Flexible hybrid system power quality improvement using advanced control architecture

Abstract

To achieve superior harmonic reduction using hybrid energy generation high power devices interfaces, an advanced fuzzy current and voltage controlled technique is proposed in this paper. It proves that the proposed fuzzy controlled way could decrease the numbers of active and passive filters in the micro grid hybrid energy system. Furthermore, a closed control loop for wind system connected rectifier is not essential as the wind voltage and speed variation can be automatically recognized by the advanced control loop. Therefore, the advanced control architecture decreases the system complexity without affecting the system performance. Control and design of both the dc/dc converter and three-phase inverter are presented. Moreover, the proposed scheme offers outstanding performance for overcoming the voltage and current distortions. The simulation model of the proposed system is developed in MATLAB Simulink environment and tested for the proposed control technique performance. Further, experimental results revealed the power and viability of the proposed method.

Keywords

Solar energy system wind energy system fuzzy system inverter boost converter

Nomenclature

P _pv-gd

Solar energy system output power

P _pv-max

Maximum power output from the solar energy system

R _s

Series resistance of solar array

R _p

Parallel resistance of solar array

P _wind-gd

Wind energy system output power

R _wind-max

Maximum power output from the wind energy system

BAT _upper

Upper limit of storage state of power charge

BAT _lower

Lower limit of storage state of power charge

BAT _int

Initial storage

C _r

Nominal capacity

V _stor

Storage voltage

P _bc

Storage charging power

P _bd

Storage discharging power

P _grids

Grid supply power

P _grids-lim

Grid supply power limit

P _gridi

Grid injection power

P _gridi-lim

Grid injection power limit

Error

eoe

Change in error

ISE

Integral square error

ITAE

Integral time absolute error

ITSE

Integral time square error

Gain factor

Fuzzy logic controller output

Pulse period

k _p

Proportional gain

k _i

Integral gain

THD

Total harmonic distortion

1 Introduction

Nowadays, Renewable energy generation systems are substituting fossil fuel based systems because of their continuous depletion, high cost, and high carbon emission. Solar energy sources provide good quality of power but due to unavailability at night, these sources cannot be used at night. While on the other hand, the wind energy sources have opposite characteristics, i.e. it gives high power at night. Therefore, a hybrid energy system should be formed for confirming uninterruptable power and for controlling the power between this system and the utility grid, high power level converters are used. An individual converter corresponding to each source can be employed because of increased complexity and control action. Hence, an idea of multi-input single output converter can be used for integrating the various renewable power systems to the micro grid as well as a utility grid. This converter can be controlled by different controllers such as P, PI, PID, fuzzy logic controller, etc. Moreover, two controllers give the best result, one for controlling dc voltage and other for controlling grid power to ensure the good quality power to the grid. The performance of these types of controllers is changed by variation in system factors. There are many types of fuzzy logic controllers are available for example variable structure sliding mode fuzzy logic control for controlling the hybrid energy system. The active filters can be controlled by a fuzzy logic controller to minimize the total harmonic distortion [1]. The optimum power extraction and the dispatch can be controlled by a fuzzy logic controller in a hybrid grid-tied system [2]. To control the power generated by grid-connected hybrid energy system, dc/dc converters, and dc/ac converter are used and for controlling this system PI controller is changed by the intelligent controller [3]. The three-phase inverter can also be controlled by PID with fuzzy logic controller [4]. The firing angle of the inverter circuit can also be controlled by the controller according to the need [5]. Among the many controller’s configurations, the fuzzy-PI controller gives the best result for grid-tied hybrid energy system and this configuration is also used for controlling the battery charging and discharging, Hence, this gives multiple advantages [6]. The gain of PI controller can be modified by the use of a fuzzy logic controller, hence this gives the reduced harmonic distortion and increases the system power quality [7]. The conventional controller can fail to attain smooth power flow and nonlinear load reparation [8]. Whereas the rule table of the fuzzy controller can come close to a single nonlinearity [9]. The intelligent control methods has been used to control the stand-alone system. Intelligent control methods i.e. Fuzzy-PI control is more efficient than a classical method such as PI control because they do not need an exact system model and increase the dynamic performance of the complete system [10, 11] and [12]. In the present literature, grid-tied hybrid energy systems are not as common as a stand-alone system in practical application. Moreover, dc/dc controllers can also be controlled by fuzzy logic so the voltage distortion is minimized. Hence it gives output voltage regulation with an excellent response. Whereas the pulse width modulation inverter with fuzzy logic controller reduces the cost of the system by reducing the harmonic distortion hence the need for active filters has been decreased [13]. If the solar system is used in a hybrid generation system then the fuzzy logic controller can also be employed to maintain the maximum power point [14]. Using the intelligent fuzzy logic controller for microgrid application is also needed nowadays [15]. The efficiency of the solar grid-connected system can be improved by using soft computing techniques [16]. The stability of the system with a wind energy system can also be improved by the controller [17]. The main work of any fuzzy logic controller to design the algorithm for its control, hence, there is the need for a proper arrangement for making the rules for the controller [18].

When the solar energy system and wind energy system are connected to the DC microgrid, the performance of the system may be degraded due to the dynamics of loads. An adaptive based controller is proposed in [19, 20] to lessen the effect of uncertain loads and the constant power loads power are engaged in a Takagi–Sugeno fuzzy-based model predictive control method to changing the introducing current of the storage system. Further, the incremental negative impedance of the constant power loads may threaten the stability of the system. In [21], an adaptive backstepping controller has proposed for a constant power loads connected microgrid. In [22], model predictive method based system is proposed to avoid the system induced delay and it may be widely used on the large power systems. Whereas, in [23], investigation of the dynamic stabilization problem of the microgrid using a non-fragile fuzzy control combination of power buffer. The fuzzy controller is used to stabilize the power between the energy storage system and the DC link. An approach for a solar energy system based controller design is proposed in [24] and the reference voltage is known using the maximum power point tracking system. The injecting power balance of microgrid is essential [25] with power loads. Different filters are proposed [26] to lessen the result of the noisy network on the network’s information. The fuzzy system is also used in solar energy based system to enhance the efficiency of the system.

