The Simple Embedded System for Three–Phase Solar Motor Pump Using Volt/Hertz

Maximum Power Point Tracking Technique

Auttawut Waiprib Electrical and Energy Engineering Department,

School of Engineering, University of the Thai Chamber of Commerce,

Bangkok, Thailand sauan_vip@utcc.ac.th

Nuttaporn Ritnoom Electrical and Energy Engineering Department,

School of Engineering, University of the Thai Chamber of Commerce,

Bangkok, Thailand nuttaporn_rit@utcc.ac.th

Abstract—This paper presents the research and development on the embedded system for three-phase solar motor pump with the Volts/Hertz (V/F) maximum power point tracking technique (MPPT) which significantly simplifies the software design complexity for PWM three-phase motor drive inverter. The software of the control system and MPPT is designed by using the microcontroller to generate the command signals for MC3PHAC chip. The experimental results show that the maximum power adjusted by the proposed system on the variation of the sunlight intensity is very close to that of the reference system. We also show the characteristics of the maximum power point tracking of the PV Array. Our design of this system can be applied and developed on many other purposes. In addition, by using our proposed software design, the implementation is simple and inexpensive.

Keywords-embedded control system; MPPT; solar cell; photovoltaic (PV); three –phase motor pump;

I. INTRODUCTION At present, the solar motor pumps are increasingly required

by general consumers such as those in agricultural sector, household, etc. The main factor for high demand in solar motor pumps is to replace the fueled motor pumps that have high operation cost due to the high fuel price. Additionally, the solar motor pump is usually easy to install and relocate. It also has very low noise and it uses clean energy, thus lowering the noise pollution and reducing global warming effect.

The solar motor pump uses the solar cell as a power source. From the characteristic of the solar cell, the generated power will be varied by the level of the sunlight intensity. Each level of sunlight intensity can generate only one point of maximum power. In order for the solar cell to generate maximum power at all levels of intensity, maximum power point tracking (MPPT) technique should be enforced.

The solar motor pump can be divided into two categories: direct current (DC) and alternate current (AC) motor pumps. In late 1990s, the solar motor pump system and MPPT with DC–DC converter, bus-less direct current (BLDC) inverter drive, and three–phase induction motor pump by the DC Bus

Voltage–Controlled technique and V/F technique, were introduced [1]-[3]. The motor is driven by the inverter and controlled by MPPT. However, these systems are rather difficult to design for application and extension. The structure of the system is shown in the Figure 1(a).

Recently, many researches are focused on the efficiency and capability of solar motor pump by introducing the MPPT to control the inverter and DC–DC converter by using Fuzzy Theory and Artificial Neural Network (ANN). The motor pump is DC motor, BLDC motor or three–phase induction motor. Control technique is V/F, Vector Control and Field Oriented Control [4] – [9]. The embedded control systems are ranged from 16-bit microcontroller to 32–bit DSP chip. The structure of the system is shown in the Figure 1(b). Again, these systems are very complex, thus leading to high cost.

(a) DC-DC + MPPT Microcontroller

3 Phase Motor PumpBLDC Motor PumpDC Motor Pump

GATEDRIVE

Solar ARRAY

V,I

DSP+MPPT

DC-DC+ MPPTInverter Drive BLDC

DC-AC Inverter+ MPPT

Hall Effect & Encoder sensor

(b) DSP + MPPT Microcontroller

Figure 1 The structure of solar motor pump and MPPT

The purpose of this paper is to simplify the complexity of software design for embedded control system for the three–phase solar motor pump with the MPPT by Volts/Hertz (V/F) technique. We choose to apply our software design to the three-phase AC motor pump due to its higher performance, better maintainability, and smaller form factor than any other motor pumps.

We organize this paper as follows. In the next section, the background of solar motor pump and MPPT technique are presented. The proposed embedded control system is explained in section III. In section IV, the experimental results are illustrated. Finally, we provide the discussion and conclusion in Section V.

II. BACKGROUND In this section, the background of electrical power from

solar cells and MPPT technique are introduced as following:

A. Solar Cells Solar cells are an electrical source that has non–linear

characteristics – the non-linear varying of electrical power due to sunlight intensity and temperature. At a particular value of sunlight intensity, there is only one point of maximum power generated from solar cells, as shown in Figure 2. Point A, B and C in this figure are the maximum power generated at the sunlight intensities at 0.50kW/m2, 0.75kW/m2 and 1.00kW/m2, respectively. In order to enhance the system efficiency, the motor pump should operate at the maximum power point, which is described in the next subsection.

B. Principle of Maximum Power Point Tracking The electrical power P of the source is directly varied to the

frequency ƒ of the motor. The principle of three–phase induction motor control by the V/F technique is to keep the ratio between voltage and frequency as a constant. This technique can generate the maximum torque at the motor at either high or low rotor speed. The maximum power can be expressed in the equations below:

ωTVIPPPP pvpvpvoutin ===== max

2ωkT = (1) 3ωkPpv = (2)

∴ 3fkP tpv ≈ (3)

where Pin is the input power of motor; Pout is the output power of motor; Ppv is the power of solar cell; Pmax is the maximum power of solar cell; Vpv and Ipv are Voltage and current of solar cell, respectively; T is the torque of motor; ω is the speed of motor; ƒ is the frequency; k is the constant of a pump; and kt is k (2π)3.

