With abundant and non-polluting benefits in nature, sources of renewable energy have reached vast concentrations. This paper first discusses the number of MPPT (Maximum Power Point Tracking) techniques utilized by wind and photovoltaic (PV) to create hybrid systems for generating wind-PV energy. This hybrid system complements each other day and night to enable continuous power output. Then, a new MPPT technique was proposed to extract maximum power using a newly developed hybrid optimization algorithm, namely the Cukoo Fire Fly method (CFF). The CFF algorithm is derived from the integration of the cuckoo search (CS) algorithm and the Firefly (FF) optimization algorithm. In addition, the proposed system comprises a LUO converter controlled by an integrated proportional controller (PI) which includes control of the power delivered to the load. The LUO converter can increase or decrease power depending on load requirements. The results are compared with Harmony Search Algorithm (HAS), Cuckoo Search (CS) and Firefly (FF). Investigation of the proposed system was carried out on the MATLAB/Simulink platform.

The requirement to widen the sources of renewable energy is increasing gradually with the drop of traditional energy sources [

DC-DC converters are placed as interface in between the DC systems of different levels of voltages. Most renewable sources possess reduced voltage output and thus they need booster in such a way to produce the desired level of output voltage. Renewable sources of energy are the better options while compared with the non-renewable sources of energy due to the fact that the renewable sources of energy are limitless and they are eco-friendly. To make any renewable energy system proficient, they need to contain the suitable converter [

The major aim of the paper is to model an efficient optimization oriented MPPT controller enabled Luo converter for wind/solar hybrid power system. The MPP for both systems were tracked optimally using the proposed CFF optimization controlled MPPT to assure better performance of the system with the reduction of ripples. The duty ratio of the POLC is controlled using the PI controller, the parameters of which are generally maintained in such a way to produce minimal time domain specifications. The proposed system develops an effective strategy to track the MPP from both input sources with same convergence instance, rapidity and tracking effectiveness in a concurrent manner, in such a way to overcome the drawbacks of conventional MPPT methods.

Contribution of CFF-enabled PI controller: The CFF algorithm is designed with the hybridization of CS in FFA to provide global convergence of the solutions. The advantages of both CS and FFA are inherited in the proposed CFF in such a way to limit the drawbacks associated with them. The solution provided by CFF offers the MPP for both solar and wind power systems in such a way to regulate the entire performance of the system by means of the PI controlled Luo converter.

The rest of the paper contains: Section 2 deliberates the analysis of few existing methods in the literature with their advantages and limitations. Section 3 deliberates the detailed explanation of the proposed system and the step-wise explanation of the optimization algorithm. The results of the proposed strategy are deliberated in Section 4, and finally, Section 5 concludes the paper.

The number of methods in the literature works related to MPPT controller is discussed as: In [

A multi-input single output (MISO) negative output Luo converter for hybrid wind-solar system was modeled in [

The major challenges of the proposed research are: The major challenge of the hybrid power system is the development of its own strategy for the management of energy to satisfy the load requirement. The selection of suitable power sources is an important issue as it decides the cost of operation and the reliability of the overall system [

This section deliberates the proposed optimization-oriented control of MPPT for optimizing the maximum power output of wind/solar hybrid power generation system. Here, a hybrid wind/solar energy system is studied as the primary sources to supply the load. The execution of a deterministic algorithm, on the basis of CS and FFA is done, in such a way to handle the flow of energy from the source, and to reduce the cost with reduced computational time. As the power generated using the hybrid power source is needed to be adequate to be applied for industrial applications, hybrid system is preferred in the proposed system. Solar and wind energy system can work individually or together. For obtaining the MPP from solar and wind energy conversion system, various number of techniques exist in literature. The proposed CFF algorithm is used in the MPPT in such a way to extract the MPP from both sources. The error produced between the reference and the actual power acts as the input to the PI controller, which in turn alters the duty cycle of the POLC. The POLC acts either as buck or boost converter with the reduction of conversion loss and enhancement of power quality and efficiency.

The solar energy is transformed into electricity by making use of a semiconductor device, which is termed as a solar cell. For practical applications requiring a particular current or voltage for their operation, a considerable number of solar cells are needed to be connected together in series and parallel to develop a solar panel, which also termed as PV module [_{s} and parallel resistance _{p}.

The characteristics equation of ideal PV cell is expressed as,_{pv,cell} represents the current produced due to light incidence, _{d} indicates the current passing through the diode, _{o,cell} implies the reverse saturation current of diode,

The output current of PV array is indicated using

where, _{pv} represents the PV array current, _{0} indicates the saturation current of diode,

The expected voltage of the PV array is fed to POLC through the variation in duty ratio using a gate pulse. The altered value of current and voltage supply the altered measure of power. The rise or fall of power makes changes in the value of current and voltage, correspondingly.

