View of IMPROVING ENERGY EFFICIENCY BY MONITORING AND USING CONVERTOR IN HOUSEHOLD ELECTRICAL CONSUMPTION

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Vol.04, Issue 11, November 2019, Available Online: www.ajeee.co.in/index.php/AJEEE IMPROVING ENERGY EFFICIENCY BY MONITORING AND USING CONVERTOR IN

HOUSEHOLD ELECTRICAL CONSUMPTION

1Sudhir Dixit, ²A.K. Jhala

Abstract:- It is proposed to implement a non-intrusive appliance load monitoring system (NIALM). It can determine the operating schedule of whole electrical loads in a target system from measurements made at a centralized location. In addition it can identify the operation of electromechanical devices from other kinds of power distribution net and distinguish loads even when many are operating at one time A novel explicit equivalent circuit model is proposed to describe the transient waveforms across and through three IGBT terminals.

Therefore, the solution of IGBT transient waveforms is non-iterative and can be generated with an ultra-small time-step (5 ns in the proposed model). Also, the gate driving circuit is modeled, which makes the proposed model applicative for different driving conditions. The parameters involved in the proposed model can be easily collected from most types of IGBT datasheet, including the output characteristics curves, the transfer characteristics curves, the capacitance curves of IGBT and the reverse recovery current curves of the anti parallel freewheeling diode. These parameters reflect intrinsic characteristics of IGBT and do not rely on the specific test circumstances. Therefore, the proposed model can be generic while maintaining the credibility

Keywords:- IGBT FPGA SMPS.

1. INTRODUCTION

The power losses should be estimated to optimally design the switching converter.

The accurate loss analysis and distribution of the converter are beneficial to improve the efficiency and save the cost.

2. REGRESSION LINE METHOD

Regression analysis is most often used for prediction. The goal in regression analysis is to create a mathematical model that can be used to predict the values of a dependent variable based upon the values of an independent variable. In other words, we use the model to predict the value of Y when we know the value of X.

(The dependent variable is the one to be predicted). Correlation analysis is often used with regression analysis because correlation analysis is used to measure the strength of association between the two variables X and Y.

In regression analysis involving independent variable and dependent variable plots are drawn to analyze the data behavior. The plots allow us to visually inspect the data prior to running a regression analysis. Often this step allows us to see if the relationship between the variables is increasing or decreasing and gives only a rough idea of the relationship. The simplest relationship between variables is a straight-line, linear and nonlinear relationship. Of course the data may well be curvilinear and in that

case we would have to use a different model to describe the relationship.

2.1 Results of 24 Hours from last 1400 hours

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Vol.04, Issue 11, November 2019, Available Online: www.ajeee.co.in/index.php/AJEEE

Fig. 1 Power used against Total hours

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Fig. 7 Power used against Total hours To calculate the electrical energy consumption of house following steps are taken up:-

1. List all the appliances or items that run on electrical supply.

2. Count the quantity of each item.

3. Check every appliances label for its electrical properties. If the label is damaged, the search for the item on the internet and you will get its electrical properties.

4. Write the current and voltage of these items. For United States, the voltage for residential homes is 120V, whereas for most of the other countries its 240V.

5. Finally write down the approximate no. of hour you use the appliance in a day.

6. For each item, multiply the Amps with Voltage and then with the no.

of hrs. This will give you the energy consumed by the appliance in watt-hour.

7. Or, if have the wattage of the appliance, just multiply it with the total time (in hours).

8. Divide the watt-hour by 1000 to get the killo-watt-hour.

9. Multiply the killo-watt-hour of an appliance by the total quantity of that type of appliance.

10. Add the killo-watt-hour values of all the electrical appliances.

11. Now to the total value multiply a factor of 0.6.

12. The final value will give you the total electrical energy consumed by your house in a single day.

3. RESULTS

1. Energy Accuracy:

95.0%

2. Difference(kWh):

6.5

3. MSE:

0.093059

4. Percentage:

16.28%

5. Truth:

130.732 (kWh)

Estimated: 124.245 (kWh)

3.1 Efficiency Factors Of Proposed Parameters

Conduction losses that depend on load:-

 Resistance when the transistor or MOSFET switch is conducting.

 Diode forward voltage drop

(usually 0.7 V or 0.4

V for schottky diode)

 Inductor winding resistance

 Capacitor equivalent series resistance

Switching losses:-

 Voltage-Ampere overlap loss

 Frequency switch*CV² loss

 Reverse latence loss

 Losses due driving MOSFET gate and controller consumption.

 Transistor leakage current losses, and controller standby consumption.

1. In the operation of buck power converters, the loss of IGBT is one of the main power dissipation.

Hence, accurate loss evaluation and loss measurement of IGBT becomes to be an important issue in designing higher power density converters.

2. Conduction losses as well as switching losses are included in the calculation using a simplified model, based on power semiconductor data sheet

There are lots of papers on DC-DC power converter loss investigation. However the temperature-related accurate design- oriented loss modeling is not analyzed.

