Maximum Power Tracking Research Articles

Enhancing the Performance of a Renewable Energy System Using a Novel Predictive Control Method

The current study concerns improving the performance of a renewable energy system using systematically designed control algorithms. The performance of the system under study is evaluated under two operating scenarios: the first in which the system consists of only a wind-driven synchronous generator connected to the utility grid; in the second scenario, the generator is combined with a photo-voltaic solar system and a battery for supplying a load. Each system component is modeled and thoroughly described. To maximize the benefits of solar and wind energies, two separate maximum power point tracking procedures are adopted. Furthermore, to enhance the generator’s dynamics, a novel predictive control scheme is designed and validated by comparing its performance with traditional predictive control. The novel predictive controller utilized a simple and unique cost function to avoid the shortages of traditional predictive controllers. For standalone operation, an effective procedure is adopted to ensure the power balance between the generation, storage, and isolated load units. To evaluate the effectiveness of the designed controllers under different operating regimes, Matlab/Simulink is utilized for this task. The obtained results confirm the superiority of the novel predictive scheme used with the synchronous generator over the classic control approach for the two operating scenarios. This has been shown in the form of reduced ripples and reduced current harmonics. The obtained results are also confirming the validity of the adopted maximum power tracking strategies with solar panels and wind turbines as well. Furthermore, balanced power delivery is achieved thanks to the adopted management strategy for standalone operation, which enhances the overall system performance.

A Maximum Power Tracker for Photovoltaic Module Arrays Based on the Improved Perturbation and Observation Method

A Maximum Power Tracker for Photovoltaic Module Arrays Based on the Improved Perturbation and Observation Method

A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading

Solar photovoltaic (PV) systems often encounter a problem called partial shading condition (PSC), which causes a significant decrease in the system’s output power. To address this issue, meta-heuristic algorithms (MHAs) can be used to perform maximum power point tracking (MPPT) on the system’s multiple-peak P-V curves due to PSCs. Particle swarm optimization was one of the first MHA methods to be implemented for MPPT. However, PSO has some drawbacks, including long settling time and sustained PV output power oscillations during tracking. Hence, some improved MHA methods have been proposed. One approach is to combine a MHA with a deterministic approach (DA) such as P & O method. However, such a hybrid method is more complex to implement. Also, the transition criteria from a DA to a MHA and vice versa is sometimes difficult to define. Another approach, as adapted in this paper is to modify the existing MHAs. This includes modifying the search operators or the parameter settings, to enhance exploration or exploitation capabilities of MHAs. This paper proposed to incorporate the stooping behaviour in the golden eagle optimization (GEO) algorithm. Stooping is in fact a hunting technique frequently employed by golden eagles. Inclusion of stooping in the GEO algorithm not only truly model golden eagles’ hunting behaviour but also yields great performance. Stooping behavior only requires one extra parameter. Nevertheless, on average, the proposed method can reduce tracking time by 42.41% and improve dynamic tracking accuracy by 1.95%, compared to GEO. Moreover, compared to PSO, GWO, and BA, the proposed method achieves an improvement of 2.66%, 3.56%, and 4.24% in dynamic tracking accuracy, respectively.

Direct speed fractional order controller for maximum power tracking on DFIG-based wind turbines during symmetrical voltage dips

Direct speed fractional order controller for maximum power tracking on DFIG-based wind turbines during symmetrical voltage dips

Comparison of Control Method System by Using Perturbation and Observation Algorithms and Fuzzy Logic for the Maximum Power Point Control in Photovoltaic Systems