When fuzzy-PI controllers are employed to control the voltage and current distortion in the hybrid energy system, then there is a needfor proper’s rules for this. Hence, rules formation affects system performance. The gain of PI controllers are varied then total harmonic distortion also varied. The optimum value of gains is selected to ensure a good quality of power. The three-phase inverter controls the grid power with fuzzy-PI controller and the exact amount of grid power is the function of microgrid voltage to maintaining good performance. In this paper, the PI controller parameters value for two-level boost converter and inverter circuit are determined by using the fuzzy logic controllers according to the need of the grid. Consequently, the proposed method comforts the grid integrated energy system complexity. The performance of the system is examined for both steady state condition as well as the transient situations such as reference dc voltage variations for two-level boost converter and current reference and the dc voltage deviations for the three-phase inverter. Moreover, the proposed scheme offers outstanding performance for overcoming the voltage and current distortions. THD reduction is the main concern in inverter line voltage, In [27], a genetic algorithm is proposed to reduce the THD in the line voltage and it gives the value of THD 10.3%. Whereas in [28]. New modulation technique for an asymmetric inverter is proposed and it gives the THD 9% but this method increases the complexity. In [29], criteria based modulation scheme is presented and it gives the WTHD 4.4%. for further reduce the harmonic distortion, this method is proposed, and it gives 2.70% THD.To achieve superior harmonic reduction using hybrid energy generation high power devices interfaces, an advanced fuzzy current and voltage controlled technique are proposed in the paper. The advanced control architecture reduces the grid-connected hybrid energy system complexity, without affecting the system performance. Further, a closed control loop for wind system linked rectifier is not essential as the wind voltage and speed variation can be automatically recognized by the advanced control loop. The advanced control architecture decreases the system complexity, without affecting the system performance. Control and design of both the dc/dc converter and three-phase inverter are presented. Further, experimental results have revealed the power and viability of the proposed method.Further, experimental results revealed the power and viability of the proposed method.

In the paper, the grid-tied hybrid energy system configuration is discussed in Section 2. Control strategies for boost converter and inverterare discussed in Section 3. Outcomes are discussed in Section 4. Lastly, the finalreports are presented in Section 5.

2 Hybrid energy system configuration

The offered grid-tied hybrid energy system block diagram illustrated in Fig. 1 is a high power system and comprises a solar system, wind system and converter modules. The converter modules for interconnecting the energy sources to the main grid are fuzzy based systems to operate in utility grid-connected mode. Generally, the solar photovoltaic system generates power at variable dc voltage, while the wind system provides power at changeable ac voltage. Hence, the power provided from wind and solar system needs a power conditioning phase before it’s connected to the grid. The three-phase inverter delivers the available power at the boost converter to the grid. Here grid currents in three phases are represented by I_a, I_b, and I_c respectively.

Fig.1

Grid-connected wind and solar energy system.

The boost converter is a dc to dc step-up converter that harvests the energy from the wind and solar system applying the charging algorithm to the storage system. The two-level boost converter regulates the storage device power based on the amount of solar and wind power generation. When the dc microgrid is linked to the main grid, the storage device consume or supply power as per the need. If the main grid is in some trouble condition, then the inverter transfers the microgrid into the islanded way. The solar system is operated in maximum power transfer mode while maintaining the desired power for the two-level boost converter. The three-phase inverter operates in fuzzy-based control configuration and the amount of inverter power to the main grid is the function of dc voltage of the boost converter for maintaining the good profile.

The transferred ac power to grid follows the output power characteristics of the boost converter and the advanced control architecture accomplishes the reduced harmonic distortion with increased efficiency by taking the maximum hybrid system power while maintaining the power in the system. The advantage of this proposed scheme is the excellent performance of the microgrid energy system for overcoming the voltage and current distortion.

2.1 Microgrid solar system description

The solar system as shown in Fig. 2. Is a well-controlled which is either using maximum power point transfer way or using an algorithm to get the good power P_pv-gd to protect the two level boost converter from overvoltage. In this system, the selected maximum power transfer method is the perturb & observe, according to that the solar source produces the maximum power P_pv-max [15]. The output of this scheme is connected to boost converter while the controller provides the duty cycle and it is given to the boost converter to control the microgrid voltage. R_s and R_p are the series and parallel resistances respectively. In case of insufficient solar power towards the dc microgrid, the storage system provides the required power to ensure the uninterrupted supply. If the surplus power is available, the storage device could be charged and microgrid linking provides the opportunity to give it back.

Fig.2

Solar system representation.

2.2 Microgrid wind system description

The wind system as shown in Fig. 3. is controlled by the algorithm to output an excellent power P_wind-gd and ac power provided by the wind generator is rectified to t dc power to protect the boost converter from voltage mismatch and overvoltage. As the wind voltage varies along with the wind speed changes, the algorithm is set to get the maximum power R_wind-max. The output of the converter is connected to main grid via the the inverter which serves as harmonic reduction device.

Fig.3

Wind system representation.

2.3 Storage system description

Lead-acid based batteries are chosen as the storage device for dc microgrid connected to the main grid via inverter, due to its low cost and developed technology. The storage power can be controlled by a control system which determines the ideal charging. The storage state of power charge should be defined by its upper and lower limits, BAT_upperand BAT_lower respectively, keep away the storage system from over discharging and overcharging as defined by Equation 1. The BAT is considered by Equation 2. with BAT_int as initial storage at t_o, C_r as nominal capacity (Ah), and V_stor as the storage voltage. ${BAT}_{upper} \leq BAT (t) \leq {BAT}_{lower}$ (1) $BAT (t) = {BAT}_{int} + \frac{1}{3600 . V_{star} C_{r}} \int_{t_{o}}^{t} (P_{bc} (t) - P_{bd} (t))$ (2)

Where P_bc and P_bd are the storage charging and storage discharging power respectively.

2.4 Microgrid connection description

The micro-grid connection is controlled by the boost converter circuit. The microgrid power is controlled by a fuzzy voltage controller which calculates the desired value of K_p and K_i of PI cthe ontroller to regulate the output of boost converter. Moreover, microgrid supply power and microgrid injection power is controlled to overcoming the problem of undesired voltage and current fluctuations.