And many recent researches are either too complicated or inaccurate. Since it is desired to use only datasheet information with minimal measurements,

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Vol.04, Issue 11, November 2019, Available Online: www.ajeee.co.in/index.php/AJEEE 4. CONTRIBUTIONS

4.1 IGBT related thermal losses

The total losses of IGBT include three aspects:-

1. The switching loss during the process of turn on and turn off;

2. The conduction loss during the state of conducting;

3. The turn-off loss during the turned off state. The turn-off loss can be ignored.

This method will be independent of circuit models with decent accuracy and simulation speed. This proposed model (GUI) will divide the power losses of buck converter into four different parts, considering the relation of each component to temperature variation 4.2 Simulation And Results

By putting each part together (GUI Model), the temperature-related power loss model for buck converter can be built. The numerical simulation will be carried out using MATLAB in temperature ranging from 25ºC to 125ºC

4.3 GUI Model for Simulation

5. FLOWCHART

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Fig 8 Loss Calculation Results The static model can be integrated into the state-of-art network solution in the system-level real-time simulation while the dynamic behavior model can produce the IGBT transient waveforms

simultaneously with a 5ns resolution. The detailed modeling principles and procedures are presented in detail and thoroughly discussed.

The real-time hardware implementations are designed based on the Lab VIEW FPGA module and optimized by the high-level synthesis tool -Vivado HLS. A paralleled and pipelined structure is adopted to ensure the strict time constraints. A four-phase floating interleaved boost converter and a three- phase five-level modular multilevel converter are simulated in real time with the proposed model in NI FPGA-based real-time test bench. By comparing the test results with Saber offline simulation results, the accuracy of the proposed model is verified.

The model is proved to be reliable with acceptable errors in exchange for significantly improved real-time simulation efficiency. The proposed model can be easily implemented in ready-made real-time platforms since the associated modeling parameters can be directly collected from the device datasheet. By using the proposed approach, an FPGA- based real-time simulator can be updated from the ideal or quasi-ideal switch models to the detailed behavioral model by simply adding the proposed modules into the FPGA-based hardware designs.

6. CONCLUSION

A temperature-related power loss model for buck converter is carried out using the information of datasheets. The consideration of temperature effect on the device characteristics and losses is recommended for high-temperature operation. The experiment results verified the accuracy of the proposed model.

The temperature-related power loss modeling method in this letter can be applicable to predict IGBT or diode junction temperature and to design power converters’ heat dissipation system under high temperatures. It can be widely used in high temperature, high power density and high frequency systems. The buck is widely used in low power consumption small electronics to step-down from 24/12V down to 5V. They are sold as a small finish product chip for well less than $1 USD having about 95% efficiency.

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Vol.04, Issue 11, November 2019, Available Online: www.ajeee.co.in/index.php/AJEEE REFERENCE

1. Klinefelter, Alicia, et al. “A 6.45 μW self powered IoT SoC with integrated energy harvesting power management and ULP asymmetric radios.” Solid-State Circuits Conference (ISSCC), 2015 IEEE International. IEEE, 2015.

2. Grossmann, Peter J., Miriam E. Leeser, and Marvin Onabajo. “Minimum energy analysis and experimental verification of a latchbased sub threshold FPGA.” Circuits and Systems II: Express Briefs, IEEE Transactions on 59, no. 12 (2012): 942- 946.

3. Ryan, Joseph F., and Benton H. Calhoun.

“A sub-threshold FPGA with low-swing dual VDD interconnect in 90nm CMOS.” In CICC, pp. 1-4. 2010.

4. Qi, He, Oluseyi Ayorinde, Yu Huang, and Benton Calhoun. “Optimizing energy efficient low-swing interconnect for sub threshold FPGAs.” In Field Programmable Logic and Applications (FPL), 2015 25th International Conference on, pp. 1-4. IEEE, 2015.

5. Betz, Vaughn, and Jonathan Rose. "VPR: A new packing, placement and routing tool for FPGA research." In Field-Programmable Logic and Applications, pp. 213-222.

Springer Berlin Heidelberg, 1997.

6. Yuan, Fang-Li, et al. “A multi-granularity FPGA with hierarchical interconnects for efficient and flexible mobile computing.”

IEEE Journal of Solid-State Circuits 50.1 (2015): 137-149.

7. Semiconductor, Lattice. “iCE40 Ultra Family Data Sheet.” DS1048 Version 1 (2015).

8. Microsemi Corporation, “IGLOO nano FPGA Fabric (User's Guide),” Version 1.4, March 2008.

9. Qi, He, Oluseyi Ayorinde, and Benton H.

Calhoun. “An energy-efficient near/sub threshold FPGA interconnect architecture using dynamic voltage scaling and power gating.” In Field-Programmable Technology (FPT), 2016 International Conference on.

IEEE, 2016.

10. Ayorinde, Oluseyi, et al. “Using island-style bi-directional intra-CLB routing in low power FPGAs.” Field Programmable Logic and Applications (FPL), 2015 25th International Conference on. IEEE, 2015.

11. Yang, S. “Logic Synthesis and Optimization Benchmarks Version 3.0. 1991.”

Microelectronics center of North Carolina.