Selective Harmonic Elimination in Multilevel Inverter with Artificial Intelligence Techniques

GRD Journals- Global Research and Development Journal for Engineering | Volume 4 | Issue 10 | September 2019 ISSN: 2455-5703

Selective Harmonic Elimination in Multilevel Inverter with Artificial Intelligence Techniques Davinder Singh Research Scholar Department of Electrical Engineering Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India Ranvir Kaur Assistant Professor Department of Electrical Engineering Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India

Gursewak Singh Brar Professor and Head Department of Electrical Engineering Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India

Abstract Nowadays, the power industries demands for high level of voltage and power signal. To switch these types of signal the multilevel inverter is developed. The multilevel inverter is accomplished to manage the wide range of voltage signal. The power and voltage signal generated in the power industries should not enclose the undesired harmonics. To get rid of the unwanted harmonics from the output waveform of multilevel voltage source inverters, a variety of modulation techniques and optimization paradigms are reviewed in this paper. Various optimization techniques to calculate the nonlinear transcendental equations in selective Harmonic Elimination are also discussed in this paper.

Keywords- Selective Harmonic Elimination (SHE), Pulse Width Modulation (PWM), Imperialist Competitive Algorithm (ICA) and Colonial Competitive Algorithm (CCA)

I. INTRODUCTION OF

MULTI-LEVEL INVERTERS

In entire inverters are used to convert the DC power supply to AC Power Supply.

In the application point of view the multilevel inverters are used regularly.

The multilevel were implement in dissimilar topologies in own applications.

Mostly the inverter should consist of power semiconductor switches and DC Voltage sources.

Fig. 1: Basic vision of an inverter with (a) Two levels (b) Three levels (c) n-levels

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Selective Harmonic Elimination in Multilevel Inverter with Artificial Intelligence Techniques (GRDJE/ Volume 4 / Issue 10 / 012)

The semiconductor switches in the inverters semi controlled in universal. The control signals for this semiconductor switches will acquire from the applicable firing circuits. Fig. 1) shows the graphic diagram of one phase crutch of inverter with dissimilar of levels in which the semiconductor device is represented by an ideal switch with more than a few positions. In the more than shape the basic view of the multilevel inverters with dissimilar number of voltage sources and voltage levels are made known temporarily.

From the above shape we can appreciate that as the number of the voltage sources is improved the number of the levels in the output voltage are also improved.

Some of the salient features of the multilevel inverters are: 1) The multilevel inverters can obtain the voltage and present with low THD. 2) Competence of the inverter depends in the lead the switching frequency.

3) Ordinary mode voltages are condensed and thus the stresses on the motor bearing are reduced.

4) The input current tired by them has low deformation.

5) There exists no EMI difficulty.

A. Selective Harmonic Elimination For a preferred original voltage V-out, the switching angles a1, a2, ... aM should be chosen, so that the initial harmonic VM(1)=Vout, and precise advanced harmonics equal to zero. Here, the need of control design is to eliminate the3th, 5th, 7th, and 9th classify harmonics as they rule the total harmonic distortion. So the switching angles can be set up by solving the following equations:-

Where the modulation file m is defined as ; rV1 1(4E).This is a system of five transcendental equations with five unidentified variables a1, a2, a3, a4 and a5. The right solution would signify that the output voltage of the 7-level inverter would not include the3th, 5th 7th and 9th order harmonic apparatus. MATLAB provides a purpose f-solve, which could find a root of the non-linear equations. It uses Gauss-Newton method with a varied quadratic and cubic stripe search method. With the function, a curriculum is used to look for the angles.

II. FIREFLY ALGORITHM (OPTIMIZATION ALGORITHM) The firefly algorithm (FFA) is a meta-heuristic algorithm, enthused by the irregular performance of fire flies. The primary principle for a firefly's flash is to act as a signal scheme to attract additional fireflies. Now this can idealize a few of the alternating characteristics of fireflies so as to consequently expand firefly inspired algorithms. For simplicity in relating our original Firefly Algorithm (FFA) [10], there are the subsequent three idealized rules. On the first rule, all firefly attracts all the extra fireflies with weaker flashes. All fireflies are unisex so that one firefly will be attracted to other fireflies regardless of their sex .Secondly, attractiveness is comparative to their clarity which is inversely proportional to their distances. For some two flashing fireflies, the not as much of bright one will move towards the brighter one. The attractiveness is proportional to the brightness and they both reduce as their aloofness increases. If there is no brighter one than a exacting firefly, it will move at random. Finally, no firefly can magnetize the brightest firefly and it moves randomly. The intensity of a firefly is artificial or dogged by the backdrop of the objective function. For a maximization trouble, the brightness can purely be proportional to the charge of the objective function.