2.5 Grid connection description

The grid connection is controlled using the inverter circuit which is controlled by a fuzzy current controller, calculates the value of K_p and K_i of PI controller to control the power of the grid. Throughout the microgrid operation, the power of grid must be regulated to meet the Equations 3 and 4. Where P_grids, P_grids-lim, P_gridi-lim and P_gridi-lim are grid supply power, grid supply power limit, grid injection power and grid injection power limit respectively. $0 \leq P_{grids} (t) \leq P_{grids - lim}$ (3) $0 \leq P_{gridi} (t) \leq P_{gridi - lim}$ (4)

3 Control strategy for hybrid energy system

The PID, PI and P controllers are usually used for controlling the different converters and can also be used in controlling the grid-connected inverter and two level boost converter in the hybrid energy system. The controller’s performance is affected by a change in system factors, hence this is the main drawback. The converters in the hybrid energy system should be effectively controlled with a good controller which can modify its condition according to the solar irradiance changes and wind velocity variations. The fuzzy-PI controller is the greatest solution which can be obtained by joining a PI controller with the fuzzy logic controller. The K_p and K_i values in two level boost converter and inverter control are obtained using the fuzzy logic controllers. The diagram showing the control way of hybrid energy system is presented in Fig. 4.

Fig.4

Schematic diagram showing the control strategy of a hybrid energy system.

3.1 Design of fuzzy-PI controller for controlling the dc microgrid

Choosing membership functions, input, and output variables are the important steps in the fuzzy logic controller design process. The boost converter voltage is regulated, hence error (e) and change in voltage error (eoe) are well-defined as the input variables to the fuzzy logic controller. These two input variables in a fuzzy logic controller have seven membership functions. The linguistic variables ‘plus high (PLH)’, “plus medium (PLM)’, plus low (PLL)’, ‘zero (ZL)’, ‘minus high (MNH)’, “minus medium (MNM)’, minus low (MNL)’ for input variablesare used to describe the fuzzy variables. To obtain the fast response, the rules are well-defined according to the need of dc microgrid and its process according to change in error and change in error inputs as shown in Table 1.

Table 1
Changing rules for the boost converter

Output Error(e) Output Error(e)

(K_p) MNH MNM MNL ZL PLL PLM PLH (K_i) MNH MNM MNL ZL PLL PLM PLH

Change in MNH MNH MNM MNL MNH PLL MNH ZL Change MNH MNH MNH MNH MNH MNH MNL MNL

MNM MNH MNL MNM MNL MNL ZL PLH in MNM MNL MNH MNL MNL MNL MNL ZL

error (eoc) MNL MNL MNL MNL MNH ZL PLL PLH error (eoc) MNL MNH MNL MNH MNH MNL ZL PLL

ZL MNM MNL MNL ZL PLL PLH PLL ZL ZL MNH MNH MNL ZL PLL PLH

PLL MNL MNM ZNL PLH PLM PLL PLH PLL PLL MNH MNL ZL PLL PLL PLH

PLM MNL ZL PLL PLH PLM PLM PLM PLM PLH MNH ZL PLL PLH PLL PLM

PLH ZL PLL PLH PLM PLM PLM PLM PLH PLM ZL PLH PLH PLH PLH PLM

Output		Error(e)	Output		Error(e)
Change in	MNH	MNH	MNM	MNL	MNH	PLL	MNH	ZL	Change	MNH	MNH	MNH	MNH	MNH	MNH	MNL	MNL
	MNM	MNH	MNL	MNM	MNL	MNL	ZL	PLH	in	MNM	MNL	MNH	MNL	MNL	MNL	MNL	ZL
error (eoc)	MNL	MNL	MNL	MNL	MNH	ZL	PLL	PLH	error (eoc)	MNL	MNH	MNL	MNH	MNH	MNL	ZL	PLL
	ZL	MNM	MNL	MNL	ZL	PLL	PLH	PLL		ZL	ZL	MNH	MNH	MNL	ZL	PLL	PLH
	PLL	MNL	MNM	ZNL	PLH	PLM	PLL	PLH		PLL	PLL	MNH	MNL	ZL	PLL	PLL	PLH
	PLM	MNL	ZL	PLL	PLH	PLM	PLM	PLM		PLM	PLH	MNH	ZL	PLL	PLH	PLL	PLM
	PLH	ZL	PLL	PLH	PLM	PLM	PLM	PLM		PLH	PLM	ZL	PLH	PLH	PLH	PLH	PLM

The fuzzy rules are chosen systematically according to system operation. In this paper, a simple approach for the formation of fuzzy rules is presented by obtaining a relationship through simulation with a learning process.

The ISE, ITAE, and ITSE are selected which could be determined by Equations 5–7, respectively, are chosen as performance indices of the offered controller. ${ISE}_{b} = \int_{0}^{\infty} e^{2} (t) dt$ (5) ${ITAE}_{b} = \int_{0}^{\infty} t | e^{2} (t) | dt$ (6) ${ITSE}_{b} = \int_{0}^{\infty} t | e^{2} (t) | dt$ (7)

3.2 Design of fuzzy-PI controller for controlling the grid

Selecting the output, input variables and membership functions are also important to stage in the fuzzy controller which controls the inverter. The three-phase inverter current is controlled, hence error (e) and change in voltage error (eoe) are used as the input variables to this fuzzy logic controller also. Similarly, these two input variables in a fuzzy logic controller have seven membership functions. The linguistic variables ‘plus high (PLH)’, “plus medium (PLM)’, plus low (PLL)’, ‘zero (ZL)’, ‘minus high (MNH)’, “minus medium (MNM)’, minus low (MNL) for input variables are used to describe the fuzzy variables. The rules are shown in Table 2.

