematical functions that hold significant potential to dramatically improve the efficiency and power quality of solar energy synchronous inverters, electric hybrid automobiles, and industrial motor controls, among many others. An executive summary can be found here, a slideshow type intro presentation here, a development proposal here, the latest calculator here, and detailed additional tutorials and design info here. Major goals of such digital sinewave generation including offering the maximum possible efficiency by using the fewest of simplest possible switching transitions; offering the lowest possible distortion by zeroing out a maximum number of low harmonics that impact power quality, whine, vibration, and circulating currents; and by using all digital techniques that are extremely low end microprocessor and/or microcontroller friendly. Magic sinewaves have two remarkable properties: Any number of desired low harmonics can be forced exactly to zero in theory, and to astonishingly low levels when quantized to 8-bit compatible levels. And magic sinewaves use the absolute minimum possible and simplest energy-robbing transitions to achieve such harmonic suppression. A typical magic sinewave might look something like this...


-- 73 . 1 --

We see that this waveform is a variation on PWM or pulse width modulation. Its highly unique characteristics are that it has far fewer energy robbing transitions than conventional PWM, that it is always exactly phase- and frequency locked to a fundamental, and uses half bridge rather than full bridge switching events for further efficiency improvment. Additional advantages include a 100 percent modulation depth allowing the carrier to never exceed the fundamental. Plus, of course, zeroing out any chosen number of low harmonics and doing so with an absolute minimum of switching events. There are several different types of Magic Sinewaves possible. Three of emerging interest are called Best Efficiency, Bridged Best Efficiency, and Delta Friendly. A Best Efficiency Magic Sinewave zeros out an additional two harmonics. When compared to conventional earlier solutions. Brought about by an invisible and zero integrated width pulse at zero degrees. A bridged best efficiency is similar but is continuous at 90 degrees, And fills in with alternate values. A delta friendly magic sinewave meets the exacting special needs of three phase power systems. There are fewer of these at present, limited to 3, 7, 11, 15, ... or more pulses per quadrant. They zero out somewhat fewer low harmonics but have a major advantage of needing only one-half the storage for amplitude data values. Magic sinewaves are extremely exacting in their solutions. A typical equation set for a seven pulse per quadrant best efficiency magic sinewave might be...

cos ( 1*p1s ) - cos ( 1*p1e ) + ... + cos ( 1*p7s ) - cos ( 1*p7e ) = ampl * pi/4 cos ( 3*p1s ) - cos ( 3*p1e ) + ... + cos ( 3*p7s ) - cos ( 3*p7e ) = 0 cos ( 5*p1s ) - cos ( 5*p1e ) + ... + cos ( 5*p7s ) - cos ( 5*p7e ) = 0 cos ( 7*p1s ) - cos ( 7*p1e ) + ... + cos ( 7*p7s ) - cos ( 7*p7e ) = 0 cos ( 9*p1s ) - cos ( 9*p1e ) + ... + cos ( 9*p7s ) - cos ( 9*p7e ) = 0 cos (11*p1s) - cos (11*p1e) + ... + cos (11*p7s) - cos (11*p7e) = 0 cos (13*p1s) - cos (13*p1e) + ... + cos (13*p7s) - cos (13*p7e) = 0 cos (15*p1s) - cos (15*p1e) + ... + cos (15*p7s) - cos (15*p7e) = 0 cos (17*p1s) - cos (17*p1e) + ... + cos (17*p7s) - cos (17*p7e) = 0 cos (19*p1s) - cos (19*p1e) + ... + cos (19*p7s) - cos (19*p7e) = 0 cos (21*p1s) - cos (21*p1e) + ... + cos (21*p7s) - cos (21*p7e) = 0 cos (23*p1s) - cos (23*p1e) + ... + cos (23*p7s) - cos (23*p7e) = 0 cos (25*p1s) - cos (25*p1e) + ... + cos (25*p7s) - cos (25*p7e) = 0 cos (27*p1s) - cos (27*p1e) + ... + cos (27*p7s) - cos (27*p7e) = 0

