Total Harmonic Distortion Reduction for n-level Cascaded H-bridge Boost Inverter using Hybrid Method

S.Veerakumar1 and Dr.A. Nirmalkumar2

Abstract -- These days to improve the battery voltage for a conventional three-phase inverter, a multilevel boost inverter is used by Electric vehicles (EV) and Hybrid Electric Vehicles (HEV) available power inverter systems for HEVs. Due to the existence of high range of Total Harmonic Distortion (THD), the present HEV inverters have low efficiency and low power density. In this paper, dc-ac cascaded n-level boost converters with Adaptive Neuro Fuzzy Interference System (ANFIS) controller have been proposed. The feature of this technique is minimization of THD and also provides the discriminatory elimination of harmonics. An n-level boost inverter is comprised in the proposed design. An ordinary three phase, three leg inverter and an H-bridge in series with each leg of the inverter (for each phase allocated one leg), which uses capacitor as a dc source. To generate the staircase output voltage waveform fundamental switching scheme is used. Knowledge base generation, ANFIS training and Fine tuning are the three steps followed in ANFIS. In the knowledge base generation, the random number of switching angles and the equivalent output voltages are generated. Next by the training dataset, the ANFIS was trained and at last produces the optimum switching angle. To develop the correctness of the system this process includes the fine tuning process. This will be finding out the error produced in the system, which is tuning to reduce the error and produce the optimum switching angle. The final output is reduced THD rate and high power density.

Key words: dc-ac cascaded n-level boost inverter, Electric Vehicles (EV), Hybrid Electric Vehicles (HEV), Adaptive Neuro Fuzzy interference System (ANFIS).

Nomenclature- Input dc voltage

and - Switching angles- Target voltage

- Phase voltage

- New switching angle- Value of switching angle

- Tuning constant- Voltage of order harmonics

1. IntroductionA global concern  has grown with vehicle

generated air  pollution  emissions, greenhouse gas emissions and energy consumption [8].Design for heavy duty hybrid electric vehicles (HEVs) that have large electric drives such as tractor trailers, transfer trucks, or military vehicles will require advanced power electronic inverters to meet the high-power demands( 100 kW) required of them[1]. For these vehicles,the improvement of large electric drive

trains will result in better fuel efficiency, lower emissions, and likely better vehicle performance acceleration and braking [2]To increase the battery voltage for a traditional three-phase inverter, power inverter systems for HEVs use a dc–dc boost converter[14].Two different approaches to the HEV modeling can be adopted the backward and forward-facing modeling with respect to the physical causality principles [9, 30].Many new applications such as contactless power supply for professional tools contact-less battery charging across large air gaps for electric vehicles, compact electronic devices, mobile phones, and medical implants have been enabled by the modern power electronics [3].Due to their potential improved safety, reliability and convenience, several CEET systems have been developed for electric vehicle battery-recharging applications [4].

These systems are designed to transfer power continuously, provided that good horizontal and vertical alignment exists between the track and power pickups [5]. The rechargeable batteries and ultra-capacitors electrochemical capacitors are the energy storage technologies that are being utilized

[6].Electric vehicles (EVs) and hybrid EVs (HEVs) have been identified to be the most viable solutions to fundamentally solve the problems associated with ICEVs [7]. To blend a preferred high voltage from a number of levels of dc voltages that can be batteries, fuel cells, etc. is the common function of the multilevel inverter [15]. For a given interaction, statistics, initial density matrix, and coupling, the potential is a functional of the time-dependent current density [10].A multilevel converter was presented in which the two separate DC sources were the second Aries of two transformers coupled to the utility AC power [11].The cascaded multilevel converter with separate DC sources can fit many of the needs of all-electric vehicles because it can use onboard batteries or fuel cells to generate a nearly sinusoidal voltage waveform to drive the main vehicle traction motor [13, 29].

Attaining high power rating is not the only goal for a multilevel it also enables the use of renewable energy sources [12].The battery model integrated to the SimPower Systems (SPS) is used in the complete simulation of an HEV power train [16]. Without excessive simulation times, the electrical model of each battery type must precisely represent the terminal voltage, state-of-charge (SOC), and power losses. The paper includes experimental confirmation of the models and simulator at a number of levels [17].It is broadly accepted that for middle- and high-speed applications back-EMF-based methods carry out well [18]. Both for the fuel cell hybrid vehicle design and on the testing side of the development cycle, achieving this goal requires state-of the-art technology [19].Electrical systems generally comprise several working modes caused by switches. To attain convergence and/or accuracy electrical systems also tend to be “stiff” by nature, requiring very small time steps or variable-step solvers [20]. A key issue in designing an effective multilevel inverter is to ensure that the total harmonic distortion (THD) in the voltage output waveform is small enough [21].Transformers less multilevel inverters are uniquely suited for these applications because of the high power ratings possible with these inverters [22].

