A FUZZY – ELEPHANT HERDING OPTIMIZATION TECHNIQUE FOR MAXIMUM POWER POINT TRACKING IN THE HYBRID WIND-SOLAR SYSTEM
3.1 OBJECTIVE:
In recent years, the attention towards the utilization of solar and wind power systems are becoming more popular because of its non-polluted and non-depleting nature. However, these power sources rely on environmental conditions for power generation. So, extracting maximum power available from the wind and solar systems is essential. Many of the existing Maximum Power Point Tracking (MPPT) techniques’ performances are not convenient in the hybrid operation of wind and solar power systems. So, to provide an optimum MPPT operation, here proposes a Fuzzy Logic Controller (FLC) based Elephant Herding Optimization (EHO) technique for MPPT in the hybrid wind-solar system. The proposed Fuzzy-EHO (FEHO) method tends to track the maximum power under the partial shading condition of the Photovoltaic (PV) system and low wind speed situations. The results are compared with the conventional MPPT methods, and it shows that the proposed optimization method efficiently tracks the maximum power point of the wind-solar power systems even with the variations in the climatic conditions. MATLAB tool is used in the design and operation of the proposed method. Furthermore, the performance of the proposed system is tested with experimental setup and provides better efficiency.
3.2 INTRODUCTION:
Other than hydropower, wind and photovoltaic energy hold the most potential to meet our energy demands. Alone, wind energy is capable of supplying large amounts of power but its presence is highly unpredictable as it can be here one moment and gone in another. Similarly, solar energy is present throughout the day but the solar irradiation levels vary due to sun intensity and unpredictable shadows cast by clouds, birds, trees, etc.
Energy is an important input ingredient for the economic development of a country, and it is derived from several sources. These sources can broadly be classified as non-renewable such as oil, natural gas, and coal, or renewable, such as biomass, wind, solar and hydel sources. Renewable energy sources can be traced to belong to the oldest form of energy utilization ever known to mankind. Yet their importance was not recognised till recently and was considered to be non-conventional. These sources are renewable in the sense of being wholly regenerated in the annual solar cycle. As well known, the wind is caused by the earth’s rotation and the uneven heating of its atmosphere by the solar radiation. Extraction of the kinetic energy of the wind to power simple machines dates back to ancient times in human history. Wind energy has been put to use to run windmill, pump water, or even to drive an electric generator to produce electricity. The size of the wind electric generator has a significant impact on the economics of the power produced. Small wind electric generators (less than 50 kW) have typically a large fraction of total price tied up in costs required independent of the size, such as operating costs, controls, and installation costs, These costs generally’ make small wind electric conversion systems unattractive on a commercial basis. The common inherent drawback of wind and photovoltaic systems are the intermittent natures that make them unreliable. However, by combining these two intermittent sources and by incorporating maximum power point tracking (MPPT) algorithms, the system’s power transfer efficiency and reliability can be improved. Combining multiple renewable resources via a common dc bus of a power converter has been prevalent because of convenience in integrated monitoring and control and consistency in the structure of controllers as compared with a common ac type. A simple control method tracks the maximum power from the wind/solar energy source to achieve much higher generating capacity factors.
Solar wind hybrid renewable power generation system is the most affordable and reliable source combination among others. Solar radiation and temperature at the time and on the day is the main parameter for solar power generation. On the other hand, there is a limitation of wind power generation. It requires a minimum wind speed say above cut-in speed should be present for a minimum power generation. There is no limitation for solar power system but radiation is available during day time only. Both the sources have their climatic disturbances for giving continuous power generation. Naturally, these two sources are complementary to each other and available in all parts of the world. It is also to be noted that wind power and solar power complement each other. During monsoon months solar power generation is reduced to a large extent due to cloudy skies, during the same period, the wind speeds are much higher than the rest of the year. During monsoon, wind turbines generate extra power to compensate for the loss of solar power. To improve the reliability of the hybrid renewable power system, the control strategy can be applied for a power converter unit. 145 PV / Wind alone is an unreliable source of energy, in a sense that it can supply energy only when the solar radiation and wind velocity is available. It can be utilized by integrating wind and solar power system in hybrid mode. With-out any storage system, such situations may lead to the total shutdown of the load. Even if the sun is available, the intensity of the solar insolation received by the PV array varies continuously, and such a scenario is quite common of the solar PV power system. The change in the shading pattern due to the cloud also changes the electrical characteristics of the array. Under such conditions, to maintain the continuity of the energy and to maintain the load, a modified configuration including a source other than the PV, is essential.