Currently, photovoltaic energy conversion has developed tremendously in the global energy industry. The enormous capacity of solar power plants generates electricity all over the world. Hundreds of gigawatts of electrical energy are supplied to the networks of various countries around the globe. In addition, up to hundreds of gigawatts of installed capacity of solar power plants are commissioned every year in all countries of the world. And this has been the trend for the past few decades. The main problem of solar photovoltaic conversion is the instability of the solar radiation flux associated with the climatic conditions in the regions where photovoltaic plants are operated. This problem can be solved by using highly efficient methods to control the generation of solar power plants, in particular, such as the use of information control systems for a particular PV system to increase the final generation of electrical energy supplied to the consumer. The photovoltaic (PV) conversion of solar radiation flux is an important renewable energy source. Due to the varying intensity of the sun, the electricity generated by PV directly from the radiation flux is not constant. The photovoltaic system currently uses maximum power point (PMP) tracking in perturbation and observation (P&O) mode to increase the final output power of the photovoltaic panels. A step-down DC-DC converter allows PMP to change the PV operating voltage and determine the maximum power output of the PV panel. This study proposes a fuzzy logic implementation. The magnitude of the perturbed voltage is determined by the change in power dq and the change in power relative to the change in voltage dq/dv is fuzzy. The performance of fuzzy logic is investigated in this work in order to optimize MPM. Fuzzy logic can simplify maximum power tracking and reduce voltage instability. According to the simulation results, the MPM method based on fuzzy computation performs better than the traditional P&O method. By using the proposed methods, we can significantly improve electric power generation and increase the efficiency of photovoltaic conversion.

Maximum power tracking for wind energy conversion systems via a high-order optimal disturbance observer-based LQR without a wind speed sensor

A high-order optimal disturbance observer (HOODO) is introduced to precisely estimate the aerodynamic torque and the variable wind speed; and consequently calculate the optimal speed of the generator without measuring the wind speed. As the immeasurable wind speed is varying fast, the aerodynamic torque, which is considered as a disturbance, is also changed fast, then the conventional assumption that the disturbance is slowly-varying (i.e., its first-time derivative is zero), is not applicable. The proposed HOODO design considers the fast and stochastic characteristics of the wind speed by relaxing aforementioned conventional assumption. Moreover, via the linear optimal control theory, the parameters of the HOODO are tuned systematically by proper selection of the elements of the diagonal weighting matrices. In this study, a compromise between the observer’s convergence rate and the ability to Gaussian noise suppression is also considered. This helps to solve the difficulties of many existing observers regarding the gain selection algorithms. Also in the article, for the first time, a control scheme is considered in combination with the proposed HOODO and the LQ controller. To maximize a power captured from the wind, a linear-quadratic regulator (LQR) is utilized to maintain the angular speed of the generator at the optimal speed reference. A stability analysis of the designed control scheme is also discussed. Comparative results of the HOODO under different orders as well as other observers are given to prove the effectiveness of the introduced estimation method. The obtained simulation results reveal that the HOODO has a superior estimation of disturbance. All the simulations are carried out in the MATLAB/Simulink software.

Active-resonance technology for wave energy utilization: An efficient offshore distributed renewable power generation alternative

Active-resonance control is an efficient utilization technology for the ocean wave energy. A Resonant Inversed-Pendulum Wave Energy Converter (RIPWEC) that can regulate the natural frequency according to the variation of wave period is introduced. The adjustment of natural vibration frequency of the RIPWEC is realized by its mass-position-adjusting mechanism. This paper reveals the resonance characteristics of the RIPWEC and its power control strategy by means of theoretical modeling, numerical simulation, and field experiments. Herein, the natural frequency adjusting range, the capture width of multi-frequency resonance, the resonant capture width ratio and the resonant generating efficiency of the physical model and the maximum power tracking algorithm are investigated specifically. The research results can contribute to the RIPWEC theoretical design and the engineering practice.

A 600 W Photovoltaic Groundwater Pumping System Based on LLC Converter and Constant Voltage MPPT

The work consists of the development of a market-friendly 600 W photovoltaic battery-less water pumping system. The system is composed of four 280 Wp solar panels, an auxiliary 10 W solar panel, a three-phase converter and a ½ horsepower (hp) submersible water pump. The electronic stage involves a resonant DC-DC converter in series with a voltage source three-phase inverter. The novelties of this work are related to the use of a resonant DC-DC converter with fixed duty-cycle and frequency for water pumping application, the use of Silicon Carbide Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) for solving converter startup issues, and the use of a constant voltage maximum power tracking (MPPT) algorithm with auxiliary panels. The work displays the design of the converter, simulation and experimental results, discussions, and cost evaluation. The feasibility of the system is studied for the water requirements of a beef cattle farm in Brazil.

