Academic Journal of Science and Technology ISSN: 2771-3032 | Vol. 14, No. 3, 2025 362 Inverter‐based Resources for Power Generation Technology Xinyi Wang1, a 1Faculty of Engineering, Architecture & Info Tech, The University of Queensland, Brisbane, Queensland, 4067, Australia awangxinyi200010@163.com Abstract: The aim of this research project is to explore the possibility of maintaining the stability of frequency control in power systems today where the share of high percentage inverter resources is gradually increasing. By conducting experiments using SCADA, this research aims to find effective ways to analyze the specific generation methods and compare them with the conventional ones, and finally to explore the possibilities of maintaining the stability of the frequency of the power system in the above mentioned scenario. Keywords: Inverter-based resources; power system frequency stability; SCADA; generation methods. 1. Introduction With the rise of renewable energy, people are becoming more aware of the importance of sustainable development and ecological protection, especially clean energy sources such as wind and solar. These energies cannot be directly connected to the grid and need to be realized through inverters, so this kind of energy is called IBR. Compared with traditional synchronous generators, these resources have lower carbon emission, more flexible use and relatively lower cost, but as more and more IBRs are added to the grid, the change of the energy structure and the instability brought by the IBRs bring unprecedented challenges to the frequency control of the power system. In this paper, we will focus on three experiments with microgrids, wind energy and photovoltaic power generation, so as to further understand the differences between the new power generation technologies and the traditional power generation methods. 2. Literature Review With the wide application of renewable energy in the power system, the role of inverter resources (IBRs) in the grid has gradually become the focus of research. Inverters not only realize the grid integration of renewable energy, but also significantly change the frequency stability and dynamic characteristics of the power system. 2.1. Characteristics of inverter resources (IBR) An inverter resource is a power electronics-based device whose output frequency is synchronized with the grid frequency, as determined by its internal controller. Compared with conventional synchronous generators, inverters lack inertial reserves, so their dynamic response is faster but their stability is relatively low [1].Droop control, as a strategy that simulates the power distribution characteristics of synchronous generators, is widely used in active and reactive power regulation of microgrids with the advantages of fast response and high adaptability [2]. 2.2. Basic theory of microgrid frequency control Droop control is one of the core technologies of microgrid frequency control, which realizes active and reactive power regulation through the following equations: 𝛥𝑓 𝑘𝑝 ⋅ 𝑃 (1) 𝛥𝑉 𝑘 ⋅ 𝑄 (2) 𝑘 and 𝑘 are Droop coefficients, which are used to regulate the relationship between active and reactive power and frequency and voltage, respectively. It has been shown that optimizing the Droop coefficient can effectively improve the dynamic performance of microgrids [3,4]. 2.3. Power control of doubly-fed induction generator (DFIG) Doubly-fed induction generator (DFIG) is the mainstream technology of modern wind turbine, which realizes the bidirectional power flow between stator side and rotor side through power electronic converter. The power output of DFIG is closely related to the wind speed and the pitch angle of the blades, and its Maximum Power Point Tracking (MPPT) technology can significantly improve the efficiency of the wind energy utilization [5]. In addition, DFIGs have advantages in fault ride-through (FRT) performance and reactive power support, which contribute to the stability of modern power grids [6]. 