1 Introduction

Power generation demand has been increasing rapidly, creating an enormous requirement of conventional sources namely petroleum and coal. The limited supply of these non-renewable sources of energy creates challenges to meet up to the requirement of power in the future. Excessive burning of fossil fuels leads to emissions of green house gases, a cause of the global warming of the earth.

In order to cater the disadvantages and limitations of present sources of energy, providing renewable power sources such as the wind, solar, fuel cell, bio-gas and bio-mass, become a need of the future for the sustainable development of society. Renewable energy sources bring with them various benefits like reducing green-house gas emission providing a sustainable and affordable source of energy. Solar PV generation is of importance as solar energy is available universally, atmosphere friendly, free of cost, low maintenance cost with less operation cost [1, 2]. The PV generation system is necessary for stand-alone as well as grid-connected so as to maximize the PV generation. This paper focuses on various types of solar PV renewable energy sources.

Maximum power point tracking (MPPT) controller is used to find the point at which the solar photo-voltaic system generates maximum power [3–6].

In the past, various types of MPPT control methods have been presented by researchers to improve the efficiency of the PV system, such as conventional Perturb and Observe (P&O), adaptive P&O, modified P&O, variable step size P&O, distributed MPPT, Incremental Conductance (INC), modified INC, Fuzzy Logic Controller (FLC), Particle Swarm Optimization (PSO) based P&O method and Neural Network (NN). These control methods vary in oscillation around the actual maximum power point, convergence speed, complexity, stability, cost and requirement of electronic equipment [7–9].

In this paper, the PV system is discussed on the basis of their MPPT control methods, complexity, kinds of converters used and hardware performance. This review of literature draws comparison between old techniques [1, 10, 11] and latest techniques leading to accumulation of current merits in the methods stating them as sound performance techniques.

The block diagram of PV system with DC–DC Buck or boost converter for stand-alone and grid connected is shown in Fig. 1. The MPPT control method is used to calculate the maximum current and maximum voltage at which the PV module operate under given irradiation and temperature for obtaining maximum power.

Fig. 1
figure 1

Block diagram of PV system with DC–DC converter for stand-alone and grid connected

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This paper has following sections: photovoltaic panel model in Sect. 2, overview of MPPT for PV systems in Sect. 3, comparison of MPPT methods in Sect. 4, conclusion in Sect. 5 and research trends in Sect. 6 are discussed.

2 Model of Photovoltaic Panel

A photo-voltaic module is a device which converts the light into electricity directly using semiconducting materials that exhibit the PV effect. The form of PV cell, is defined as the device whose electrical distinctiveness, such as voltage, current and resistance varies when exposed to light [12].

The equivalent circuit of the solar cell is shown in Fig. 2.

Fig. 2
figure 2

Equivalent circuit of solar cell

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The working of solar cell based on semiconductor type, requires minimum energy to excite electrons from valance band to conduction band which generates controllable current in the circuit. This model consists of series–parallel resistances, light generated source and a diode. The mathematical expressions of the SPV cell are given by Eq. (1), (2) and (3).

$$I = I_{ph} - I_{d} \left[ {exp\left( {\frac{qV}{{k_{b} TA}}} \right) - 1} \right]$$
(1)
$$I_{ph} = S\left[ {I_{scr} + k_{i} \left( {T - T_{r} } \right)} \right]$$
(2)
$$I_{d} = I_{rr} \left[ {\frac{T}{{T_{r} }}} \right]^{3} exp\left( {\frac{{qE_{g} }}{kQA}\left[ {\frac{1}{{T_{r} }} - \frac{1}{T}} \right]} \right)$$
(3)

where V is the output voltage, I is the current, T is the cell temperature (K), q is the electron charge, ki is the coefficient of short-circuiting temperature, S is the solar irradiance (W/m2), A is the ideality factor, kb and k are the Boltzmann’s constant, Id is the PV saturation current, Iph is the load current, Iscr is the short-circuit current at reference condition, Tr is the reference temperature, Irr is the saturation current at Tr, Eg is the band-gap energy of the material, Q is the total charge of electron.

