1. Introduction
The growing demand for electricity, the rapid depletion of fossil fuels, and the threat of global warming and climate change have necessitated the development of RES for power generation. Hydropower, wind, solar, and geothermal are examples of RES that deliver clean energy with zero carbon emissions [
1,
2,
3]. The increased integration of renewable energy sources, distributed generations, and growing customers has resulted in the development of a concept called the Microgrid. According to CIGRE/US DoE, a Microgrid [
4] is defined as a small-scale self-controllable power system that integrates several Distributed Energy Resources (DERs), loads, and energy storage systems to provide electrical power to a particular area. The Microgrid is beneficial for both electricity customers and power grid operators as it improves power quality, efficiency, and system reliability, reduces emissions, network congestion, and power losses. Microgrids are categorized into three types: AC Microgrids, DC Microgrids, and hybrid AC/DC Microgrids. Among them, the DC Microgrids are gaining popularity over traditional AC Microgrids due to various advantages, such as no consideration of reactive power regulation, synchronization in islanded mode, and harmonic or frequency conflicts. Based on operating conditions, the DC Microgrids are further classified as islanded and grid-connected modes [
5,
6,
7].
Among the different Renewable Energy Sources (RES), photovoltaic (PV) and wind power generation systems have seen massive growth in recent years as they are more accessible and provide clean energy without any harmful emissions [
8]. The electrical energy generated by PV panels is delivered to the connected load, grid, and batteries through proper power electronic interface. The output voltage of a PV panel is generally low due to varying weather conditions, which necessitates converters to enhance the voltage before connecting it to a grid-connected inverter [
9]. To improve the DC-DC conversion operation and obtain a controlled output voltage, different converters such as zeta converter [
10], boost converter [
11], SEPIC converter [
12], Cuk converter [
13], buck-boost converter [
14], Luo converter [
15], etc., have been reported. Converters such as boost, buck-boost, and cuk generate high voltage gain when operating at high-duty cycles and cause high voltage stress across the switches. The presence of large input current ripples in cuk and SEPIC converters inhibits their MPPT performance.
For significant enhancement of converter operation in terms of percentage THD, unity power factor, settling time, overshoot, steady-state error, rise time, etc., adopting a suitable controller is crucial. The conventional PI controller is the most desirable in the PV grid-connected systems since it is simple in design, regulates DC quantities and improves system performance [
16]. However, it includes a non-zero steady-state error, performance deterioration during load changes, and difficulty in determining appropriate controller values due to non-linearity and changing operating conditions [
17,
18]. To address these issues, Artificial Intelligence (AI) techniques such as FLC or Neural Networks (NN) are used to control the switching operation of the converter. However, NN based control approach is computationally intensive and suffers from control complexity, especially in PV applications [
19,
20]. In order to address the aforesaid issues in the existing literature, this research paper proposes a Cascaded Fuzzy Logic Control (CFLC) to enhance the operation of a high-gain LUO converter and to effectively enhance the DC link voltage of the DC microgrid system. The proposed cascaded control approach is capable of managing disruptions and tackling problems related to non-linearity and uncertainty.
Nowadays, DFIG-based Wind Energy Conversion Systems (WECS) are widely used for various reasons. It includes reactive power support, improved efficiency, low power loss, reduced acoustic noise, and low-rating power converters. At the same time, the growth in the number of DFIG-based wind farms results in the minimisation of system inertia, which in turn develops stability issues in Microgrids [
21,
22]. In the absence of Inertia, the DC Microgrids suffer from voltage stability issues, whereas the AC Microgrids suffer from frequency stability issues. The voltage fluctuations cause havoc on renewable energy sources and sensitive loads connected to the DC Microgrids. These voltage instabilities in DC bus voltage occur mainly due to sudden load variations and intermittent power generation from renewable energy sources.
In addition to inertia control, proper damping control is also essential to keep the voltage stable and ensure adequate power balance in DC Microgrids [
23]. The stable and reliable operation of the DC Microgrids is further improved through droop control, which offers effective DC voltage regulation and ensures proper power-sharing [
24]. A linear method based on virtual impedance has been studied in [
25], where the damping control has enhanced voltage stability. However, this approach cannot provide the required recovery voltage for stabilization downstream. The DC Microgrids’ rapid and unexpected voltage variation is stabilized by providing proper inertial support [
26]. A combined approach of the virtual inertia concept and damping control is adopted for the PV system [
27]. However, this strategy does not apply to WECS; therefore, the contribution of inertia support from the wind turbine to the DC Microgrids for stability enhancement is not achieved. To address this issue, in this paper, an effective inertia and damping control method along with droop control is used for DFIG-based WECS to achieve effective DC link voltage enhancement in the DC Microgrid system.