Table 2
Rules for changing the (K_p) and (K_i) output variables in three phase inverter control

Output Error(e) Output Error(e)

(K_p) MNH MNM MNL ZL PLL PLM PLH (K_i) MNH MNM MNL ZL PLL PLM PLH

Change in MNH MNM MNM MNM MNH MNH PLH PLH Change MNH MNL MNH MNL MNH MNH MNL MNL

MNM MNH MNH MNM MNL MNM ZL PLM in MNM MNL MNH MNL MNL MNH MNL ZL

error (eoc) MNL MNM MNL MNM MNH ZL PLL ZL error (eoc) MNL MNH MNL MNH MNL MNL ZL PLM

ZL MNM MNL MNL ZL PLL PLH PLM ZL PLL MNM MNH ZL ZL PLL PLH

PLL MNL ZL ZL PLL PLM PLL PH PLL PLL MNH MNL PLL PLM PLL PLM

PLM MNM MNL PLH PLH PLM PLM PLM PLM ZL MNH ZL PLL PLH PLH PLH

PLH ZL PLL PLH PLM PLM PLM PLM PLH PLM ZL PLH PLH PLH PLH PLH

Output		Error(e)	Output		Error(e)
Change in	MNH	MNM	MNM	MNM	MNH	MNH	PLH	PLH	Change	MNH	MNL	MNH	MNL	MNH	MNH	MNL	MNL
	MNM	MNH	MNH	MNM	MNL	MNM	ZL	PLM	in	MNM	MNL	MNH	MNL	MNL	MNH	MNL	ZL
error (eoc)	MNL	MNM	MNL	MNM	MNH	ZL	PLL	ZL	error (eoc)	MNL	MNH	MNL	MNH	MNL	MNL	ZL	PLM
	ZL	MNM	MNL	MNL	ZL	PLL	PLH	PLM		ZL	PLL	MNM	MNH	ZL	ZL	PLL	PLH
	PLL	MNL	ZL	ZL	PLL	PLM	PLL	PH		PLL	PLL	MNH	MNL	PLL	PLM	PLL	PLM
	PLM	MNM	MNL	PLH	PLH	PLM	PLM	PLM		PLM	ZL	MNH	ZL	PLL	PLH	PLH	PLH
	PLH	ZL	PLL	PLH	PLM	PLM	PLM	PLM		PLH	PLM	ZL	PLH	PLH	PLH	PLH	PLH

The controlled approach of three-phase inverter is established for sharing the grid active power along with the wind and solar systems supplying the power needed by the grid. The inverter voltages are defined as follow, $V_{abi} = \sum_{n = 1, 3, 5}^{\infty} \frac{4 V_{dc}}{n π} cos \frac{n π}{6} sin n (wt + \frac{π}{6})$ (8) $V_{bci} = \sum_{n = 1, 3, 5}^{\infty} \frac{4 V_{dc}}{n π} cos \frac{n π}{6} sin n (wt - \frac{π}{2})$ (9) $V_{cci} = \sum_{n = 1, 3, 5}^{\infty} \frac{4 V_{dc}}{n π} cos \frac{n π}{6} sin n (wt + \frac{5 π}{6})$ (10)

The inverter currents and voltages are changed into d-q-0 frame and it is defined in matrix form as, $[\begin{matrix} V_{di} \\ V_{qi} \\ V_{oi} \end{matrix}] = \frac{2}{3} [\begin{matrix} 1 - 1 / 2 - 1 / 2 \\ 0 \sqrt{3} / 2 - \sqrt{3} / 2 \\ 1 / 2 - 1 / 2 - 1 / 2 \end{matrix}] [\begin{matrix} V_{ai} \\ V_{bi} \\ V_{ci} \end{matrix}]$ (11) $[\begin{matrix} V_{di} \\ V_{qi} \end{matrix}] = \frac{2}{3} [\begin{matrix} 1 - 1 / 2 - 1 / 2 \\ 0 \sqrt{3} / 2 - \sqrt{3} / 2 \end{matrix}] [\begin{matrix} V_{ai} \\ V_{bi} \\ V_{ci} \end{matrix}]$ (12)

Similarly for current signals, $[\begin{matrix} I_{d} \\ I_{qmes} \\ I_{oi} \end{matrix}] = \frac{2}{3} [\begin{matrix} 1 - 1 / 2 - 1 / 2 \\ 0 \sqrt{3} / 2 - \sqrt{3} / 2 \\ 1 / 2 1 / 2 1 / 2 \end{matrix}] [\begin{matrix} I_{ai} \\ I_{bi} \\ I_{ci} \end{matrix}]$ (13)

In the balanced system I_o = 0, $[\begin{matrix} I_{d} \\ I_{qmes} \end{matrix}] = \frac{2}{3} [\begin{matrix} 1 - 1 / 2 - 1 / 2 \\ 0 \sqrt{3} / 2 - \sqrt{3} / 2 \end{matrix}] [\begin{matrix} I_{ai} \\ I_{bi} \\ I_{ci} \end{matrix}]$ (14)

These equations shows that measured quantities can be assessed. If the investigation will be done in the rotating type dq-Synchronous Reference Frame then this can be defined as, $[\begin{matrix} I_{d} \\ I_{qmes} \\ I_{oi} \end{matrix}] = \frac{2}{3} [\begin{matrix} \cos θ cos (θ - 2 π / 3) cos (θ + 2 π / 3) \\ - sin θ - sin (θ - 2 π / 3) - sin (θ + 2 π / 3) \\ 1 / 2 1 / 2 1 / 2 \end{matrix}]$ $[\begin{matrix} I_{ai} \\ I_{bi} \\ I_{ci} \end{matrix}]$ (15) $I_{d} = \frac{2}{3} [I_{q} cos θ + I_{b} cos (wt - 120^{\circ}) + I_{c} cos (wt + 120^{\circ})]$ (16) $I_{qmes} = \frac{2}{3} [I_{a} sin θ + I_{b} sin (wt - 120^{\circ}) + I_{c} sin (wt + 120^{\circ})]$ (17)

From Equation 12, $[\begin{matrix} V_{di} \\ V_{qi} \end{matrix}] = \frac{2}{3} [\begin{matrix} sin θ cos θ \\ - cos θ - sin θ \end{matrix}]$ $[\begin{matrix} 1 - 1 / 2 - 1 / 2 \\ 0 \sqrt{3} / 2 - \sqrt{3} / 2 \end{matrix}] [\begin{matrix} V_{ai} \\ V_{bi} \\ V_{ci} \end{matrix}]$ (18)

From Equation 18, the V_di and V_qi are the DC type, therefore it can be used for controlling the reference current. Whereas the V_dl and V_ql are the outputs of dq axis, it can be described as, $V_{dl} = K_{p} (I_{dl}^{ref} - I_{dl}) + 2 K_{i} (I_{dl}^{ref} - I_{dl}) dt$ $V_{ql} = K_{p} (I_{ql}^{ref} - I_{ql}) + 2 K_{i} (I_{ql}^{ref} - I_{ql}) dt$