3 Overview of MPPT for PV Systems

MPPT is an automatic control algorithm which provides maximum power at MPP of the PV module. The module can be connected to stand-alone system and to grid through inverters, solar, battery, DC–DC converter and can use similar devices in order to acquire the maximum achievable power as of one or more than one PV systems.

MPPT control method has an important role in solar PV system which determines the maximum power from PV system. The PV system is frequently incorporated into an electric power converter system to facilitate current or voltage conversion, regulation and filtering for driving a different kind of loads together with power grids, motors and batteries etc.

From Fig. 3, I–V characteristic of solar PV system, it is observed that power is maximum at a single point. This point indicates, the value of maximum voltage (Vm) and maximum current (Im)under specified solar irradiations and ambient temperature. The maximum power from the solar cell can be extracted at this operating value of current or voltage.

Fig. 3
figure 3

I–V and P–V characteristic of a solar PV system

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P–V characteristic of Fig. 3 indicates the maximum power point of PV panel [13, 14]. The same phenomenon is used in MPPT as impedance matching with tap varying transformer during alternating current (AC). The DC–DC converters are used in this regard that adjusting duty cycle of the DC–DC converter by MPPT control method in order to match the source impedance of the load.

Performance analysis of MPPT control method for any applications can be determined by evaluating its fundamental design feature, efficiency, response time, static and dynamic error, peak overshoot, sensors needed, MPP tracker and implementation complexity.

A novel MPPT control method with varying parameter for different weather conditions has been proposed [15]. This method tracks quick MPP when key technologies are providing a relationship between the control signal and different weather parameters at MPP. Simulated result shows that the transient-state performance is better as compared to conventional P&O technique.

The MPPT control methods have obtained good results for tracking efficiency using simulation method and also provide a rapid response to different weather parameters. Genetic Algorithm (GA) technique provides the optimized control rules and membership function simultaneously which are required for FLC to enhance the efficiency. MPPT algorithm has been implemented in FPGA (Field Programmable Gate Array) chip by VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) programming [16].

Shaik et al. presented the simulation based MPPT using MATLAB/SIMULINK for PV system with partial shading conditions, DC–DC boost converter and controlled with varies techniques. GA-MPPT method, to solve the problem of fails in partial shading conditions to achieve the maximum power from the modules [17].

Anew algorithm of MPPT to track the MPP under rapid partial shading conditions has been developed. The validity of simulation results has also been performed with MATLAB/SIMULINK [18].

The mathematical model of PV system for MPPT techniques with different weather conditions has been developed. The transient-state performance of the power output can be improved under different conditions. The simulated result shows the better performance of the transient-state than conventional P&O [19].

The simulation of a new hybrid MPPT technique can be used online as well as off-line. In the off-line technique, irradiations are the inputs to the system which estimates maximum power approximation based on analytical solar cell equations. In the online technique, classic P&O method is used for tuning and tracking the MPP [20].

A new technique for MPPT of PV panel which explores the effects of inherent PV resistance characteristics has been proposed. Experimental results are obtained successfully with 100 W of PV panel. This technique is used for stand-alone PV power generation system [21].

A two-stage MPPT for PV system under partial shading conditions has been developed. In first stage, rules segmentation of existing products is used and in the second stage, a new variable step size P&O technique is used to enhance the speed of searching actual MPP [22].

A novel Direct Adaptive Neural Control (DANC)-MPPT technique is used for an effective and simple solution, fast learning and straight forward digital implementation under consideration of DC–DC converter to regulate the power output of PV module. It also compares with the conventional P&O method. DANC-MPPT technique has dynamic performance and great improvement in efficiency as compared to the conventional P&O control method [23].

Literature regarding partially shaded conditions (PSC) indicates that there occur losses in the overall system. Global-MPPT (G-MPPT) control method for PSC with hardware architecture is used to achieve maximum power point has been discussed. This paper also points the difference between Local MPP and Global MPP [24].