Energy Storage Systems (ESSs) are other key constituents in DC Microgrids because they improve power quality, reliability, security, energy balance, and minimise energy losses. It mitigates the reverse flows caused when DER generation exceeds the load, resulting in the reduction of energy losses. When there is an excess energy generation, the ESS stores the appropriate amount of electrical energy and delivers it to the grid and load when there is a need. To attain this, ESS is connected to the DC bus through a bidirectional converter, which avoids the potential risk caused by the direct connection of ESS with the DC bus [
28]. In the present work, an ANN-based droop control approach is proposed for ESS to effectively regulate the DC link voltage during normal operating conditions and during insufficient power generation from PV and WECS.
In summary, the interfacing of several DERs, ESSs, and loads to the DC bus renders the DC Microgrids a low-inertia power system with poor damping, resulting in DC link voltage fluctuations even for small disturbances. Hence, combating these current technical issues is instrumental in improving DC Microgrid’s performance. To overcome the aforesaid challenges, a new control approach has been proposed for the DC microgrid system.The main contributions of the proposed work are summarized as follows,
A hybrid PV, wind, and ESS-based hybrid DC microgrid system is designed to meet the increasing load demand.
A high-gain Luo converter with CFLC is proposed to enhance the PV output voltage for providing high-gain outputs, which helps for effective stabilization of the DC link voltage of the Microgrid.
In addition, with the aid of virtual inertia and damping control approach along with droop control strategy for DFIG-based WECS, DC link voltage is effectively regulated under all operating conditions.
An ANN-based droop control technique is proposed for ESS to adequately enhance the DC link voltage during insufficient power generation from the PV and WECS.
The simulations are done in MATLAB/ Simulink and the results are experimentally validated using the FPGA Spartan 6E controllers.
In
Table 1, the current technical status of the existing literature along with its advantages and drawbacks are summarized. In addition, the merits of the proposed control approach to overcome the challenges in existing literature are listed.
The remaining part of the paper is organized as follows.
Section 2 covers the literature review. A brief description of the structure and operation of the proposed DC Microgrid is covered in
Section 3. Moreover,
Section 3 also entails the modelling of each of the components present in the proposed Microgrid architecture. The theoretical analysis is verified through MATLAB simulation, and the derived results are discussed in detail in
Section 4. In
Section 5, the experimental verification is carried out using the FPGA Spartan 6E controller. The conclusion for the proposed research work with the future scope is provided in
Section 6.
3. Proposed Microgrid and Modelling
The schematic diagram of the proposed DC Microgrid consisting of PV, wind, and an ESS is shown in
Figure 1. In this work, a Luo converter with CFLC is incorporated to boost the output voltage of the PV system and interface it with VSI. The optimized output from the CFLC is used to generate PWM pulses for controlling the switching operation of the Luo converter. In DFIG-based WECS, the controlled output is acquired by executing droop control together with virtual inertia and damping control. By feeding this combined control approach, the required pulses to control the duty cycle of the rectifier to generate a controlled DC output are obtained. Moreover, in ESS, ANN with droop control is adopted to promote its operation and to maintain its state of charge significantly. The regulated output initiates the pulse width modulator to generate pulses to control the operation of the bidirectional converter. A buck converter with a PI controller provides a controlled DC output voltage for the load. Finally, RESs and ESS are connected to the utility through VSI.
3.1. Luo Converter
Generally, the output voltage from a solar panel is low. To boost its voltage level and to meet varying parameter conditions, suitable converters are to be employed. In this proposed work, a Luo converter with CFLC is utilised to upgrade the conversion process and to obtain a controlled output voltage without a reversal in polarity. Implementing such a converter reduces cost and the required number of solar PV panels for power generation.
The Luo converter is a high-efficiency buck-boost DC-DC converter that provides a high output voltage with minimal current ripples. The circuit representation of the Luo converter is shown in
Figure 2 with the power switch S and the freewheeling diode. The operation of the Luo converter is analyzed by two modes, which are explained in
Figure 3.
In mode 1, the switch
is in ON condition and the inductor
gets charged by the supply voltage
. The inductor
gets charged by the capacitor
and the source voltage. The diode D gets reverse-biased by the voltage of the capacitor
, whereas the capacitor
supplies energy to the load. The voltage across an inductor
depends on the input voltage
and is expressed as follows:
The voltage across an inductor
is,
Here, indicates the voltage across the capacitor , denotes the voltage across the inductor , and indicates the output voltage.
