A Unity Power Factor Converter with Isolation for Electric Vehicle Battery Charger

ABSTRACT:  

This paper deals with a unity power factor (UPF) Cuk converter EV (Electric Vehicle) battery charger having a high frequency transformer isolation instead of only a single phase front end converter used in vehicle's conventional battery chargers. The operation of the proposed converter is defined in various modes of the converter components i.e. DCM  (Discontinuous Conduction Mode) or CCM (Continuous Conduction Mode) along with the optimum design equations. In this way, this isolated PFC converter makes the input current sinusoidal in shape and improves input power factor to unity. Simulation results for the proposed converter are shown for charging a lead acid EV battery in constant current constant voltage (CC-CV) mode. The rated full load and varying input supply conditions have been considered to show the improved power quality indices as compared to conventional battery chargers. These indices follow the international IEC 61000-3-2 standard to give harmonic free input parameters for the proposed circuit.

KEYWORDS:

1.      UPF Cuk Converter

2.      Battery Charger

3.      Front end converter

4.      CC-CV mode

5.      IEC 61000-3-2 standard

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

Fig. 1 General Schematic of an EV Battery Charger with PFC CUK Converter

EXPERIMENTAL RESULTS:

Fig.2 Simulated performance of the isolated Cuk converter in rated condition

(a) rated input side and output side quantities (b-c) harmonic analysis of the

current at source end

Fig.3 Simulated performance of the isolated Cuk converter while input is

varied to 270V (a) rated input side and output side quantities (b-c) harmonic

analysis of the current at source end

Fig.4 Simulated performance of the isolated Cuk converter while input is

reduced to 270V (a) rated input side and output side quantities (b-c) harmonic

analysis of the current at source end

Fig.5 Simulated performance of the isolated Cuk converter at light load

condition (a) rated input side and output side quantities (b-c) harmonic analysis

of the current at source end

CONCLUSION:

An isolated Cuk converter based battery charger for EV with remarkably improved PQ indices along with wellregulated battery charging voltage and current has been designed and simulated. The converter performance has been found satisfactory and well within standard for rated as well as different varying input rms value of supply voltages. The considerably improved THD in the current at the source end makes the proposed system an attractive solution for efficient charging of EVs at low cost.

The proposed UPF converter performance has been tested to show its suitability for improved power quality based charging of an EV battery in CC-CV mode. Moreover, the cascaded dual loop PI controllers are tuned to have the smooth charging characteristics along with maintaining the low THD in mains current. The proposed UPF converter topology have the inherent advantage of low ripples in input and output side due to the added input and output side inductors. Therefore, the life cycle of the battery is increased. MATLAB based simulation shows the performance assessment of the proposed charger for the steady state and dynamics condition which clearly state that the proposed charger can sustain the sudden disturbances in supply for charging the rated EV battery load. Moreover, during whole disturbances in supply voltage, thepower quality parameters at the input side, are maintained within the IEC 61000-3-2 standard and THD is also very low.

REFERENCES:

[1] Limits for Harmonics Current Emissions (Equipment current ≤ 16A per Phase), International standards IEC 61000-3-2, 2000.

[2] Muhammad H. Rashid, “Power Electronics Handbook, Devices, Circuits, and Applications”, Butterworth-Heinemann, third edition, 2011.

[3] N. Mohan, T. M. Undeland, and W. P. Robbins, Power Electronics: Converters, Applications and Design. Hoboken, NJ, USA: Wiley, 2009.

[4] B. Singh, S. Singh, A. Chandra and K. Al-Haddad, “Comprehensive Study of Single-Phase AC-DC Power Factor Corrected Converters With High-Frequency Isolation”, IEEE Trans. Industrial Informatics, vol. 7, no. 4, pp. 540-556, Nov. 2011.

[5] A. Abramovitz K. M. Smedley "Analysis and design of a tapped-inductor buck–boost PFC rectifier with low bus voltage" IEEE Trans. Power Electron., vol. 26 no. 9 pp. 2637-2649 Sep. 2011.

