Enhancing Performance and Optimizing Energy Utilization and Voltage Regulation in Hybrid Wind-Solar Pumping Systems

The increase in energy request due to industrial development and modernization is projected to rise by 56.0% over the next three decades, this substantial surge in energy demand will inevitably have environmental repercussions. addressing this increased energy request through modern energy solutions, such as RESs, can reliably power supply, and affordable electricity generation and reducing the reliance on conventional energy sources and mitigating their environmental impacts. RESs play a crucial role not only in industrialization and modernization, but also for water pumping applications. By harnessing renewable energy for water pumping, it is possible to achieve sustainable water management while reducing carbon emissions and environmental degradation associated with the use of fossil fuels [1-6].

Unlike conventional energy sources, individual renewable energy systems like wind turbines and solar panels cannot reliably meet continuous load demands. Therefore, hybrid energy systems, which harness two or more renewable resources, have emerged as more reliable and carbon dioxide-free alternatives [3, 6]. Therefore, hybrid renewable energy systems (HRES) present an attractive solution for standalone power generation. They offer promising prospects for supplying reliable power while also promoting environmental cleanliness. HRES can play a crucial role in transitioning island nations and other remote regions away from costly and environmentally harmful fossil fuels towards sustainable and affordable energy sources.

1.1 Literature review

Bhayo et al. [3] discussed the analysis and optimization of a HRES for a housing unit, utilizing rainfall harvesting and a PV-battery system. The study aims to minimize component oversizing and ensure reliable power supply management using the particle swarm optimization technique (PSO). The study demonstrates that integrating rainfall harvesting with a PV-battery system can achieve environmentally appropriate, reliable, and cost-effective power generation. The proposed system reduces dependency on battery storage, which is the most expensive component with a short lifespan in integrated renewable systems. Harndi et al. [4] aimed to enhance optimization techniques for maximizing the efficiency of a PV pumping system, The results showed the superior performance of sliding-neuron control in reducing chattering and deviation from the reference. Abuashour et al. [5] presented a hybrid system (HS) consists of a wind turbine connected to a self-excited induction generator, a PV array is integrated with a DC-DC converter, inverter, and LC filter. The performance of the HS was evaluated under varying conditions. The results indicate that the system can operate effectively over a wide range of solar intensity levels and IM loading conditions. Obaid et al. [6] presented the design of a hybrid power system for water pumping applications. It was utilized two main RESs: solar PV panels and wind turbines. The design took into account variations in weather conditions, such as humidity, wind speed, and temperature, as these factors impact the performance of both wind turbines (wind speed) and solar panels (solar irradiance). A neural network was employed for weather forecasting based on five years of historical data for temperature, wind speed, and humidity in Sharjah. A robust control method for a two-stage water pumping system was suggested by Massaq et al. [7], the maximum power point (MPP) was tracked in the first stage using a boost converter controlled using a fuzzy logic technique that had a configurable step size. a nonlinear predictive controller was employed in the second stage, which consists of a three-phase inverter controlling the DC-link voltage and the rotor speed of the IM, in comparison to the old system (P&O algorithm for MPPT and field-oriented control (FOC) control of the IM), the results showed that the advanced control approaches give superior performance, such as fast tracking of the MPP and stable output power. An Analytical MPPT Control (AMPPTC) for a PV water pumping system based on an IM was presented in the study [8]. The AMPPTC system shows rapid dynamic performance and minimal overshoot with the water pumping system. Additionally, the AMPPTC scheme offers robust absolute state stability under variations in solar irradiation/operational parameters, optimizing system stability while ensuring regulation of the DC-link voltage. Hosseinzadeh and Heydari [9] suggested using a Linear Quadratic Regulator (LQR) for controlling a standalone single-phase PV pumping system, utilizing a permanent magnet synchronous motor (PMSM). The reference speed is determined using the P&O MPPT method. The system behavior was evaluated under various environmental conditions. Total harmonic distortion (THD) of the stator currents was assessed, showing that the proposed control system provides low harmonic distortion. An adaptive neuro-fuzzy inference system (ANFIS) was adopted in the study [10] for controlling a PV pumping system. Additionally, the performance of the vector SMC integration with a neural network was evaluated to improve the performance of the pumping system, which is based on IM. The results demonstrated the success of the proposed control method under varying load torque conditions. Add to that, the use of a neural network also contributed to reducing the phenomenon of chatter that results from the use of a switching function in a SMC. This chatter phenomenon may cause the application of high-frequency control signals, causing significant distortions in the electrical signals or in the electromagnetic torque signal of the IM. But with the use of a neural network to adjust control signals, this can be reduced, as the researchers used two neural networks in parallel to accomplish the equivalent command.