The wind power system is more comfortable and the advantages of hybrid PV systems are
Flexibility
It provides the terms of effective utilization of renewable sources. x Provide stable operation
A hybrid system can make use of the complementary nature of the other available sources, which helps in reducing the power oscillations or power outs.
PV system energy output is maximum on sunny days, while the energy output of the wind generator is higher during strong wind days. Hence, combining a PV source with a wind generator can reduce the zero power intervals and give continuous supply to the load.
The hybrid wind-solar power generation has widespread and fast growth in the utilization of energy. To track and to optimize the use of maximum available power from PV array MPPT algorithms has been employed. Among those MPPT techniques, the “Perturb and Observe” (P&O) scheme and Incremental Conductance (INC) scheme are most common because of its ease of implementation. The disadvantage of these approaches is they can only track a single maximum, which is absent when solar panels are partially shaded. If the P–V (or P–I) characteristic is not unimodal, these approaches could only effectively touch a local maximum. Swarm-based algorithms are extensively utilized in many research algorithms. The example of met heuristic methods is Swarm Intelligence (SI) techniques which are most widely used in various applications. The most widely used SI algorithms comprise Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO).
In recent times, many soft computing techniques are involved to track the MPP under normal and partial shading conditions, and the new methods are compared. During the normal conditions, to increase the power and efficacy of the solar system, Hopfield Neural Network-based Fuzzy Logic system is employed to extract the MPPT. For the same principle, the traditional methods such as P & O and Incremental Conductance algorithms were operated with the Artificial Neural Network (ANN) and FLC to develop a hybrid technique, which enhanced the solar system performance, particularly in steady-state conditions. In traditional methods, the step size was fixed. So, during partial shading conditions, these methods were frequently confined in the point of the local maximum. In the partial shading conditions, which has got more attraction recently, all the computational algorithms like ANN and FLC can track the Global Maximum Power Point (GMPP). However, they needed a high level of training during the difficult natural conditions, bio-inspired algorithms based MPPT techniques are considered as a better option in this unique conditions, where the swarm intelligence and evolutionary algorithms are taken as effective and main classes. Moreover, consolidating the bio-inspired algorithms with the traditional ones leads to specific outcomes, particularly to the extent that decreasing oscillations and convergence speed. Among the other bio-inspired algorithms, the Flower Pollination Algorithm (FPA)is appropriate for MPPT applications. To meet the practical implementation of the systems, the PSO algorithm is mostly applied in recent studies. Similarly, various bio-inspired algorithms such as Modified FPA, Modified Firefly algorithm, Modified Genetic and Firefly algorithm, Glowworm Swarm Optimization, multi-core PSO, Bat algorithm, Improved Shuffled Frog Leaping algorithm, Enhanced Simulated Annealing, Monkey King Evolution Algorithm, Artificial Fish Swarm Algorithm are used for tracking MPPT in the solar systems under partial shading conditions.
The hybrid wind-solar system is highly regarded because it is the major available renewable energy source works without any noise and little maintenance and it has several issues because of its efficiency and nonlinearity that change the amount of generated power due to irradiance and temperature. To overwhelm this drawback of low tracking efficiency and considerable convergence time, the hybrid technique is employed. Elephant Herding Optimization is a new optimization algorithm which has better performance in the optimization function of precise and fast convergence time. Moreover, the Fuzzy logic controller system is utilized for better optimization and minimization of error from the data. So, this proposed technique has been used to improve the accuracy of the MPPT algorithm effectively. The following objectives are regarded in this proposed technique
To optimize the output power of the wind-solar system.
To maintain the power maximum in case of unavailability of wind power to achieve reactive power, compensation of harmonic current and the DC Voltage regulation.
To improve the power quality of the hybrid wind-solar system.