Power polynomials of this complexity are unlikely to have a direct solution. Instead, Newton's Method, otherwise known as "shake the box" has proven to be an effective solution route. In which a good guess is made based on a previously useful result or a nearby amplitude. This is followed by one or more iterations of improvement to the good guess.
-- 73 . 2 --

Such an initial guess presupposes one and only one solution for a given magic sinewave equation. Some experiments using Monte Carlo Methods do strongly suggest that single solutions are likely the case. Per this example code and this result. The general concept is to generate tens to hundreds of millions of random pulses, filter them to low distortions, and seek out any exceptions to the known solution set. Things rapidly get out of hand beyond n=4. But all of the lower order models strongly support uniqueness. An extensive set of older JavaScript based interactive calculators is found here. These earlier calculators use a brute force iterative method that had demanded repeated trig calculations to seek the harmonic distortion minimums. While quite effective and useful, their initially slow computing times became excessive when many dozens or hundreds of harmonics are to be zeroed. In GuruGram #72, some very preliminary and tentative work showed an improved and quasi-deterministic approach to Magic Sinewave solutions. However, these new solutions still remained quite slowly converging. Here we will explore some extensions to these techniques that has led to a brand new approach to Magic Sinewave calculations that is both exceptionally fast and quasi-deterministic. Speedups beyond 1000:1 have been demonstrated. With typical calculation times of well under one second. As per this current calculator demo.

The Approach
There is a fundamental mathematical proof that no direct deterministic solutions exist for independent polynomial equation sets above order four. But on the other hand, there are trigonometric identities that might somehow indirectly relate the variables in the above equations. And, as our results clearly prove, it certainly should be possible to modulate a carrier without distortion. Whether a useful direct and deterministic solution to Magic Sinewaves exists remains an open question. The approach here uses a two step process of a good guess that is followed by a fast converging improvement. In some cases, a single iteration can give engineeringly useful results. And repeated iterations can end up amazingly fast. While converging to aesthetically and mathematically satisfying harmonics zeroed to well beyond fourteen decimal places. As an additional bonus, the current technique converges simultaneously on the zeroed harmonics and on a chosen target amplitude.

Making Some Good Guesses
A better guess can start by working backwards from a known Magic Sinewave solution. While attempting to stay as close as possible to the "real" math. Here is how the n=7 Best Efficiency Magic Sinewave angles vary with amplitude...
-- 73 . 3 --




72.000 pulse starting or ending angle position in degrees










12.000 best efficiency 7 pulse per quadrant magic sinewave pulse positions 0.0000 0.0 0.5 input amplitude



-- 73 . 4 --

We first note that very low amplitudes start off with a group of carefully locked carrier phase impulses. Having zero width and zero energy for zero amplitude. In the case of a best efficiency, seven pulse per quadrant magic sinewave, there will be impulses that start near 12.000, 24.000, 36.000, 48.000, 60.000, 72.000, and 84.000 degrees. These impulses will mirror over the 90 to 180 degree range and invert over the 180 to 360 degree range. There will also be two "invisible" carrier phase impulses you'll find at 0 and 180 degrees. Whose very small and bipolar energy will integrate to zero. And thus can be completely ignored. These invisible impulses are the key to a seven pulse per quadrant best efficiency magic sinewave being able to reject and zero all the harmonics through the 28th. Or two more harmonics than would normally be expected. Because there really are 7-1/2 pulses per quadrant. As the amplitudes increase, each of the carrier phase impulses will widen. This widening appears to be somewhat proportional to the sine squared of the carrier impulse phase angle. The fractional contribution of each carrier phase impulse can be found by summing the squares of the sines of all impulses and dividing. Because of Fourier Series constant considerations, the sought amplitude will end up as pi/4 or 0.785398163 of the 0 to 1 desired final amplitude. As the carrier impulses fatten, they do not do so linearly. Instead, they will trend downward at very high amplitudes. Sadly, polynomials directly and accurately synthesizing these curves turn out to be incredibly complex and high order. Instead, a "two step" guessing process is made. First a linear expansion get done based on sine squared cosine distributions. This is "good enough" for all but the highest amplitudes of certain magic sinewave solutions. It is important to note that these first guess angles expand as their cosines and NOT as degrees!. Because you want just as much energy above the carrier pulse center as below. Should an amplitude fraction of .007 be wanted, you can use...
starting angle = acos ( cos(center angle) - .007) ending angle = acos ( cos(center angle) + .007)