Speed Controller of Switched Reluctance Motor

ABSTRACT:  

Fuzzy logic control has become an important methodology in control engineering. The paper proposes a Fuzzy Logic Controller (FLC) for controlling a speed of SRM drive. The objective of this work is to compare the operation of P& PI based conventional controller and Artificial Intelligence (AI) based fuzzy logic controller to highlight the performances of the effective controller. The present work concentrates on the design of a fuzzy logic controller for SRM speed control. The result of applying fuzzy logic controller to a SRM drive gives the best performance and high robustness than a conventional P & PI controller. Simulation is carried out using matlab simulink.

KEYWORDS:

1.      P Controller

2.      PI Controller and Fuzzy Logic Controller

3.      Switched Reluctance Motor

SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig. 1 Block diagram of SRM speed control.

 EXPERIMENTAL RESULTS:

Figure 2. Output flux.

Figure 3. Output current.

Figure 4. Output torque.

Figure 5. Speed.

Figure 6. Output flux.

Figure 7. Output current.

Figure 8. Output torque

Figure 9. Speed.

Figure 10. Output flux.

Figure 11. Output current.

Figure 12. Output torque.

Figure 13. Speed.

CONCLUSION:

Thus the SRM dynamic performance is forecasted and by using MATLAB/simulink the model is simulated. SRM has been designed and implemented for its speed control by using P, PI controller and AI based fuzzy logic controller. We can conclude from the simulation results that when compared with P & PI controller, the fuzzy Logic Controller meet the required output. This paper presents a fuzzy logic controller to ensure excellent reference tracking of switched reluctance motor drives. The fuzzy logic controller gives a perfect speed tracking without overshoot and enchances the speed regulation. The SRM response when controlled by FLC is more advantaged than the conventional P& PI controller.

REFERENCES:

1. Susitra D, Jebaseeli EAE, Paramasivam S. Switched reluctance generator - modeling, design, simulation, analysis and control -a comprehensive review. Int J Comput Appl. 2010; 1(210):975–8887.

2. Susitra D., Paramasivam S. Non-linear flux linkage modeling of switched reluctance machine using MVNLR and ANFIS. Journal of Intelligent and Fuzzy Systems. 2014; 26(2):759–768.

3. Susitra D, Paramasivam S. Rotor position estimation for

a switched reluctance machine from phase flux linkage.

IOSR–JEEE. 2012 Nov–Dec; 3(2):7.

4. Susitra D, Paramasivam S. Non-linear inductance modelling of switched reluctance machine using multivariate non- linear regression technique and adaptive neuro fuzzy inference system. CiiT International Journal of Artificial Intelligent Systems and Machine Learning. 2011 Jun; 3(6).

5. Ramya A, Dhivya G, Bharathi PD, Dhyaneshwaran R, Ramakrishnan P. Comparative study of speed control of 8/6 switched reluctance motor using pi and fuzzy logic controller. IJRTE; 2012.

Design and Control of SR Drive System using ANFIS

ABSTRACT:  

This paper presents the modeling and simulation of an adaptive neuro-fuzzy inference strategy (ANFIS) to control the speed of the switched Reluctance motor .The SRM control is thus a  difficult to be in use in the nonlinear applications, particularly in the control of speed in automobiles. The Neuro-fuzzy system incorporates the advantages of both neural-network and fuzzy system. This controller is great additional effectual than Fuzzy  logic and neural network based controller, while it has the ability of  self-learning the gain values and acclimatizes accordingly to situations, thus accumulating more flexibility to the controller. A complete simulation, well-designed to the nonlinear model of Switched Reluctance Drive was premeditated using MATLAB /SIMULINK.

KEYWORDS:

1.     SR Drive

2.     ANFIS

3.     ANN

4.     FLC

 SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig.1.Block Diagram of ANFIS Controller for SRM Plant

EXPERIMENTAL RESULTS:

Fig.2: Response of the speed control of SRM using FUZZY, ANN and ANFIS with speed Command 3000 RPM under no load conditions.

Fig.3: Response of the Speed and Torque Control of SRM using ANFIS with Speed Command 3000 Rpm under no load conditions.

Fig.4: Response of The Speed and Torque Control of SRM using Fuzzy, ANN and ANFIS with Speed command 4000 rpm.

Fig.5: Response of the Speed and Torque Control of SRM using ANFIS with Speed Command 4000 rpm.