3.3 PROBLEM STATEMENT:
Solar Panels help in maximum utilization of solar energy during the day. However, shading can have a huge impact on the performance of solar photovoltaic panels. A common misconception is that partial shading does not affect the output of solar panels. The solar photovoltaic panels consist of several cells which are wired together into a series circuit. Because of this, the performance of the solar panel is significantly reduced even if the smallest section of the panel is in shade. Another possible issue from partial shading is overheating. Because of partial shading, one part of the solar panel generates a lower amount of energy as compared to the other non-shaded part. As the amount of power generated in shaded & non-shaded parts differs, it leads to overheating which in turn reduces the total power output of the solar panel. Setting up the solar panel where there is no shade is the best way to avoid the loss of output. But this is not always possible. So to overcome the shading problem a novel effective method needs to gets implemented.
3.4 PROPOSED METHODOLOGY:
This research proposes an MPPT for a hybrid wind-solar power system using Fuzzy Logic Control based EHO algorithm. It is recognized that Elephant Herding Optimization algorithm implements numerous benefits which comprise of fast convergence and efficiency through lesser tuning parameters. Moreover, the Fuzzy Logic Controller provides fast response and high performance due to the atmospheric variation in climate.
Figure 3.1 Representation of MPPT Algorithm
From Figure 3.1 MPPT algorithms are used to acquire maximum regulation of the hybrid wind-solar power system. The peak acquired in a characteristic current-voltage (IV) curve is called maximum PowerPoint, and it is most efficient when the Photovoltaic array utilizes around at every time. As the intensity of the sunlight radiation varies, the load of the system also changes to achieve the maximum power which becomes more complicated. To achieve MPP operation under varying sunlight and loading conditions, there is a requirement of an intelligent controller.
3.4.1. Elephant Herding Optimization
EHO algorithm was originally proposed by Wang and is essentially a swarm intelligence algorithm. It is a metaheuristic search method which arises from the modelling of herding behaviour of elephants in nature. This particular behaviour can be summarized as follows. The population of elephants contains several subgroups, known as clans, which comprise several elephants. Each clan moves under the leadership of a matriarch, while several male elephants that reached adulthood leave the clan they belong to and live in solitude. In terms of EHO, these behaviours can be modelled with two operators: clan update (which updates the elephants and matriarch current positions in each clan) and a separation (which enhances the population diversity at the later search phase).
EHO has superiority when equated to existing algorithms that are applied to the pattern matching issues. Separating the operator and Clan updating operator are the classification of the behaviours in the EHO technique. An elephant group consists of numerous numbers of clans under the leadership of matriarch, often the oldest cow (Sukumar 1993). EHO algorithm is defined as follows.
Each member ‘j’ of the clan ‘i’ moves according to matriarch where matriarch is the elephant ci with the best fitness value in a generation:
〖_(new,c_(i,j) )〗=x_(c_(i,j) )+α(x_(best,ci)-x_(ci,j) )×r (3.1)
In clan i〖 x〗_(new,c_(i,) ) depicts an elephant j in a new position and xci,jis its old position, The best solution of clan ci is the xbest,ci, where α ∈ [0, 1] is the parameter of the algorithm that determines matriarch position of the best elephant in clan xbest,ci which is obtained in the following equation:
x_(new,c_i ) = β×x_(center,ci) (3.2)
Where β ∈ [0, 1] is the second parameter of the algorithm which controls the influence of the x_(center,〖 c〗_i )defined as:
x_(center,c_(i,d) ) = 1/n_ci ×∑_(l=1)^(n_ci)▒x_(ci,l,d) (3.3)
Where 1 ≤ d ≤ D is the dimension of the dth term and represents the dimension total space and nci determines total elephants number in a clan i.
In each clan i elephants the objective values have the worst function which is moved to the new positions according to the following equation:
x_(worst,ci)=x_min+(x_max-x_min+1) ×rand (3.4)
Where 〖_〗mind x_maxare smaller and higher bound of the search space, respectively.
3.4.2. MPPT Algorithm of Elephant Herding Optimization
A Fuzzy logic controller is used to identify the peak solution of the hybrid wind-PV System, and it is shown in figure.4.2. The cuk-Sepic converter is interfaced with the PWM inverter and the load where the inverter converts DC. The EHO Algorithm is implemented to MPPT Controller. The stepping up operation is done by adjusting the duty cycle of the converter by the MPPT controller, and the control signal is produced by EHO that is transferred to the load.