1.2 Problem statements

From these reference studies, the following points can be concluded:

Focus on improving energy efficiency and dynamic control of pumping systems based on RESs, through the use of advanced control techniques such as radial control, fuzzy control, and nonlinear predictive control systems. The focus is on reducing noise and performance overruns as well as enhancing the overall stability of the system, especially under input fluctuations such as solar radiation intensity. Studies indicate the possibly of operating the systems in diverse conditions and under different loads, demonstrating the must of flexibility and adaptability of the control systems to meet different operating requirements.

Some of the shortcomings in the published articles include not evaluating the voltage value at the dc-link with changes in environmental conditions and not evaluating the motor’s performance when there is a change in the rotor resistance value, or operating at a constant speed due to the presence of batteries within the pumping system, knowing that the presence of batteries increases the cost of the system. With most research dealing with the issue of control focusing on PV systems only, it is noted that there is a shortage of articles that address the issue of controlling a HS without batteries.

Managing a battery-free HS poses specific challenges:

1. It is challenging to maintain an immediate supply and demand balance in the absence of an energy storage buffer.

2. advanced control algorithms are needed to match available power to the load dynamically.

3. Because RESs, like wind and solar are erratic, it is difficult to keep the power supply steady.

4. it may be need to connecting the system to grid in order to absorb excess energy or provide backup during shortages.

This research aims to study and simulate a generation HS (wind-solar) feeding a 3-ph IM work as a pump. Fuzzy logic controller (FLC) will be used to determine the reference speed of the IM in order to fully exploit the energy provided by the generation HS and maintain the DC link voltage within allowed values.

The research also proposes a control method based on the SMC to regulate the IM speed and rotor magnetic flux to overcome the state of magnetic saturation that can occur with using of the traditional FOC method when changing the rotor coil resistance value of the IM.

3.1 PV system model

PV systems harness energy from the sun through PV cells, converting sunlight directly into electricity. It consists of PV panels connected with boost converter controlled in order to achieve the maximum power of the PV panels. PV cells, which are composed of semiconductor materials, are the main component of the PV panel [7, 12]. The PV cell is commonly represented by the electrical circuit model shown in Figure 2.

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Figure 2. Equivalent circuit for a single PV cell model

The mathematical model of PV cell is given as:

$I=\left[I_{s c}+k_I\left(T_c-T_{\text {ref }}\right)\right] \frac{G}{1000}-I_D-\frac{V+I R_s}{R_{s h}}$     (1)

$I_D=I_s\left\{\exp \left[\frac{e\left(V+I R_s\right)}{N_s A k T_c}\right]-1\right\}$     (2)

$I_s=\left(\frac{I_{s c}}{\exp \left\{\frac{e V_{o c}}{N_s k A T_c}-1\right\}}\right)\left(\frac{T_c}{T_{r e f}}\right)^3 \exp \left\{\frac{e . E_g}{k A}\left[\frac{1}{T_{r e f}}-\frac{1}{T_c}\right]\right\}$     (3)

The boost converter model used in this paper is depicted in Figure 3, its dynamic model is provided below.

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Figure 3. The structure of the boost converter

$\frac{V_o}{V_s}=\frac{1}{1-D}$     (4)

$\frac{d I_s}{d t}=\frac{-(1-D) V_o+V_s}{L}$     (5)

$\frac{d V_o}{d t}=\frac{(1-D) I_s-I_o}{C}$     (6)

3.2 Wind-PMSG model

The variable speed wind energy generating system (VS-WEGS), which has several advantages that make it a competitive option for the generation of sustainable green energy, is one of the renewable energy technologies that is growing the fastest. The usage of PMSG with high power density and simplicity of implementation allows VS-WEGS to achieve great efficiency. It also lessens the requirement for intricate mechanical parts, which raises system dependability and lowers maintenance expenses. Better wind energy utilisation and increased annual energy production are made possible by VS-WEGS's flexible operation. Since VS-WEGS emits no harmful emissions, it contributes to environmental preservation and the slowing of climate change. PMSG’s are also very effective at low wind speeds, which makes them ideal for a variety of wind energy applications. Furthermore, Because the PMSG does not have brushes or sliping rings, wind-PMSG systems have a number of benefits. These include high efficiency, low maintenance requirements. Add to that, the generator's use of permanent magnets removes the requirement for a separate excitation mechanism, streamlining the overall design [13-15].