Whereas the fuzzy logic controller input is $△ I = I_{qref} (k) - I_{qmes} (k - 1)$ (19)

The fuzzy duty cycle variation can be described as, $△ I = \frac{\sum_{l = 1}^{n} l 4 I_{qmse, l} I_{qref, l}}{\sum_{l = 1}^{n} 2 I_{qref, l}}$ (20)

The voltage controlled technique is designed to decrease the harmonic distortion and any other disturbance in the inverter, it can be derived as, $\begin{matrix} \frac{{dV}_{dl}}{dt} - I_{qse} = \frac{{dK}_{p} (I_{dl}^{ref} - I_{dl}) + 2 K_{i} (I_{dl}^{ref} - I_{dl})}{dt} \\ - I_{qse} = K_{p} K_{i} I_{dl}^{ref} \frac{I_{dl} (s)}{I_{dl}^{ref} (s)} = \frac{K_{p} K_{i}}{s} \end{matrix}$

The adjuster function for PI is $\begin{matrix} I_{dl}^{ref} & = (K_{P} + \frac{2 K_{i}}{s}) (V_{dc}^{*} - V_{dc}) \\ = K_{P} \frac{(2 T_{v} s + 1) (V_{dc}^{*} - V_{dc})}{T_{v} s} \end{matrix}$

Relating to DC side voltage the transfer function is $\frac{1}{2 T_{v} s + 1}$ . nd the PI controller transfer function is defined as $T = k_{vp} e + k_{vi} \frac{2 {es}^{2}}{S^{2} + ω^{2}}$ (21)

Where k_vp, k_vi, e is the poportional and integral gain and error signal.

let $n (s) = k_{vi} \frac{2 e (s) s^{2}}{s^{2} + ω^{2}}$ (22) $n (s) + n (s) \frac{ω^{2}}{s^{2}} = 2 k_{vi} e (s)$ (23) $m (s) = \frac{1}{(z (s) \frac{ω^{2}}{s^{2}} - 2 k_{vi} e (s))}$ (24)

These are the control variables to control the overall design. It can be further solved to accurately design the voltage control loop $\begin{matrix} \frac{dn (t)}{dt} & + \int 2 \frac{ω^{2}}{t} z (t) dt = 2 K_{vi} (I_{qref} (t) - I_{qmes} (t)) \\ + K_{vp} (2 I_{qref} (t) - I_{qmes} (t)) \end{matrix}$ $m (t) = \frac{1}{(\int \frac{ω^{2}}{t^{2}} z (t) dtz - 2 k_{vi} e (t))}$

$\frac{dn (t)}{dt} = m (t) + K_{vp} (2 I_{qref} (t) - I_{qmes} (t))$ (25)

It can be further described as, $\begin{matrix} \frac{d}{dt} (\begin{matrix} I_{qmes} (t) \\ n (t) \\ m (t) \end{matrix}) = (\begin{matrix} \frac{- 1 + K_{vp}}{2} & \frac{1}{2} & 0 \\ K_{vi} & 0 & - 2 \\ 0 & ω^{2} & 0 \end{matrix}) \end{matrix}$ $\begin{matrix} (\begin{matrix} I_{qmes} (t) \\ n (t) \\ m (t) \end{matrix}) + (\begin{matrix} \frac{K_{vp}}{2} \\ K_{vi} \\ 0 \end{matrix}) I_{qref} (t) \end{matrix}$ (26)

Similarly for a current controlled technique for boost converter,

The fuzzy logic controller input for boost converter $Δ V = V_{dref} (k - 1) - V_{dc}^{*} (k)$ (27)

For the boost converter, the fuzzy duty cycle variation can be described as, $Δ I = \frac{\sum_{k = 1}^{n} k 2 V_{dc, k}^{*} V_{dref, k}}{\sum_{k = 1}^{n} V_{dreff, l}}$ (28)

The current controlled technique is derived as, $\begin{matrix} \frac{{dV}_{dl}}{dt} & = \frac{{dK}_{p} (V_{dref} - V_{dc}^{*}) + 0.5 K_{i} (V_{dref} - V_{dc}^{*})}{dt} \\ = 0.5 K_{p} K_{i} V_{dref} \end{matrix}$ $\frac{V_{dc}^{*} (s)}{V_{dref} (s)} = \frac{0.5 K_{p} K_{i}}{s}$

The adjuster function for PI is $\begin{matrix} V_{dref} & = (K_{p} + \frac{0.5 K_{i}}{s}) (I_{dc}^{*} - I_{dc}) \\ = K_{p} \frac{(0.5 T_{c} s + 1) (I_{dc}^{*} - I_{dc})}{T_{c} s} \end{matrix}$

The PI controller transfer function is defined as $T = k_{cp} e + k_{ci} \frac{0.5 {es}^{2}}{s^{2} + ω^{2}}$ (29)

Where k_cp, k_ci, e is the poportional and integral gain and error signal.

let $p (s) = k_{ci} \frac{0.5 e (s) s^{2}}{s^{2} + ω^{2}}$ (30) $p (s) + p (s) \frac{ω^{2}}{s^{2}} = 0.5 k_{ci} e (s)$ (31) $q (s) = \frac{1}{(z (s) \frac{ω^{2}}{s^{2}} - 0.5 e (s))}$ (32)

The above equations show that the control variables. It can be solved as, $\begin{matrix} \frac{dp (t)}{dt} & + \int 0.5 \frac{ω^{2}}{t} z (t) = 0.5 k_{ci} (V_{dref} (t) - V_{dc}^{*} (t)) \\ + K_{cp} (V_{dref} (t) - V_{dc}^{*} (t)) \end{matrix}$ $q (t) = \frac{1}{\int 0.5 \frac{ω^{2}}{s^{2}} z (t) dt z - k_{ci} e (t))}$

The value of q (t) from the equation to equation $\frac{dp (t)}{dt} = q (t) + K_{cp} (V_{dref} (t) - V_{dc}^{*} (t))$ (33)