Figure 3.2 Block Diagram of EHO based MPPT Algorithm
3.4.3. Fuzzy Logic Control
The fuzzy logic theory is utilized to solve this different issue and enables to track the maximum power point quickly in the employed fuzzy based algorithm. If the operating point of the system moves closer to the optimum point, then the step size is changed to small, and the step size is big if the operating point moves far away from the distant point. The major structure of the FLC Technique is classified as follows, i.e., fuzzification, IF-THEN Rule base, Inference engine, and defuzzification. Figure 3.3 shows that analogue input values represent the mathematical system that is analyzed using the logical variables between 0 and 1.
Figure3.3 Representation of Fuzzy Logic Controller
3.4.3.1 Usage of fuzzy logic:
Fuzzy logic offers several unique features that make it a particularly good choice for many control problems.
· It is inherently robust since it does not require precise, noise-free inputs and can be programmed to fail safely if a feedback sensor quits or is destroyed. The output control is a smooth control function despite a wide range of input variations.
Since the Fuzzy logic controller processes user-defined rules governing the target control system, it can be modified and tweaked easily to improve or drastically alter system performance. New sensors can easily be incorporated into the system simply by generating appropriate governing rules.
Fuzzy logic is not limited to a few feedback inputs and one or two control outputs, nor is it necessary to measure or compute rate-of-change parameters for it to be implemented. Any sensor data that provides some indication of a system’s actions and reactions is sufficient. This allows the sensors to be inexpensive and imprecise thus keeping the overall system cost and complexity low.
· Because of the rule-based operation, any reasonable number of inputs can be processed (1-8 or more) and numerous outputs (1-4 or more) generated, although defining the rule base quickly becomes complex if too many inputs and outputs are chosen for a single implementation since rules defining their interrelations must also be defined. It would be better to break the control system into smaller chunks and use several smaller FL controllers distributed on the system, each with more limited responsibilities.
· FL can control nonlinear systems that would be difficult or impossible to model mathematically. This opens doors for control systems that would normally be deemed unfeasible for automation.
3.4.4 Partial Shading of PV Array
In the present scenario, Photovoltaic System has a wide range of growth because of the solar energy that has the advantage of low maintenance and free of pollution. In partial shading condition, due to the different levels of solar irradiation and temperature which is received by the PV modules limits the current and voltage so that the PV system power generation also decreases. To maximize the power output, the current compensation technique is used that is dependent on the MPPT algorithm. The specifications of the PV module are as shown in Table 4. 1.
Table 3.1. PV Module Specifications
Parameter Rating
No of parallel strings 64
Series connected module per string 5
Maximum power 315.072W
Cells per module 96
Open circuit voltage 64.6V
Short circuit current 6.14
Voltage at MPP 54.7V
Current at MPP 5.76A
The temperature coefficient of Voc -0.27269 (%/deg.C)
The temperature coefficient of Isc 0.61694(%/deg.C)
Light-generated current IL 6.1461 A
Diode saturation current Io 6.5043e-12 A
Diode ideality factor 0.9507
Shunt Resistance (Rsh) 430.0559 Ohms
Series Resistance(Rs) 0.43042 Ohms
Table 3.1. PV Module Specifications
3.4.4.1 PV Array Properties under the Impact of Partial Shade
The significant improvement of the study is to cover the diverse behaviour of PV power characteristics under both ordinary and mismatching weather conditions without diving in the physical and internal analysis of semiconductors characteristics of solar cells. The hot-spot is a physical phenomenon that appears when one of the panel string is shaded; it can then act as an electric load. The shaded photovoltaic cells absorb an important quantity of electrical energy generated by other photovoltaic cells receiving high irradiation and converting it to heat. Further, the addition of a bypass diode between specific numbers of cells in the series circuit is given as a good solution. In the case of the shaded cells, the anti-parallel connection of bypass diodes with each chain of cells lets the current flows through the bypass diode in a single direction.
Figure 3.4 shows the flow chart to determine the MPP position, the voltage and current measured by using Fuzzy Logic Controller based EHO technique. Due to the ease of implementation, stability, and robustness, the artificial intelligence-based technique is followed by using Fuzzy Logic Controller – EHO which is a novel algorithm that is considered in the wind-PV field. The power at the maximum is achieved from the wind-PV System with the FEHO controller design technique.
Figure 3.4 Flowchart of Fuzzy Logic Controller based EHO Technique
The maximum output voltage (Vdc) is required as a reference signal for the voltage based MPPT controller. At the same time, the peak output energy of the PV Array system is calculated by the Global maximum power (Pdc). The differences between the Vdc and the V*dc, denoted with error (e) simultaneously with parameters of tuning are used as inputs for the fuzzy logic controller to produce the control signal, and the output of FLC is interfaced with the EHO Technique. This control signal is used to adjust the duty ratio of CukSepic converter.