The MPP can be tracked effectively using the proposed CFF algorithm even under irregular variation in level of solar irradiance. Hence, output from the SPV is given to the load through the Luo converter after tracking the MPP using CFF. The parameters of SPV module is presented in

HB-12 100 SPV module | 24 |
---|---|

Cells in each module | 21 |

Open circuit voltage | 40 V |

Short circuit current | 7 A |

Series resistance | 0.8Ω |

Irradiance | 1000 W/m^{2} |

Temperature | 18.5^{0}C |

Wind is the highest emergent renewable energy source, generally used for the production of power. The wind energy system make in use of the wind turbine that is coupled to a generator. Thus, the mechanical energy of the wind runs the turbine that drives the generator to generate the electrical energy. The power of wind turbine varies with the speeds of wind as the turbine speed varies with the wind speed. The power _{P} is the power coefficient. According to Betz's law, the maximum theoretical measure of _{P} is 0.593. In this paper, the MPP is tracked using the MPPT controller in the presence of the proposed CFF algorithm.

The proposed optimization strategy termed as CFF is developed with the hybridization of the algorithms, namely CSA [

The proposed CFF algorithm balances the advantages and the disadvantages of both the optimization algorithms. The CSA works on the basis of the life of a bird known as ‘cuckoo’, and the fundamental of this algorithm is the precise egg laying and breeding characteristics of cuckoo bird. CS generally involve in solving scheduling problems and design optimization problems in structural engineering. In involves in the global optimization of problems in case of different applications in electrical field.

FA is selected as it is a modern type swarm intelligence technique that involve in solving very tedious problems of optimization. FA is motivated due to the flash light characteristics of fire flies that identify a set of solution in random manner. The lower level of this algorithm, generally tries in the production of new solutions available in the space of search to produce the better outcome, whereas randomization involves in searching to overcome the solutions that are identified as local optimum. The solution is enhanced with the local search until finding enhancements. The meta-heuristic algorithms generally rely on the ability to balance between the two major phases, namely exploration and exploitation [

The algorithmic procedure of CFF is stated as below,

Initialization of population_{i} is the number of fireflies, with

Determination of light intensity and light absorption coefficient: The FFA depends mainly on two key things. One is the alteration of intensity of light and the other is the generation of attractiveness. Light intensity reduces with respect to the decrease in distance from that of the source and the light is also immersed by air. Hence, attractiveness must be permitted to change with the varying rate of absorption. The expression for light intensity

The light absorption coefficient γ is considered as the primary value of

Position update using modified expression: The proposed algorithm that involves the hybridization of FFA and CS is formulated in this step. The progress of ^{th} firefly is fascinated by the firefly

where, ^{th} firefly at (^{th} iteration and ^{th} iteration, respectively,^{th} firefly ^{th} at iteration, _{i} indicates the random quantity developed using the Gaussian distribution, and the value of γ varies between 0 and ∞._{0} is the attractiveness with _{ij} = 0.The notation _{ij} is the space between two fireflies, and is represented as,

The standard expression of FFA is modified using the CS in such a way to improve the system operation as the characteristics of cuckoos are useful in evaluating the solution to optimization problems.

The standard equation of CS is given as,

^{th} solution in ^{th} iteration, ^{th} solution in (^{th} iteration, _{0} is the step constant, u and v are the standard normal variables in random, and

Substitute

Thus, using

Evaluation of fitness for best outcome: The measure of fitness is found with

Stopping condition: Repeat from steps (ii) to (iv) till the completion of the maximum iterations. The flowchart of CFF is depicted in

The CFF algorithm is effective in tracking the MPP of hybrid arrangement. The transient response characteristics of wind power system in the presence of CFF in MPPT enhance the system operation. In the same way, the CFF is effective in tracking the MPPT of solar array in an accurate and quick manner accurately. The output power produced from the MPPT controller is compared with the actual power output using a comparator, and the mismatch thus obtained is supplied to the controller. The parameter setting of CFF-PI algorithm is tabulated in

Parameters | Values |
---|---|

Population size, _{i} |
25 |

No of iteration, _{max} |
100 |

Light intensity, L | 1 |

Absorption coefficient, |
0.1 |

step constant, _{0} |
0.01 |

Standard normal random variables, |
random |

Control factor of levy flight, |
1.1 |

Mutation Co-efficient | 0.8 |

Attractiveness, |
0.2 |

The error generated is supplied to the PI controller, which is a control loop feedback strategy utilized for the rectification of error on the basis of both reference power and the measured power. It works in the basis that, when the voltage is a controlled variable, it is evaluated and given back to the controller. The Proportional gain _{p} generates the output that is directly proportional to the present error measure. The system becomes unstable with the higher value of _{p}. To maintain a stable system, the integral action must be introduced. The governing equation of the PI controller is expressed as,_{t} is the output response, _{s} indicates sampling time, _{p} indicates the proportional gain, and _{i} represents the integral gain.

