Approximate Algorithm for Determining Pulse Edges of a PWM Inverter Based on Natural Sampling

Abstract

The paper presents a new algorithm for determination of pulse edges of a modulated wave of a PWM voltage inverter which offers a possibility that natural sampling is realized with an arbitrary accuracy without applying an iterative procedure. The basic idea is to express the angles which determine pulse edges of the modulated signal as polynomials of amplitude modulation index. Geometric interpretation of sampling of the polynomial algorithm is identical with the geometric interpretation of natural algorithm, but the transcendental equation whose solution defines pulse edges of the modulated signal is replaced by a simple procedure of finding values of a polynomial whose coefficients are determined in advance by an exact procedure. This approach gives the possibility of digital implementation of polynomial sampling method using the low-cost microprocessor platforms.

1. Introduction

Pulse width modulation is an important method of the technique of control of power converters. At the current technological level, three areas within the pulse width modulation possess certain autonomy as regards the accomplished solutions [1]. These are switching strategy of pulse width modulation, concerned with generation of edges of switching pulses [2–10], range of regulation of amplitude of the fundamental harmonic of inverter output voltage [11–15], and degree of harmonic distortions of inverter output voltage and current [16–21]. The first PWM techniques are based on the method of the modulating function.

The modulating function contains information regarding the desired waveform, whereas the signal carrier contains information concerning the switching frequency. Pulse edges of a modulated signal are determined by crossings of the modulating function and signal carrier, which makes the basis of all analog implementations of the modulating function methods involving natural sampling. Signal at the output of a pulse width modulator defines the switching function of the inverter branch which controls the on-state of the switch within the branch. Synthesis of the ac voltage at inverter's output, regulation of the amplitude, and frequency are accomplished by the switching actions in inverter branches.

The basic PWM method is related to the mode of determination of pulse edges. Determination of pulse edges based on crossing of the modulating and carrying signals belongs to the group of natural sampling techniques.

The basic shortcoming of the natural sampling technique is tied to the transcendental relation which connects the angles determining pulse edges and modulating function. This aggravated the application of natural sampling technique in digital and microprocessor systems applying PWM methods.

Regular sampling method is the basic solution for digital realization of the modulating function method. The regular sampling method is considerably more flexible since the modulating function is specified at discrete points; thus it is possible to calculate its values in advance and interpret them by means of digital words [5, 6, 22]. The natural sampling method has the advantage over the regular method in so far as the harmonic content linearity, and range of regulation of the amplitude of the fundamental harmonic are concerned.

Middle of the eighties marks the beginning of the application of the space vector modulation as the vector approach to PWM for three-phase inverters. The basic advantages of SVM are related to the following.

(i)Expanded linear range of modulation without injection of the third harmonics into the sine modulating function [23–25].(ii)Lower harmonics content in relation to the regular methods based on the sine modulating function [26]. (iii)Lower switching losses are conditioned by only one change of state [24, 27] (iv)Simple digital implementation[28, 29]

On the other side in the papers [30, 31], it is shown that the conventional method of SVM is equivalent to the regular method with modified sine-triangle uniform sampling PWM with triplen harmonic injected. In the papers [32, 33] a detailed analysis of carrier based PWM methods is given and their relation with the space vector approach as well. Using the simple transformations, modulating functions translate from the time domain to the complex domain, where the reference vector rotates with the angular speed equal to angular frequency of time modulating functions.

Finally, in the paper [34] it was demonstrated that the calssical method SVM is identical with the method of modulation sine function injected with triplen harmonics and regular sampling technique.

Taking into account the relevant literature[34–36], we propose an aproximate algorithm for PWM sampling according to the modulating function method. Unlike some other works, in which the natural sampling method was rejected [2, 3, 15], we have accepted the elements of this technique but have substituted the transcendental equation by an algebraic expression derived, applying an exact procedure.

2. Modulating Function Method

General characteristic of this group of methods is that the synthesis of the pulse at the output of the modulator is carried out on the basis of the reference signal and signal carrier.

