An Analysis of Variable-Speed Wind Turbine Power-Control Methods with Fluctuating Wind Speed

This paper reviews the existing models, connection schemes and maximum power point tracking (MPPT) methods of PV arrays. The partial shading causes significant power losses. It is very difficult to maintain uniform irradiance over the entire PV array, especially for rooftop or building-integrated PV systems. The aspects such as connection scheme of PV array, shading pattern and maximum power point tracking (MPPT) techniques, etc., decide the power generated by the PV array. The conventional MPPT methods are only capable of reaching maximum power point (MPP) in identical insolation situations. Hence, conventional MPP tracking becomes inept due to the existence of several peaks in the P-V characteristics. There are different global maximum power point (GMPP) tracking techniques available in the literature that operate in partial shading scenarios. The reconfiguration of the PV array reduces the number of multiple peaks. This reduction in several peaks makes conventional MPP tracking techniques competent even under partial shading conditions with low implementation complexity and higher tracking speed. This paper can assist scientists in choosing a precise objective-based PV module model, connection schemes and MPP tracking methods out of numerous schemes available in the literature.

Implementation of Solar thermal energy as a source of renewable electricity is currently being developed. The main problem with photovoltaic systems is the result of power efficiency is low. The maximum power point tracking (MPPT) method can increase the efficiency of photovoltaic output power. This research will use the MPPT method with an artificial bee colony (ABC) algorithm. MPPT design will be simulated using Power Simulation (PSIM) software. Simulation results will be compared with no MPPT and MPPT human psychology optimization (HPO) algorithm. The results show MPPT ABC gets the best average accuracy from the average accuracy without MPPT and MPPT HPO, which is 99.95%. And the MPPT ABC has a response time of MPP tracking faster than MPPT HPO, during irradiation 800 W/m 2 , 900 W/m 2 , 1000 W/m 2 .

For maximum utilization of solar energy, photovoltaic (PV) power systems should be operated at the maximum power point (MPP) which can be achieved using maximum power point tracking (MPPT) methods. However, the occurrence of multi-peak on P-V curve of a PV array due to the changing environmental conditions such as being partially shaded increases the complexity of the tracking process. The global MPP cannot always be achieved by the conventional MPPT methods. Therefore a novel MPPT method for PV systems using flower pollination (FP) algorithm is proposed in this paper and the Levy flight is used to improve the convergence of FP algorithm. MPPT model of the PV system is established in MATLAB to verify the effectiveness of the proposed method, and the proposed method is compared with two well established MPPT methods. The simulation results indicate that the proposed MPPT method can quickly track the changes in external environment and effectively handle the partially shaded condition.

Under partial shading condition or imbalanced temperature distribution, the power-voltage characteristic curve of photovoltaic (PV) array exhibits complicated multiple peaks. Traditional maximum power point tracking (MPPT) method is usually easy to trap into local extreme power point. To solve this issue, this paper proposes a novel MPPT method using quantum-behaved particle swarm optimization (QPSO) algorithm. This algorithm adopts detect-search strategy based on voltage closed-loop control, making voltage particles search photovoltaic characteristic curves in quantum-behaved paths. By setting a modulation strategy of reunion and separation factor and stopping criterion, the proposed QPSO algorithm improves the precision of stable state, and eliminates power oscillation under stability condition. Experimental results show that QPSO algorithm has good performance with less parameters, superior global optimization ability, favorable stability and better adaptability compared with traditional particle swarm optimization (PSO) algorithm in PV MPPT.

태양전지의 출력 특성은 비선형이고 온도와 일사량에 많은 영향을 받는다. 최대전력점 추종 제어는 태양광 발전의 출력을 최대로 하기 위해 사용되는 제어기법이다. 태양광발전 시스템의 출력 및 효율을 증가시키기 위해 더욱 우수한 최대전력점 추종 제어기법이 필요하다. 본 논문에서는 태양광 발전 시스템의 효율을 개선하기 위해 일사량을 고려한 새로운 최대전력점 추종 제어 알고리즘을 제시한다. 제시한 알고리즘은 종래의 P&O방법과 CV 방법을 혼합한 것이며, PSIM 시뮬레이터를 통하여 종래의 MPPT 알고리즘과 다양한 일사량 조건에서 성능시험을 비교하였다.<BR> 제시한 알고리즘은 종래의 알고리즘에 비해 출력의 자려진동없이 다양한 일사량에서 우수한 성능을 나타냈다. 이로서 본 논문에서 제시한 HB 방법의 최대전력점 추종 제어 알고리즘의 타당성을 입증하였다.

Improved particle swarm optimization for maximum power point tracking in photovoltaic module arrays

The use of solar energy as an alternative energy source continues to increase along with global energy needs and environmental awareness. However, the efficiency of the photovoltaic (PV) panel system is greatly influenced by variations in sunlight intensity, especially in partial shading conditions. This condition causes the emergence of several local maximum power points, which makes it difficult for conventional MPPT (Maximum Power Point Tracking) systems to find the global maximum power point (GMPP). In this study, an MPPT method based on the Firefly Algorithm (FA) was developed, a metaheuristic optimization algorithm inspired by the behavior of fireflies in attracting partners through light intensity. This method was chosen because of its ability to explore non-linear search spaces and avoid traps at local maximum points. The study was conducted through modeling of the PV panel system and simulations in various partial shading scenarios using MATLAB/Simulink software. The Firefly Algorithm was then applied to search for the maximum power point and its performance was compared with conventional MPPT methods such as Perturb and Observe (P&amp;O) and Incremental Conductance (IC). The discussion plan includes analysis of power tracking efficiency, convergence time, and system stability under changing lighting conditions. It is expected that the results of this study can contribute to the development of a more adaptive and efficient MPPT control system for modern solar power systems, especially in areas with high levels of shade.

