Fuzzy Control of Hybrid Power System to Improve Power Quality

For many years, small autonomous grids have been in place in isolated villages where connecting to the main power grid is impractical for technical or financial reasons. Fossil-fuel production methods have been the most popular option for supplying electricity in these remote grids because of their scalability, affordable investment costs, and adaptable operation. However, integrating greener generation technologies based on solar, wind, hydrogen, and hydro power has become a priority in microgrids due to their proven technical and financial viability [1]. Currently, the electrical industry is dealing with two main issues: (a) increasing the number of people who are not currently served by the grid and (b) satisfying the growing demand from those who are served by the grid. Globally, 20% of electricity generated in the next ten years (by 2020) must come from renewable energy sources [2]. The concept of renewable energy systems (RES) presents a more sustainable technology that can meet the increasing electrical needs of remote and networked populations. Microgrids (MGs) have recently piqued the curiosity of scientists as a possible future alternative to traditional energy sources. With MGs' assistance, it is believed to be feasible to integrate various sources of RES within conventional grid [1].

Despite unexpected events and crowded distribution grids, MGs offer great dependability in their ability to function, in addition to cleanly and dependably integrating distributed energy throughout the main grid. This lowers the loss of electricity throughout the transport and distribution processes and shortens the period needed for investing and implementation [3]. The studies [4, 5] were reviewed, and some of them were put into practice. Several instances are found in various nations across the globe. The bulk of MGs are located in Europe, as are the United States and Japan.

A low-voltage distribution power system MG is characterized by the connection of small modular generation systems, which include distributed generators (DGs), sustainable sources of energy, and additionally intermediate storage containers, to meet load demands. The utility grid has the ability to handle this specific power system as either a generator or a controlled load [6]. According to the type of source that provides the control, Microgrids fall into one of three categories: hybrid, alternating current (AC), or direct current (DC) [7]. Microgrids are the best option for delivering electricity to isolated regions of a nation where the output of the national grid network is constrained by design, rendered inoperable by bad weather, or interrupted by human activity [8].

As a result, the microgrid's economic operation is its primary concern. Numerous scholars are investigating ways to maximize microgrid operation activities [9]. Some studies are particularly focused on AC MG because of its compatibility with the main grid, even though MG systems can be entirely DC, AC, or a hybrid of the two technologies [3, 10, 11].

A better design approach for FLC systems for a DC microgrid under variable atmospheric conditions is suggested by this study. It will enhance the reaction stability and dependability of the system. An FLC uses DC-DC boost converters to assess PV and FC in order to achieve this improved accuracy and response time [12]. The bacteria foraging optimization (BFO) algorithm was used in the article to optimize an isolated microgrid model that incorporates both a traditional diesel-fueled generating unit and renewable energy sources like solar and wind. Two-Swim Modified BFOA (TS-MBFOA) and Normalized TS-MBFOA (NTS-MBFOA) are two new iterations of the BFOA that were put into practice and evaluated. The results demonstrated that TS-MBFOA outperformed NTS-MBFOA and the evolutionary algorithm LSHADE-CV in terms of numerical solutions. But from a mechatronic perspective, the optimal solution identified by NTS-MBFOA is superior since it extends the IMG's lifespan, which eventually leads to financial savings [13].

In recognition of the nature of most renewable energy methods, MG designs incorporate power electronics. The injected power supplied to the MG must be managed [11]. Its power quality issues might be resolved if suitable closed-loop control methods are used [14]. Parallel inverter utilization has the potential to enhance the performance of many technologies and MG arrangements [15]. The main energy resource in a typical hybrid energy system is typically less available than the secondary resource, which is typically more available. For the situation, especially during the winter months, when solar radiation is at its lowest, wind speed is typically very high. While wind energy has been effectively used, sunlight can to be used to generate power throughout the night. In addition to this, meeting consumer load requirements through the simultaneous use of diverse energy resources is greatly improved [16].

The purpose of this research is to improve the power quality of a proposed hybrid MG system and to reduce AC and DC switching to improve efficiency, and enhance the quality of the power it produces, which consists of PV, wind and battery with a control system for each unit. As a result, a fuzzy logic-based controller for PV systems is designed. The controller connects the battery through BBDC and a permanent magnet synchronous generator (PMSG) wind power system, and synchronizes its output with the power grid to improve the power quality. The VSC controller is coordinated with the PWM of the inverter installed on the power supply system with the power grid.