III. PROPOSED SYSTEM The system architecture of the embedded control system

and the software design are explained in this section as follows:

A. System Architecture We use PIC16F877A, 8–bit microcontroller, as the main

processor of the system. The digital I2C–pattern command signals are generated from this microcontroller and are converted to analog command signal (0–5Vdc) by using PCF8591 A/D converter. These analog signals are forwarded to pin 26 of MC3PHAC – monolithic intelligent motor controller, which is an inexpensive commodity motor controller. This motor controller adjusts the frequency from 1Hz to 128Hz to maximize Ppv according to the analog command signals by using sine-wave PWM inverter. The system status can be monitored on PC via serial port communication (RS232).

This system architecture, as shown in Figure 3, is much simpler than what we discuss previously, specifically the control software design which is described in the next subsection.

B. Software Design of the Control System and MPPT The software design can be depicted in Figure 4. In the

Initialization process, all system variables and configuration parameters of MC3PHAC motor controller are set up. The present voltage Vpv(n) and present current Ipv(n) from the solar array are constantly read and forwarded to the Interrupt Program. This Interrupt Program compares Vpv(n) and Ipv(n) to the upper and lower thresholds. If any of them is more than the upper threshold or less than the lower threshold, the system will be shutdown and alarm signal is generated. This is a safety mechanism to protect the motor pump.

If both Vpv(n) and Ipv(n) are in the operation range, the main program will calculate the present power Ppv(n) and compare it with the previous calculated power Ppv(n – 1). If Ppv(n) is greater than Ppv(n – 1), the main program will compare present voltage Vpv(n) and that of the previous stage Vpv(n – 1). If Vpv(n) > Vpv(n – 1), the main program will update C(n) by C(n) = C(n – 1) + 5%. On the other hand, if Vpv(n) < Vpv(n – 1), C(n) is updated by C(n) = C(n – 1) – 5%.

Figure 2 V-I Characteristic Curve of The Solar Cell.

On the contrary, if Ppv(n) is not greater than Ppv(n – 1), C(n) is updated according to the following conditions:

C(n) = C(n – 1) – 5%, if Vpv(n) > Vpv(n – 1) or

C(n) = C(n – 1) + 5%, if Vpv(n) < Vpv(n – 1) or

C(n) = C(n – 1), if Vpv(n) = Vpv(n – 1)

After updating C(n), the main program will stop for 3 seconds. This duration is long enough for microcontroller to save some energy and fast enough to catch the changes of sunlight intensity. When the main program resumes, it updates Ppv(n), Vpv(n) and C(n) and repeats the same aforementioned steps as illustrated Figure 4. Note that this software design is just a simple decision tree which is much simpler than the fuzzy systems or ANN mentioned earlier.

IV. EXPERIMENTAL RESULTS In order to appreciate our proposed system, we conduct a

referenced system in parallel with our proposed system. The referenced system is manually fined tune to get the maximum power point at all time of particular sunlight intensity. Then, we compare the referenced system with our proposed automatic system.

A. Referenced System The architecture of the referenced system is shown in

Figure 5(a). Variable resistor load is used to manually tune the system to get the maximum power point. Two sets of six serial solar arrays (750W, 110VDC) are connected to motor pump. The measured power from this referenced system is the upper bound of the power at particular sunlight intensity.

B. Our Proposed System The system configuration of our proposed system is the

same as that of reference system except replacing the variable resistor load with our proposed MPPT controller, as shown in Figure 5(b).

Both systems operate in parallel from 10AM to 1PM. The maximum powers (Pmax) of both systems are collected every 15 minutes, as shown in Figure 6. The results show that our proposed embedded system can control the power very close to the maximum power in the reference system. On average, it is only approximately 6% less than the perfect values.

In Figure 7(a), the steady-state system response in terms of voltage and current is shown. In order to simulate the change of sunlight intensity, we issue the “increase sunlight intensity”

Figure 3 Schematic Diagram of the Proposed Embedded System.

control command for 100ms and show the system response in Figure 7(b). We can notice that the system adjusts itself by increasing the frequency, increasing speed and giving more power.

Figure 4 Flowchart of Control Software for MPPT

(a) Referenced System

(b) The Proposed System

Figure 5 Simplified Schematic Diagrams.

Figure 6 Performance of the Proposed System.

(a) Instantaneous System Response

(b) System Response from the Simulation of the Changes in Sunlight Intensity

Figure 7 System Response

V. DISCUSSION AND CONCLUSION The existing systems, which use the fuzzy algorithm or

ANN, require special DSP chips to process the signals. Moreover, they can produce the weird system behaviors due to the unpredictable fuzziness. With the DSP chips, the system cost is normally higher than the simple microcontroller.

The simple embedded system for the three–phase solar motor pump with the Volts/Hertz (V/F) maximum power point tracking technique (MPPT) is presented. Based on a very simple decision tree approach, the complexity of the software design is much lower than the existing systems. Without fuzzy system or ANN, the proposed algorithm can offer “near” maximum power to the overall system. This is great for low computation power controller, such as 8-bit microcontroller and very simple software coding. This simplification allows lower system cost, the cost of implementation is about US$20.00, and more stability of the control software. The limitation of the system is it can be used with three – phase motor only.

ACKNOWLEDGMENT The authors sincerely wish to give an appreciation to the

Department of Electrical and Energy Engineering, School of Engineering, University of the Thai Chamber of Commerce (UTCC) for all facilities, including the engineering laboratory, equipments and tools.

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