In the several last years, numerous Maximum Power Point Tracking (MPPT) methods for photovoltaic (PV) systems have been proposed. An MPPT strategy is necessary to ensure the maximum power efficiency provided to the load from a PV module that is subject to external environmental perturbations such as radiance, temperature and partial shading. In this paper, a new MPPT technique is presented. Our approach has the novelty that it is a MPPT algorithm with a dynamic hysteresis model incorporated. One of the most cited Maximum Power Point Tracking methods is the Perturb and Observer algorithm since it is easily implemented. A comparison between the approach presented in this paper and the known Perturb and Observer method is evaluated. Moreover, a new PV-system platform was properly designed by employing low cost electronics, which may serve as an academical platform for further research and developments. This platform is used to show that the proposed algorithm is more efficient than the standard Perturb and Observer method.

Marine current power is attracting more attention as a renewable energy option. Similar to wind power, marine current power often requires a maximum power point tracking (MPPT) method to optimize power extraction from the free‐flowing water. Research into MPPT methods for marine current power remains limited. Therefore, this paper presents a comprehensive investigation of MPPT methods for marine current power, building upon similar research in wind power. Three methods, namely the optimal tip speed ratio (OTSR), optimal torque (OT), and two variants of the perturb and observe (P&amp;O) method, are explored. Using a simulation model developed for a specific marine current energy converter, where hydrodynamic calculations are coupled with electrical simulations, the study demonstrates that the OTSR method achieves MPPT with a comparably fast convergence time. After a change in water speed, the OTSR method achieves optimal operation within two turbine rotations. Additionally, the P&amp;O methods are shown to achieve MPPT, albeit with a significantly longer convergence time. However, the P&amp;O methods can be more convenient since no model of the system is required, and no water speed measurements are necessary. The proposed implementation of the OT method underperforms but positions the system close to the optimal operational point.

This paper presents an improved reinitialisation condition for time invariant maximum power point tracking (MPPT) methods used in photovoltaic (PV) systems experiencing partial shading conditions (PSC). Time invariant (MPPT) methods, such as Particle Swarm Optimisation (PSO), overcome the limitations of existing MPPT by tracking the global maximum power point (GMPP) of a PV system operating under PSC. However, due to the time invariant structure of these MPPT methods, they also require a reinitialisation condition to be defined for when a change in irradiance or temperature occurs. Testing was performed using simulations of a model built in Matlab/ Simulink, where the performance of existing and developed conditions was evaluated using test cases with changes in solar irradiance. Limitations of existing conditions were identified and a more robust reinitialisation condition developed. The developed reinitialisation condition used sentry particles to monitor the PV voltage range for changes in the measured power of any sentry. The developed condition had a 96 % rate of successful detection, as compared to as low as 68 % successful detection for existing methods, demonstrating improved performance and robustness.

Simulation and experimental design of adaptive-based maximum power point tracking methods for photovoltaic systems

Compared with typical current controlled PV inverters, voltage controlled PV inverters based on droop control are easier to achieve seamless transfer between the stand-alone mode and the grid-connected mode. This paper achieves both seamless transfer and the maximum power point (MPP) tracking (MPPT) by adjusting droop curves dynamically, combining MPPT and P-f droop control. The DC voltage is difficult to control during grid connecting which will affect both the stability of the system and the MPPT efficiency. To solve this problem, a control method is proposed which includes an active power inner loop and a DC voltage outer loop. At last simulation and experimental results validate the proposed control strategy and show that DC voltage response is fast and stable. Both seamless transfer and MPPT operation modes are realized.

Combined hydro-wind frequency control scheme: Modal analysis and isolated power system case example

An MPPT (Maximum Power Point Tracking) plays a vital role in a photovoltaic(PV) power generation system. Applying the optimum MPPT is a significant way to achieve an efficient PV system. The MPPT methods’ efficiency counts on the two MPPT factors, including the perturbation period and the perturbation amplitude. Nevertheless, realizing MPPT in the photovoltaic array is very hard when the natural conditions change, especially for the temperature and light intensity. This paper proposed a new method based on Conjugate Gradient (CG) to address the problem of hard reasonable considering both dynamic and steady-state performance in some existing MPPT algorithms. Meanwhile, the presented algorithm can effectively adjust the output of PV cells by controlling the boost DC/DC converter. Finally, the experimental and simulation results show the system can quickly get the maximum power point and have performance.

The maximum power point tracking technique is applied in wind power generation system to improve the utilization of wind energy. A review of existing maximum power point tracking methods is presented in this paper, based on which an intelligent maximum power point tracking is developed by the authors to improve the system performance and to facilitate the control implementation. The developed maximum power point tracking algorithm adopts variable duty cycle step size to maximize the output power of the wind generator, without the need for either knowledge of wind turbine characteristics or the measurements of mechanical quantities such as wind speed and turbine rotor speed. System simulation results have confirmed the functionality and performance of this method.