Distributing the paper in this manner: Part 2 describes the general structure of a microgrid. Each element of the proposed hybrid microgrid system is explained in Section 3. Proposed model and then modeling photovoltaic (PV) in subsystem (3.1) and subsystem for battery storage of electricity (3.2) are covered in detail in their respective subsections. Modeling the PMSG wind turbine in subsystem (3.3). The voltage source converters (VSC) within the AC/DC bus subsystem are fully described in section 3.4. In Section 4, the controlling techniques applied to the system's hybrid microgrid sources were explained. Section 5 displays the simulated findings within MATLAB/Simulink software. The results of the three technique scenarios have been tallied and compared. Lastly, but not least, Section 6 contains the conclusions.

This section provides an explanation of the suggested design. In the MATLAB/Simulink environment, the system was implemented with the appropriate Simscape program. The purpose is to present researchers with an instrument and a deeper comprehension of the MG behavior and all of its constituent parts, as well as the control techniques that are used for this proposed HMG system, in addition to their general effects for enhancing power quality in various operational prerequisites.

Figure 2 shows a depiction of the proposed hybrid MG. The schematic below shows how the MG is connected to an electricity sub-transmission scheme to the main grid. This MG is made up of a battery, PV array, and a conversion of wind energy at a changing speed based on the PMSG. The maximum power point tracking (MPPT) algorithm controls PV sources of energy that are connected to a DC bus. Whereas in the WT situation, we will utilize a proportional integral (PI) controller to act on the pitch angle control in order to maximize power extraction amounts and control the quantity of power produced at its rate value. The battery served as a storage device, and it connected to the DC bus through a bidirectional DC/DC Buck Boost converter. Weather-related factors affect wind and solar power systems, and solar electricity is nonexistent at night. As a result, a battery by itself is unable to meet the load demand in conditions of prolonged no wind or low sun. Weather-related factors affect wind and solar power systems, and solar electricity is nonexistent at night. And a battery by itself is unable to meet the load demand in conditions of prolonged no wind or low sun. Furthermore, the VSC acts as a control system on DC/AC buses, utilizing feedback signals for voltage and current. Droop controllers can then produce reference signals, allowing for the intelligent application of another device to regulate the microgrid's voltage and frequency. The bacteria-foraging optimization approach is used to modify the controller's parameters (Kp, Ki). For both real and DC its elements, this PI-BFO system controller acts like a current regulator. In the suggested hybrid power generation system, VSC will safeguard the renewable energy sources.

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Figure 2. Proposed hybrid MG system

3.1 Solar-photo voltaic system (PV)

PV modules' voltage-current along with voltage-power relationships is evident that the PV panel's maximum power is achieved in proximity to its open circuit voltage [20-22]. In Eq. (1), the PV model's voltage-current (V-I) characteristics are provided by the study [23]:

$I_{p v}=I_{p h}-I_s e^{\left(\frac{q\left(V_{p v}+R_s I_{p v}\right)}{n k T}-1\right)}-\frac{V_{p v}+R_s I_{p v}}{R_{s h}}$          (1)

where, I_ph is the photo-current, I_s is the diode saturation current, q is the electron charge, T is the temperature in (K), n is the P-N junction ideality factor, and R_s and R_sh are the intrinsic series and shunt resistances of the PV cell, respectively.

The most significant of the distributed sustainable energy supplies utilized in MG is solar-PV technology, which uses the MATLAB/Simulink software to model the PV cells' performance. At a temperature of 25℃, an overall value of G=1000 W/m² is applied to the dual arrays during operation across the cells that produce electricity. The data from each module in this PV is used to produce a standard power output of 213.15 kW for the DC Bus.

3.2 Wind energy control system (WECS)

All wind system needs wind turbines. They work as the main movers of generators that produce electricity and are attached to their shafts. Axis wind turbines can be broadly classified as either vertical axis wind turbines (VAWT) or horizontal axis wind turbines (HAWT), based on the direction in which the turbines rotate. Depending on how they operate, they are further classified into constant speed and varying speed varieties. The wind energy conversion system's wind generator models and wind turbine parameters are the main factors affecting the WECS's mode of performance [24].