Three factors affect a wind turbine: rotational speed, pitch angle, and wind speed. The torque applied to the generator mechanically is the output signal that results. The following formula is used to calculate the kinetic energy carried by wind currents:

$P_{\text {wind }}=0.5 \rho \pi R^2 V^3$     (7)

The following formula determines how much mechanical power the turbine can extract from the wind's motion:

$P_t=P_{\text {wind }} C_p(\beta, \lambda)$     (8)

$C_p=c_1\left(c_2\left(\frac{1}{\lambda+0.08 \beta}-\frac{0.035}{\beta^3+1}\right)-c_3 \beta-c_4\right) e^{-c_5\left(\frac{1}{\lambda+0.08 \beta}-\frac{0.035}{\beta^3+1}\right)}+c_6 \lambda$     (9)

$\lambda=\frac{R \omega_m}{V}$     (10)

The torque furnished by the wind turbine can be computed using the equation:

 $T_{\text {mech }}=\frac{P_t}{\omega_m}$    (11)

The following form represents the dynamical model of a PMSG in d, q frame [14, 15]:

$L_{g d} \frac{d i_{g d}}{d t}-\omega_g L_{g q} i_{g q}+R_g i_{g d}=V_{g d}$     (12)

$L_{g q} \frac{d i_{g q}}{d t}+\omega_g L_{g d} I_{g d}+\omega_g \phi_m+R_g i_{g q}=V_{g q}$     (13)

$\frac{d \omega_g}{d t}=\frac{1.5 P_g^2 \Phi_{\mathrm{m}} \mathrm{i}_{\mathrm{gq}}}{J_g}-\frac{P_g \mathrm{~T}_{m e c h}}{J_g}-\frac{\mathrm{f}_g}{J_g} \omega_g$     (14)

3.3 IM model

The mathematical model for a three-phase IM in d, q frame is given as follows [16, 17]:

$\frac{d i_{s d}}{d t}=-a_5 i_{s d}+\omega_k i_{s q}+a_3 \Phi_{r d}+a_4 \omega \Phi_{r q}+b v_{s d}$     (15)

$\frac{d i_{s q}}{d t}=-\omega_k i_{s d}-a_5 i_{s q}-a_4 \omega \Phi_{r d}+a_{3_{r q}}+b v_{s q}$     (16)

$\frac{d \Phi_{r d}}{d t}=a_2 i_{s d}-a_1 \Phi_{r d}+\left(\omega_k-\omega\right) \Phi_{r q}$     (17)

 $\frac{d \Phi_{r q}}{d t}=a_2 i_{s q}-\left(\omega_k-\omega\right) \Phi_{r d}-a_1 \Phi_{r q}$    (18)

$\frac{d \omega}{d t}=\left(\frac{P}{J}\right)\left(T_e-T_L\right)-\left(\frac{f}{J}\right) \omega$     (19)

$T_e=\left(\frac{P L_m}{L_r}\right)\left(\Phi_{r d} i_{s q}-\Phi_{r q} i_{s d}\right)$     (20)

where,

$\begin{aligned} & a_1=R_r / L_r, a_2=L_m R_r / L_r, a_3=L_m R_r /\left(\sigma L_s L_r^2\right) \\ & a_4=L_m /\left(\sigma L_s L_r\right), a_5=\left(L_r^2 R_s+L_m^2 R_r\right) /\left(\sigma L_s L_r^2\right) \\ & b=1 /\left(\sigma L_s\right), \sigma=1-L_m^2 /\left(L_s L_r\right)\end{aligned}$

3.4 Design of pump

When the IM operates as a pump, the load torque is proportional to the rotational speed. This fact comes by considering that the energy absorbed by the pump is equal to the mechanical energy generated on the IM shaft, which can be measured by the study [13]:

$P_{\text {pump }}=k_{\text {pump }} \Omega^3=T_L \Omega \rightarrow T_L=k_{\text {pump }} \Omega^2$     (21)

where, $k_{\text {pump }}=\frac{T_{L_{\text {nominal }}}}{\Omega_{\text {nominal }}^2}$  is called the speed-torque coefficient.