The Luo converter otherwise called as Positive output Luo converter (POLC) is a circuit, which can carry out both step-down and step-up process. The circuit model of POLC is indicated in

Mode I: During the ON state of switch S, _{1} is charged with the input voltage E and diode D is inverse biased. Meanwhile, energy is absorbed by _{2} from the source and _{1}. The capacitor _{2} supplies the load. The equivalent circuit of mode I is depicted in

Mode II: When the switch S is in OFF condition, the current absorbed from the source become zero and the currents of inductors _{1} and _{2} passes through D and the equivalent circuit is depicted in _{L1} passes through D to charge _{1} , whereas current _{L2} passes through the capacitor _{2}, resistor R and the freewheeling diode D to maintain as incessant [

The current _{L2} across the inductor is expressed as,

The duty cycle of Luo converter is expressed as,

The output voltage equation of Luo converter circuit is specified as,

The voltage across _{1} in an average is expressed as,

Peak to peak inductor current across the inductor _{1} is given as,

The value of inductor _{1} using the above expression is given as,

Current across the inductor L_{2} is expressed as,

The rate of inductor L_{1} using the above expression is given as,

The charge across _{1} raises at off time by _{L2} and reduces on time by _{L1}. The variation in charge on _{1} should be nil [

The ripple voltage in capacitor _{1} is specified as,

Value of capacitor _{1} from the above expression is given as,

The ripple voltage in capacitor _{2} is expressed as,

The rate of capacitor _{2} from the expression given above is given as,

The parameters of the Luo converter with their ratings are tabularized in

Parameters | Values |
---|---|

Capacitance, _{1} |
0.1mF |

Inductance, _{1} |
0.3 mH |

Capacitance _{2} |
0.1 mF |

Inductance, _{2} |
0.3 mH |

Diode D | 0.6 |

Duty Cycle | 80% |

Switching frequency |
50 kHz |

Load Resistance R_{L} |
16 |

Simulation of wind/solar hybrid power system fed Luo converter based MPPT optimization connected to a load is done in Simulink environment of MATLAB as given in

A hybrid energy source is fed to the load through the Luo converter. Due to hybrid energy source, the parameters of the system vary that leads to the production of harmonics. Hence, to overcome this limitation, Luo converter is connected between the load and the source. The efficiency of Luo converter in the presence of PI controller, and the optimized gain parameters of PI controller by HAS, CS, FF and CFF method are presented in the simulation results.

The effectiveness of MPPT-CFF-PI technique is analyzed under a variety of states, such as HAS-MPPT with PI, CS-MPPT with PI, FF-MPPT with PI and CFF-MPPT with PI.

Optimization technique | Gain values |
---|---|

HSA | _{p}= 0.01, _{i}= 0.04 |

Cuckoo Search | _{p}= 0.02, _{i}= 0.04 |

Fire Fly | _{p}= 0.03, _{i}= 0.05 |

MPPT-CFF-PI | _{p}= 0.04, _{i}= 0.06 |

In this technique, the existing PI control is combined with HSA method to obtain the optimized gain values. Hence, it forms a MPPT-PI-HSA control technique to lessen the harmonics in the system.

In this technique, the existing PI control is combined with CS method to obtain the optimized gain values. Hence, it forms a MPPT-PI-CS control technique to lessen the harmonics in the system compared to MPPT-PI-HSA.

In this technique, the existing PI control is combined with FF method to obtain the optimized gain values. Hence, it forms a MPPT-PI-FF control technique to lessen the harmonics in the system compared to MPPT-PI-HAS and MPPT-PI-CS.

In this technique, the existing PI control is combined with CFF method to obtain the optimized gain values. Hence, it forms a MPPT-PI-CFF control technique to lessen the harmonics in the system compared to MPPT-PI-HAS, MPPT-PI-CS and MPPT-PI-FF.

Due to the consequences of hybrid system, the converter output is overstated, and hence the precision of the waveform may get affected.

The comparative chart on terms of efficiency is depicted in

S. no | Techniques | Efficiency (%) |
---|---|---|

1 | HAS | 97.8% |

2 | CS | 98.2% |

3 | FF | 98.9% |

4 | Proposed CFF | 99.2% |

S. no | Techniques | Output Voltage(V) | Output Current (A) | Output Power(W) |
---|---|---|---|---|

1 | HAS | 294 | 4.6 | 1350 |

2 | CS | 296 | 4.8 | 1425 |

3 | FF | 298 | 4.9 | 1455 |

4 | Proposed CFF | 300 | 5 | 1500 |

From

This paper introduces a newly developed MPPT algorithm called Cuckoo-Firefly (CFF) for standalone wind-solar hybrid system. Due to the varying characteristics of solar and wind sources, it is important to find out the optimal voltage that guarantee the acquiescent of maximum power from both wind-solar hybrid power generation systems. The CFF algorithm is the hybridization of CS and the FF. The proposed model comprises of a Proportional Integral controller (PI) controlled LUO converter that involve in controlling the power given to the load. The Proposed technique produces 99.2% efficiency. Fore future this work is compared with various other novel optimization techniques to improve the efficiency.

The author with a deep sense of gratitude would thank the supervisor for his guidance and constant support rendered during this research.