The reference signal is a periodic function having the frequency equal to the fundamental frequency of the inverter output voltage and amplitude which is proportional to the amplitude of the fundamental harmonic. The modulating function is analytical representation of the reference signal. In general, this is a function of the following general form [37]:

𝑚(𝑡)=𝐹(𝑀,𝜔𝑡),|𝐹(𝑀,𝜔𝑡)|≤1. (2.1)

Parameter 𝑀 stands for the modulation amplitude index, and parameter 𝜔 for the fundamental frequency of inverter ac voltage Function 𝐹(𝑀,𝜔𝑡) can be continuous or discontinuous having first-order interruptions in terms of 𝑀 [38]. However, a great number of modulating functions is of the following simpler form:

𝑚(𝑡)=𝑀𝐹(𝜔𝑡),|𝐹(𝜔𝑡)|≤1,𝐹(2𝜋−𝜔𝑡)=−𝐹(𝜔𝑡). (2.2)

In classical PWM methods the carrier signal is an ac signal of triangular half-periods, of frequency 𝑓_𝑐, and amplitude equal to one. The ratio of the carrier signal frequency and the reference signal frequency determines the frequency index of modulation. In cases when this is an integer, this index is pulse number 𝑝 which is characteristic for the synchronized methods of modulating function. Onother class of these methods is characterized by a continuous variation of the fundamental frequency for a constant frequency of the carrier signal. In this case, owing to nonperiodicity of pulses, appearance of subharmonics is possible having particularly harmful influence on the load at lower fundamental frequencies.

The basic characteristics of the signal at modulator output, irrespective of the method of edge determination, are the binary form and modulated width. They are transferred to the states of switching elements by means of the switching function.

Pulse edges at modulator output are most frequently determined by the natural or regular sampling method on the basis of the reference and carrier signals.

In case of a three-phase inverter modulation function may contain, in addition to the basic component, components of the order 3𝑘th which neutralize across the load. Application of the vector modulating function [23] extends the amplitude range.

2.1. Regular Sampling Method

In the regular sampling method the reference signal is discretized, and the pulse edges are determined by comparing the carrier signal with the reference signal modified in this way, which implies a digital realization of the method. Therefore, the regular sampling method has the advantage that microprocessor control is possible compared to the natural sampling method where the pulse edges should be determined by iterative solution of the transcendental equation obtained from the condition for crossing of the reference and carrier (triangular) signals. Figure 1 presents the synthesis of the output voltage of a half-bridge inverter by applying the modulating function method and regular sampling.

Figure 1 

Synthesis of the output voltage of a half-bridge inverter by applying the modulating function method and regular sampling.

The interval of 2𝜋 radians corresponding to the period of the modulating function will be divided in 𝑝 intervals of length Δ𝛼=(2𝜋)/𝑝, as shown in Figure 1. Denote the center of 𝑘th interval by 𝛼_𝑘. At these points modulating function 𝑚(𝛼_𝑘) is discretized to a set of 𝑝 values which are compared with a triangular signal carrier of the frequency equal to the switching frequency of the inverter. The average value of the switching function of the inverter branch is

𝑎_𝑘=12(1+𝑀𝐹(𝛼_𝑘)). (2.3)

The pulse edges centered at 𝛼_𝑘are

𝛼_1,𝑘=𝛼_𝑘−12𝑎_𝑘𝛼Δ𝛼,_2,𝑘=𝛼_𝑘+12𝑎_𝑘Δ𝛼. (2.4)

Switching function 𝛼_𝑘(𝜔𝑡) of the half-bridge is fully determined if the angles of the pulse edges are known. From (2.4) it can be concluded that for complete determination of the switching function, it is necessary to determine 𝑝 pulse widths which correspond to the cycle of the switching frequency. Alternative voltage at the output is determined by means of switching function 𝛼(𝜔𝑡) by the following expression:

𝑢_𝐴0=𝑈_𝑑[𝑎(𝜔𝑡)−0.5]. (2.5)

The voltage waveform 𝑢_𝐴0 represents a copy of the periodic sequence of modulator output pulses. This voltage may be expressed using the Fourier series:

𝑢_𝐴0=^∞_𝑛=1(𝐵_𝑛sin𝑛𝜔𝑡+𝐴_𝑛cos𝑛𝜔𝑡), (2.6) where

𝐴_𝑛=1𝜋^𝑝_𝑘=1^{𝛼𝑘+0.5𝑎(𝛼𝑘}_𝛼^)Δ𝛼_{𝑘−0.5𝑎(𝛼𝑘)Δ𝛼}=2cos𝑛𝑥𝑑𝑥𝜋𝑛^𝑝_𝑘=1cos𝑛𝛼_𝑘sin(𝑛𝜋2𝑝(1+𝑀𝐹(𝛼_𝑘𝐵))),_𝑛=1𝜋^𝑝_𝑘=1^{𝛼𝑘−0.5𝑎(𝛼𝑘}_𝛼^)Δ𝛼_{𝑘−0.5𝑎(𝛼𝑘)Δ𝛼}=2sin𝑛𝑥𝑑𝑥𝜋𝑛^𝑝_𝑘=1sin𝑛𝛼_𝑘sin(𝑛𝜋2𝑝(1+𝑀𝐹(𝛼_𝑘))). (2.7a)(2.7b) The coefficients obtained using (3.7) for any modulating function determine polynomial formulas (3.12a)–(3.12d) for switchning angles:

Π_1𝑖(𝑀)=𝑖𝜋𝑝+𝐴₁Π(𝑖,𝑝)𝑀,_2𝑖(𝑀)=Π_1𝑖(𝑀)+𝐴₂(𝑖,𝑝)𝑀²Π_3𝑖,(𝑀)=Π_2𝑖(𝑀)+𝐴₃(𝑖,𝑝)𝑀³Π_4𝑖,(𝑀)=Π_3𝑖(𝑀)+𝐴₄(𝑖,𝑝)𝑀⁴. (3.12a)(3.12b)(3.12c)(3.12d)

From expressions (3.12a)–(3.12d) it is possible to obtain the approximate solutions of transcendental equations (3.9). In view of the phase synchronization between the reference and carrier signal, it is sufficient to apply some of the expression 𝑝−1 times to obtain the (𝑝−1)th successive pulse edge angle. The remaining angles are determined by simply using the symmetry property from the natural sampling method, which has been preserved in expressions (3.12a)–(3.12d).Using the property of the symmetry of angles about the quarter-period of the reference signal, for an odd frequency index value, it is sufficient to apply the stated expressions and calculate the (𝑝−1)/2 successive pulse edge angle.

3.2. Influence of Polynomial Degree to the Level of Approximation of Natural Sampling

The deviations of angles 𝛼_𝑖,𝑖=1,2,…,2𝑝 obtained using formulae (2.13), (2.14), (2.15), and (2.16) from the values found by applying the iterative procedure to expression (3.9) represent the measure of the remainder term 𝐸_𝑁(𝑖,𝑝,𝑀). The maximum values of absolute remainder terms for 𝑝=6,9,12,15 for the amplitude index value varying from zero to one are shown in Table 1. As can be seen, polynomial approximations yield the same accuracy in determining the pulse edge angles as that achieved by applying some of the iterative numerical procedures (e.g., the Newton-Raphson or Gauss-Saidel methods).

Table 1 

Maximum turncation error values upon a change in 𝑀 from 0 to 1.

However, unlike the mentioned numerical method, formulae (2.13), (2.14), (2.15), and (2.16) are directly applicable in the form of a microprocessor algorithm. The symmetry properties of the PWM sequence are preserved even for the low values of frequency index 𝑝, and this permits the algorithm to be applied to the cases where the regular method shows a tendency toward degrading the harmonic and amplitude performances of inverter output voltage regulation. It can be seen from Table 1 that the maximum of remainder term is inversely proportional to the frequency index and directly proportional to the amplitude index.

3.3. Modified Polynomial Approximation by Using Chebyshev Polynomials

To achieve a higher accuracy of pulse edge with polynomial of lower order, we will apply the economization procedure by using Chebyshev polynomials [42]. In this way we obtain the following polynomials:

𝛼_𝑖=Π^𝑐_1𝑖=(𝑖𝜋𝑝−𝐴₄(𝑖,𝑝)8)+(𝐴₁(𝑖,𝑝)+3𝐴₃(𝑖,𝑝)4𝛼)𝑀,_𝑖=Π^𝑐_2𝑖=Π^𝑐_1𝑖+(𝐴₂(𝑖,𝑝)+𝐴₄(𝑖,𝑝))𝑀². (3.13)(3.14)

The use of polynomial (3.4) provides a level of accuracy whose order of magnitude corresponds to the use of a third-degree polynomial, as shown in Table 2.

Table 2 

Maximum truncation error values upon a change in 𝑀 from 0 to 1 when a chebyshev polynomial is Used.

When used as a PWM sampling algorithm, the modified polynomial permits computer time savings and yields a maximum error of 0.1297 degrees, which is comparable to the use of a nonmodified third-degree polynomial.

