Performance Enhancement of PV Panel using Fuzzy Logic Control

Project Title

Performance Enhancement of PV Panel using Fuzzy Logic Control

Project Area of Specialization Wearables and ImplantableProject Summary

Photovoltaic cell (PV) is the renewable energy source, it is an outstanding green electricity source for one of the kind applications like mobile and stationary, as a backup source of energy. But inspire of all this at the same time preserving the constant voltage and current and maximum power from the PV in the presence of input (temperature, irradiance) and load variations , and to make sure that maximum power be delivered from the PV to load is the predominant trouble we are facing.  So the goal we have to obtain is to maintain the PV system should work at maximum power point (MPP) in the variant environmental condition, so that maximum power can be extract from the PV, and in this way efficiency of the system will also be increased.

We can overcome the cited problem by making use of designing and with aid of enforcing suitable techniques and algorithm. Already there are algorithms which are used to operate PV at MPP. But what we are doing, we apply three different techniques perturb and observer (P & O), incremental conductance (IC), and fuzzy logic control to extract maximum power from PV and compared the result of these techniques and conclude which one is the efficient one.

P&O and IC show better performance with environmental conditions and implementation is also easy but they possesses an extra PI control loop which make the tracking process of these two algorithms too slow. P & O always hover across the MPP but not able to grab that, IC hover across MPP but for short period of time .On the other hands fuzzy have very small response time and track the MPP with sudden variation in environmental conditions and give comparatively excellent results, it not hover across MPP and exactly give the desire result with 97.6% accuracy.

We design hardware for the practical implementation and demonstration of fuzzy logic control in real time.

Project Objectives

• Performance enhancement of Photovoltaic (PV) using fuzzy logic control.
• Software based comparison of different techniques for maximum power point tracking (MPPT)
• Hardware implementation of most efficient technique.

Project Implementation Method

In the first phase three different MPPT techniques Perturb and Observer (P & O), Incremental conductance (IC) and Fuzzy Logic Control are implemented in MATLAB software and results are compared. From the obtained results we make comparison and conclude that Fuzzy Logic Control is the best and efficient technique in all.

In the second phase we design the hardware in Proteus and MATLAB.

The hardware of fuzzy logic control system is divided into four parts. The Photovoltaic (PV) module, the Buck-Boost Converter, battery bank and the Inverter. PV module supply power to the Buck-Boost converter, Buck –Boost converter make sure that the PV should work at maximum power point (MPP) and maximum power should extract from the PV. This is done by the Fuzzy logic controller, which goes on monitoring the voltage and current of PV with the changing conditions of environment. Fuzzy Logic Controller rules are defined in MATLAB, and then programed in Arduino to operate the Buck-Boost converter switching accordingly. If the PV voltage decreases due to environmental changes the boost convert get activated and boost up the voltage to the rated voltage of battery and charge it, similarly if the PV voltages rises then the Buck converter get activated and step down the PV voltage to rated value of battery. The voltage control sensor and the current control sensor goes on sensing any change in voltage and current, and accordingly the fuzzy controller extract the maximum power from the PV and control the switch of converter, and make sure that constant rated voltage should deliver to battery bank. After the battery bank we have inverter which convert the DC into AC. To enhance the efficiency of inverter efficient modulations is performed to control the switching of Mosfets.

The whole system is continuously monitored and result are displayed on LCD displays.

Benefits of the Project

1. To increase output POWER/COST ratio:

Under the pressure of limited available energy resources and environmental policies, electrical power generation using renewable energy has rapidly increased in recent years. Photovoltaic (PV) system is gaining increased importance as a renewable source due to advantages such as the absence of fuel cost, little maintenance and no noise and wear due to the absence of moving parts.  A PV panel is a non-linear power source, i.e. its output current and voltage (power) depends on the terminal operating point. The maximum power generated by the PV panel changes with the intensity of the solar radiation and the operating temperature. To increase the ratio output power/cost of the installation it is important that PV panel operates in the maximum output power point (MPP).

2. To increase the live span of battery:

It is very important to respect the correct battery charge curves because it will prolong its correct operation and live. As we know, frequent charging and discharging will shorten the life time of a battery. With such a system, the problem is how to determine when the battery should be charged to provide the best energy efficiency and to prolong the life time. A fuzzy controller achieve the optimal results of the battery charging and discharging performance.

Technical Details of Final Deliverable

Photo voltaic (PV) module is a nonlinear device whose output varies with change in operating temperature, irradiance and load.

The control objective is to track maximum power will lead consequently to effective operation of the PV panel.

Fuzzy Logic controller (FLC) used due to their heuristic nature associated with simplicity, effectiveness and its multi-rule-based variable’s consideration for both linear and non-linear parameter variation of the system. Fuzzy system is composed of knowledge based rules system; the main part of FLC is knowledge of base consisting of the If-Then rules. Fuzzy Logic is implemented to obtain the MPP operation voltage point faster with less overshoot and also it can minimize the voltage fluctuation after MPP has been recognized. To design the FLC, variables which represent the dynamic performance of the system should be chosen as the input to the controller.

In our project the PV voltage and current are chosen as the input variables of FLC. The FLC continuously monitoring the change in PV voltage and current, and control the switching of boost converter which ensure that the circuit impedance faced by the PV module should remain constant, and the load line will cut the maximum power point. Fuzzy controller not only ensure that the maximum power is extracted from the PV but also make sure the batteries should charge at rated voltage and current. After the batteries we can connect both DC and AC load. For AC load we have inverter which convert the DC to AC. After inverter transformer are installed to step up the AC voltages to rated standard voltage level.

Monitoring is done through displays on both input and load side to show the efficiency of the whole system.

Final Deliverable of the Project HW/SW integrated systemType of Industry Energy , Manufacturing Technologies OthersSustainable Development Goals Affordable and Clean Energy, Decent Work and Economic Growth, Industry, Innovation and InfrastructureRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	71327
Arduino Mega	Equipment	3	1100	3300
PV module	Equipment	2	13500	27000
FETS	Equipment	16	165	2640
Gate Driver	Equipment	8	205	1640
terminal blocks	Equipment	12	36	432
LM2576 Module	Equipment	4	670	2680
Voltage sensor module	Equipment	7	445	3115
current sensor module	Equipment	8	330	2640
Battery	Equipment	2	2800	5600
variable power supply	Equipment	1	5740	5740
Documentation and thesis	Miscellaneous	6	1650	9900
LCD Displays	Equipment	4	215	860
PWM IC	Equipment	4	345	1380
IRF3205	Equipment	8	75	600
Transformer	Equipment	2	750	1500
Ardopter	Equipment	2	1150	2300