A technique for arranging solar array optimally for the linear load without a converter is used and its performance is compared with classical MPPT technique. This technique provides the results with the purely resistive load. The MPPT-based solar array system improvement was acknowledged [25].

The professional design, development and analysis in favour of PV systems in electric power generation systems, is important to inspect and evaluate, performance, operating principles and advantages of MPPT control methods. These parameters are used in the renewable solar industry. The classification of different MPPT control methods is shown in Fig. 4.

Fig. 4
figure 4

Classification of different MPPT control methods

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3.1 Constant Voltage and Constant Current Methods

Constant voltage (CV) control method is surveillance based technique which provides actual MPP that occurs in percentage between 72 and 78 % of open circuit voltage (Voc) used for criterion atmospheric condition. The solar photovoltaic (SPV) module is forever operated at constant voltage while constant current (CC) control method is the similar incidence of CV control method although the MPP arrives in percentage between 78 and 92 % of short circuit current Isc [26]. The CC equation is given as

$$I_{M} = KI_{SC}$$
(4)

where K is the constant value from 0.78 to 0.92.

3.2 Perturb and Observe Methods

P&O control method usually used for finding MPP of SPV modules does not necessitate measurement of either short-circuit current or open-circuit voltage. This method operates by perturbing the module terminal voltage or current at regular intervals then comparing the power output of PV system along with the earlier perturbation cycle. When perturbing of PV module is increased, the operating voltage causes enhancement of the output power. If the control system moves the PV module operating point along the rising track, continue the perturbation otherwise change the perturbation in order to reverse direction. In this method, the process continues until the MPP is achieved [27–29].

The perception in the rear of P&O control method is to amend the operating voltage or current of the PV module until maximum power from PV module is achieved. To explain in the form of example, increase the voltage of array continuously to produce the increased power output of the SPV array. Once maximum output power of SPV array is obtained and if further voltage is increased, the power output of SPV will reduce. In such case reduce the voltage of PV array. As shown in Fig. 5. Pk−1 is the previous power, Pk is the current power. If previous power is greater than current power, the magnitude of voltage decreases to find repeal towards the MPP. This perturbance indefinitely contineous and output power oscillates about an MPP.

Fig. 5
figure 5

Perturb and observe method

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3.3 Current Sweep Control Methods

Current sweep (CS) control method uses a sweep waveform of the SPV module. In this method, I–V characteristic of the SPV module is found and simplified by fixed time intervals. This MPPT technique is used to calculate the MPP reached at fixed time interval. This control method also takes around 50 ms which mean a few power losses are available. CS method is only feasible when the power utilization of tracking system is less as compared to the increase in power to fetch the PV generation system [30].

3.4 DC Link Capacitor Droop Control

This Control technique is specifically designed for SPV system. PV system is connected in parallel with DC link, and then connected to AC system line. The DC link does not require computation power of SPV module [31]. This technique directly depends on the response of DC voltage control loop of the inverter.

3.5 dP/dV or dP/dI Feedback Control

Microcontroller and digital signal processing (DSP) techniques are able to control complex computations. These are appropriate ways of performing MPPT control method to calculate the slope (dP/dI or dP/dV) of the SPV generation system and feedback to the converter of the system by few controls in order to drive it to zero [32–36].

3.6 Incremental Conductance Methods

The incremental conductance (INC) control method provides the advantages over P&O control method which can verify the value of MPP with reducing the oscillations [37]. It can perform MPPT control method under rapidly unreliable irradiation conditions by superior accuracy as compared to P&O control method. INC control method considers the rise of the P–V curve of SPV system is zero at the MPP, positive at the left and negative at the right of the MPP. MPP of the PV module is determined by comparing the instantaneous conductance (I/V) with the incremental conductance (ΔI/ΔV). Once the MPP is obtained, it can be maintained unless a modification in V or I occur. This MPPT technique is the novel MPP in Fig. 6.