4. Analysis of the Results

Because of the odd symmetry of the ac component of the pulse sequence generated by the method proposed, the Fourier expansion of inverter ac voltage contains only sinusoidal components. The amplitude of the nth harmonic of voltage is given by expression:

𝐶_𝑛=(−1)^𝑠1𝜋𝑛[1+(−1)^𝑝+𝑛+2^𝑝−1_𝑖=1(−1)^𝑖cos(𝑛𝛼_𝑖)]. (4.1) After substituting the expressions for pulse edge angles, 𝛼_𝑖 into (4.1) and then 𝐶_𝑛 into (2.11), we find the current distortion factor as a function of the amplitude index, with the index 𝑝 figuring as a parameter. The regulation amplitude factor is obtained directly by applying formula (3.5) for 𝑛=1, depending on the amplitude index 𝑀. As the second-order polynomial algorithm gives relatively small deviations of pulse edge angles from those obtained using the natural method, the same conclusion applies to the amplitude of the fundamental harmonic generated by the second-order polynomial algorithm. The use of polynomial algorithm (3.14) produces a considerable decrease in the distortion factor in the upper part of amplitude modulation. The results obtained by numerical analysis and simulations clearly indicate the advantages of the proposed sampling method, at low-frequency index values, with respect to both the distortion factor and the maximum achievable modulation amplitude factor.

When the second-degree polynomial algorithms are applied to the sinusoidal modulating function, the results obtained for 𝑝=6 are already close to the ideal natural sampling method (Tables 3 and 4). At higher frequency index values, all algorithms converge to the same distortion factor values and to a common amplitude characteristic of voltage regulation, as confirmed by experiment.

Table 3 

Comparison of amplitude characteristic values for natural (SPWM), regular (RSPWM), and derived polynomial algorithmic sampling.

Table 4 

Comparison of distortion factor values for natural (SPWM), regular (RSPWM), and derived polynomial algorithmic sampling.

All that has been mentioned above can be applied to the results for a three-phase inverter, since its bridge structure consists of three half-bridge structures. The proposed polynomial algorithms exhibit their major advantages when applied to the vector modulating function as well. At frequency index values 𝑝≤15, a suitably chosen algorithm may give the maximum values of amplitude characteristic, which are considerably better compared with the regular sampling method. In addition, the distortion factor is decreased. To illustrate this, let us say that the polynomial algorithm (3.13) for 𝑝=6 and 𝑀=1 yields the reduced amplitude of the fundamental harmonic of 0.5672 and a distortion factor DIS = 5.6761%. In case the regular sampling method is applied under identical conditions, the reduced amplitude is 0.5521 with a distortion factor DIS = 8.4087%. Algorithm (3.14) gives the reduced amplitude of 0.5773 and a distortion factor DIS = 10.0245%.

The odd values of frequency index 𝑝 imply the use of algorithm (3.14). As far as the amplitude characteristic is concerned, the best results for 𝑝=9,21,… are achieved with synchronized half-periods of opposite signs (𝑠=0), whereas for 𝑝=15,27,…. half-periods of the same sign (𝑠=1) should be synchronized. These results are presented in Figures 3 and 4.

Figure 3 

Maximum amplitudes of the fundamental harmonic of inverter output voltage obtained using the vector modulating function (2.22) and (a) polynomial sampling method (3.2), (b) polynomial sampling method (3.4) for 𝑠=0, (c) polynomial sampling method (3.4) for 𝑠=1, and (d) regular sampling method.

Figure 4 

The distortion factor DIS(𝑙,𝑝)% of inverter output line voltage obtained using the vector modulating function (2.22) and (a) polynomial sampling method (3.2), (b) polynomial sampling method (3.4) for 𝑠=0, (c) polynomial sampling method (3.4) for 𝑠=1, and (d) regular sampling method.

For higher pulse number values, all algorithms converge toward the same values of amplitude and distortion harmonic indicators (Figures 3 and 4).

5. Implementation of Polynomial Algorithm and Experimental Results

It is known that the basic characteristics of the digital implementation of the natural sampling are the iterative procedure and the work with the data in the floating point numbers format, which is a consequence of the transcendental nature of the expressions by which the angles of impulses edges are determined. Algebraic nature of the formula for the angles (2.13), (2.14), (2.15), (2.16), (3.13) and (3.14) give the possibility to replace the iterative procedure faster method of direct computing.

By transition to the time domain we find the formulas which determine the position of the 𝑘th impulse inside the appropriate periods of switching frequency. The software implementation of a polynomial algorithm is achieved by simulation of the process which is shown in Figure 5 with the characteristic times determined by the following formulas which represents the times of intersection of carrier signal and reference signal, given in the explicit algebraic form of polynome with 𝑀 as the argument

Figure 5 

Graphical representation of main elements of impulse synthesis using polynomial algorithm.