A Novel MPPT-Based Lithium-Ion Battery Solar Charger for Operation under Fluctuating Irradiance Conditions

Fluctuant irradiance conditions constitute a challenge in front of a proper battery charging process, when originated from a PhotoVoltaic Array (PVA). The behavior of the PVA under such conditions (i.e., reflected by a disturbed PV characteristic curve) increases the complexity of the total available power’s extraction process. This inconvenient fact yields eventually to a decreased overall efficiency of PV systems, especially with the presence of imprecise power-electronics involved circuits. Accordingly, the purpose of this paper is to design a complete battery solar charger, with Maximum Power Point Tracking ability, emerged from a PVA of 1.918 kWp, arranged in Series-Parallel topology. The targeted battery is of Lithium-Ion (Li-I) type, with 24 VDC operating voltage and 150 Ah rated current. The design began by configuring an interleaved synchronous DC-DC converter to produce a desired voltage level, with low inductor ripple current and low output ripple voltage. The DC-DC converter is in turns condemned by a modified Perturb and Observe (P&O) algorithm, to ensure efficient maximum power tracking. Progressively, the design encountered a layout of the bi-directional DC-DC converter to ensure safe current charging values for the battery. Under the same manner, the role of the bi-directional converter was to plug the battery out of the system, in case when the Depth of Discharge (DoD) is below 25%, thus sustaining the life span of the battery. The entire setup of the proposed sub-systems then leads to the relatively fastest, safest, and most reliable battery charging process. Results show an effectiveness (in terms of PV power tracking) ranging from 87% to 100% under four swiftly changing irradiance conditions. Moreover, this paper suggested the design’s future industrialization process, leading to an effective PV solar charger prototype.

New high performance PV system architecture for mitigation of partial shading effects

Partial shading (PS) is a common phenomenon that significantly affects the performance of photovoltaic (PV) systems. Various techniques such as maximum power tracking (MPPT), PV module interconnection and reconfiguration, and power electronics- based schemes have been used. The performance of these system highly depends on shading patterns. Also, the cost of hardware and control complexity are the other limitations to apply them. The paper presents a new hybrid system that combines conventional interconnected PV array using total cross tied (TCT) with partial power processing converters (PPPC) to enhance power generation from photovoltaic (PV) systems. The paper also highlights the impact of partial shading (PS) on the performance of PV systems, which reduces power output and increases mismatch losses. The severity of these effects depends on the shading area and the PS pattern.The proposed hybrid system offers a cost-effective, easy-to-control solution that can be used to enhance the performance of PV systems and promote their widespread adaptation. The performance of the proposed system has been verified using different PS conditions and compared with series-parallel (SP) and TCT interconnected array. The results demonstrate that the proposed system outperforms the SP and TCT interconnected array under PS conditions, highlighting the effectiveness of the PPPC in improving the efficiency of the PV system.

Maximum power tracking of wave power generation system based on improved particle swarm optimization

In a wave power generation system, traditional particle swarm optimization (PSO) is prone to premature convergence and local optimal solution when it is used to control the maximum power tracking. Therefore, an improved PSO is proposed, and simulated annealing adaptive particle swarm optimization (SAAPSO) is constructed. The algorithm controls the inertia weight coefficient factor by hyperbolic tangent function and makes the nonlinear adaptive change. Using a linear change strategy to control social learning factors and self-learning factors, a simulated annealing operation is introduced, and a temperature is set according to the initial state of the population. The population is guided to compare the fitness value with the random number in the Metropolis criterion and temperature, and whether a new solution is generated is judged, to improve the problem that the algorithm falls into a locally optimal solution. The simulation results show that the algorithm can effectively prevent the wave power generation system from falling into the local maximum power point, and the maximum wave energy capture ability is significantly improved.