Where as the duty cycle of boost converter defined as, $\frac{I_{dc}^{*} - I_{dc} (n + 2)}{(1 - D) T} = \frac{V_{dref} - V_{dc}^{*} (k)}{V_{dc}^{*} I_{dc}^{*}}$ (34)

Where T is the pulse period and it can be changed according to the need of the demand. In the closed-loop current control system, the V_dref is the controlled with the help of loop which tries to keep $V_{dc}^{*}$ constant by adjusting V_dref. The V_dref for the boost converter can be written as: $V_{dref} (k) = V_{dref} (k - 2) + GF$ $V_{dref} (k) = V_{dref} (k - 2) + {GK}_{ci} K_{cp}$

Where G and F are the gain factor and the fuzzy logic controller output respectively. The error e(k) and change of error eoe(k)can be written as: $e (k) = V_{dref} (k) - V_{dc}^{*} (k)$ $eoe (k) = e (k) - e (k - 2)$

The time instant k can be chosen for different sampling time, which is related to the duty cycle of the boost converter. Fuzzy-PI controller is used to lessen the error and improve the system power quality. The presented fuzzy-PI based inverter is simulated. The fuzzy logic controller regulates the value of K_p and K_i of the PI based controller. The modulation index is produced by PI cthe ontroller according to the error in currthe ent. The performance indices of the offered controller for inverter circuit, which could be known by Equations 35–37, correspondingly. ${ISE}_{inv} = \int_{0}^{\infty} e^{2} (t) dt$ (35) ${ITAE}_{inv} = \int_{0}^{\infty} t | e (t) | dt$ (36) ${ITSE}_{inv} = \int_{0}^{\infty} t | e^{2} (t) | dt$ (37)

4 Simulation results and discussions

The proposed hybrid energy system with an advanced control scheme is simulated using MATLAB/Simulink. A voltage sensor is employed to sense the output voltage of boost converter. The fuzzy logic controller is employed to regulate the value of K_p and K_i of PI controller to regulate the output voltage of boost the t converter by changing the duty cycle of it. Consequently, these fuzzy-PI controllers are used to keep the constant voltage for proper operation of grid connected energy system. The solar and wind output voltages are presented in Figs. 5 and 6 correspondingly. The output voltage boost converter is shown in Fig. 7.

Fig.5

The output voltage of the solar system.

Fig.6

The output voltage of wind system after rectification.

Fig.7

The output voltage of two-level boost converter.

Parameter Specification for flexible hybrid energy system is given below:

Wind power system: 10kW

Solar power system: 10kW

Solar system voltage: 96Volts

Wind system voltage after rectification: 96 Volts

Two level boost converter specifications:

L₁, L₂, C₁, C₂: 4.56μH, 4.56μH, 62.30μF, 62.30μF respectively.

The PV modules are connected with a total power of 10kW are tested in the proposed scheme, for which the design circuit parameters are given below:

The open circuit voltage is 90 Volts

The short circuit current is 9 Amp

PV array power is 10kW

The wind turbine is selected accordingly to generate 10kW power. Since the beginning of the smart grid, a microgrid with power balancing, power quality enhancement, and the main grid interfacing is proposed with an effective control technique. Based on the PV system and wind system, power grid connection, the main aim of this flexible microgrid is to efficiently supply the power with good performance. Still, the boundary of PI control is that the power quality is affected in higher level. Hence, the fuzzy-PI controller gives better performance while maintaining the system power quality.

The overall hybrid system with control strategy is considered that the output voltage of the boost converter must be approximately 230 V.

Moreover, this is also simulated where boost converter is controlled by fuzzy-PI control scheme, with changing the reference voltage as display in Fig. 8:

Fig.8

The output voltage of two-level boost converter with changing the reference voltage.

t = 1 sec to t = 1.5 sec: V_d = 220 Volts

t = 1.5 sec to t = 2.5 sec: V_d = 130 Volts

t = 2.5 sec to t = 3.5 sec: V_d = 220 Volts

t = 3.5 sec to t = 4.5 sec: V_d = 175 Volts

t = 4 sec to t = 5 sec: V_d = 230 Volts

Furthermore, this voltage can be increased or decreased according to need. For further analyses, simulation is done for different values of K_p and K_i.

The fuzzy base controllers are used to regulate the K_p and K_i gains of the PI base controller and fuzzy logic controllers are considered with fuzzy toolbox in MATLAB. The current of inverter I_qmes and the reference current I_qref are shown in Fig. 9 a and b. The reference current of inverter is same at t = 0.1 s and at t = 0.8, Furthermore, this current can be increased or decreased according to need. The output current of inverter which is controlled by fuzzy-PI controller tracks the reference current with less settling time. The inverter current is tested for dc voltage deviation, the inverter current follows the reference current when there is change in output voltage of two level boost converter. Figure 10 displays inverter current also follows the reference current as shown in Fig. 9b. The inverter current is same at t = 1 s to at t = 5, while voltage is not same over this period. Hence, fuzzy-PI controller control the output effectively.

Fig.9

a and b. Inverter output current I_qmes and reference current I_qref.

Fig.10

Inverter output current under input DC voltage variations.

From 0 to 0.5 s, the effectiveness of the control scheme is clearly shown in inverter output current I_qmes and reference current I_qref. The effect of changing the reference current on the inverter current is clearly shown. At t = 2s to t = 4s, when dc voltage changes, a inverter current clearly track of the reference current is shown in the simulation. At the end of the simulation, again dc voltage reaches its original location, and inverter current modification is obtained by fuzzy-PI controller. From these simulations and fuzzy-PI controlled output power of two level boost converter, it is finalized that the controller obtain the stable microgrid operation. To demonstrates the working of controlled strategy for hybrid energy system, the display units are used to monitors the changes of the input variables as well as the output variables.

However, it is beneficial to change the dc voltage during simulation compared to constant voltage, more accurate results are generated. It is shown that the proposed controllers offers the stable operation and improved solution when hybrid system is employed. Hence, a hybrid system is formed by using the proposed concept of controlling the output power to grid for efficiently using a dc microgrid connected to the main grid via inverter.