3.2.1 Features of a wind turbine

The following represents the aerodynamic power factor, or Cp, for the wind turbine, which is a measure utilized for expressing the turbine's electrical power generation as in Eq. (2):

$P=0.5 \rho_a A C_P V^3$          (2)

where, A describes the swept area in m², V represents the wind velocity speed in m/sec, and ρ represents the air density in kg/m³. Eq. (2) shows how wind power is affected by air density, rotor area, and wind speed [24].

As follows: One measure of a wind turbine's performance is its aerodynamic power factor, which is established using the blade pitch angle (β) and tip-speed ratio (λ). A general Cp equation is determined by the properties of the turbine [23]. With varying values of β, the Cp varies with λ as explained by Eq. (2). Maintaining λ at the optimal level for all wind speeds is necessary for operating the WECS with the highest C_p value [25, 26]. This is just possible with operation at variable speeds using DFIG or PMSG, which allows the turbine's speed of rotation to be changed according to changes in wind speed [27]. In large wind systems, in order to set the electrical energy produced by the wind turbines within a predetermined limit, pitch angle control is usually utilized, pitch angle control is usually utilized, particularly during high speeds of wind [27].

3.2.2 PMSG

Typically, a PMSG in a wind energy conversion system is constructed with multiple poles. It therefore operates slowly in synchronization with the wind turbine's rotational speed. Hence, PMSG and the rotor shaft of the wind turbine are intimately related.

One such activity that doesn't need additional gearbox arrangements is the direct-drive operations [28]. According to their greater efficiency, these generator types are favored in designs that are small. Permanent magnetic materials are expensive, which has constrained their application, even though large-scale designs have been explored. The system for electricity from wind conversion with PMSG that employs a standard AC-DC-AC transformation process [29, 30]. Through the utilization of electrical power conversion devices, the stator can be linked to the electrical grid. A standard wind energy transformation system utilizing PMSG. Because without a spare rotor management is present, the electrical power converters in utilize operate at their full capacity, increasing the efficiency of energy from wind conversion throughout a wide range of wind speeds. Additionally, full capacity power converters don't need extra equipment to be used in fault ride-through situations and help to comply with various grid rules [28].

3.3 Battery energy storage system (BESS)

The irregular variable behavior of distributed energy from renewable sources causes it to be extremely difficult to manage and operate the micro grid. The PV system thus requires an energy storage system (ESS). In order to maintain frequency as well as voltage at the scheduled levels, the ESS adjusts the power output. This allows it to provide the islanded micro grid with the appropriately defined voltage and frequency supports [29].

The BBES unit in this HMG system is connected over a DC bus, which has a voltage higher than the battery connected across it. Consequently, the battery and a buck-boost converter are linked, which adjusts the electrical voltage of the battery based on the operational parameters of the PV system, for the purpose of managing its energy use.

3.4 A DC-DC buck boost bidirectional converter (BBDC)

The solar system and batteries are integrated into a bidirectional converter that converts DC to DC. Through the use of a bidirectional DC-DC converter, electricity can flow both ways—from a load into a BESS. Assuming there is enough available irradiance to generate the essential voltage for running the load, power moves from the PV system towards the BESS, simultaneously charging the BESS [30]. Currently, the bidirectional converter will work in the buck state. The power is transferred from the BESS towards the load, and the BESS discharges as a result whenever the voltage required for the load can to be produced with the available irradiance. Bidirectional DC-DC converters are now operating in boost state. Bidirectional converter specifications are constructed with the assistance of Eqs. (3)-(5) [24]. Table 1 displays more information about this proposed hybrid microgrid.

Table 1. The microgrid system's parameters and specifications

$C_H=\frac{D}{R_H f_s\left(\Delta V_H / V_H\right)}$          (3)

$L_{b, \min } \geq \frac{D(1-D)^2 R_H}{2 f_s}$          (4)

As for the operation's buck:

$L_{b, \min } \geq \frac{D(1-D) R_L}{2 f_s}$          (5)

where, the load resistance at the boost and buck sides is denoted by R_H and R_L, respectively. C_H is the boost side capacitance value. The switching frequency is f_s, and the inductance's minimum value is L_b,min [24].