Fig.6: Response of the speed control of SRM using FUZZY, ANN and ANFIS with speed Command 3000 RPM under load Conditions

Fig.7: Response of the speed and torque control of SRM using ANFIS with speed Command 3000 RPM under load conditions

CONCLUSION:

In this paper, ANFIS-based controller was presented for SR drives. The speed and torque control method existing in this  paper and comparing with the previous control schemes(fuzzy &ANN), while it can be used in both no load and load operating speeds and conditions including speed and torque transients, zero-speed standstill, and startup, and does not suppose the linear characteristics of the SR motor. Moreover, the proposed technique does not need of complex calculations to be carried out during the real-time operation, and no complex mathematical model of the SR motor is required. A main thought in the research was the robustness and reliability of the speed controlling method.

 REFERENCES:

[1] J. P. Lyons, S. R. MacMinn, and M. A. Preston, “Flux/current methods for SRM rotor position estimation,” in Proc. IEEE Industry Application Soc. Annu. Meeting, vol. 1, 1991, pp. 482–487.

[2]S. R. MacMinn, C. M. Steplins, and P. M. Szaresny, “Switched reluctance motor drive system and laundering apparatus employing same,” U.S. Patent 4 959 596, 1989.

[3] M. Ehsani, I. Husain, S. Mahajan, and K. R. Ramani, “New modulation encoding techniques for indirect rotor position sensing in switched reluctance motors,” IEEE Trans. Ind. Applicat., vol. 30, pp. 85–91, Jan./Feb. 1994.

[4] G. R. Dunlop and J. D. Marvelly, “Evaluation of a self commuted switched reluctance motor,” in Proc. Electric Energy Conf., 1987, pp. 317– 320.

[5] Ramesh.Palakeerthi,Subbaiah.P ,2014, ‘High Speed Charging and Discharging Current Controller Circuit to Reduce Back EMF by NeuroFuzzy Logic ‘, International Journal of Applied Engineering Research,vol. 9, no.22

Volume/Weight/Cost Comparison of a 1MVA 10 kV/400V Solid-State against a Conventional Low-Frequency Distribution Transformer

ABSTRACT:  

Solid-State Transformers (SSTs) are an emergent topic in the context of the Smart Grid paradigm, where SSTs could replace conventional passive transformers to add flexibility and controllability, such as power routing capabilities or reactive power compensation, to the grid. This paper presents a comparison of a 1000 kVA three-phase, low-frequency distribution transformer (LFT) and an equally rated SST, with respect to volume, weight, losses, and material costs, where the corresponding data of the SST is partly based on a full-scale prototype design. It is found that the SST’s costs are at least five times and its losses about three times higher, its weight similar but its volume reduced to less than 80 %. In addition, an AC/DC application is also considered, where the comparison turns out in favor of the SST-based concept, since its losses are only about half compared to the LFT-based system, and the volume and the weight are reduced to about one third, whereas the material costs advantage of the LFT is much less pronounced.

KEYWORDS:

1.      Induction Motor (IM)

2.      Indirect Field-Oriented Control (IFOC)

3.       Pulse Width Modulation (PWM)

SOFTWARE: MATLAB/SIMULINK

CIRCUIT DIAGRAM:

Fig. 1. Power circuit of one converter cell used in the SST’s MV side phase

stack

EXPERIMENTAL RESULTS:

Fig. 2. MV side output voltage and resulting line current for (a) the cascaded  1000 kVA MV converter, and (b) corresponding LV side waveforms for one of the 500 kVA LV converter units (cf. Fig. 1(b)) for full-load active power operation.

CONCLUSION:

This paper provides a comparison of a 1000 kVA three-phase LFT and an equally rated SST with respect to material costs, weight, volume and losses. As a direct AC/AC replacement for an LFT, the SST solution realizes benefits with respect to volume, but on the other hand is significantly less efficient and has at least five times higher material costs. However, SST-based solutions can clearly outperform conventional transformers plus LV rectifier systems in modern AC/DC applications, achieving about half the losses and one third of the weight and volume, respectively. All in all, SST technology has significant potential also in grid applications, especially with the Smart Grid being heavily promoted and becoming a reality in the foreseeable future, which increases the requirements in terms of flexibility, intelligence and controllability. However, the usefulness of an SST can only be judged in the context of a given application; there is not a general SST solution that fits every need. Current state-of-the-art LFT technology evolved during more than a hundred years, and represents therefore a truly experienced competitor. Thus SSTs, and explicitly also their relation to various application scenarios, regarding both, technical and economical aspects, should be prominently included in any power electronics or energy research agenda.