Fig. 6
figure 6

Incremental conductance method

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3.7 Other MPPT Techniques

Other MPPT control methods comprise module reconfiguration [38], whereby SPV modules are arranged in various parallel and series combinations such as the resultant MPP meet precise load necessities. This control method is slow, time-consuming and not suitable for real-time tracking MPP implementation.

3.8 Ripple Correlation Control Methods

Ripple Correlation Control (RCC) method takes values of the signal ripple which is without human intervention, readily available during power conversion. The ripples are taken as perturbation and analyzed for maximum power point tracking with optimization [39, 40].

3.9 Beta Methods

In Beta control technique, a constant value of beta (β) is specified in the given formula [39, 40].

$$\beta = lnI_{pv} / V_{pv} {-} q/\left( {\eta \times K \times T } \right) \times V_{pv}$$
(5)

where T is the ambient temperature in Kelvin (K), k is the Boltzmann’s constant, q is the electronic charge and η is the diode quality factor. The above equation shows that the β value depends on upon the ambient temperature, although independent from the solar irradiation.

In this control technique, the SPV operates close to the value of β, if the ratio of fluctuation amplitude and standard voltage is constant.

3.10 Fuzzy Logic Control Methods

Microcontrollers with fuzzy logic control (FLC) [41] are designed for MPPT control method over last decade. FLC algorithm has advantages of functioning by imprecise inputs, handling non-linearity and no need to build mathematical model [42]. FLC usually consists of three stages such as fuzzification, lookup table for rule base and de-fuzzification. In fuzzification process, variables of numerical inputs are transformed into variables of linguistic. Linguistic variable uses fuzzy levels such as NS (Negative Small), NB (Negative Big), PS (Positive Small), PB (Positive Big) and ZE (Zero) [43].

FLC MPPT inputs are generally an error E and a change of error ∆E. The user has the flexibility to select the linguistic variables. As dP/dV vanishes at the MPP, [41] uses the estimate

$$E\left( n \right) = \left( {P\left( n \right) - P\left( {n - 1} \right)} \right)/\left( {V\left( n \right) - V\left( {N - 1} \right)} \right)$$
(6)
$$\Delta {\text{E}}\left( {\text{n}} \right) = {\text{E}}\left( {\text{n}} \right) - {\text{E}}\left( {{\text{n}} - 1} \right)$$
(7)

The above equations are enormously and frequently used. The calculated values of E and ∆E are converted into the linguistic variables. The FLC output adjusts the duty ratio D of the power converter. It can be seen in a table of rule base in Table 1 [44]. MPPT control method using FLC have been publicized to perform healthy under unreliable environment conditions.

Table 1 Fuzzy rule base
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Usefulness of FLC depends on the knowledge of the user or engineer to choose the precise error computationally and coming up by the rule base Table 1. In Patcharaprakiti and Premrudeep-Reechacharn [44], proposed a novel FLC to facilitate continuously follow the rule base table and the membership functions, to provide optimum performance. Using FLC method Experimental results show a fast response to the MPP and reduce fluctuation about MPP [45].

3.11 Neural Network Methods

Neural network (NN) with microcontrollers is also one of the control methods for MPPT technique [46]. NN control method consists three layers such as input layer, hidden layer and output layer. These layers are illustrated in Fig. 7. The number of nodes in each layer can be different and is user dependent. The PV module has state variables such as Voc and Isc, and input variable of atmospheric data such as temperature and irradiance. The output of PV module may be single or multiple signals such as current, voltage and duty cycle which can be used to drive the converter in order to operate close to MPP. MPP depends on the NN control method using the hidden layer and well trained NN. All nodes are connected by weighted link. The nodes are denoted by i and j and having a weight of Wij as shown in Fig. 7. To precisely identify the MPP, Wij’s have to be wisely calculated over the training process. The SPV module is tested over months or years. Patterns are recorded in NN between inputs and outputs. Since different SPV modules have different characteristics, NN has to be specifically trained as per the SPV panel design. As characteristics of SPV panel varies with time, NN control method has to be trained to guarantee precise MPPT.