To verify the effectiveness of the proposed techniques for controlling the hybrid energy system, an experimental test has been done with dSPACE as shown in Fig. 14. There area solar panel and induction generation which are used as solar energy and wind energy system respectively. A dSPACE controller board is employed as a control platform for the inverter and it provides the relating of MATLAB/Simulink model to the real hardware. This can be done by using the dSPACE input-output interface blocks into the model. The Simulink model along with the dSPACE block is changed to C-code using the real-time workshop function in the MATLAB. Further, this C-code is compiled and associated with the real-time dSPACE board. The graphical user interface software is used to monitor the behavior and performance of the system in real time.

Fig.11

The grid side voltages and currents of the inverter and without controller (THD level: 28.85%).

Fig.12

The grid side voltages and currents of the inverter and with PI controller alone (THD level: 8.25%).

Fig.13

The grid side voltages and currents of the inverter with fuzzy-PI controller (THD level: 2.70%).

Fig.14

Experimental Setup.

An advanced fuzzy current and voltage control techniques are used to effectively control the hybrid energy system and reduced the harmonic distortion; the circuit parameters are the output voltage of DC/DC converter = 230 V, sampling period = 0.15 ms. Figure 15 shows the grid side voltages and currents of the inverter with fuzzy-PI controller- experimental results and Fig. 16 show the FFT analysis- experimental results. The use of power electronics devices is going through a transitory stage in the maingrid-connectedapplications. Dissimilar characteristics of the inputs could be combined to provide the preferred output operation by control strategy which is a combination of fuzzy and PI controller modifying the output power accordingly. Such a control scheme provides a better performance to ensure the reliable operation of the hybrid energy system. A complete system is designed for providing the renewable energy power to the grid effectively. Transferring power to the grid includes the power quality improvement which is put up by three-phase inverter connected with the main grid. A phase locked loop is employed to sense the grid voltages, frequency, and phase.

Fig.15

The grid side voltages and currents of the inverter with fuzzy-PI controller-experimental results.

Fig.16

FFT analysis with THD 2.85% - experimental results.

The Total harmonic distortion (THD) level of the inverter current is calculated without using any controller as 28.85% as shown in Fig. 11 and THD level with the PI controller as 8.25% as display in Fig. 12. Besides, the Total harmonic distortion level of inverter current with a fuzzy-PI controller is calculated as 2.70% as shown in Fig. 13 and this value fulfill the requirements of the international values. The power factor for all cases are calculated and this shows better performance. the THD analysis of the inverter output current shows that fuzzy-PI based controller has better results in assessment with the conventional PI controller as shown in Table 3. The discussed control technique offers a quick transient response and follows the reference value efficiently.

Table 3

Simulation outcomes – fuzzy-PI and PI alone for the three-phase inverter circuit

Control	Simulation results
Technique	PF	ISE	ITSE	ITAE	THD	THD%
Fuzzy-PI	0.99	0.015	0.030	0.041	270	2.70
PI(Kp = 2.8, KI = 1800)	0.99	0.016	0.032	0.044	771	7.71
PI(Kp = 3.0, KI = 1850)	0.99	0.018	0.031	0.043	706	7.06
PI(Kp = 3.4, KI = 1900)	0.99	0.018	0.031	0.045	825	8.25
PI(Kp = 3.6, KI = 1920)	0.99	0.019	0.041	0.046	750	7.50
PI(Kp = 3.9, KI = 1970)	0.99	0.021	0.036	0.045	780	7.80
PI(Kp = 4.1, KI = 1990)	0.99	0.023	0.035	0.043	708	7.08

In [27], a genetic algorithm is presented to decrease the THD value in the line voltage and it provides10.3% THD. While in [28], New modulation technique for an asymmetric inverter is presented and it provides the 9% THD and in [29], criteria based modulation scheme is presented and it gives the 4.4% THD. These results are compared with the obtained results as shown in Table 4 and Fig. 17. The comparison outcomes reveals that the proposed advanced control architecture offers the stable operation and better solution when the hybrid system is used.

Fig.17

Results comparison.

Table 4

Comparison of results

Method	THD %
Genetic algorithm [27]	10.3
New modulation technique for a asymmetric	9.0
inverter [28]
Criteria based modulation scheme [29]	4.4
Proposed Technique	2.70

5 Conclusion

In the paper, the fuzzy-PI current and voltage controlled hybrid energy system has been designed. The value of K_p and K_i of the PI controller for two level boost converter and inverter are calculated using the fuzzy logic controllers according to proposed system operation. Thus, this method eases the grid connected energy system complexity. The performance of the scheme is examined for both steady state condition as well as the transient situations such as reference dc voltage variations for two level boost converter and current reference and the dc voltage variations for three phase inverter circuit. Also, the proposed scheme provides the outstanding performance for overcoming the voltage and current distortion. Further, experimental results have shown the power and viability of the proposed method.

The obtained results confirmed that the harmonic distortion level satisfies the international standards. Moreover, in order to further analyze the effectiveness of control scheme, a simulation case for changing the gains of the PI controller is done and the value of THD level is taken for each case. This strategy gives better outcomes in comparison with the PI controller. Hence, it is shown from the results that the proposed advanced control architecture offers the stable operation and improved solution when the hybrid system is deployed.

References

Elgammal

A.A.A.

and El-naggar

M.F.

, MOPSO-based optimal control of shunt active power filter using a variable structure fuzzy logic sliding mode controller for hybrid (FC-PV-Wind-Battery) energy utilisation scheme, IET Renew Power Gener 11(8) (2017), 1148–1156.

Bhattacharjee

and Roy

B.K.

, Advanced fuzzy power extraction control of wind energy conversion system for power quality improvement in a grid tied hybrid generation system, IET Gener Transm Distrib 10(5) (2016), 1179–1189.

Tan

, Squirrel Cage Induction Generator System Using Wavelet Petri Fuzzy Neural Network Control for Wind Power Applications, IEEE Trans Power Electron 31(7) (2015), 1–1.

Tao

C.W.

, Wang

C.M.

and Chang

C.W.

, A design of a DC-AC inverter using a modified ZVS-PWM auxiliary commutation pole and a DSP-based PID-like fuzzy control, IEEE Trans Ind Electron 63(1) (2016), 397–405.

Wai

R.J.

and Liu

Y.K.

, Design of fuzzy neural network control for single-stage boost inverter, Proc – Int Conf Mach Learn Cybern 2(12) (2015), 560–565.