In the proposed model, the IM nominal power is 15kw, and the PMSG nominal power is 6kw. The parameters of PV panels are shown in Table 2.

Table 2. The parameters of used panel

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Figure 9. The changes in wind speed

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Figure 10. The changes in irradiation

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Figure 11. The rotational speed of PMSG

Figure 9 shows the changes in wind speed, where it was assumed that the wind speed would be 8m/s during the period 0-3sec, 10m/s during the period 3-9sec, and 12m/s during the period 9-15sec. Figure 10 shows the changes in Irradiation, where It was assumed that the Irradiation would be 800w/m² during the period 0-6sec, 900w/m² during the period 6-12sec, and 1000w/m² during the period 12-15 sec. It must also be taken into account that an increase in the rotor resistance value of 30% was assumed.

Figure 11 shows the changes in the rotational speed of PMSG, it is noted that the PMSG speed increases in proportion to the wind speed in order to track the point of maximum power that PMSG can provide.

As the wind speed increases and the solar radiation increases, the energy supplied increases, and thus the pump rotation speed increases, as shown in Figure 12, which shows the response of the two driving systems to regulating IM speed. It can be seen that both driving systems achieve high dynamic performance, in terms of accuracy and fast tracking the reference speed imposed by the RSA system. However, from Figure 13, it can be noted that the advanced drive system is superior in its ability to maintain the value of the rotor flux within the nominal value (1wb) despite the increase in the value of the rotor resistance, this contributes to preventing the occurrence of magnetic saturation, which contributes to reducing the malfunctions that can occur in the IM.

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Figure 12. Responses of IM speed control systems

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Figure 13. Responses of rotor flux control systems

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Figure 14. The voltage wave applied on the IM stator

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Figure 15. The voltage frequency applied on the IM stator

The phenomenon of magnetic saturation can be explained by the fact that the voltage to frequency ratio is greater when using a traditional FOC system. Figures 14 show the two voltage waves applied for both systems, and Figure 15 shows the electrical frequency. It is clear that the applied voltage in the case of a traditional FOC system is greater, and correspondingly, the electrical frequency is lower compared to the use of an advanced FOC system.

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Figure 16. The electromagnetic torque of IM

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Figure 17. The current wave drawn by the IM stator

An increase in IM speed will be accompanied by an increase in torque based on relationship 21, and thus an increase in the current drawn by the IM, as shown in Figures 16 and 17, respectively. Obviously in Figure 16, torque ripples when using the advanced FOC system are less. It is noted from Figure 17 that the value of the current is slightly larger when using the advanced FOC system, because the voltage applied in this case is less compared to the traditional FOC system, as the IM in both cases needs the same value of electrical power in order to provide the ability to rotate at the same speed and generate the same torque value.

Stability in the rotational speed reflects the accuracy and effectiveness of the RSA system work, which works to adjust the DC link voltage and fully utilize the generated energy form PV system and PMSG. It can be seen from Figure 18 that the DC link voltage remains at its nominal value without any unwanted excesses. The presence of a difference between the power supplied and the power consumed will cause current to flow through the DC link capacitor, and thus increase the voltage to high and inappropriate values, while it is noted that the voltage value here is stable, which indicates the effectiveness of the proposed system efficiency.

Figure 19 shows the power values for each PV system and the wind generation system, and the power consumed in the IM. It can be noted that the power provided by PV panels increases with increasing solar radiation and reaches its nominal value (11.38kw) when the radiation value is 1000w/m², On the other hand, the power provided by the PMSG reaches its nominal value (6kw) when a wind speed is equal to 12m/sec. It is noted that all the energy provided by the PV panels and the wind generator is converted into mechanical energy on the IM shaft, taking into account the value of the losses consumed in it in addition to the losses present in the electronic transformers.

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Figure 18. The DC link voltage

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Figure 19. The power for each PV system, PMSG, and IM