Ijems v1is90006

International Journal of Engineering, Management & Sciences (IJEMS) ISSN: 2348 –3733, Volume -1, Issue-9, September 2014

Performance Enhancement of Evolutionary Algorithm Based Multilevel Inverter: A Comparative Study Rajat Sunia, Abhishek Soni , Shifali Gupta  Abstract— This paper aims to extend the knowledge about the performance enhancement of cascaded multilevel inverter by using heuristic optimization methods. For improving efficiency of multilevel inverter, harmonics has to be eliminated. To eliminate the harmonics, Selective Harmonics Elimination Pulse Width Modulation technique is used for a better power quality output. This technique eliminates the pre-selected lower order harmonics which are more dominant into the nature while satisfying the fundamental component of output voltage, but the major issue is to solve the set of nonlinear harmonic equations derived from Fourier analysis of the output waveform of multilevel inverter. Using Conventional approach to solve those equations does not guaranteed the optimal solution and may stuck in the local minima. Hence, Evolutionary optimization algorithms are used for solving those equation. Evolutionary algorithm consist a subset of evolutionary computation and the results are obtained. Among of all a comparison is done between simulated annealing, Genetic Algorithm and multi objective genetic algorithm. Whereas, Evolutionary optimization algorithm are inspired by the biological process of evolution.

Using those algorithm the main concern is to find the notches angles for giving the gate pulse to the individual switches of multilevel inverter. Using all of the three algorithms data sets are collected for different modulation indexes and output are verified by results. Results also shows the comparison between genetic algorithm and multiobjective genetic algorithm for getting the optimum results.

Index Terms— Genetic Algorithm (GA), Multilevel Inverter (MI), Selective harmonics elimination PWM (SHEPWM), Simulated Annealing(SA), Multi objective genetic algorithm (MOGA).

I. INTRODUCTION Nowadays electricity plays an important role. For improving the efficiency of the power quality, harmonics has to beeliminated. Achieving high power, high voltage situation and eliminate the harmonic components of the output voltage a single phase eleven level inverter in cascaded with equal DC supply is designed. Multilevel inverters continue to receive more and more attention because of their high voltage operation capability, low switching losses, high efficiency Manuscript received September 12, 2014.

Rajat Sunia, Lecturer, Department of Electrical Engineering, Govt.

Polytechnic College, Chittorgarh , Rajasthan India Abhishek Soni, Asst. Prof., Department of Electrical Engineering, SwasthyaKalyan Technical Campus, Jaipur, Rajasthan ,India.

Shifali Gupta, B.Tech Scholar, Poornima College of Engineering, Jaipur , Rajasthan ,India.

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and low output of electromagnetic interference (EMI). The preferred output of a multilevel inverter is combined by several sources of dc voltages. With an increasing number of dc voltage sources, the inverter voltage waveform approaches a nearly sinusoidal waveform while using a low switching frequency scheme. This results in low switching losses, and because several dc sources are used to synthesize the total output voltage, each experiences a lower dv/dt compared to a single level inverter.Consequently the multilevel inverter technology is a promising technology for high power electric devices such as utility applications [1-4].

Today, in many electric utility applications multilevel inverter is used such as flexible AC transmission system (FACTS) equipment [1], high voltage current lines [2], electrical drives [3], solar, generating system. Three types of multilevel inverters are used: diode clamped [4], flying capacitor [5], and cascaded H-bridge multilevel inverter with separate dc sources [6]. Referring to the literature reviews, the cascaded multilevel inverter (CMI) with separated DC sources is clearly the most feasible topology for use as a power converter for medium & high power applications due to their modularization and extensibility.Improve the efficiency at inverter level, switching frequency techniques are used. They are categorized on the basis of high frequency and low frequency. To diminish the harmonic components from the output voltage of multilevel inverter various switching strategies are used, such as sinusoidal pulse width modulation (SPWM), Space vector pulse width modulation (SVPWM), Selective harmonics elimination PWM (SHEPWM), Optimized harmonic stepped waveform (OHSW) [7], [8].

The other method is using a low pass filter in the output of inverters to eliminate higher order harmonics. To eliminate the lower order harmonics in multilevel inverter output voltage SHEPWM strategy is widely used. In this method the aim is to eliminate the lower order harmonics, while the fundamental component is retained. In this approach by solving the n number of equations, (n-1) lower order harmonics from the fifth order can be eliminated and the fundamental component gets satisfied. The nonlinear equations involve switching angles as unknown variables.In this paper we are using heuristic optimization methods. These methods have also been introduced based on evolutionary optimization techniques. These methods involves continues optimization and combinational optimization problems.

Evolutionary methods have advantage that the global minima is most of the time assured. These methods not only find the

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Performance Enhancement of Evolutionary Algorithm Based Multilevel Inverter: A Comparative Study

solutions for infeasible M, where lower order harmonics can be completely removed but also give the global minima too for the optimal output. An evolutionary method provides the optimum angles and can be used for any number of inverter levels. GA is one of such heuristic optimization technique [14], [15] that has been used in the work. In addition Particle swarm optimization (PSO) method [16], Ant colony optimization [17] and Bee Algorithm [18] have also been introduced.