Another gotcha is forgetting that JavaScript works in radians, not degrees. The conversion constants are...
radians = degrees * pi/180 degrees = radians * 180/pi

To make sure the highest amplitudes converge, a second guess can be made that slightly tilts the highest amplitude angles downward...
-- 73 . 5 --

correction = fudge * (amplitude)^4 * (angle/90)

... with a typical fudge value of .02 or .03 getting subtracted. Summarizing, a good guess is made by first linear expanding to the sought amplitude in a sine squared weighted proportion. A second guess then slightly adjusts the highest amplitude values to guarantee convergence. Exact details can be found by using view source on the calculator demo.

Exploring a Trig Identity
It turns out the "improver" portion of our two-step algorithm is in fact fully deterministic when very near a given Magic Sinewave solution. To understand exactly why this is so, we can look at this trig identity...
cos( a + x ) = cos ( a ) cos ( x ) - sin ( a ) sin ( x )

This identity is true for all values of a and x. Useful simplifications can result if we are in the first quadrant and if a is much larger than x. If x is very nearly zero, its cosine will be close to one and its radian value will nearly equal its argument. Which simplifies to...
cos( a + x ) approximates cos( a ) - x sin( a ) if a >> x

This expression exactly matches that used by Newton's Method! Where you make a better approximation to a solution by multiplying its present error by the slope of the function and add this to the present value.
Note that the slope of the cosine is minus the sine. And also that the slope of cos ( nx ) is - n * sin ( nx ).

It can also be of interest to find an even better approximation. The power series definition of sines and cosines are...
sin(x) = x - x3/3! + x5/5! - ... cos(x) = 1 - x2/2! + x4/4! - ...

... which, when substituted in the original trig identity gives us a somewhat more precise approximation of...
-- 73 . 6 --

cos( a + x ) closely equals cos( a )*(1 - x2/2 ) - sin( a )*(x - x3/6)

While this result is not needed for our current "improver" algorithm, it may prove highly useful for further refinements.

The "improver" algorithm
The "improver" algorithm ends up very close to fully deterministic when near a valid Magic Sinewave solution. It is based on taking our initial equations above and substituting each cosine value with cos ( lastguess + error ). Rearranging constant and variable terms will leave fourteen linear equations in fourteen unknowns. These are easily and rapidly solved using Gauss Jordan Elimination. As the fundamental amplitude error is treated as an error in the same way as a nonharmonic zero error, the solution rapidly converges both on the desired amplitude and on totally zeroed harmonics. This completely eliminates the small amplitude errors of the previous calculators. And the need for repeat trips. A functional and super fast demo Magic Sinewave calculator appears here. Summarizing our "improver" rules...
Each cosine term in the basic Magic Sinewave equations gets substituted with cos ( bestguess + error ) This gets approximated by cos(bestguess) - error*slope. Note that the slope of cos(nx) is -n*sin(nx). Terms are rearranged, leaving an array of n linear equations in n unknowns. The equations are solved, either using Gauss Elimination and back substitution. Or else Gauss-Jordan Elimination. Errors are replaced using the cos ( a+x ) trig identity. Leaving a very close and nearly deterministic solution.