𝑡⁽¹⁾_𝑘𝐴=𝑇_𝑐4−𝑇_𝑐4𝑀(^𝑁−1_𝑠=0𝑎_𝑠⁽¹⁾(𝑘,𝑝)𝑀^𝑠𝑡),_(2)𝑘𝐴=𝑇_𝑐4+𝑇_𝑐4𝑀(^𝑁−1_𝑠=0𝑎_𝑠⁽²⁾(𝑘,𝑝)𝑀^𝑠𝑡),_(1)𝑘𝐵=𝑇_𝑐4−𝑇_𝑐4𝑀(^𝑁−1_𝑠=0𝑏_𝑠⁽¹⁾(𝑘,𝑝)𝑀^𝑠𝑡),_(2)𝑘𝐵=𝑇_𝑐4+𝑇_𝑐4𝑀(^𝑁−1_𝑠=0𝑏_𝑠⁽²⁾(𝑘,𝑝)𝑀^𝑠𝑡),_(1)𝑘𝐶=𝑇_𝑐4−𝑇_𝑐4𝑀(^𝑁−1_𝑠=0𝑐_𝑠⁽¹⁾(𝑘,𝑝)𝑀^𝑠𝑡),_(2)𝑘𝐶=𝑇_𝑐4+𝑇_𝑐4𝑀(^𝑁−1_𝑠=0𝑐_𝑠⁽²⁾(𝑘,𝑝)𝑀^𝑠). (5.1)(5.2)

In the formula (5.1) we suppose that 𝑎₀⁽¹⁾(𝑘,𝑝)=𝑎₀⁽²⁾(𝑘,𝑝)=𝐹(𝛼_𝑘);𝑘=1,2,…,𝑝;𝑠=0, where 𝛼_𝑘 is the angle of the 𝑘th sample of the modulation function determined in such a way that the polynomial algorithm becomes a regular algorithm with the characteristic times, given in the following formula:

𝑡⁽¹⁾_𝑘𝑋=𝑇_𝑐4−𝑇_𝑐4𝑀𝐹_𝑋(𝛼_𝑘),Δ_𝑘𝑋=𝑇_𝑐2𝑀[1+2𝐹_𝑋(𝛼_𝑘)],𝑋∈(𝐴,𝐵,𝐶). (5.3)

The synthesis of impulses on the modulator output is performed by simulation of the process which is shown in the Figure 6. Detailed microprocessor implementation of regular symmetrical algorithm is given in [43–45].

Figure 6 

Main elements of impulse synthesis, using regular symmetrical algorithm.

Modern microcontrollers are equiped with integrated peripheral units designed for the synthesis of the modulated signals which control the state of switching of inverter branch. Intel’s microcontroller 8XC196MC/MD is optimized for implementation of PWM using the method of modulation function [46]. It contains the generator of the modulated signal which significantly simplifies the programe implementation of selection algorithm and reduces the need for external hardware devices, enabling the synthesis of three independent complementary sets of modulated signals, with common switching frequency and conservatively determined “dead’’ time. Digital word 𝑊𝑇𝐶 which determines the period of switching frequency 𝑇_𝑐 is defined by

𝑊𝑇𝐶=10⁶𝑓4𝑝𝑓^XTAL_max𝐹_max𝐹, (5.4) where

𝐹_max is the value of the register of maximum frequency of output inverter's voltage; (𝐹𝐹𝐹𝐹) is the 𝑓_max maximum value of frequency in (Hz); 𝑝 pulse number;𝑓_XTAL is the clock frequency on the pin XTAL1 in (MHz).

The times of leading impulses edge are determined in the form of digital words which are calculated using

𝑊𝑇_𝐴1=𝑊𝑇𝐶2−𝑊𝑇𝐶2𝑀(^𝑁−1_𝑠=0𝑎_𝑠⁽¹⁾(𝑘,𝑝)𝑀^𝑠),𝑊𝑇_𝐵1=𝑊𝑇𝐶2−𝑊𝑇𝐶2𝑀(^𝑁−1_𝑠=0𝑏_𝑠⁽¹⁾(𝑘,𝑝)𝑀^𝑠),𝑊𝑇_𝐶1=𝑊𝑇𝐶2−𝑊𝑇𝐶2𝑀(^𝑁−1_𝑠=0𝑐_𝑠⁽¹⁾(𝑘,𝑝)𝑀^𝑠). (5.5) Similarly, we find the formula for digital words of the trailing edges of impulses, the value of which is set in the appropriate registers WG_COMPx when the counter WG_COUNT reaches the value 0001 H:

𝑊𝑇_𝐴2=𝑊𝑇𝐶2+𝑊𝑇𝐶2𝑀(^𝑁−1_𝑠=0𝑎_𝑠⁽²⁾(𝑘,𝑝)𝑀^𝑠),𝑊𝑇_𝐵2=𝑊𝑇𝐶2+𝑊𝑇𝐶2𝑀(^𝑁−1_𝑠=0𝑏_𝑠⁽²⁾(𝑘,𝑝)𝑀^𝑠),𝑊𝑇_𝐶2=𝑊𝑇𝐶2+𝑊𝑇𝐶2𝑀(^𝑁−1_𝑠=0𝑐_𝑠⁽²⁾(𝑘,𝑝)𝑀^𝑠). (5.6)

The values of the coefficient 𝑎_𝑠⁽¹⁾(𝑘,𝑝)⋯𝑐_𝑠⁽¹⁾(𝑘,𝑝) are normalized and are represented as double-complementary quantities in fixed point formats in the following way:

KOEF_𝑥=2⁻¹⁵(−𝑏₁₅2¹⁵+¹⁴_𝑖=0𝑏_𝑖2^𝑖);𝑏_𝑖∈{0,1}. (5.7)

In this kind of representation all previously calculated coefficients are shown with the accuracy greater than 10⁻⁴ in the range of changes from −1 to 0.999 within which are also all polynomial coefficients.

The coefficients of the leading and trailing edges are placed in the form of pairs of memory tables so that one table corresponds to the leading impulse edges and the other to the trailing impulse edges. The number of tables depends on the polynomial degree and the number of different values of the pulse number. The coefficients which correspond to the different phases are determined from the same table by applying the suitable index addressing of the data from the tables. The basic element which determines the conditions under which a polynomial algorithm can be applied is that the time necessary for realization of the program must be shorter than half of the carrier signal period. Concerning the memory table it is possible to perform certain optimizations using the properties of the symmetry of pulse angles about the half-period. The amplitude modulation index can be obtained also from the memory table which shows the output amplitude dependency on the frequency. In the experiment performed this dependency is linear, and it maps the ratio 𝑈/𝑓=const.

The experimental model (Figure 7) is made for the practical realization of the polynomial algorithm, and it contains three basic units.

Figure 7 

Experimental model

(a)Voltage three-phase inverter, with the implemented first degree polynomial algorithm and the vector modulation function (2.22). (b)Three-phase asynchronous motor ZK100L4;(i)nominal voltage: 380𝑉Δ;(ii)rated current: 5,3𝐴Δ/3,05𝐴Υ;(iii)power factor : cos𝜑=0,81;(iv)rated power: 2,2𝑘𝑊;(v)rated speed: 1410(ob/min).(c)Electric brake WBII 2GA1351–1B(i)rated power: 2𝑘𝑊;(ii)rated speed: 1500(ob/min).

Experimentally, the possibility of application of the polynomial algorithm as the modification of the regular selection algorithm for the switching frequency of 2,4 kHz is confirmed. For higher switching frequencies, the times needed for online computation of the positions of the impulse edges are critical regarding their asymmetry about the switching segment. The applications of the polynomial algorithm of the first degree for the output frequency of 40 Hz with the pulse number 𝑝=60 are achieved. Oscilloscope recordings of the line voltage and current, obtained by the synthesis according to polynomial algorithm with the vector modulation function, are shown in the Figure 8.

(a)

(b)

Figure 8 

Oscilogram of line current and phase voltage with vector modulation and polynomial algorithm of the first degree.

6. Conclusion

The suggested new sampling PWM algorithm by using the method of modulating function offers a possibility that natural sampling can be realized without any iterative procedure. It is shown that the angle of pulse edges can be approximated by a polynomial, the highest degree of the polynomial determining the measure of approximation of the obtained solution compared to natural sampling. The economizing procedure of using Chebyshev polinomials offered the possibility of using polinomials of a lower order, usually of the first- or second-order, still acomplishing a good approximation of the natural sampling. In terms of simplicity, this algorithm is comparable to a version of regular sampling method. As regards the performance concerning the quality of inverter’s output voltage, improvement by application of the suggested algorithm, which retains all good properties of the natural technique of PWM, is confirmed by simulation. In addition, possibility of implementation of polynomial algorithm in the real application is experimentally confirmed.