3.5 Results and Discussion
Figure 3.5 Simulink design of proposed method
The MATLAB Simulink model of the proposed system is designed with PV panel array, wind power system, a Fuzzy logic controller with Incremental Conductance method and Elephant Herding Optimization that is controlled by the power converter and given to the load shown in figure 3.5.
The Simulink model of the proposed EHO optimization technique is shown in Figure 3.6. Photovoltaic or PV modules and wind power systems are interconnected with the CukSepic Converter to the load or distributed grid side. For improvement in the Efficiency of the System, PV array needs MPPT. This chapter proposed a FEHO MPPT modelling and control of the hybrid wind-PV system. Generally, the MPPT is achieved by interjecting a CukSepic converter between the PV module and the load, thus, controlling the converter duty cycle (D). The results show an optimized output which will be explained in the next chapter. This model is simulated in various partial shading conditions in the MATLAB/Simulink.
Figure.3.6 Simulation of Fuzzy with EHO method
General steps for EHO:
The pseudo-code of the Elephant Herding Optimization Algorithm
Step 1: Initialization.
Initialize generation counter t=1; and
Population = 1; the maximum generation MaxGen
Step 2: While t<MaxGen do
According to their fitness sort all the elephants.
Develop updating operator of the clan as depicted in Figure 1.
Develop separating operator of the clan as depicted in Figure 1
Evaluate the population by the newly updated position
t=t+1.
Step 3: end while
3.5.1 Simulation Results
Under various irradiance conditions, the simulation results are predicted. The PV array response is measured by the rapid change in isolation which is given to the PV module as intensity pulse. Using the Beta method, the maximum power point is tracked effectively under the rapid change of isolation. Hence the results are simulated as given below.
Figure 3.7 Current-Voltage characteristics
Figure 3.7 and 3.8 illustrates the I-V and P-V characteristics of the PV array under partial shading conditions. The P-V and I-V characteristics of a Solar cell, PV array, or module show detailed information about the efficiency and ability of solar energy conversion. The curves render the details needed for the configuration of a solar energy system, such that the system operates as close to the optimal peak as possible. The difficulty deliberated by MPPT procedures is to habitually discover the voltage VMPP or current IMPP at which a PV array must function to get the maximum power output PMPP below a specified temperature as well as irradiance. It is probable to have multiple local maxima, but generally, there is only one MPP. Most of the procedures react to fluctuations in both irradiance as well as temperature, but some are precisely more beneficial if the temperature is almost constant.
Figure. 3.8 Power – Voltage characteristics
Figure 3.9 depicts the input power tracking response of the output power obtained from the proposed Fuzzy-EHO based Cuk Sepic Converter under partial shading conditions. This shows the maximum power tracked by the proposed MPPT based Cuk Sepic Converter.
Figure 3. 9 Power Tracking Response under partial shading condition
Figure 3.10 Inverter Vab voltage waveform before the filtering process
Figure 3.10 shows the inverter output voltage between the phases a and b, and it has the magnitude of -280 to 280. By using the LC filter, this is changed to a sinusoidal output, and it is shown in figure 3.11.
Figure 3.11 Inverter lab voltage waveform after the filtering process
Figure 3.12 Simulated result of Load Voltage of the PV-Wind System
Figure 3.12 shows the load side output voltage of the proposed system. Even with the dynamic conditions, the sinusoidal output is provided by the proposed control strategy.
Figure 3.13 shows the output voltage at the DC link of the inverter terminal. It shows that even with the variation in the power sources the DC link voltage is maintained at a constant level of 300V. This will improve the reliability of the hybrid wind-solar system.
Figure 3.13 Simulated results of DC-link voltage
Figure 3.14 Simulated and the experimental I-V curves without shading and simulated with shading.
Figure 3.14 shows the comparison between the load side output voltage and current of the PV-system with and without shading. The shaded solar cell is like a clog in a pipe. The current flowing through an entire string module can be reduced.
Figure 3.15 Simulated and the experimental P-V curves without shading and simulated with shading.