Let's look at some more detail. Our fundamental equation from above was...
cos ( 1*p1s ) - cos ( 1*p1e ) + ... - ... + cos ( 1*p7s ) - cos ( 1*p7e ) = ampl * pi/4

Replace each cos with a sum of our known guess and unknown error xn...
-- 73 . 7 --

cos (p1sg + x1) - cos (p1eg + x2) + cos (p2sg + x3) - cos (p2eg + x4) + ... + cos (p7sg + x14) - cos (p7eg + x14) = ampl * pi/4

Assume xn is very small and substitute its cos ( a+x ) approximation...
cos (p1sg) - x1 * sin (p1sg) cos (p1eg) + x1 * sin (p1sg) + ... = ampl * pi/4

Note that signs alternate between starting and ending angles. Since p1xg is known, its sine and its cosine will be constants. Change sign and rearrange all constants to the right side of the equation...
x1 * sin(p1sg) - x2 * sin(p1eg) + x3 * sin(p2sg) - x4 * sin(p2eg) + ... - ... = ampl * pi/4 + cos(p1sg) - cos(p1eg) + cos(p2sg) - cos(p2eg) + ...

When all constants are substituted and combined, this becomes a fourteen term linear equation of form...
[j0,0](x1) + [j0,1](x2) + [j0,3](x3) + ... + [j0,13](x14) = [ k00 ]

Solving the harmonic equations are similar noting the slope of cos(nx) will be - n *sin(nx). Giving us a linear equation set of 14 variables in 14 unknowns...
[j0,0](x1) + [j0,1](x2) + [j0,3](x3) + ... + [j0,13](x14) = [ k00 ] [j1,0](x1) + [j1,1](x2) + [j1,3](x3) + ... + [j1,13](x14) = [ k01 ] . . . . . . . . . . [j13,0](x1) + [j13,1](x2) + [j13,3](x3) + ... + [j13,13](x14) = [ k13 ]

Which, despite its apparent complexity, can easily be solved by either Gaussian elimination followed by back substitution. Or else by Gauss-Jordan elimination. The latter is preferable when expanding to larger magic sinewave solutions. Once the x errors are found, they are easily combined with the guess angles using the above exact cos ( a+x ) trig identity.
-- 73 . 8 --

Convergence is amazingly rapid and speed appears at least a thousand times faster than the earlier calculations. Again, a demo can be found here.

Some Delta Friendly Considerations
If three phase loads are to be driven without needing rewiring and using only three half bridge drivers, special delta friendly magic sinewaves are required. These are summarized in this tutorial. Known three phase magic sinewave solutions are presently limited to n = 3, n = 7, n = 11, and higher ( 4x + 3 ) pulses per quadrant. Because all triad harmonics must be explicitly cancelled, delta friendly magic sinewaves zero out a fewer number of low harmonics. But their benefits include having to solve only one half the usual number of linear equations and require only one half of the data storage. For instance, a 7 pulse per quadrant magic sinewave might use seven of its pulse edges to guarantee explicit triad cancellation, one pulse edge ( used in obscure combination with the others ) to set the amplitude, and the remaining six edges ( again in combination ) used to zero out harmonics 5, 7, 11, 13, 17, and 19. Since 21 is a triad harmonic and no even harmonics are present, the first uncontrolled harmonic would be the 23rd. Compared to the 29th for a single phase, seven pulse best efficiency magic sinewave. Again for n=7, it is convenient to make the controllable edges p4s, p4e, p5s, p6s, p6e, p7s, and p7e. The other edges must be forced to obey this rule set...
p1s = 60 - p5s p1e = p6e - 60 p2s = p7s - 60 p2e = 60 - p4e p3s = 60 - p4s p3e = p7e - 60 p5e = 120 - p6s