Additional forms of brightness can be apparent in a similar method to the form function in inherent algorithms. Based on these three rules, the basic ladder of the firefly algorithm (FFA) can be summarized as the fake code exposed underneath. Switching angles based on Firefly algorithm. The ensuing equations for the calculation of output voltage totality harmonic distortion (THD) of a multilevel inverter are used as the objective function. This objective function is worn to lessen the THD in the output voltage of a multilevel inverter. While minimizing the objective function, the selective harmonics such as the 5th, 7th, 11th and 13th can be prohibited by using the Firefly algorithm. The simulations are performed for an 11 level cascaded multilevel inverter with one and the same and non-equal dc sources to give you an idea about the authority of the planned method. The results demonstrate that the planned firefly algorithm can get rid of selective harmonics in the output voltage of a multilevel inverter.

Begin FFA process; Initialize algorithm parameters: Objective function f(x), x = (x1, x2, . . . , xd)T Initialize a population of fireflies xa (a = 1, 2, . . ., n) Define light absorption coefficient Îł while (t < Max Generation) for a = 1: n all n fireflies for b= 1: a all n fireflies Light intensity Ii at xi is determined by f(xa) if (Ib > Ia) Move firefly a towards b in all d dimensions

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Selective Harmonic Elimination in Multilevel Inverter with Artificial Intelligence Techniques (GRDJE/ Volume 4 / Issue 10 / 012)

end if Attractiveness varies with distance r via exp [−γr2] Evaluate new solutions and update light intensity end for b end for a 1) “Selective Harmonic Elimination for a Cascade Multilevel Inverter”, presents selective harmonic elimination method for cascade multilevel converters to get rid of the particular harmonics in the output voltage. The opinion of the converter is analyzed. Then the switching angles are computed to eliminate the short order harmonics in hypothesis. And the gating signals for the converter are certain. An investigational 7-level H-bridge multilevel converter was used to realize the algorithm and to authorize the methods. The investigational results show that the method can effectively eliminate the specific harmonics as predictable.

2) “Selective harmonic elimination PWM using ant colony optimization”, Selective harmonic elimination pulse width modulation (SHEPWM) is a helpful switching technique in multilevel inverters since of low number of switching and at the same time, high quality of output voltage waveform. The main difficulty in SHE-PWM is discovery the optimum switching angles with the aim of minimizing specific harmonics. Population-based optimization algorithms have been established to be an effectual tool for such evils with transcendental equations and disparity constraints. In this document, the ant settlement optimization for nonstop domains (ACOℝ) is practical to harmonic elimination difficulty in a 7th-level cascaded inverter.

Simulation results in MATLAB atmosphere are compared with subdivision crowd optimization (PSO) algorithm, results show superior union rate of ACOℝ in similarity with PSO.

III. RESULT The path topology was given in the fig. 1. In the given path there are twelve switching campaign coupled with diodes in antiparallel. Between the three H-bridges in the path the thyristors are confidential into two, which are superior thyristors in addition to lower thyristors. We have numbered the upper thyristors with the abnormal numbers and the lower thyristors with the even numbers. Actually there fourteen instants in the synthesized output voltage. For every instant exacting campaign should only occupation and the take it easy should turned off. Now the thyristors are curved on only in the attendance of the gate signal.

In case, of the zero stage there are two promising switching patterns to manufacture the zero level.

Example 1) upper thyristors or 2) lower thyristors We enumerated the switching possibilities for both switching instant of the output voltage. And firstly we considered the firing angles spaced proportioned to the π/2 and the 12th switching waveforms for the plans are exposed below:

Fig. 2: switching signals for the strategy t1, t2, t3, t4

Fig. 3: switching signals for the strategy t5, t6, t7, t8 All rights reserved by www.grdjournals.com

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Selective Harmonic Elimination in Multilevel Inverter with Artificial Intelligence Techniques (GRDJE/ Volume 4 / Issue 10 / 012)

Fig. 4: switching signals for the strategy t9, t10, t11, t12

Fig. 5: Simulation figure of the cascaded inverter

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Selective Harmonic Elimination in Multilevel Inverter with Artificial Intelligence Techniques (GRDJE/ Volume 4 / Issue 10 / 012)

Fig. 6: Output voltage of the cascaded multilevel inverter

Fig. 7: Output current of the cascaded multilevel inverter

Now the simulations were complete on the MATLAB Simulink, the more than diagrams show the output voltage and current waveform of the cascaded multi-level inverter. In the simulation that we performed for the seven levels cascaded multilevel inverter with the voltage of 80 volts to all H-bridge inverter and lagging load is well thought-out to include the consequence of the realistic situations.

