Maximum Power Point Tracking: Adaptation Linear Prediction for Wind Energy Conversion Systems

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 .

This paper deals with the modeling, analysis, control and simulation of a doubly-fed induction generator (DFIG) driven by a wind turbine. This grid connected wind energy conversion system (WECS) is composed of DFIG and two back-to-back PWM voltage-source converters in the rotor circuit. A mathematical model of the machine, derived in an appropriate dq reference frame is established. The grid voltage oriented vector control is used for the grid side converter (GSC) in order to maintain a constant DC bus voltage and to compensate for reactive power at the power network. The stator voltage orientated vector control is adopted in the rotor side converter (RSC) control strategy, providing efficient handling of active and reactive power at the stator, as well as a maximum power point tracking (MPPT) method for the DFIG-based wind turbine. The proposed system is simulated for different operating conditions to illustrate the reliability of the control technique. Corresponding system simulation results under nonlinear load variations and wind speed transients are presented to demonstrate the significance of MPPT in WECS, and the effectiveness of adopted control technique.

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.

In this study, a single sensor‐based maximum power point tracking (MPPT) algorithm is presented for permanent magnet synchronous generator‐based wind energy conversion system (WECS). The MPPT method used in this study uses only the wind speed sensor to generate the reference point required for the maximum power. A discrete‐time controller is proposed to bring the system to the reference point determined by the MPPT algorithm. The designed MPPT controller consisting of a new discrete‐time reaching‐law approach and a discrete‐time integral sliding mode control (SMC). The designed discrete‐time SMC‐based controller is implemented by using an Acorn RISC Machine (ARM)‐based LM4F120 microcontroller, and its performance is examined on a WECS emulator prepared in the laboratory. The results got from the real‐time studies prove that the proposed controller has the satisfactory performance to bring the system to the MPP. The performance of the proposed controller is also compared with the conventional sliding mode controller and perturb and observe method. The obtained results proved that the proposed controller has a better performance than the other controllers in terms of the settling time and output voltage's ripple.

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&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&O methods are shown to achieve MPPT, albeit with a significantly longer convergence time. However, the P&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

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.

Novel standalone hybrid solar/wind/fuel cell power generation system for remote areas

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 power-voltage characteristic of the photovoltaic (PV) array shows multiple maximum power points (MPP) under partially shaded conditions. This makes the conventional maximum power point tracking (MPPT) methods ineffective due to their inability to discriminate between the local and global MPP. This paper proposes a new MPPT method based on tabu search (TSMPPT) for PV systems under partially shaded conditions. It combines three different search strategies, i.e. diversification search, local search and intensification search, to avoid the risk of trapping into the local MPP. Simulation results show that the proposed method could accurately track the global MPP with low complexity. (4 pages)

Due to the complex-shading and ununiform-corrosion problems caused by the oceanic climate, the working conditions of photovoltaic (PV) system in port are poor. In this study, some effective PV topologies are developed and compared under typical oceanic conditions. In order to improve the output of port PV system, a novel maximum power point tracking (MPPT) method is developed, in which the convolutional neural network (CNN) and bidirectional long short time memory network (BiLSTM) are introduced to forecast the real-time optimal operation point. Finally, the developed topology and MPPT method are verified by a comprehensive set of case studies. Experimental results show that the total-cross-tied (TCT) and gradient (GT) topologies can achieve the best performance. However, by considering the complex structure and higher failure rate due to the large number of joints in TCT topology, the PV arrays installed on the roofs, in the free space of green belt, freight yard and near the seaside are suggested to be connected in GT topology to balance operation efficiency, failure rate and maintenance costs. Moreover, the developed CNN-BiLSTM-based MPPT method and PV topology can effectively work together, and the average forecasting error is just 3.482%, which outperforms the compared state-of-the-art methods significantly.