2. Related WorksIlhamiColaket al. [23].have proposed to most

common multilevel inverter topologies and control schemes have been reviewed. Multilevel inverter topologies (MLIs) were increasingly being used in medium and high power applications due to their many advantages such as low power dissipation on power switches, low harmonic contents and low electromagnetic interference (EMI) outputs. The

selected switching technique to control the inverter will also have been effective role on harmonic elimination while generating the ideal output voltage. Intensive studies have been performed on carrier-based, sinusoidal, space vector and sigma delta PWM methods in open loop control of inverters. The selection of topology and control techniques may vary according to power demands of inverter review results constitute a useful basis for matching of inverter topology and the best control scheme according to various application areas.

Mauricio Rotella et al.[24].have applied a non-redundant three-stage 27-level inverter using “H” converters was analyzed for medium- and high-power machine drive applications. The main advantage of this converter was the optimization of levels with a minimum number of semiconductors. However, the system needs six bidirectional and isolated power supplies and three more unidirectional if the machines were not using regenerative braking these nine power supplies were reduced to only four, all of them unidirectional, using three strategies: 1) the utilization of independent and isolated windings for each phase of the motor; 2) the utilization of independent input transformers; and 3) the most important of them, the application of special pulse width modulation (PWM) strategies on the 27-levelconverter, to keep positive average power at the medium power bridges and zero average power at the low-power bridges. The generation of this PWM and control of this multi converter was implemented using DSP controllers, which give flexibility to the system.

C. Thanga Rajet al. [25] have presented due to robustness, reliability, low price and maintenance free, induction motors (IMs) used in most of the industrial applications. The influence of these motors (in terms of energy consumption) in energy intensive industries was significant in total input cost a review of the developments in the field of efficiency optimization of three-phase induction motor through optimal control and design techniques. Optimal control covers both the broad approaches namely, loss model control (LMC) and search control (SC). Optimal design covers the design modifications of materials and construction in order to optimize efficiency of the motor. The use of Artificial Intelligence (AI) techniques such as artificial neural network (ANN), fuzzy logic, expert systems and nature inspired algorithms (NIA), Genetic algorithm and differential evolution in optimization were also included. Experimental and simulation examples on efficiency optimization were illustrated.

Chris Mi et al. [26].have proposed to power electronics circuits play an important role in the success of electric, hybrid and fuel cell vehicles.

Typical power electronics circuits in hybrid vehicles include electric motor drive circuits and DC/DC converter circuits. Conventional circuit topologies, such as buck converters, voltage source inverters and bidirectional boost converters were challenged by system cost, efficiency, controllability, thermal management, voltage and current capability, and packaging issues. Novel topologies, such as isolated bidirectional DC/DC converters, multilevel converters, and Z-source inverters, offer potential improvement to hybrid vehicle system performance, extended controllability and power capabilities gives an overview of the topologies, design, and thermal management, and control of power electronics circuits in hybrid vehicle applications.

KonstantinosLaskariset al.[27].have proposed Permanent magnet synchronous machines with non-overlapping concentrated fractional-slot windings on improved electrical characteristics compared to full pitch windings configurations describes the design process and construction of two 10-pole permanent magnet synchronous motors, featuring full-pitch and fractional-pitch windings these two configurations in terms of performance and efficiency. Both motors have been designed for direct-drive applications with low speed and high efficiency capability and were intended to be used as a traction drive in an electric prototype vehicle. The proposed motors have been external rotor configuration with surface mounted NdFeB magnets. The electromagnetic characteristics and performance were computed and analyzed by means of finite elements analysis. These results were finally compared with the experimental measurements on respective prototypes.

One of the factors that affect the overall performance of multilevel inverters is harmonics. Harmonics are mainly generated due to non-linear loads connected to the inverters. Some of the problems that occur due to harmonics are wiring overload, transformer heating etc. The harmonics are generated in each order and for controlling these harmonics certain controllers are utilized. For output regulation of inverters, different linear controller schemes such as PID, H-based or deadbeat controllers are used. However, these controllers are unable to yield satisfactory results in the case of disturbances. Some works, have used filters to eliminate the harmonics, but the harmonic contents are not fully eliminated by them. Certain other works have attempted to reduce the harmonics by varying the switching angles using techniques that control the switching angles like fuzzy logic, neural network etc. These controlling methods eliminate the harmonic contents, but not in all circumstances. Now, the increasing demand of electrical vehicles leads to eliminate the harmonic contents under all

circumstances in a critical way. Moreover, less works have been considered in the literature in perspective of applying harmonic elimination efforts for inverter that could be applied in electric vehicle and appliances. These problems motivated to find a solution and to do this research work.

