Wind energy is one of the green energy sources that can decrease reliance on fossil fuels such as oil and coal. Wind turbines harness the kinetic energy of the wind and transform it into mechanical energy, which is subsequently converted into electrical energy using a DC generator. The generated DC voltage is transformed into AC voltage by an inverter unit. However, conventional inverters generally produce square-wave outputs with high total harmonic distortion (THD), whereas AC power systems ideally require sinusoidal waveforms with low harmonic content. This research proposes the design of a renewable energy–based power supply system capable of generating both DC and AC voltages for low-power applications. The novelty of the proposed system lies in the use of a five-level inverter with fewer switching devices than the cascaded inverter topology, combined with a T-model inductive-capasitive-inductive (LCL) filter to suppress harmonic distortion at the inverter output. The method involves modeling and simulating the system design using MATLAB Simulink. Simulation results show that the five-level inverter produces an output voltage amplitude of 270 V at 50 Hz with a THD of 28.48 %. After applying the LCL filter (L1 = L2 = 0.01 H and C = 1 mF), the THD decreases to 2.56 % for a 10 Ω resistive load, 2.61 % for an RL load (100 Ω, 0.8 mH), and 2.54 % for an RC load (10 Ω, 1 µF). These values comply with the IEEE 519 standard requiring THD below 5 %. Therefore, the proposed system has potential for small-scale renewable energy applications.
Photovoltaic efficiency is frequently compromised by physical obstructions, resulting in partial shading conditions. This non-uniform irradiance condition severely distorts system characteristics by inducing multiple power peaks. This study proposes a novel fractional tent map-chaotic horse herd optimization (FTM-CHHO) algorithm for global maximum power point (GMPP). By integrating fractional-order memory and chaotic maps, FTM-CHHO enhances global search capabilities and prevents entrapment in local maxima. The method was rigorously validated through simulations and hardware experiments using a SEPIC converter. Simulations demonstrated that FTM-CHHO achieved 99.52 % to 100 % tracking accuracy with rapid convergence times of 0.32 to 0.62 s. Furthermore, hardware tests under real-world shading confirmed its robustness, maintaining 95.54 % to 98.26 % accuracy and converging within 10.1 s. FTM-CHHO significantly outperformed perturb and observe (P8O) and standard horse herd optimization (HHO). These findings confirm that FTM-CHHO provides a highly reliable, fast, and efficient solution for maximizing solar energy extraction under complex environmental variability.
Generally, multilevel inverters function as voltage step-down or step-up converters. This paper discussed a new type of multilevel inverter, specifically five-level and seven-level designs that use six active switches, which can increase or decrease the output voltage. The first study on using equal DC sources (E + E), known as symmetric, in the five-level setup for adjusting voltage was conducted, and then it was expanded to seven levels using an uneven DC source (2E + E), called asymmetric, with a new control method that works differently for each function. This multilevel voltage step-up (boost) and step-down (buck) inverter fundamentally has two components: a DC voltage level generator and a voltage step-up and polarity generator. This work provides a detailed look at how the multilevel voltage boost and buck inverter is built to show the features of the suggested design. Simulation data is provided to confirm how well each proposed design works with five-level and seven-level voltage increases and decreases in an inverter, using the sinusoidal pulse-width modulation (PWM) index and PWM duty cycle for better understanding. By the simulation, the total harmonic distortion (THD) value of the inverter when it operates as a buck is 1.26-1.32 %, and when it operates as a boost, it is 1.32-1.36 %. These values are still below 5 % in accordance with IEEE 519 standards and demonstrate a unity power factor.
This study develops a generalized displacer motion equation for a beta-type Stirling engine. The proposed equation approximates the ideal Stirling cycle while maintaining sinusoidal piston motion to ensure stable power extraction. The displacer trajectory is modeled with a Fourier series and optimized. The resulting trajectory is then generalized as a piecewise function to improve applicability across different geometries. This approach improves control of working fluid distribution, allowing the expansion and compression processes to more closely approach isothermal conditions. The results show that the optimized Fourier trajectory achieves 91.9 % of the ideal Stirling-cycle work output, outperforming conventional drive mechanisms, where it only achieves 59.8 % for crank mechanism, 66 % for Scotch yoke, and 68.5 % for rhombic drive. For practical implementation, the optimized Fourier trajectory is generalized using a piecewise formulation. The generalized trajectory maintains approximately 80–90 % of the ideal Stirling-cycle work over a range of compression ratios without requiring re-optimization. These results demonstrate that the proposed approach provides both high thermodynamic performance and improved adaptability compared with conventional Stirling engine drive mechanisms.
This work presents an efficient motion planning framework for four-wheel steering (4WS) autonomous vehicles operating in complex and unknown environments. To improve planning efficiency, the framework employs a lazy edge-based A* (LEA*) algorithm for global path planning, adaptive fourth-order Runge–Kutta model predictive control (RK4-MPC) for trajectory tracking and motion execution, and wheel force distribution control (WFDC) to ensure stable motion during steering maneuvers. Quantitative results show that the LEA* reduces planning time by 87.5 % edge evaluations by 96.1 % compared to conventional A*, while improving path smoothness by 51 %. The integration of adaptive RK4-MPC with WFDC achieves the lowest tracking error and heading error of 34.8 % and 37.5 % compared to OMNI, and 28.6 % compared to S-4WS. In addition, the proposed method reduces the wheel slip ratio 88.4 % better than OMNI and 46.7 % better than S-4WS, while also reducing yaw acceleration by 50 % compared to both baselines. For computational efficiency, the proposed framework achieves a search time of 0.5234 s, 83.1 % faster than OMNI, and 37.1 % faster than S-4WS, and an optimization time of 1.4892 s, 30.3 % faster than S-4WS. Overall, the proposed framework improves motion planning efficiency while maintaining smooth and stable motion in simulation.