2.4. MPPT technique for PV inverters PV inverters maximize the efficiency of solar energy utilization through MPPT algorithms. Current mainstream MPPT algorithms include perturbation observation (P&O) and incremental conductance (IC), both of which exhibit high tracking efficiencies under different irradiance and temperature conditions [7]. It has also been shown that optimizing the control strategy of the inverter can reduce the impact of the PV system on the local grid and improve the quality of the grid-connected voltage [8]. 2.5. Power system frequency stability While conventional synchronous generators maintain grid frequency stability through rotational inertia, inverter resources need to provide frequency support through virtual 363 inertia (VSG) technology due to the lack of physical inertia. The core of VSG technology lies in the simulation of synchronous generator's dynamic characteristics including inertial response and damping response [9]. Studies have shown that combining the VSG technique with Droop control can further improve the frequency regulation of inverter resources [10]. 2.6. Shortcomings and outlook of current research Most of the existing studies focus on the control strategies of inverter resources and single-scenario experiments, and the research on system-level behaviors under large-scale high- penetration inverter resource conditions is still relatively insufficient. Therefore, in this paper, the performance of Droop control, DFIG and PV inverters in frequency control will be discussed in depth from three experiments of microgrid, wind and PV power generation, so as to provide experimental basis for the frequency stability of future power systems. 3. Research 3.1. Microgrids Grids with power sources, loads (power consuming equipment) and energy storage units that can be switched to “islanded operation mode” or “grid-parallel operation mode” are called microgrids. This modern power grid system allows for operation on the traditional utility grid as well as self- sufficiency with local power supply. Switching between the two modes of operation usually allows for a seamless transition. The objective of this study is to investigate the P-droop and Q-droop capabilities of synchronous motors for primary automatic control in isolated microgrids 3.1.1. Equipment The equipment, part number and designation of microgrids is shown below in Table 1: Table 1. Microgrids 3.1.2. Wiring Diagram The wiring diagram of microgrids is shown below in Figure 1: 364 Figure 1. Microgrids 3.1.3. Research Steps Automatic reactive power control based on Q Droop (with inductive loads) (1) Open the servo test bench, generator HMI unit, excitation voltage controller and power quality meter. (2) Open the SCADA viewer from Labsoft (3) Open the SCADA file (4) Configure the IP address of the generator HMI and power quality meter in SCADA (5) Select the Power Quality Meter and click on “Properties”. Once a new window opens (6) Then enter the IP address of the Power Quality Meter, leaving the “Port Number” as 502. (7) Press the “Run” button in the SCADA toolbar to run SCADA. (8) In SCADA, set the voltage of the “Master” generator to 400V and the frequency to 50Hz. (9) Voltage drop is enabled by default, for this activity, set it to 5% as the default setting. (10) From the “Instrument” menu, turn on the SCADA recorders (1 and 2) to observe and synchronize the display of the motor parameters and record the experimental readings of active power P, reactive power Q, machine voltage U, and the duration of the speed. (11) Press the “Run Idle” button on the SCADA to start the synchronous machine. (12) Wait for the synchronous machine to stabilize and reach 1500 rpm. 3.1.4. Results Table 2. Initial data Droop Settings (%) Reference/Previous Voltage (V) Inductive Load – Q (VAR) Measured Voltage (V) Calculated Voltage (V) Difference (V) 3 400 -247.9 396 396.2815 -0.2815 3 396 0 400 400 0 5 400 -244 394 393.9 0.1 5 394 0 400 400 0 7 402 -241 392 391.565 0.435 7 392 0 