Figure 3.15 shows the comparison between the load side output voltage and power of the PV-system with and without shading. The step size of the PV System modulation index is changed so that the fast MPP (Maximum Power Point) tracking can be performed, but the oscillation power around MPP will be large. In contrary, it changed to long tracking time and small oscillation when step size is small.
Figure 3.16 THD analysis of load voltage
Figure 3.16 shows the Total Harmonic Distortion (THD) analysis of the load voltage waveform. It showed that even with the variation in the load and source side, the THD of the proposed hybrid system is very low.
Tracking Method Power Tracking (Watt) Voltage(V) Tracking Period (sec) Tracking Efficiency%
FLC Standard 199.80 38.90 0.33 99.89
Proposed FLC-EHO 199.96 39.23 0.19 99.95
Table 3.2 Performance Comparison of MPPT Methods
From Table 3.2, it is noticed that the Proposed Fuzzy Logic Controller based EHO has better system performance in maximum power point conditions. The method of MPPT based FLC standard slows down in tracking the maximum available PowerPoint when the radiation level is changing very fast whereas the second method FEHO rectifies this problem. The MPPT efficiency has been increased from 99.89% to 99.95%, and the accuracy is further increased by 0.33 seconds to 0.19 seconds respectively, and this proves that the FEHO is applicable to achieve the optimized value.
3.5.2 Experimental setup
The performance of the proposed Fuzzy-EHO MPPT method is tested in a real-time environment by using an experimental setup in the laboratory as shown in figure 3.17.
Figure 3.17 Experimental setup of the proposed system
The proposed Fuzzy-EHO algorithm is written in MATLAB M-Script and converted to C-language by using a compiler and implemented on the microcontroller. PIC 16-F877A, 8-bit micro-controller is used to implement the proposed MPPT control algorithm. The current and voltage of the hybrid wind-solar system are read by the two-built in Analog to Digital converter channels and these values are sent to the proposed control algorithm. The duty ration of the power converter is controlled by pulses generated in the built-in PWM modules with the frequency of 50KHz. A resistive load is utilized for consuming the power produced by the hybrid wind-solar system.
Controlling the level of irradiation and wind speed is a difficult task in the real-time scenario. However, the proposed algorithm is tested with the experimental setup with the help of the commercial micro-controller. The performance of the proposed system has been analyzed with different test conditions. For a summer day, the PV irradiation is around 790 W/m2 and the PV panel temperature is about 450C. The anticipated maximum power of the PV array is 25W, but only 24 W is achieved. This is due to the losses in the connection cables. The specification of this experimental setup is given in Table 3.3.
Table 3.3. Specifications of the experimental setup
PV array Power Rating 25 Watts
Wing Generator Rating 5 Watts
System Voltage 48 V
Rating of the inverter 25
AC voltage output 230 V
Frequency 50 HZ
Even with the variation in the solar irradiation and wind power, high power output is achieved by the proposed system with the sinusoidal output at the load side. This ensures the efficacy of the presented Fuzzy-EHO system. The below-given table 3.4. shows the performance comparison of the proposed technique with and without experimental data. The results are improved in the experimental data case as compared to results of without considering the experimental data for the proposed system.
Table 3.4. Performance Comparison of Proposed FLC-EHO with experimental data
Tracking Method Power Tracking (Watt) Voltage(V) Tracking Period (sec) Tracking Efficiency%
Proposed FLC-EHO 24.96 17.23 0.19 99.95
Proposed FLC-EHO (With experimental data) 24.98 17.85 0.16 99.98
3.6 SUMMARY:
Here In this chapter proposed a Fuzzy Logic Controller based Elephant Herding Optimization technique for MPPT in the hybrid wind-solar system. The proposed Fuzzy-EHO method tracked the maximum power under the partial shading condition of the Photovoltaic system and when the availability of wind speed is low. The MPPT implemented using the proposed FLC based EHO has a much better performance as estimated to the other type of controllers. The overall system efficiency, power potential, and performance of the system and reliability have been improved by using the MPPT controller developed using the proposed FEHO. The results were compared with the conventional MPPT methods, and it showed that the proposed optimization method efficiently tracks the maximum power point of the wind-solar power systems even with the variations in the climatic conditions. Furthermore, the performance of the proposed system was evaluated experimentally and high efficacy in maximum power tracking. In future work, the proposed FEHO method performance will be tested in the optimal power flow operation in the grid-connected hybrid wind-solar system with the presence of battery systems.