3. Proposed MethodologyThe proposed adaptive technique reduces

the harmonics by combining the fuzzy logic and the neural network. This technique can eliminate harmonics selectively by choosing the switching angles of the Hybrid Electric Vehicles (HEV) optimally and adaptively. By selecting optimal switching angles, harmonics generation can be avoided in inverters that are used in HEV. Consider that the HEV utilizes a dc-ac Cascaded H-Bridge Multilevel Boost Inverter with No Inductors for Electric/Hybrid Electric Vehicle Applications.

The general mathematical model of the inverter output voltage with s steps can be represented as,

(1) Where, is the input dc voltage and and are the switching angles.The phase voltage of the multi level inverter can

be represented as,

(2)

Figure.1. Dc–ac cascaded H-bridge n- level boost inverter.

N-level dc-ac cascaded H-bridge multilevel boost

inverter (Figure.1) for EV and HEV applications is

described in this paper. The dc-ac cascaded H- bridge

n-level boost inverter has the standard three phase

inverter and each phase of the inverter is connected in

series with the H-bridge. The H-bridge capacitor is

used as a dc power source. For this proposed

topology the usage of large inductors can be

eliminated. Usually fundamental switching scheme is

used to do modulation control and to output staircase

voltage waveform. The proposed dc–ac cascaded H-

bridge n-level boost inverter without inductors will

provide a boosted ac voltage output. The operation of

the dc-ac cascaded H-bridge n-level boost inverter is

explained in next section 3.1.

3.1 Mode of Operation

The single phase structure of the dc-ac cascaded n-level inverter is shown in Figure. 2. The circuit operates with the help of switching actions. The outputs are generated according to the switching actions. If the switch is closed in the bottom level inverter, the output voltage of will be.Similarly the switch closed the output of the is

.This output is given to the H-bridge, which is supplied by a capacitor voltage. The capacitor is kept charged to , then the switches and is closed. Similarly switches and are closed, the H-bridge output is taken to .If the output voltage is 0, when the switches and or and

will be closed.

Figure.2. Single phase of the dc-ac cascaded H-bridge multilevel boost converter.

In this inverter which uses fundamental frequency switching modulation control, a staircase waveform output is produced with a sinusoidal load current waveform. Accordingly the capacitor voltage regulation depends on the phase angle difference of the output voltage and current otherwise the highest output ac voltage of the inverter depends on the displacement power factor of the load.

The magnitudes of the Fourier coefficients when normalized with respect to Vdc are as follows,

(3)Where n = 1, 3, 5, 7...The switching angles , can be

selected such that the voltage total harmonic distortion is minimum. In general these angles are chosen so that the major lower frequency harmonics, 5th, 7th, 11th, and 13th, harmonics are eliminated. The switching angles are chosen with the help of Adaptive Neuro Fuzzy Interference System (ANFIS). The ANFIS technology was explained in next section 3.2.

3.2 Example of Mode Operation: (Switch , and Closed)

This section describes the multilevel inverter mode operation with , and switches are closed, which is shown in Figure.3. The output voltage

of leg of the bottom inverter (with respect to the ground) is when the switch is closed. This leg is connected in series with a full H-bridge and it is supplied by a capacitor voltage. The capacitor is kept charged to , and then the output voltage of the H-bridge can take on the values switches

and are closed. The output 0 means and is closed. The number of levels chosen high to produce better sinusoidal voltage waveform. Similarly the other modes are operated.

Figure.3. Mode of operation example

4. ANFIS Based On Switching AnglesThe optimum choosing of switching angle using

ANFIS mainly consists of three steps,1. Knowledge base generation2. ANFIS training3. Fine tuning

4.1 Knowledge Base Generation

The switching angles are maintained at, (4)

The THD (odd harmonics) can be calculated using

the following relation

(5)

ANFIS used to play fundamental role in Total Harmonic Distortion (THD) reduction technique. It is used for training the neural network according to the switching angles and the harmonics generated by them. It provides a random generation of switching angles and also corresponding output voltages. The ordinary switching angles range is divided into different intervals and a switching angle pattern is generated. The switching angle intervals and the corresponding voltages are determined using the following table.