In this section, the reported results of the quality and efficiency variables of the power will also be established to demonstrate the efficacy of the control techniques applied to improve the hybrid microgrid's power quality in Figure 2. As previously mentioned, MATLAB software is used for all simulations, and the controller has been inspected. Table 1 presents the system parameters. The following results confirm the efficacy of the controlling methods employed to enhance the quality of the power across the proposed HMG system.

5.1 Simulation results of different MPPT control methods for a PV system

A dynamic representation of a solar electricity system using the controller with fuzzy logic, conventional P&O, and IC algorithm on the solar system as that to MPPT, could be used to analyze the characteristics of the converter, including the voltage of the input, the voltage at the output, in addition to electrical current, power, along efficiency. To determine each algorithm's near to the MPP values, the initial three variables were chosen. The output voltage was selected to confirm that there are apparent oscillations at the output converter, and to ascertain whether an FLC generates a notably higher efficiency, the efficiency value was utilized. This is further demonstrated by the outcomes obtained, which are present in the accompanying Figure 12 and Table 3.

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(a) Power

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(b) Current

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(c) Voltage

Table 3. Comparison of different methods for obtaining MPPT

As demonstrated by the figures above, the FLC approach yields higher efficiency, power, voltage, and current in comparison with both P&O and IC algorithms at the same environmental parameters; nevertheless, FLC takes longer to stabilize. Despite FLC's long settling time compared to other methods, this indicates that the controller reached the correct MPP to provide higher power than the traditional algorithms.

As a result of the efficiency value being more stable, the oscillations are slightly smaller for FLC when they reach the MPP, as is primarily visible in Figure 12(a). To show how beneficial it is at reducing oscillations with MPP under steady-state circumstances. The specific interval's environmental conditions 1000 W/m² of solar radiation along with a 25°C ambient temperature are identical to the PV panels' test environment, so that the boost converter's voltage, current, and power output should almost exactly correspond for the results of MPP listed in Table 2. This is verified by Figure 12.

Because every research employs a converter with specific characteristics and looks at how PV systems operate under different combinations of ambient temperature and solar irradiation, it is exceedingly difficult to compare the results achieved for the suggested algorithms to those of other research directly. As a result of the work's observation indicating that an elevation-value capacitor, along with a little value inductor, favorably affects the converter's efficiency, after comparing FLC with traditional algorithms, this study indicated that FLC is the best approach, which gives high output power and optimal efficiency for use in the proposed MG system in this study.

5.2 BESS

A description of the HMG's battery as having a standard voltage of 384 volts, a current capacity of 2000 Ah, and an initial state of charge rate (SOC) is 50% SOC rate. Since the SOC has been set to 50%. Depending on the battery's condition, charging will occur to the required level when the PV's irradiance is less than 100 W/m²; the battery will start charging at that proportion. Figure 13 shows that the battery was operated in discharge mode for five seconds and the SOC of the battery dropped while maintaining the line voltage of 48V. Moreover, the charge a battery at SOC required to achieve 80% of its capacity at 418 volts. Notice that the SOC for the battery is choice to 50% charged in order to safeguard and ensure it has a long life.

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Figure 13. DC-bus and SOC under charge and discharge

5.3 Simulation results of the wind turbine

Features that enable the wind turbine to generate power in the range of 100 to 600 kW. The blades have a diameter of forty meters. The operating wind speed is between 6 and 40 m/s. Eight PMSG generator poles with a 500-volt output voltage are coupled to an AC-DC-AC converter. Figure 14 illustrates the features of when wind speeds fall between 10 and 15 m²/sec. The figure shows that the mechanical torque is less than zero at first, increases over the course of 0.1 sec, and then remains at that value. Additionally, the figure will show that the wind turbine speed for the generator starts at zero and increases over the course of 0.2 sec, reaching almost 480 rad/sec.

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Figure 14. Mechanical torque and speed of the generator and wind

5.4 Simulation results of Hybrid AC/DC Microgrid (HMG)

Renewably sourced electricity is used in this planned hybrid microgrid, including solar (PV) arrays and wind turbine generators (WTG). Here, MATLAB software is used to simulate the microgrid for the purpose of reducing both AC and DC switching to enhance efficiency, and enhancing the quality of the power of the proposed hybrid microgrid. Furthermore, preventing overcharging or excessive discharge of the battery, which could harm them and shorten their usable lives, is another essential component of the control system on a hybrid microgrid.