Fig. 7
figure 7

Neural network

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3.12 System Oscillation

System oscillation (SO) is based on the maximum power transfer theorem which compares the AC component and the average input of the power conversion phase of the system for finding the duty cycle [47].

3.13 Particle Swarm Optimization Based MPPT Methods

To express the application of particle swarm optimization (PSO) used for MPPT control method, a vector of work cycles resolution by particles (Np) is calculated. The control method transmits three duty cycles of Di (i = 1, 2, 3, 4, …, Np) of the power converter. The value of duty cycle is about constant following next iteration and consequently, the maximum power operating point will be maintained [48–51]. The PSO algorithm is efficient for non-standardized lighting situations, however, its convergence depends on the preliminary position in favour of the agents as shown in Fig. 8.

Fig. 8
figure 8

Movement of particle in the optimization process

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3.14 Ant Colony Optimization Based MPPT

Ant colony optimization (ACO) algorithm is an optimization technique which is used for tracking maximum power changes. In this controlling algorithm [52], parameters such that, € is the convergence rate, Q is the locality in favour of the research process, M is the number of ants, k is the archives of the solution which is decisive through the user. While selecting number of ants, there should be cooperation stuck between tracking accuracy and convergence speed. With the high value of Q, number of iterations to converge is less (Fig. 9).

Fig. 9
figure 9

a Ants using the double bridge, b ants chose the shortest path

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3.15 Genetic Algorithm Based MPPT

Genetic algorithm (GA) control method is defined as the metaheuristic adaptive search (MAS) algorithm which initializes the people as specified in Eq. (8) and generated power of SPV system is the objective function. It is used to estimate the members in favour of the population [53].

$$\left[{parent\,\left(1\right)\,parent\,\left(2\right)\,parent\,\left(3\right)\,parent\,\left(4\right)} \right] = \left[ {0.8 \,0.6\, 0.4\, 0.2} \right].2 V_{oc}$$
(8)

Two steps are performed for Crossover as follows

$$Child\,\left( k \right) = r{\cdot}Parent\,\left( {k + \left( {1 - r} \right)\,Parent \left( {k + 1} \right)} \right)$$
(9)
$$Child\,\left( {k + 1} \right) = \left( {1 - r} \right){\cdot}Parent \left( k \right) + r. Parent \left( {k + 1} \right)$$
(10)

If the reset is not accomplished, GA will end by a home search, and if reset is completed in subsequent situations following equations will be evaluated.

$${\text{V}}\left( {{\text{k}} + 1} \right) - {\text{V}}\left( {\text{k}} \right) <\Delta {\text{V}}$$
(11)
$$\left| {\frac{{P_{pv} \left( {k + 1} \right) - p_{pv} \left( k \right)}}{{P_{pv} \left( k \right)}}} \right| >\Delta P$$
(12)

where Ppv is the generated power from the PV module. Figure 10 shows flow chart of GA based MPPT technique with basic steps of GA control method for SPV systems [53].

Fig. 10
figure 10

Flowchart of GA based MPPT

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3.16 Firefly Algorithm Based MPPT

Firefly algorithm (FA) is defined as the novel metaheuristic control method inspired with flashing fireflies [52, 54]. This optimization technique was introduced in the year 2009 at the University of Cambridge (UC) by Yang. FA based MPPT facilitates random solutions which will be measured by fireflies. FA has brilliance to allocate the objective. The significant rule of FA the entire fireflies must be unisex. In this MPPT control method, regardless of sex firefly are concerned with brightness. FA then calculates objective utility function. The light intensity obtained at a meticulous distance preparatory to light sources obeys the inverse square law. It is an attractive option for searching object, to directly proportional with brightness and decreases by distance (Fig. 11).