Instead of the usual 14 equations in 14 unknowns, we should be able to come up with only 7 equations in 7 unknowns instead. With each of the new variables representing a curious vector sum of the paired original edges...
cos cos cos cos (1*(p4s-30))* 1.732 - cos (1*(p4e-30))* 1.732 + (1*(p5s-30))* 1.732 + cos (1*(p6s+30))* 1.732 (1*(p6e-30))* 1.732 + cos (1*(p7s-30))* 1.732 (1*(p6e-30))* 1.732 = amplitude * pi/4

-- 73 . 9 --

Yes, these equations are truly bizarre. A complete derivation is included in the demo, which you can access through the usual "view source" route.
Note that the fourth term is different from the others. Because it relates a leading and trailing pulse edge. Also note that 1.732 more precisely is 2*sin(60).

The harmonic equations are similar to the above, except the "1" gets replaced by the non-triad harmonic numbers of 5, 7, 11, 13, 17, and 19. And the output gets divided by the harmonic number. Also, the overall harmonic signs invert for 5, 7, 17, and 19. Thus the equation for 5h produces minus the actual fifth harmonic. Once again, a derivation appears in the demo calculator.

Calculator Design and Structure
The new ultra speed calculators differ dramatically from the earlier versions. Here are some of the key differences...
"N" INDEPENDENT CODE -- As many of the functions are made

as independent of the pulse-per-quadrant and display box counts as possible. This enormously simplifies rewrites for different sizes of magic sinewaves.
NORMALIZATION -- Internal calcs are done with JavaScript

preferred radian angles and Fourier rather than absolute amplitudes. Final values are limited to the display only.
ARRAY TECHNIQUES - A numerically accessed Angles[x] and a supporting Harms[x] array eliminates keeping track of

fancy variable names and display positions.
CODE SPLITTING - The code is in two halves, an "analyze"

portion that keeps the display happy and the "adjust" portion that provides newer and better values. Central to this is "pivoting" on the Angles[x] array. Which is the primary link between the two.
EXTENSIVE LOOPING - Used when and where possible to keep the code compact and to encourage "n" independence. IMPROVED GAUSS-JORDAN - Latest versions of the required n x n linear equation solvers are ultra compact, amazingly fast, and fully "n" independent. EXPORT AREAS - New cut and paste regions can greatly

simplify extracting all angles for further use.

-- 73 . 10 --

A Brief Gauss-Jordan Tutorial
Gaussian elimination is the process of playing around with some array values ahead to time to greatly simplify a final solution. Consider five linear equations in five unknowns...
A0*v A1*v A2*v A3*v A4*v + + + + + B0*w B1*w B2*w B3*w B4*w + + + + + C0*x C1*x C2*x C3*x C4*x +D0*y +D1*y +D2*y +D3*y +D4*y + + + + + E0*z E1*z E2*z E3*z E4*z = = = = = K0 K1 K2 K3 K4

While all sorts of solution methods exist, we seek one that is computationally efficient. If we dink around with some manipulations ahead of time, we can eventually end up with a solution that will be obvious by inspection! Arrange the coefficients into a group of arrays...
[ [ [ [ [ A0 A1 A2 A3 A4 B0 B1 B2 B3 B4 C0 C1 C2 C3 C4 D0 D1 D2 D3 D4 E0 E1 E2 E3 E4 K0 K1 K2 K3 K4 ] ] ] ] ]

The rules for our "Gauss" part of rearrangement are that any row can be scaled by any constant term by term without changing the results. And that any row can be subtracted from any other row term by term and substituted. Again without changing the results. In interests of sanity, let "~" be any coefficient that resulted from any and all previous manipulation. Scale the top row by dividing by its initial value...
[ [ [ [ [ 1 A1 A2 A3 A4 ~ B1 B2 B3 B4 ~ C1 C2 C3 C4 ~ D1 D2 D3 D4 ~ E1 E2 E3 E4 ~ K1 K2 K3 K4 ] ] ] ] ]