Bastos

R.F.

, Aguiar

C.R.

, Gonçalves

A.F.Q.

and Machado

R.Q.

, An Intelligent Control System Used to Improve Energy Production From Alternative Sources With DC/DC Integration, IEEE Trans Smart Grid 5(5) (2014), 2486–2495.

Ozdemir

, Kaplan

, Sefa

and Altin

, Fuzzy PI controlled inverter for grid interactive renewable energy systems, IET Renew Power Gener 9(7) (2015), 729–738.

Singh

and Chandra

, Real-time implementation of ANFIS control for renewable interfacing inverter in 3P4W distribution network, IEEE Trans Ind Electron 60(1) (2013), 121–128.

Viswanathan

, Oruganti

and Srinivasan

, Nonlinear function controller: A simple alternative to fuzzy logic controller for a power electronic converter, IEEE Trans Ind Electron 52(5) (2005), 1439–1448.

10.

Garcia

, Garcia

C.A.

, Fernandez

L.M.

, Llorens

and Jurado

, ANFIS-Based Control of a Grid-Connected Hybrid System Integrating Renewable Energies, Hydrogen and Batteries, IEEE Trans Ind Informatics 10(2) (2014), 1107–1117.

11.

Kamel

R.M.

, Chaouachi

and Nagasaka

, Three control strategies to improve the microgrid transient dynamic response during isolated mode: A comparative study, IEEE Trans Ind Electron 60(4) (2013), 1314–1322.

12.

Wang

and Thi

M.S.N.

, Stability enhancement of large-scale integration of wind, solar, and marine-current power generation fed to an sg-based power system through an lcc-hvdc link, IEEE Trans Sustain Energy 5(1) (2014), 160–170.

13.

Uddin

M.N.

, Radwan

T.S.

and Rahman

M.A.

, Fuzzy-logic-controller-based cost-effective four-switch three-phase inverter-fed IPM synchronous motor drive system, IEEE Trans Ind Appl 42(1) (2006), 21–30.

14.

Alajmi

B.N.

, Ahmed

K.H.

, Adam

G.P.

and Williams

B.W.

, Single-Phase Single-Stage Transformer less Grid-Connected PV System, IEEE Trans Power Electron 28(6) (2013), 2664–2676.

15.

Thounthong

, Luksanasakul

, Koseeyaporn

and Davat

, Intelligent model-based control of a standalone photovoltaic/fuel cell power plant with supercapacitor energy storage, IEEE Trans Sustain Energy 4(1) (2013), 240–249.

16.

Basu

and Mohan

, A High-Frequency Link Single-Stage PWM Inverter With Common-Mode Voltage Suppression and Source-Based Commutation of Leakage Energy, IEEE Trans Power Electron 29(8) (2014), 3907–3918.

17.

Wang

and Nguyen

Thi, Stability Enhancement of a PMSG-Based Offshore Wind Farm Fed to a Multi-Machine System Through an LCC-HVDC Link, IEEE Trans Power Syst 28(3) (2013), 3327–3334.

18.

Fischer

, Grass

, Zouzou

, Dascalescu

, Greil

and Hopf

, Smart Home Precipitator for Biomass Furnaces: Design Considerations on a Small-Scale Electrostatic Precipitator, IEEE Trans Ind Appl 50(3) (2014), 2219–2224.

19.

Vafamand

, Yousefizadeh

, Khooban

M.H.

, Bendtsen

J.D.

and Dragicevic

, Adaptive TS Fuzzy-Based MPC for DC Microgrids With Dynamic CPLs: Nonlinear Power Observer Approach, IEEE Syst J (2018), 1–8.

20.

Vafamand

, Asemani

M.H.

, Khayatiyan

, Khooban

M.H.

and Dragicevic

, TS Fuzzy Model-Based Controller Design for a Class of Nonlinear Systems Including Nonsmooth Functions, IEEE Trans Syst Man, Cybern Syst (2017), 1–12.

21.

Yousefizadeh

, Bendtsen

J.D.

, Vafamand

, Khooban

M.H.

, Blaabjerg

and Dragicevic

, Tracking control for a dc microgrid feeding uncertain loads in more electric aircraft: Adaptive backstepping approach, IEEE Trans Ind Electron 66(7) (2019), 5644–5652.

22.

Vafamand

, Khooban

M.H.

, Dragičević

and Blaabjerg

, Networked Fuzzy Predictive Control of Power Buffers for Dynamic Stabilization of DC Microgrids, IEEE Trans Ind Electron 66(2) (2019), 1356–1362.

23.

Vafamand

, Khooban

M.H.

, Dragicevic

, Blaabjerg

and Boudjadar

, Robust non-fragile Fuzzy Control of uncertain DC Microgrids Feeding Constant Power Loads, IEEE Trans Power Electron 8993(c) (2019), 1–1.

24.

Vafamand

and Rakhshan

, Dynamic Model-Based Fuzzy Controller for Maximum Power Point Tracking of Photovoltaic Systems: A Linear Matrix Inequality Approach, J Dyn Syst Meas Control 139(5) (2017), 051010.

25.

Rakhshan

, Vafamand

, Khooban

M.H.

and Blaabjerg

, Maximum power point tracking control of photovoltaic systems: A polynomial fuzzy model-based approach, IEEE J Emerg Sel Top Power Electron 6(1) (2018), 292–299.

26.

Kardan

M.A.

, et al., Improved Stabilization of Nonlinear DC Microgrids: Cubature Kalman Filter Approach, IEEE Trans Ind Appl 54(5) (2018), 5104–5112.

27.

Yousefpoor

, Fathi

S.H.

, Farokhnia

and Abyaneh

H.A.

, THD minimization applied directly on the line-to-line voltage of multilevel inverters, IEEE Trans Ind Electron 59(1) (2012), 373–380.

28.

Espinosa

E.E.

, et al., A new modulation method for a 13-level asymmetric inverter toward minimum THD, IEEE Trans Ind Appl 50(3) (2014), 1924–1933.

29.

Dagan

K.J.

and Rabinovici

, Criteria-based modulation for multilevel inverters, IEEE Trans Power Electron 30(9) (2015), 5009–5018.