Switching Angles Output Voltage

Figure.4. Reconfigured dataset

Where, are random integers generated within the corresponding intervals,

is the voltage of order

harmonics. The ANFIS was trained by using the reconfigured dataset . When the training process is completed, the ANFIS is ready to use and to obtain the output voltage for the given switching angle.

4.2 ANFIS Training

The system consists of an adaptive network and FIS that aids in the elimination of harmonics that are generated by the Electric Vehicle and Hybrid Electric Vehicle. The ANFIS network to be utilized for the system is illustrated in Figure. 5.

Figure.5. the ANFIS structure used in the proposed technique

ANFIS consists of five layers, fuzzy layer, product layer, normalized layer, defuzzy layer, and total output layer. The input consists of THD and voltage of the multilevel inverter. The dataset will be needed to train the network. The dataset is shown in Figure.4. This can be made capable by taking as the reference dataset and extracting the patterns from R. The R dataset class satisfies the dataset in same class, this form is one of the training elements in training dataset .In the same way, this process is performed for all the patterns present in R and therefore the total training dataset is formed.

Switching Angles Output Voltage

Figure.6. Training dataset

4.3 Fine Tuning

Generally the ANFIS output is not accurate i.e., the switching angle generation and the corresponding voltage and THD is not equal but equivalent. So it is required to increase the

performance and accuracy of the system by using tuning process. In this tuning process the constant elements like and the taken into account. The tuning process is illustrated below.

Step 1To find the error THD value that is denoted

by Error ( )

Where, is Target Total Harmonic Distortion, THD is actual Total Harmonic Distortion.

Step 2To find the error voltage V that is denoted

by Error

Where, is target voltage, is phase voltage.Step 3Determine the average of the error value

Step 4Find the new switching angle

Where, is new switching angle, is tuning constant normally ,r is random numbers selected within the range 0 to 1, is the value of switching angle calculated.

Step 5To check the result that is must satisfy the

following;

If the result not satisfied means, it will be corrected in the following way is the error condition,

Recover the value using the following relation

To check if the condition is

satisfied will be taken to next steps.If it is not satisfied

Where, (Random numbers)Then repeat the steps 4 and 5. It will be

proceeding when the result is satisfied.

5. Experimental ResultsThe execution of the proposed technique is

performed in the working platform of MATLAB

7.12.0(R2011a) and we have utilized the provided ANFIS toolboxes. In the implementation process, we have considered that the HEV has the cascaded H-bridge multilevel inverter, which is dependable for generating the harmonics affected voltage waveforms. The multilevel inverter has n-levels and the corresponding switching angles. The selections of switching angles are needed to be optimally. The technique is executed in such a way that it can eliminate the selective order harmonics. During the random generation of switching angles are considered and accordingly the rules are generated for the corresponding angles, which is used to the network training. Once the training process was finished, which is ready to work. To improve the performance of the technique by adding tuning technique (tuning constant ). The implementation and the corresponding results are illustrated below.

Figure.6. proposed method without harmonic elimination

Figure.7. Proposed method without tuning

Figure.8: Proposed 3rd order harmonic with tuning

Figure.9.Proposed 3rd order harmonic& fundamental harmonic with tuning

Figure.10. 3rd order magnitude without harmonic elimination

Figure.11. 3rd order magnitude without fine tuning

Figure.12. 3rd order magnitude with fine tuning

Figure.13. 3rd order magnitude with neural network based fine tuning

Figure.14. 5th order magnitude without harmonic elimination

Figure.15. 5th order magnitude without fine tuning

Figure.16. 5th order magnitude with fine tuning

Figure.17. 5th order magnitude with neural network based fine tuning

Figure.18. 7th order magnitude without harmonic elimination

Figure.19.7th order magnitude without fine tuning

Figure.20. 7th order magnitude with fine tuning

Figure.21. 7th order magnitude with neural network based fine tuning

Figure.22. 9th order magnitude without harmonic elimination

Figure.23. 9th order magnitude without fine tuning

Figure.24. 9th order magnitude with fine tuning

Figure.25. 9th order magnitude with fine tuning neural network

6. DiscussionsFigure 10 illustrates the inverter output without

harmonic elimination technique whereas Figure 11, 12 and 13 illustrates inverter output with selectively mitigated 3rd order harmonics using proposed technique without fine tuning, proposed technique with fine tuning and neural network-based harmonic elimination technique, respectively. When no harmonic elimination technique is implemented, 3rd

order harmonic voltage magnitude is 290% of normalized fundamental voltage magnitude, which is fundamental voltage magnitude (normalized) and proposed technique without fine tuning is implemented the resultant magnitude is 220% of normalized fundamental voltage magnitude. The 3rd

order harmonic elimination with proposed fine tuning technique magnitude is reduced compared to the other two techniques in which magnitude range is 78% of normalized fundamental voltage magnitude. The neural network based tuning technique reduced the magnitude level that is not better than the proposed fine tuning technique in which magnitude is