REFERENCES:

[1] W. McMurray, “Power converter circuits having a high frequency link,” U.S. Patent 3,581,212, 1970.

[2] S. D. Sudhoff, “Solid state transformer,” U.S. Patent 5,943,229, 1999.

[3] M. Kang, P. Enjeti, and I. Pitel, “Analysis and design of electronic transformers for electric power distribution system,” IEEE Trans. Power Electron., vol. 14, no. 6, pp. 1133–1141, 1999.

[4] M. Manjrekar, R. Kieferndorf, and G. Venkataramanan, “Power electronic transformers for utility applications,” in Conf. Rec. IEEE Industry Applications Conf., Rome, Italy, 2000, pp. 2496–2502.

[5] L. Heinemann and G. Mauthe, “The universal power electronics based distribution transformer, an unified approach,” in Proc. 32nd Annu.  IEEE Power Electronics Specialists Conf. (PESC), Vancouver, Canada, 2001, pp. 504–509.

Fault Detection and Classification In Electrical Power Transmission System Using Artificial Neural Network

ABSTRACT:

This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB® environment.

KEYWORDS:

1.      Artificial neural networks

2.      Feedforward networks

3.      Back propagation algorithm

4.      Levenberg–Marquardt algorithm

SOFTWARE: MATLAB/SIMULINK

 CIRCUIT DIAGRAM:

Figure 1 Snapshot of the studied model.

 EXPERIMENTAL RESULTS:

Figure 2 Data pre processing illustration.

Figure 3 Regression FIT of the outputs vs. targets for the network.

 CONCLUSION:

In this paper we have studied the application of artificial neural networks for the detection and classification of faults on a three phase transmission lines system. The method developed utilizes the three phase voltages and three phase currents as inputs to the neural networks. The inputs were normalized with respect to their pre-fault values respectively. The results shown in the paper is for line to ground fault only. The other types of faults, e.g. line-to-line, double line-to-ground and symmetrical three phase faults can be studied and ANNs can be developed for each of these faults. All the artificial neural networks studied here adopted the back-propagation neural network architecture. The simulation results obtained prove that the satisfactory performance has been achieved by all of the proposed neural networks and are practically  implementable. The importance of choosing the most appropriate ANN configuration, in order to get the best performance from the network, has been stressed upon in this work. The sampling frequency adopted for sampling the voltage and current waveforms in this research work is 1,000 Hz. Some important conclusions that can be drawn from the research are:

1. Artificial neural networks are a reliable and effective method for an electrical power system transmission line fault classification and detection especially in view of the increasing dynamic connectivity of the modern electrical power transmission systems.

2. The performance of an artificial neural network should be analyzed properly and particular neural network structure and learning algorithm before choosing it for a practical application.

3. Back propagation neural networks delivers good performance, when they are trained with large training data set, which is easily available in power systems and hence back propagation networks have been chosen for proposed method.

The scope of ANN is wide enough and can be explored more. The fault detection and classification can be made intelligent by nature by developing suitable intelligent techniques.  This can be achieved if we have the computers which can handle large amount of data and take least amount time for calculations.

REFERENCES:

Alanzi EA, Younis MA, Ariffin AM (2014) Detection of faulted phase type in distribution systems based on one end voltage measurement. Electr Power Energy Syst 54:288–292 Jamil et al. SpringerPlus (2015) 4:334 Page 13 of 13

Aziz MS, Abdel MA, Hassan M, Zahab EA (2012) High-impedance faults analysis in distribution networks using an adaptive neuro fuzzy inference system. Electr Power Compon Syst 40(11):1300–1318

Bouthiba T (2004) Fault location in EHV transmission lines using artificial neural networks. Int J Appl Math Comput Sci 14(1):69–78

Chaturvedi DK (2008) Soft computing techniques and its applications in electrical engineering. Springer, Berlin, Heidelberg

Chaturvedi DK, Mohan M, Kalra PK (2004) Improved generalized neuron model for short-term load forecasting. Soft Comput 8:370–379