Fig. 11
figure 11

Flowchart of FA based MPPT for PV system

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3.17 Hybrid Methods

Hybrid control methods provide precise MPP. This control method is used for controlling the signals coupled by control techniques which consist of two parts. Each part is created based on various controlling algorithmic loop [1]. First part is defined using off-line control methods, such as the constant value method which depends on the weathering situation of SPV system. In this part, the control signal offers a sudden response to environment changes. Another part of the control signal is based on online control methods. This part does a steady-state survey which signifies accurate track MPP. The different control signal of first part decreases steady-state error and does not impose a fast response to the change of atmosphere.

The hybrid control method flow chart is given in Fig. 12. The control signal of the first part is produced using off-line control method to locate the approximation loop whereas the second part is produced using online control method in termination control loop.

Fig. 12
figure 12

General algorithm of hybrid method

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Two loops are manageable in the hybrid control method. First Loop of the MPP is calculated based on the Voc by consistent temperature [55]. In second loop, MPP is calculated using P&O control method. Maximum power output gives better transient and steady state responses. The hybrid control methods are used in an off-line method to acquire data of PV system which is operating close to MPP [56, 57]. The online INC control method approach is used to track the accurate MPP, and to control the power converter, when the preliminary operating point is found matching the load impedance and source impedance. It is also directly proportional to the ratio of Voc and Isc associated by the SPV system. In this algorithm, the MPP is tracked when the multiple maxima are confined to that point.

It has been conferred that the FLC is used to achieve improved transient and steady-state responses [58, 59], and adjusts The duty cycle of the DC–DC converter which drives the operating point of the MPP, to increases the transient response. In this control method, vibrations are reduced and power output is increased during the steady-state situation. The loop should be implemented for determination of MPP using FLC method. Due to this development, the current control point increases the transient response and reduces the power loss during steady-state conditions [58]. Additional features of hybrid control method are described in [60–62]. Using a point analog hybrid control method, designed for maximum power tracking, Voc and off-line P&O MPPT control method can be combined. It was determined directly and to use for the preliminary measure of MPP [63]. The results such as Voc of the SPV can be determinant during interruptions and switching.

3.18 Extremum Seeking Control Methods

This method is an efficient extremum seeking control (ESC) method which is supported by rigorous theories such that perturbation of sprawl and singular [64, 65]. In this algorithm, dynamically non-linear system is considered for optimization in real-time simulation. The ESC algorithm is efficiently applied to various systems like vehicle and maximization of anti-lock braking system [64], used for flight for reduction of power maximization [66], for engine compressor for maximization of the pressure, autonomous vehicle goal tracking [67], PID controller to improve the Pulse Width Modulation (PWM) [68]. ESC technique is particularly modified to track the MPP designed for the SPV system [69–72].

ESC method is used to optimize the PWM for the DC–DC converter, to obtain maximum operating point of the SPV system. It is used to control objectives of fast track members, focus on uncertainty and disruptions from the SPV module with an external load.

Block diagram of the ESC algorithm implement in SPV module is shown in Fig. 13. It shows that the Iref is the reference value of the current in ampere (A), Imax is the current at which the SPV system has the maximum power Pmax, ω, φ and a are the frequency, phase shift and amplitude of disturbance signal of the sinusoidal accordingly. Oh is the frequency of high pass filter (HPF), k is the positive adaptive integrator cut-off gain, C(s) is the transfer function of the compensator. Various kinds of MPPT control methods like the ESC algorithm is used to detect the ripple of power in order to follow the MPP. This technique is also called RCC [72].

Fig. 13
figure 13

ESC algorithm for the PV system

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The ESC algorithm has mainly two advantages, first, the optimization through maximizing strength which are plainly solved by dynamic amendment based feedback control law of perturbation. This is assured technique to achieve MPP as control converges. Second advantage does not require setup approach. The ESC algorithm has a disadvantage of the complex organization during implementation for considering the moderately low amplitude signal.

4 Comparison of MPPT Methods

Both P&O and INC control methods can find the true maximum power at MPP of the PV array. These are hill climb control methods [73]. The P&O technique provides the oscillations of PV output power around the MPP in steady-state situations.

