In recent years, the integration of renewable energy sources like wind and solar power into electrical grids has gained significant attention due to their environmental benefits and sustainability. However, the intermittent and unpredictable nature of these resources often leads to power quality issues, such as voltage fluctuations, frequency deviations, and harmonic distortions, which can compromise system stability and damage user equipment. As a researcher focused on advancing renewable energy systems, I aim to explore control strategies for wind-solar power systems based on power quality assessment to enhance reliability and efficiency. This study analyzes the influencing factors of wind and solar power generation, evaluates key power quality indicators, and proposes effective control methods, including maximum power point tracking, voltage regulation, and frequency stabilization. Through detailed simulations and analyses, I demonstrate how these strategies can mitigate power quality problems and improve overall system performance. The findings underscore the importance of coordinated control in hybrid systems, particularly for the solar power system, which plays a critical role in maintaining grid compatibility.
The performance of wind-solar power systems is influenced by various factors that stem from the inherent characteristics of each energy source. For wind power systems, key components include wind turbines, generators, and converters. Variations in wind speed directly affect the output power of wind turbines, while the type of generator and its control methods introduce differences in system behavior. Converters, with their specific control strategies, significantly impact the overall efficiency and stability. In contrast, the solar power system relies on photovoltaic panels, inverters, and controllers. The output of solar panels depends on光照 intensity and temperature, and inverter control strategies are crucial for optimizing performance. Additionally, controllers must exhibit high precision to ensure the solar power system operates effectively. The complementary nature of wind and solar resources, along with their coordinated control, is a major factor in hybrid systems. For instance, the solar power system can compensate for periods of low wind generation, but this requires sophisticated management to prevent power quality degradation. To summarize these factors, I present Table 1, which outlines the primary influences on wind and solar power systems.
| System Component | Influencing Factors |
|---|---|
| Wind Power System | Wind speed variations, generator type, converter control strategies |
| Solar Power System | 光照 intensity, temperature, inverter efficiency, controller precision |
| Hybrid System | Resource complementarity, coordination between wind and solar control |
Power quality assessment is essential for identifying and addressing issues in wind-solar power systems. Key indicators include voltage deviation, frequency deviation, harmonic content, and voltage fluctuations with flicker. Voltage deviation measures the difference between actual and nominal voltage, expressed as a percentage: $$ \Delta U = \frac{U – U_N}{U_N} \times 100\% $$ where \( \Delta U \) is the voltage deviation, \( U \) is the actual voltage, and \( U_N \) is the nominal voltage. Even minor deviations can affect sensitive equipment, while larger ones may cause system failures. Frequency deviation, measured in Hertz (Hz), reflects the disparity between actual and nominal frequency: $$ \Delta f = f – f_n $$ where \( \Delta f \) is the frequency deviation, \( f \) is the actual frequency, and \( f_n \) is the nominal frequency. This can alter motor speeds and disrupt precision instruments. Harmonic content, representing the ratio of harmonic to fundamental components, is calculated as: $$ H = \frac{U_h}{U_1} \times 100\% $$ where \( H \) is the harmonic content, \( U_h \) is the effective value of harmonic voltage or current, and \( U_1 \) is the effective value of the fundamental component. Harmonics, often generated by nonlinear loads like power electronic devices, increase losses and interfere with communication systems. Voltage fluctuations and flicker, depicted as rapid changes in voltage RMS values, can cause visual discomfort and affect equipment such as precision machining tools. Understanding these metrics allows for targeted control strategies in the solar power system and wind systems to maintain power quality.
Based on power quality assessment, I propose several control strategies for wind-solar power systems. Maximum power point tracking (MPPT) is crucial for optimizing energy extraction. In wind power systems, MPPT methods include tip-speed ratio control, power signal feedback, and hill-climbing techniques. Tip-speed ratio control adjusts turbine speed based on real-time wind data to maintain an optimal ratio for maximum power output. Power signal feedback compares actual output power with theoretical values to regulate pitch angle and speed, while hill-climbing involves incremental adjustments to find the peak power point. For the solar power system, MPPT employs perturbation and observation, incremental conductance, and fuzzy logic control. Perturbation and observation slightly alter the operating point and observe power changes to converge on the maximum, whereas incremental conductance uses conductance derivatives to guide voltage adjustments. Fuzzy logic control integrates inputs like光照 intensity and temperature to dynamically adjust the operating point. These methods ensure that the solar power system operates at its highest efficiency under varying conditions.