400 400 0 9 401 -239.1 391 389.2405 1.7595 9 391 0 400 400 0 Table 3. Automatic reactive power control based on Q Droop (with capacitive loads) Droop Settings (%) Reference/Previous Voltage (V) Capacitive Load – Q (VAR) Measured Measured Voltage (V) Calculated Voltage (V) Difference (V) 3 400 208 404 403.12 0.88 3 404 0 400 400 0 5 400 209 406 405.225 0.775 5 406 0 401 400 1 7 401 212 408 407.42 0.58 7 408 0 401 400 1 9 401 212 409 409.54 -0.54 9 409 0 401 400 1 Table 4. Automatic active power control based on P Droop (with resistive loads) Droop Settings (%) Droop Speed (rpm) Resistive Load – P (W) Measured Speed (rpm) Calculated Speed (rpm) Difference (rpm) Droop Settings (%) Droop Speed (rpm) Resistive Load – P (W) 3 1527.75 0 1544.5 1545 0.5 3 1527.75 0 3 1545 -306 1527.5 1527.78 0.28 3 1545 -306 5 1546 0 1575 1575 0 5 1546 0 5 1575 -306 1546 1546.3 0.3 5 1575 -306 365 3.1.5. Research Conclusion Q Droop reactive power control With inductive loads The experimental data show that under inductive load conditions, the reactive power is linearly related to the Droop setting. When Droop is increased from 3% to 9%, the deviation of the reference voltage from the measured voltage gradually increases. This is due to the fact that the voltage variation in Droop control is linearly related to the reactive power with the expression: 𝛥𝑉 𝑘 ⋅ 𝑄 (3) where k is the Droop coefficient and Q is the reactive power. Under inductive load conditions, the reactive power is negative and the voltage drop is significant, which indicates that Droop control can effectively limit the voltage instability caused by reactive power under inductive load conditions. With capacitive load The experiments with capacitive loads show that as the Droop increases from 3% to 9%, the measured voltage rises gradually, but the deviation is always less than 1 V. This indicates that the positive deviation characteristics of the Droop control are well reflected under capacitive loads. The experiment verifies the adaptability of the Droop control to different load types, and also shows that the voltage compensation effect of the capacitive load can alleviate the grid pressure. Active Power Control for P Droop The experimental results show that when Droop is set to 3% and 5%, the speed changes caused by the active power are small, with deviations of 0.5 rpm and 0.3 rpm, respectively, indicating that the Droop control can effectively balance the load fluctuations in islanded operation. The results further support the applicability of Droop control in active power management, especially in the islanded operation mode, where its dynamic response performance is more excellent. 3.2. Wind This study describes the working principle and function of modern wind turbines doubly fed induction generator (DFIG). Experiments will be conducted to control and observe the active and reactive power distribution of the wind turbine in grid-connected operation. It will also be investigated how the DFIG generator depends on the wind speed at the point of its operating maximum power point (MPP) when supplying power to the grid and the effect of the pitch angle of the paddles on its power output. 3.2.1. Equipment The equipment, part number and designation of wind is shown below in Table 5: Table 5. Wind 366 3.2.2. Wiring Diagram The wiring diagram of wind is shown below in Figure 2: Figure 2. Wind 3.2.3. Research Steps DFIG active and reactive power control (1) Open the machine test bench and DFIG control unit (2) Turn on the power supply (three-phase grid) and make sure that the transformer circuit is normal and the circuit breaker has not tripped. (3) Open the “Labsoft” software from the “Start Menu”. (4) from the Labsoft “Instrument” menu to open the “Power Control” tool (5) Select “Stator Power” mode on the virtual instrument. (6) On the servo base, select “Speed Control” and set the speed to 1200rpm. (7) Press the “Enable” button on the virtual instrument. 