In open voltage control method (constant voltage ratio), the current value of the PV module must be zero, to calculate the open circuit voltage (Voc), then with a predetermined ratio, the voltage generated is approximately 76 % typically. When the current is set to zero, in this duration, power may be exhausted [74]. The estimated percentage is 76 % although the ratio between MPP to Voc is not fundamentally precise [74].

Even if low-cost and easy implementation, the interruptions decrease module efficiency and there is no guarantee to measure the concrete maximum power point. The efficiency of some systems can reach beyond 95 % [40] and additional characteristics [75–93] are given in following points.

  1. 1.

    The parameters voltage and current are sensed by P&O, INC, RCC, current sweep, constant current, constant voltage, dP/dI or dP/dV feedback control, system oscillation, beta method, array reconfiguration, state-based MPPT and slide control MPPT methods.

  2. 2.

    The voltage is sensed by FLC, NN, Voc, DC link capacitor droop control methods.

  3. 3.

    The parameters irradiations and temperature are sensed by linear current control methods.

  4. 4.

    The current is sensed by Isc MPPT technique.

  5. 5.

    P&O, Voc, RCC, DC link capacitor droop control, constant current, constant voltage provide low implementation complexity.

  6. 6.

    INC, Isc, dP/dV or dP/dI feedback control, linear current control, system oscillation, slide control beta method provide medium implementation complexity.

  7. 7.

    FLC, NN, current sweep, system oscillation, array reconfiguration, state-based MPPT techniques have high implementation complexity.

  8. 8.

    Convergence speed of P&O and INC are varied, current sweep, array reconfiguration are slow, Voc, Isc, DC link capacitor droop control are medium and FLC, NN, RCC, current constant, voltage constant, dP/dV or dP/dI feedback control, system oscillation, state-based MPPT and slide control have fast convergence.

  9. 9.

    P&O, DC link capacitor droop control, system oscillation, beta method and state-based MPPT techniques can be used for both analog and digital signals but INC, FLC, NN, Current Sweep, dP/dV or dP/dI feedback control, linear current control, array reconfiguration and slide control are used for only digital and RCC, current constant and voltage constant are used for analog only.

  10. 10.

    Voc, Isc, FLC, NN, current sweep, system oscillation, LCC, AR and state-based MPPT are MPPT methods provide periodic tuning.

  11. 11.

    True MPPT techniques are P&O, INC, FLC, NN, RCC, Current Sweep, dP/dV or dP/dI feedback control, system oscillation, beta method, state-based MPPT and slide control.

  12. 12.

    P&O, INC, RCC, DC link capacitor droop control, CC, CV, dP/dI or dP/dV feedback control, beta method and slide control are MPPT techniques which are not dependents on PV array.

5 Conclusions

Different systems to obtain MPP in solar PV have been discussed in this paper. The majority of the times various constraints are linked with MPPT in a system like tracking time, cost, easiness of implementation and precision etc. Among these only one or two of them are more vital for a different application of solar Photo-Voltaic. The MPPT techniques for a precise application are decided by these essential constraints. For instance, P&O and INC are commonly used where low cost is a crucial factor. This review of MPPT system helps to choose particular MPPT technique for a specific application and also for further research in the area of MPPT like particle swarm optimization (PSO), modified PSO, genetic algorithm (GA) and modified GA, use of these methods may improve the output.

6 Research Trends

Based on literature reviews the following research gaps are identified which is useful for future research.

  1. 1.

    The MPPT may include the use of a different DC/DC converter, and also some different MPPT algorithms such as CMPPT. Multi-input energy systems for the hybrid wind/solar energy systems need to be developed.

  2. 2.

    Modification of the conservative P&O technique can be done for the maximum power operating point to be reached much earlier as compared to that in the conventional P&O method.

  3. 3.

    Environmental changes in parameters like ambient temperature or solar irradiation or even both can be simulated by MATLAB/SIMULINK models to carry out MPPT in MATLAB using variable inputs instead of constant values of the parameters.

  4. 4.

    Optimization in DC/DC converter design by MPPT algorithms would achieve improved efficiencies and power tracking capabilities.