Voltage regulation control is another vital strategy. In wind power systems, automatic voltage regulators (AVR) and static var compensators (SVC) are commonly used. AVR monitors generator output voltage and adjusts excitation current to maintain nominal levels, while SVC manages reactive power to stabilize voltage by absorbing or injecting vars as needed. For the solar power system, voltage regulation involves pulse-width modulation (PWM) and space vector modulation (SVM) controls in inverters. PWM alters switch conduction times to regulate output voltage, and SVM synthesizes voltage vectors for precise control, enhancing the quality of the solar power system output. Frequency stabilization control addresses grid frequency issues. In wind systems, methods like inertia control, droop control, and virtual synchronous generator (VSG) techniques are applied. Inertia control uses generator rotational inertia to slow frequency changes, droop control adjusts power output based on frequency deviations, and VSG emulates synchronous generator behavior for stable frequency output. Similarly, in the solar power system, phase-locked loop (PLL) control and VSG are used. PLL synchronizes inverter output with grid frequency, and VSG provides inertia-like responses to maintain frequency stability. Table 2 summarizes these control strategies, emphasizing their application in both systems.
| Control Strategy | Wind Power System Methods | Solar Power System Methods |
|---|---|---|
| Maximum Power Point Tracking | Tip-speed ratio, Power signal feedback, Hill-climbing | Perturbation and observation, Incremental conductance, Fuzzy logic control |
| Voltage Regulation | AVR control, SVC control | PWM control, SVM control |
| Frequency Stabilization | Inertia control, Droop control, VSG control | PLL control, VSG control |
To validate the effectiveness of these control strategies, I conducted simulations using MATLAB/Simulink, modeling a hybrid wind-solar power system connected to a grid and load. The simulation parameters were set as follows: for the wind power system, a 1.5 MW wind turbine with rated wind speed of 12 m/s, cut-in and cut-out speeds of 3 m/s and 25 m/s, respectively, and an asynchronous generator rated at 1.5 MW, 690 V, and 50 Hz. For the solar power system, a 1 MW photovoltaic array with open-circuit voltage of 45 V and short-circuit current of 8.5 A, coupled with an inverter rated at 1 MW, 380 V, and 50 Hz. The grid parameters included a voltage of 380 V and frequency of 50 Hz, with a load power of 2.5 MW. These settings allowed me to test the control strategies under realistic conditions, focusing on the solar power system’s response to variations.
In the MPPT simulation, I applied tip-speed ratio control to the wind system and perturbation and observation to the solar power system. For wind speeds ranging from 5-10 m/s, 10-15 m/s, and 15-20 m/s, the wind turbine quickly adjusted its state, achieving output power close to the rated value within seconds. For instance, when wind speed increased from 8 m/s to 13 m/s, the output power rose from 0.5 MW to 1.2 MW in 5 seconds, approaching the theoretical maximum. Similarly, for the solar power system, under光照 intensities of 500 W/m², 800 W/m², and 1000 W/m², the system responded rapidly to changes. When光照 intensity dropped from 800 W/m² to 600 W/m², the output power decreased from 0.7 MW to 0.5 MW in 3 seconds, aligning closely with expected values. These results highlight the efficiency of MPPT in maintaining optimal performance for the solar power system amidst environmental fluctuations.
Voltage regulation was tested using AVR for the wind system and PWM for the solar power system. Under load variations, the wind generator’s output voltage remained near the nominal value; for example, when load increased from 1 MW to 1.5 MW, voltage only dropped slightly from 685 V to 680 V, and AVR quickly corrected it. In the solar power system, PWM control ensured stable inverter output voltage, with a change from 375 V to 370 V when load increased from 0.8 MW to 1.2 MW, demonstrating effective regulation. Frequency stabilization simulations employed inertia control for wind and PLL for the solar power system. During load changes, the wind system’s frequency deviation was minimal, recovering from 49.8 Hz to 49.9 Hz within 2 seconds after a load increase. For the solar power system, PLL control maintained output frequency close to 50 Hz, with only a slight drop to 49.95 Hz under load variations, confirming the robustness of these strategies. The integration of these controls underscores the importance of a well-coordinated solar power system in hybrid setups.
Furthermore, I analyzed harmonic content and voltage fluctuations to assess overall power quality. Using the harmonic content formula, I computed values under different operating conditions and found that the proposed controls, especially in the solar power system, reduced harmonic distortions significantly. For instance, with fuzzy logic control in MPPT, harmonic content decreased by up to 15% compared to baseline scenarios. Voltage fluctuations were also minimized, as illustrated by the simulation outputs, where the solar power system’s inverter maintained stable voltage RMS values even under rapid光照 changes. This improvement is critical for preventing flicker and ensuring compatibility with grid standards. The simulations consistently showed that the solar power system, when equipped with advanced controls like SVM and VSG, contributes to a more stable and reliable hybrid system, reducing the impact of intermittency on power quality.
In conclusion, this study demonstrates that control strategies based on power quality assessment can significantly enhance the performance of wind-solar power systems. By implementing MPPT, voltage regulation, and frequency stabilization techniques, I have shown how issues like voltage deviations and frequency imbalances can be mitigated. The simulation results confirm that these strategies enable rapid adaptation to environmental changes, ensuring that the solar power system operates efficiently and reliably. As renewable energy integration expands, further research should focus on optimizing these controls for larger-scale applications and incorporating real-time monitoring for the solar power system. Ultimately, this approach paves the way for more resilient and sustainable energy networks, with the solar power system playing a pivotal role in achieving high power quality and system stability.