Now the idea of the selective harmonic elimination is functional in the firing circuit which was not revealed which is hard to understand at this instant but the main observation which we should consider is in the output voltages for different modulation indexes is suitable to the inaccuracy in the solution vectors of the switching angles and the truncation and the rounding off errors in this digital reproduction results the indecent implementation of the total reproduction circuit that is the most wanted harmonics which should be eliminated are not totally receiving eliminated.

Fig. 8: FFT analysis of the output voltage for M(m-1) equal to 2.2

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Selective Harmonic Elimination in Multilevel Inverter with Artificial Intelligence Techniques (GRDJE/ Volume 4 / Issue 10 / 012)

Fig. 9: The output voltage for M (m-1) equal to 2.2

In this page the FFT analysis for similar modulation is observed.

Fig. 10: FFT analysis of the output voltage for M (m-1) equivalent to 1.5

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Selective Harmonic Elimination in Multilevel Inverter with Artificial Intelligence Techniques (GRDJE/ Volume 4 / Issue 10 / 012)

Fig. 11: The output voltage for M (m-1) equivalent to 1.5

Fig. 12: The output voltage for M (m-1) equivalent to 1.5

IV. CONCLUSION Nature-inspired metaheuristic algorithms have gained attractiveness, which is moderately due to their capability of production with nonlinear global optimization harms. We have reviewed the basics of firefly algorithm, the latest developments with diverse applications. As the time of writing, a sudden Google search suggests that there are about 323 papers on firefly algorithms from 2007-08. This assessment can only cover a portion of the literature. There is no disbelief that firefly algorithm will be applied in solving more challenging harms in the in the neighborhood of future, and its literature will continue to spread out. On the additional hand, we have also decorated the significance of utilization and investigation and their effect on the efficiency of an algorithm.

Then, we make use of the irregular search tactic theory as a beginning foundation for analyzing these key components and ways to find the maybe optimal setting for algorithm-dependent parameters. With such approaching, we have used the firefly algorithm to find this most favorable balance, and confirmed that firefly algorithm can certainly make available a good balance of utilization and examination. We have also exposed that firefly algorithm requires far fewer function evaluations. Nonetheless, the massive differences between alternating search theory and the behavior of metaheuristics in perform also suggest there is still a huge gap sandwiched between our understanding of algorithms and the actual behavior of metaheuristics. More studies in metaheuristics are highly needed. It is worth pointing out that there are two types of optimality here. One optimality concerns that for a given algorithm what best types of harms it can crack. This is comparatively simple to reply since in principle we can examination an algorithm by a extensive range of harms and then select the best category of the harms the algorithm of attention can solve. On additional hand, the other optimality concerns that for a given problem what best algorithm is to find the solutions efficiently. In principle, we can evaluate a set of algorithms to solve the same optimization difficulty and expect to find the most excellent algorithm(s). In reality, there may be no such algorithm at all, and all experiment algorithms may not perform well. Look for for new algorithms might acquire substantial research efforts. The academic sympathetic of met heuristics is still absent behind. In fact, there is a huge gap stuck between theory and applications. Though theory lags at the back, applications in difference are very varied and energetic

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Selective Harmonic Elimination in Multilevel Inverter with Artificial Intelligence Techniques (GRDJE/ Volume 4 / Issue 10 / 012)

with thousands of credentials appearing both year. Moreover, there is a supplementary huge space stuck between small-scale problems and large-scale problems. As most published studies have focused on small, toy problems, there is no agreement that the methodology that works well for such toy problems will work for large-scale harms. All these issues still remain unresolved equally in theory and in put into practice. As additional research topics, nearly everyone met aheuristics algorithms involve good Modifications so as to resolve combinatorial optimisation correctly. However with great interest and many all-embracing studies, additional studies are highly needed in the area of combinatorial optimisation using met aheuristic algorithms. In adding together, most current met aheuristic investigate has listening carefully on small scale troubles, it will be particularly helpful if additional research can focus on large-scale real-world applications.

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