3.2.4. Results Table 6. Initial data Speed(rpm) 1200 1250 1300 1350 1400 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 DFIG (W) Stator Power 400 398 399 398 399 399 398 399 399 397 398 398 398 398 398 LSC (W) -189 -174 -162 -144 -128 -114 -95 -83 -70 -57 -44 -32 -16 -6 5 Grid (W) 208 223 235 253 269 283 301 314 327 340 353 366 380 391 403 Speed (rpm) 1300 1400 1500 1600 1700 1800 DFIG (W) Stator Power 400 399 398 400 398 398 DFIG (VAR) Stator Power 80 77 80 78 81 78 LSC (W) -169 -140 -109 -82 -55 -29 LSC (VAR) 0 0 0 0 0 0 Grid (W) 228 256 287 316 342 366 Grid (VAR) 80 79 80 79 77 78 367 Table 7. Active power control and grid synchronization of DFIG at different wind speeds Wind Speed (m/s) Pitch Angle (degrees) Rotor Speed (rpm) Torque (Nm) Pm (W) Ps (W) Pr (W) Pg (W) Efficiency (%) 3 0 0 0.2 0 0 -20 -20 3.5 0 875 0.3 25 0 -24 -24 -96 4 0 881 0.5 48 5 -91 -87 -181.25 4.5 0 945 0.7 71 33 -100 -68 -95.77464789 5 0 1038 1 103 55 -104 -48 -46.60194175 5.5 0 1128 1.2 149 78 -104 -25 -16.77852349 6 0 1217 1.4 179 107 -101 3 1.675977654 6.5 0 1301 1.7 226 133 -97 38 16.81415929 7 0 1399 1.9 293 168 -91 77 26.27986348 7.5 0 1499 2.3 355 205 -82 124 34.92957746 8 0 1590 2.5 431 242 -67 176 40.83526682 8.5 0 1684 2.9 523 285 -50 231 44.16826004 9 0 1786 3.3 607 328 -31 300 49.42339374 9.5 0 1879 3.7 715 376 -3 373 52.16783217 10 0 1894 4.1 813 448 6 464 57.07257073 10.5 0 1894 4.7 927 514 24 532 57.38942826 11 0 1897 5.1 997 571 24 598 59.97993982 11.5 0 1899 5.3 1065 614 35 646 60.657277 12 0 1899 6.4 1133 656 39 697 61.51809356 12.5 0 1900 6.1 1197 708 44 746 62.32247285 13 1 1903 6.2 1227 724 47 778 63.40668297 13.5 5 1902 6.1 1242 724 51 776 62.47987118 14 8 1904 6.2 1232 724 54 772 62.66233766 Table 8. Fault ride-through performance of the DFIG Gain IQ 40% Voltage dip 60% Voltage dip U(g_pos_d)(pu) I(g_pos_q)(pu) U(g_pos_d)(pu) I(g_pos_q)(pu) 0 0.6 0 0.4 0 2 0.6 0.6 0.4 1 3 0.6 0.8 0.4 1.1 4 0.6 1.11 0.4 1.1 6 0.6 1.15 0.4 1.15 8 0.6 1.15 0.4 1.15 3.2.5. Research Conclusion DFIG active and reactive power control The experimental results show that the output power of the DFIG increases nearly linearly when the rotational speed is gradually increased from 1200 rpm to 1900 rpm. Specifically, the grid synchronous power increases from 208W to 403W, while the reactive power remains stable. This indicates that modern WTGs have strong stability and power output capability at high rotational speeds. The experimental results also support the theoretical model: 𝑃 𝜔 ⋅ 𝑇 (4) where P is the power, ω is the rotational speed, and T is the torque. As the rotational speed increases, the power increases linearly, verifying the accuracy of the theoretical formula. Power characteristics at different wind speeds When the wind speed changes from 3m/s to 14m/s, the system efficiency gradually increases from negative to 62.66%. The low torque and power output under low wind speed conditions even cause negative efficiency, which is due to the fact that the DFIG is difficult to reach the rated speed when the wind speed is insufficient. And when the wind speed exceeds 7 m/s, the system gradually enters the stable output stage, and the power factor and efficiency are significantly improved. This indicates that the DFIG can effectively convert wind energy into electric energy under optimized wind speed, and its performance is closely related to the blade pitch angle and motor speed. 3.3. Photovoltaic The objective of this study is to investigate the operation and characterization of modern photovoltaic (PV) systems operating under grid coupling. 