In mode 2, the switch
is in OFF condition, and hence the current obtained from the source becomes zero. To charge the capacitor
the current
flows through the freewheeling diode. The current
flows through the capacitor
and load and keeps itself continuous through the diode D. Now, the diode becomes forward-biased to carry the inductor current. The KVL equations for mode 2 are given below,
Applying the voltage second balance principle on the inductors
and
,
where
refers to duty cycle value, t
on refers to the ON time period, and T refers to the total time period. Depending on the duty cycle (D) of the switch, the output voltage of the Luo-converter may be less than, equal to, or greater than the source or input voltage. When D is less than 0.5, the output voltage is less than the input voltage. When D is equal to or more than 0.5, the output voltage is larger than the input voltage. Therefore, the resulting equation for output voltage is given as,
The inductors
and
values have a ripple content of
and are given as,
where the terms
and
refer to peak-to-peak ripple current of inductors.
The capacitors
and
values have a ripple content of
and are given as
Here, the terms and specify the peak-to-peak ripple voltage of the capacitors. The operation of a high-gain Luo converter is further enhanced by applying CFLC.
3.2. Cascaded Fuzzy Logic Controller
The application of a suitable controller in the form of CFLC provides the required error compensation and improves the dynamic performance of the Luo converter. To ensure overall system control, cascaded control [
31] is adopted, which can offer quick responses to disruptions. Since fuzzy sets are incredibly effective in tackling the problem of uncertainty and non-linearity in a system, fuzzy cascade control structures are developed and utilised in this present work.
Figure 4 shows the structure of the proposed cascaded control technique consisting of two FLCs connected in a series. The reference control signal from the first FLC drives the second FLC to generate the necessary duty cycle
command that controls the switching pulses of the converter. The second FLC is designed to minimise distortions, which have a detrimental effect on the output of the Luo converter.
The voltage output of the converter is compared to the reference voltage and an error is obtained. The error in addition to the change in error , is fed as input to the CFLC. The CFLC optimised output is then given to the pulse width modulator to provide the required pulses for enhancing the converter output efficiently. The CFLC technique used in this paper is a closed-loop control methodology applied to maintain a constant output voltage of the PV system.
Figure 5a,b illustrates the triangular membership functions of inputs
and
in addition to the CFLC rule base. The rule base is usually represented by fuzzy linguistic variables. For the designed
rule base, the
linguistic control rule is given as,
Here, the numeric value of the control is specified as , change of error is specified as , the output is specified as , the error is specified as , and the kth rule is specified as . The linguistic value of change of error and error is specified as , respectively.
3.3. Virtual Inertia and Damping Control
The term inertia is a system parameter that refers to the ability of the Microgrid to store and infuse kinetic energy into the electric power system. Inertia is considered to be quite an essential property required to control and maintain the smooth functioning of the entire electric power system. Insufficient inertia results in frequency instability in the AC Microgrid and voltage instability in DC Microgrids. The voltage instability causes deteriorating effects on the renewable power sources and sensitive loads interfaced with the DC Microgrid. The fluctuations in DC bus voltage result from massive distortions caused by step changes, an arbitrary connection of loads, and the variable, uneven power introduced by renewable power sources. The damping effect is considered essential to DC Microgrid owing to its inherent nature to subdue voltage fluctuations. Hence, for the stable operation of the Microgrid, the inertia effect, in addition to damping capability, is critical. An ideal inertia and damping control technique for WECS is adopted in this research to enhance the DC Microgrid’s voltage stability and power balance, as illustrated in
Figure 6.
The dc-link active power balance equation is represented in (23)
where
is the active power output of the PWM rectifier,
is the active power injected into the DC Microgrid,
is the dc capacitor voltage, and
is the dc bus voltage. During the steady-state operating condition,
since constant DC voltage is maintained. During unbalanced operating conditions or uncertainties, the variation in
may occur, leading to either the charging or discharging of the DC capacitor. It results in the reduction of the dc voltage change rate. The wind turbine is a controllable power source and can reduce this dc voltage change rate. It provides additional power through the application of inertia and damping control [
32]. The inertial power supplied (
) by the WECS to maintain the dc-link power balance is represented by the following Equation (24),
The value of inertial power
is determined as
By adjusting the value of the PWM rectifier’s virtual inertial control coefficient
, the DC Microgrid inertia is regulated as per the following equation,
The effect of inertia power
on the DC Microgrid can be described as a virtual capacitor
connected in parallel with a capacitor
. The capacity demands of actual capacitor
can be reduced by increasing the magnitude of
, which benefits the DC Microgrid’s working ability. The variable speed of the wind turbine results in an alteration of output electric power according to the motion equation of the rotor.