5.4.1 Simulation result of PV-side

When using FLC with an MPPT controller is give best outcomes are achieved with results found from using the two conventional algorithms, IC and P&O, as shown in Figure 15 displays supplying the DC load with the voltage from the DC bus with a slight ripple under constant temperature (25℃) and variable irradiance at the optimal duty cycle to the converter from the PV system. It is noticeable that the output DC voltage becomes almost stable after a short period of time is 0.35 seconds from the start time. It is necessary to deliver the DC load at 500 V, that being the electrical voltage across the DC bus.

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Figure 15. Voltage of DC bus

Figures 16 and 17 show the voltage and current that exit the PV system and connect to the grid through an inverter that needs six pulses to change the DC power to AC voltage. Next, the VSC control is employed for the purpose of maintaining the voltage 380 × √2 ≈ 540 V throughout the DC link. The inverter pulses that produced these outcomes are displayed in Figure 18(a) shows the PWM output of switching (1, 3, and 5) and Figure 18(b) shows the PWM output of switching (2, 4, and 6) that represented after converting the voltage from AC to DC, it is employed to supply power to the AC load through a DC/AC the bus. by level-stabilizing and converting the AC output voltage from the DC bus to AC.

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Figure 16. Voltage of DC-AC converter PV-side

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Figure 17. Current of DC-AC converter PV-side

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(a) Output of switching (1, 3, and 5)

18b.png

(b) Output of switching (2, 4, and 6)

Figure 18. PWM of VSC controller PV-side

5.4.2 Simulation result of the WT-side

Considering wind speed affects both the frequency and speed of WTG, variations in wind speed will have an impact on the stability of the results.

The three-phase electric currents, voltages, and electric power delivered across the converter are displayed in Figure 18. That is, six pulses are required for the AC-DC-AC conversion to convert the DC voltage to the voltage of AC. After that, VSC control is utilized to keep the voltage at the required level (380 V, 50 Hz) regularly. The output three-phase voltage wave forms in Figure 19(a) demonstrate that they are stable, identical in value, and have the exact same frequency. However, the current levels will start to rise steadily and eventually stabilize at roughly constant values. In Figure 19(b), as indicated. Lastly, as seen in Figure 19(c), the power of the AC-DC-AC WT converter starts to rise and fluctuate a short while after zero. Then, the power waveforms start to stabilize and remain steady and stable. Even with variations in wind speed, the WT-system continues to provide steady power and voltage after a brief period of time from the beginning of generation. That is because using a pitch angle regulator to control the rotating speed through the usage of a PI controller has both advantages and disadvantages. Additionally, the WT-system-connected VSC has a significant impact on maintaining these variables' constant values following brief oscillations before reaching stability.

19a.png

(a) Voltage

19b.png

(b) Current

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(c) Power

Figure 19. The WT-side AC-DC-AC converter's

5.4.3 Simulation result of HMG system

As depicted in Figure 20, the evaluation looked at the changes in WTG and PV generation power based on the results. Here, the power results for the wind and solar systems show that their power is higher than that of the HMG system, which provides a higher power value of about 600 kW. The power results also show that the power of the network is efficient in producing electricity, as it starts to stabilize after 0.3 seconds.

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Figure 20. Power taken from PV, WT and unity grid

As seen in Figure 21, the frequency of all buses in this assessment gave findings that showed a distortion at first. It then enters a steady condition, which lasts for 0-0.3 seconds, or the time it takes to reach stability at (50 Hz). And also see that, as is apparent, there is little distortion from the start and small frequency variation, which is regarded as an advantageous feature of the electrical circuit's general design.

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Figure 21. Frequency of all buses

Figure 22 illustrates the detection of total harmonic distortion (THD) in the line-line voltage signal along the transmission lines of the HMG system. THD occurs when a sinusoidal signal is fed to the inverter, causing it to cut and distort. THD starts at zero, after that it increases and then slows down for a period time is 0.7 seconds before stabilizing and reaching to value of roughly 0.03. As illustrated in Figure 23, it takes some time before stabilizing at 120 degrees associated with a phase difference for the output voltage along the transmission lines of the HMG system in less than 0.1 seconds.

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Figure 22. THD of L-L voltage

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Figure 23. Phase shift between 3 phases on all buses

This work is supported by the Southern Technical University, College of Technical Engineering, Basrah, and the Department of Electrical Technology Engineering.