In this paper GA is applied to solve nonlinear equations obtained from SHE technique for obtaining switching angle.

Result introduced the probability of reaching the global minima has been compared without using the GA. Experimental results are presented to confirm the simulation results.

This paper is divided into following parts: Introduction, Multilevel Inverter, Evolutionary Optimization techniques, and Experimental results .

II. MULTILEVEL INVERTER In this work cascaded type multilevel inverter is considered.

In the multilevel inverter, we connect number of individuals together. A single H-bridge is a three-level converter, as its output waveform has three stepped waveform. The switches in the inverter are turned on only at the fundamental frequency and the voltage stress across the switches is only the magnitude of dc sources voltage. For an eleven level inverter five dc source voltages are used for controllingthe four H-bridges of the multilevel inverter. Each H-bridgehas four IGBT. Each bridge is fed by separate sources. The four switches S1, S2, S3 and S4 are controlled to generate three discrete outputs out V with levels Vdc, 0 and - Vdc. When S1and S4 are on, the output is Vdc; when S2 and S3 are on, the output is – Vdc; when either pair S1 and S2 or S3 and S4 are on, the output is 0. Total output voltage is the sum of all the individual voltage and symmetric with respect to neutral point. All the inverters can produce staircase output voltage waveform as shown in fig2. The number of output phase voltage levels in a cascaded multilevel inverter is 2N + 1, where N is the number of dc sources. For example, phase voltage waveform for a 11-level cascaded inverter with three isolated dc sources (N= 5) is shown in Fig. 2. Each H-bridge unit generates a quasi-square waveform by phase-shifting the switching timings of its positive and negative phase legs Discrete, Ts = 5e-005 s.

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(3) Switching angles has the constraints between 0 to 90 {0 ≤ α i< 90} , because of quarter half wave symmetry characteristics, Even harmonics becomes zero and the triplen harmonics are also absent since they will not show their effect in 3-phase balanced systems. The general expression for an output equations will equals to

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The popular selective harmonic elimination methods provide us the opportunity to eliminate the lower order harmonics by selecting the more dominant harmonics. A eleven level inverter waveform has five variables a1, a2, a3, a4 and a5, where Vdc1, Vdc2, Vdc3, Vdc4 and Vdc5 are assumed to be equal. By taking equal amplitude of all dc sources, the Fourier series for a periodic function v0

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III. SELECTIVE HARMONICS ELIMINITATION

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Figure 1. Eleven level inverter with five separate dc sources

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International Journal of Engineering, Management & Sciences (IJEMS) ISSN: 2348 –3733, Volume -1, Issue-9, September 2014 IV. COMPARISIONBETWEEN DIFFERENT EVOLUTIONARY OPTIMIZATION TECHNIQUE A. SIMULATED ANNEALING Simulated annealing is the heuristic method based on thermodynamic free energy. It is the procedure of heating and controlled cooling of a material. Temperature and thermodynamic free energy affected by heating and cooling.

It based on the concept of that obtaining good solution in a fixed time that is acceptable, but it does not give best solution.

Figure 4. Output waveform of 11- level inverter by using GA

Table1. Data collection of firing angles with respect to modulation index using simulated annealing MI α1 α2 α3 α4 α5 1

21.42

28.66

58.25

60.92

Figure 5.FFT Analysis of 11 level Inverter usingGA

80.85

C. MULTIOBJECTIVE GENETIC ALGORITHM In a multi-objective optimization problem, multiple objective functions need to be optimized simultaneously. There does not necessarily exist a solution that is best with respect to all objectives because of incommensurability and confliction among objectives. There exist infinite number of optimal solution which is called a pareto optimal.

Table 3. Data collection of firing angles with respect to modulation index using multiobjective genetic algorithm MI α1 α2 α3 α4 α5

Figure 2.. Output waveform of 11- level inverter by using simulated annealing

1

12.88

31.82

42.37

64.33

84.19

Figure 3.FFT Analysis of 11 level Inverter usingsimunnal annealing B. GENETIC ALGORITHM Genetic Algorithms are search and optimization techniques based on Darwin’s Principle of Natural Selection. Basic principle of GA is “Select the best Discard the Rest”. .

Basically this algorithm is a set of instructions that is repeated to solve a problem. Conceptually steps explained ahead are inspired by the biological processes of evolution (gradual working or development). Also better solutions evolve from previous generations until a near optimal solution is obtained. ”. A basic flowchart is shown in figure with the steps involved in Genetic algorithm.

Figure 7. FFT Analysis of 11 level Inverter using Genetic Algorithm (M=1)

Table 2.Data collection of firing angles with respect to modulation index using GA MI α1 α2 α3 α4 α5 1

8.91

20.74

50.46

52.26

Figure 6.Output waveform of 11- level inverter by using Multi Objective Genetic Algorithm (M=1)

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