where the terms
and
refer to the electromagnetic and mechanical power of the DFIG, respectively. The rotor speed is represented by
and the inertial time constant is represented by
. When the DC Microgrid experiences voltage fluctuations, the kinetic energy stored in the rotor provides the required virtual inertial power
.
where
refers to the virtual inertial control coefficient of WECS. The dc bus voltage is related to the wind speed through the following equation,
where the optimum power coefficient of the wind turbine is represented by
. The terms
and
refer to the dc bus voltage of the earlier sample and the dc bus voltage in the vicinity of the rated value. The virtual inertial coefficient
is given as,
3.4. PI Controller Based Droop Control
The main objective of applying the PI controller-based droop control technique [
33] is to ensure the optimal power deliverance of WECS according to its capacity. Both the voltage control and current control loop of the droop control use a PI controller for error compensation. The PI controller of the voltage control loop compares the actual DC output voltage of the PWM rectifier
with the reference voltage
and generates a reference current
. The reference current generated from the PI controller of the current control loop is compared with the actual current
obtained from the PWM rectifier to produce a control signal. The resultant control signal controls the switching operation of the PWM rectifier to deliver a controlled DC output, as shown in
Figure 6.
3.5. Modelling of the ESS with ANN Based Droop Control
3.5.1. Description of ANN
The ANN replicates the working of the human brain to find the solution to complex problems. In this work, the feed-forward neural network (FFNN) is selected. The FFNN comprises of an input layer, an output layer, and a hidden layer. Its performance relies on the training process, the number of neurons in the hidden layer, and the activation function used by the hidden layer. The total number of neurons in the hidden layers is 50. The tan sigmoid activation function is used for the hidden layer, while the linear activation function is used for the output layer. The number of training epochs is 1000, and the Levenberg-Marquardt algorithm is used as the learning algorithm. The learning rate is 0.01. The training time of ANN is approximately 20 min, and the attained accuracy is about 92%. The input signal passes through the network layers, and the corresponding weights are adjusted during training. Similar the PI controller, the ANN controller is also provided with an error signal and an integral of the error signal as input.
where
,
, and
specify the reference signal, actual signal, and error signal, respectively. Finally, the output of the ANN controller is used to tune the parameters of the PI controller.
Utilising ANN for ESS control has a number of benefits, one of which is their capacity to learn and adjust to changing circumstances. The ANN is capable of modifying its settings as system data is gathered to optimise performance based on the system’s current state and provide more precise and accurate control. Thus, ANN is a potential technique for controlling ESS in a variety of applications since it offers a flexible and effective approach to ESS control.
3.5.2. Structure of Bidirectional Converter with ANN Optimized Droop Control
A bidirectional power converter is used to interface the ESS to the dc link since it effectively assists the process of charging and discharging. The surplus power generated from both renewable sources is stored in the ESS. In a short supply of power, the stored energy from the ESS acts as the secondary backup power source. To overcome the voltage imbalance issues of the DC Microgrid due to ESS over-discharge/charge conditions, an ANN-optimised droop control technique [
34] is introduced, as shown in
Figure 7.
The reference voltage
of the ESS maintained at the input is
. The output of the voltage control loop is the desired reference current value
. The output of the current control loop enables the PWM generator to initiate pulses that control the switching operation of the bidirectional converter, as seen in
Figure 8.
The droop control technique also controls the SOC condition of the ESS. The battery gets charged at a state of 20% SOC value and gets discharged at a state of 80% SOC value. The battery SOC, which refers to the charge contained in the battery in relation to its volume, is expressed as,
where the battery capacity, in addition to the battery charging current, is specified as Q and
, respectively. When operated in buck mode, the bidirectional converter has the ESS in the place of load and the DC link as the source, and thus, the charging of the battery takes place. In the boost mode of the operation of the converter, battery discharges and ESS serve as a source in this mode. The capacitor and inductor value during boost mode and buck mode of the operation is given as,
where the ripple voltage and ripple current are specified as
and
, respectively. The terms
refers to output resistance, switching frequency, and duty ratio.