120% of normalized fundamental voltage magnitude. Figure 14-17 shows the multilevel inverter output without harmonic elimination, proposed technique with selectively eliminates 5th order harmonics without fine tuning, proposed technique with fine tuning and neural network based fine tuning harmonic elimination, respectively. The 5th order harmonic voltage magnitude is 170% of normalized fundamental voltage magnitude, when no harmonic elimination technique is implemented. The proposed method without fine tuning process is implemented; the resultant is 140% of normalized fundamental voltage magnitude and implements the proposed method with fine tuning technique, in which magnitude of the harmonics range is reduced by 90% of normalized fundamental voltage magnitude. The 5th order selective harmonic mitigation using neural network fine tuning technique is 140% of normalized fundamental voltage magnitude.

The multilevel inverter output without harmonic elimination ,selectively mitigated the 7th

order harmonic technique using proposed technique without fine tuning, proposed method with fine tuning and neural network based fine tuning is illustrated in figures 18-21 respectively. The without harmonic elimination technique is implemented, which 7th order harmonic voltage magnitude is 240% of normalized fundamental voltage magnitude and the proposed technique without fine tuning result is 140% of normalized fundamental voltage magnitude. In proposed technique with fine tuning is implemented, which reduced the harmonic magnitude is 120%of normalized fundamental voltage magnitude and the neural network based fine tuning technique is implemented, which harmonic magnitude is 130% of normalized fundamental voltage magnitude. The figures 21-25 shows the output of the multilevel inverter without harmonic elimination, selectively mitigated the 9th order harmonics using proposed technique without fine tuning, proposed technique with fine tuning and neural network based fine tuning respectively. When no harmonics elimination technique is implemented, the output of the harmonic magnitude is 190% of normalized fundamental voltage magnitude. The 9th

order selective harmonic elimination using proposed technique without fine tuning is implemented, which harmonic magnitude is 140% of normalized fundamental voltage magnitude. The proposed technique with fine tuning is reduced the harmonic voltage magnitude in 90% of normalized fundamental voltage magnitude. Neural network based fine tuning is to mitigate the 9th order harmonic is implemented, which harmonic voltage magnitude is 7800% of normalized fundamental voltage magnitude. The comparative results are proved that

the ANFIS controller with fine tuning technique is more sufficient to mitigate the selective harmonics.

ConclusionIn this paper, the HEVs n-level cascaded H-bridge

inverter was represented and the THD was controlled by the ANFIS controller. The input to be given for ANFIS controller was harmonic voltage to be minimized and the required THD value of the system. It was successfully trained with the random generation of switching angles and the corresponding output voltages. The ANFIS output controls to obtain optimal switching angles, which is not equal but equivalent. So that fine tuning process was introduced to improve the accuracy of the system. From the results, it was seen that the proposed technique reduced THD and mitigated the selected harmonic order using the determined optimal switching angles when compared to the previous techniques. The comparative results proved that the proposed technique mitigated the selected harmonics to sufficient level which are competent over the other techniques.

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S.Veerakumar obtained his Bachelor’s degree in Electrical and Electronics Engineering from Bharathiyar University Coimbatore. Then he obtained his Master’s degree in Power Electronics from Vellore Institute of Technology, Deemed University. Currently, he is a Assistant Professor (Senior Grade) in Electrical and Electronics Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India. His specializations include Power Electronics and Drives, Microprocessor, Electric Vehicle.

Dr. A. Nirmalkumar completed his graduation and post graduation in Electrical Engg from Calicut and Kerala University in 1972 &1976 respectively. Completed his Doctorate from Bharathiar University in 1992. He is currently an Professor and Head in the Department of Electrical and Electronics Engineering at INFO Institute of Engineering, Kovilpalayam, Coimbatore, Tamil Nadu, India. His area of specialization includes Power converters for renewable energy application and drives. He has more than 30 years of teaching experience. He is guiding at present 20 research scholars. He is the recipient of Institution of Engineers Gold Medal for the year 1989. He has many publications in national and international journals to his credit.