3.3.1. Equipment The equipment, part number and designation of photovoltaic is shown below in Table 9: 368 Table 9. Photovoltaic 3.3.2. Wiring Diagram The wiring diagram of photovoltaic is shown below in Figure 3: Figure 3. Photovoltaic 3.3.3. Research Steps Solar PV inverter operating point MPPT (MPP tracking with different irradiance levels) (1) Turn on the main power supply and the inverter (2) Set the load resistance to 750 ohms and manually adjust the line-to-neutral voltage to 220V for the local network transformer. (3) Open “Solar Panel Emulator” hardware. (4) Open Labsoft's “Solar Panel” virtual tool. (5) Set SHADOW [%] = 0; SHADED MODULES = 0; IRRADIANCE [%] = 100%. (6) Press the “Power” button to activate the “Solar Panel Virtual Instrument”. (7) Wait until the PV inverter tracks its “maximum power point” and synchronizes itself to the grid. 3.3.4. Results Table 10. Initial data Irradiance (%) DC Current (A) DC Voltage (V) DC Power (V) AC Current (A) AC Voltage (V) AC Power (V) Efficiency (%) 100 4.01 359 1439 2.1 221 1388 96.45587213 80 3.16 364 1121 1.7 220 1109 98.92952721 60 2.43 361 878 1.3 220 843 96.01366743 40 1.61 359 576 0.8 220 546 94.79166667 20 0.83 358 288 0.4 220 262 90.97222222 0 0 388 5 0.2 220 0 0 369 Table 11. Solar PV inverter operating point MPPT (MPPT tracking and shading module) Shadow (%) DC Current (A) DC Voltage (V) DC Power (V) AC Current (A) AC Voltage (V) AC Power (V) Efficiency (%) 20 3.5 371 1300 1.9 221 1257 96.69230769 50 3.97 272 1078 1.6 220 1035 96.01113173 80 3.98 271 1080 1.6 220 1035 95.83333333 100 4 270 1080 1.6 220 1035 95.83333333 Table 12. Active power derating and MPP tracking inverter operating point Active Power Derating in % P [W] (PV – Inverter) Calculated P [W] (PV – Inverter) Difference P [W] (VLNT) 0 1391 1500 -109 -1093 10 1352 1350 2 -1056 20 1224 1200 24 -925 30 1064 1050 14 -765 40 904 900 4 -604 50 745 750 -5 -441 Table 13. Effect of different irradiance on voltage drop of localized network transformers Irradiance (%) Power VLNT (W) AC Voltage (V) 100 -1169 415 80 -900 406 60 -628 393 40 -357 381 20 -85 366 0 5 351 3.3.5. Research Conclusion MPPT performance of PV inverters The PV inverter shows high efficiency under different irradiance. For example, under 100% irradiance, the DC power is 1439W and the AC power is 1388W with an efficiency of 96.45%. As the irradiance decreases to 20%, the efficiency decreases slightly but remains above 90%. This indicates that the MPPT algorithm of the PV inverter has good robustness and can effectively track the maximum power point. In the partial shadow shading experiment, the DC power loss of the system is less than 15%, e.g., when the shadow covers 50%, the DC power is 1078W and the efficiency is 96%. This indicates that the system can still maintain a high conversion efficiency under partial shading conditions, which verifies the applicability of the PV system in complex lighting environments. Effect of localized network voltage drop As the irradiance decreases from 100% to 0%, the local network voltage gradually decreases from 415 V to 351 V. The experimental data show that the PV system is able to provide effective support to the local grid at high irradiance, whereas the grid voltage relies on the support of other power sources when the irradiance decreases. This further illustrates the potential of distributed PV systems in improving grid voltage stability. 4. Analysis of Conclusions 4.1. Utility of Droop control Whether it is active power or reactive power control, the dynamic response performance of the Droop method in microgrid islanding operation is excellent. Its voltage deviation regulation of inductive and capacitive loads is particularly significant, providing a reliable technical guarantee for the stable operation of islanded microgrids. 4.2. Economy and stability of wind energy system The experiment verifies the high efficiency of DFIG under high wind speed conditions and shows its power factor regulation capability for the grid, proving that it is suitable for renewable energy deployment in high wind speed areas. 4.3. Adaptability of PV system The high efficiency and immunity of the PV inverter to different irradiance and shading conditions demonstrate its suitability for complex distributed energy environments. In particular, the high efficiency of the MPPT algorithm provides an important support to further enhance the application scenarios of PV systems. 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