Scale the top row by A1 and subtract it from the next row down and replacing...
[ [ [ [ [ 1 0 A2 A3 A4 ~ ~ B2 B3 B4 ~ ~ C2 C3 C4 ~ ~ D2 D3 D4 ~ ~ E2 E3 E4 ~ ~ K2 K3 K4 ] ] ] ] ]

Similarly, scale the top row by A2 and subtract it from the middle row. Then scale by A3 for row 3 and A4 for row4...
-- 73 . 11 --

[ [ [ [ [

1 0 0 0 0

~ ~ ~ ~ ~

~ ~ ~ ~ ~

~ ~ ~ ~ ~

~ ~ ~ ~ ~

~ ~ ~ ~ ~

] ] ] ] ]

Now, scale the second row down by its first nonzero coefficient...
[ [ [ [ [ 1 0 0 0 0 ~ 1 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ] ] ] ] ]

Next, force zeros in the second column the same as we did with the first, but using the second row for subtraction and substitution...
[ [ [ [ [ 1 0 0 0 0 ~ 1 0 0 0 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ] ] ] ] ]

Keep working your way through the array, this time scaling the third row down by its first nonzero term and then using scaled subtractions to zero out everything below in the same column. Eventually, you should end up with...
[ [ [ [ [ 1 0 0 0 0 ~ 1 0 0 0 ~ ~ 1 0 0 ~ ~ ~ 1 0 ~ ~ ~ ~ 1 ~ ~ ~ ~ ~ ] ] ] ] ]

This completes the Gauss part of the process. The lower right squiggle will be z by inspection! Relabel the above array...
[ [ [ [ [ 1 0 0 0 0 c01 c02 c03 c04 j05 ] 1 c12 c13 c14 j15 ] 0 1 c23 c24 j25 ] 0 0 1 c34 j35 ] 0 0 0 1 z ]

where cxx is the row and column coefficient for the left side equation terms, and jxx is the similar row and column coefficient for the right side equation term.
-- 73 . 12 --

The traditional way to solve this was by back substitution. You can start off with y = j35 - z*c34 and so on. And then work your way up a row at a time, making more complex calculations until you have v through z all solved. The Jordan approach starts off the same way, but it works one column at a time, greatly simplifying computer programming. Especially when more than one n x n equation set size is to be accommmodated. The new rule is that any constant can
be subtracted from one term in the left side of the equation as long as that same constant get subtracted from the right side of the equation.

Subtract z*c34 from row 4...
[ [ [ [ [ 1 0 0 0 0 c01 c02 c03 c04 j05 ] 1 c12 c13 c14 j15 ] 0 1 c23 c24 j25 ] 0 0 1 0 y ] 0 0 0 1 z ]

So far, this is the same as the usual back substitution. We now can observe y by inspection The difference with Jordan is to continue by working columns instead of rows. Modify the rows by subtracting z*c24, z*c14, and z*c04 to get...
[ [ [ [ [ 1 0 0 0 0 c01 c02 c03 1 c12 c13 0 1 c23 0 0 1 0 0 0 0 0 0 0 1 j05 j15 j25 y z ] ] ] ] ]

Next, modify column three by subtracting y*c23, y*c13, and y*c03. And then column two by subtracting x*c12 and x*c02. And finally column one by subtracting w*c01 to get...
[ [ [ [ [ 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 v] w] x] y] z]

Your values v through z are now instantly readable by inspection! Once again, the Jordan method takes just as many calculations as does a back substitution, but it greatly simplifies computation. In that loops do not have any multiple calculations or complicated cross-coefficients in them. This is especially handy when it comes to making the working code independent of n .

-- 73 . 13 --

A Code Example
Here's a JavaScript program that solves n x n linear equations. It is amazingly compact, offers 64 bit arithmetic, and works for most any sane value of n. But it does not yet trap out any div0's or accomodate wildly varying coefficients. Here is the main proc...
function solveGaussJordan() { gjNsize = eqns.length ; for (var iii = 0; iii