AN EFFICIENT OPTIMIZED MPPT FOR PV SYSTEM UNDER EXTREMELY FAST CHANGING IRRADIANCE – HYBRID HONEY BADGER OPTIMIZER

In closing a long time, renewable power means have entered lesser hobbyhorse as the choice for of strength is growing. Solar power is truly the maximum functional salutary useful resource in the fineness of renewable powers as it's long hauls to be had transnational. Solar energy can be converted into energy in approaches; via CSP energy factory life or Photovoltaic structures. The solar energy is converted to lukewarmness and also this energy is converted to energy in CSP energy shops. Still, this revision of power can be finished in an unattached- step system by the use of sun panels and PV period. This generation is fantastically trusted sun irradiance and temperature. For every particular 2d, there can be a maximum electricity aspect (MPP) and its charge is reckoned on irradiance and temperature. The conventional strategies cannot perform sufficiently. Metaheuristic optimization algorithms may be used to song MPP in every normal circumstance and partial shading script. The pivotal gain of the use of them is that they will be speedy in operation, and they don't trap in near optimums. This oil uses Honey Badger Optimization set of rules in Maximum Power Tracking (MPPT) in PV systems.

An Maximum Power Point Tracker of Photovoltaic Module Arrays Based on Improved Firefly Algorithm

In this paper, an improved firefly algorithm (FA) was proposed for application on photovoltaic module arrays (PVMAs) with partial modules under shading so that the maximum power point tracking (MPPT) could be implemented. Firstly, a new model of high voltage step-up converter was developed for executing the MPPT of the PVMA. For the energy storage inductor of the developed converter, the architecture of the coupled inductor was adopted so that the converter switch did not need to operate under an excessive duty cycle, which could increase the voltage gain and reduce the ripple of the output voltage at the same time. To prevent shading on the partial modules within the PVMA and generate more than one peak on the power-voltage (P-V) output characteristic curve, where the conventional MPPT could only track the local maximum power point (LMPP) and reduce the output power of the PVMA, the maximum power tracker, based on the improved FA, was proposed in this paper. Such a tracker could perform an automatic online adjustment on the FA iteration parameters, according to the slope of the P-V output characteristic curve, for the PVMA and, at the same time, perform at 0.8 times of the maximum power point (MPP) voltage for the module array under standard test condition (STC), serving as the initial tracking voltage for implementing the global maximum power point tracking (GMPPT). Lastly, the actual test results were applied to verify that the proposed converter indeed contained a high voltage step-up and low-ripple output voltage. The improved FA could also track GMPP faster and further improve the power-generating efficiency of the PVMA.

Efficient Integration of Photovoltaic Solar Generators in Monopolar DC Networks through a Convex Mixed-Integer Optimization Model

The problem regarding the optimal siting and sizing of photovoltaic (PV) generation units in electrical distribution networks with monopolar direct current (DC) operation technology was addressed in this research by proposing a two-stage convex optimization (TSCO) approach. In the first stage, the exact mixed-integer nonlinear programming (MINLP) formulation was relaxed via mixed-integer linear programming, defining the nodes where the PV generation units must be placed. In the second stage, the optimal power flow problem associated with PV sizing was solved by approximating the exact nonlinear component of the MINLP model into a second-order cone programming equivalent. The main contribution of this research is the use of two approximations to efficiently solve the studied problem, by taking advantage of convex optimization models. The numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness of the proposed approach when compared to multiple combinatorial optimization methods. Two evaluations were conducted, to confirm the efficiency of the proposed optimization model. The first evaluation considered the IEEE 33-bus grid without current limitations in all distribution branches, to later compare it to different metaheuristic approaches (discrete versions of the Chu and Beasley genetic algorithm, the vortex search algorithm, and the generalized normal distribution optimizer); the second simulation included the thermal current limits in the model’s optimization. The numerical results showed that when the maximum point power tracking was not regarded as a decision-making criterion, the expected annual investment and operating costs exhibited better performances, i.e., additional reductions of about USD 100,000 in the simulation cases compared to the scenarios involving maximum power point tracking.

Chimp Optimized Fractional-Order Tilt Integral Derivative Controller (NFO-TIDC) Assisted Battery for EV Charging

The vast usage of electric vehicles (EVs) has raised to minimize the dependency on fossil fuels. The Photovoltaic (PV) is used in the charging station to supply the required power to the EV. Batteries’ charging and discharging control have become a major challenge in RES interconnected EV charging stations. To tackle this issue, a novel fractional-order tilt integral derivative controller (NFO-TIDC) is used in the battery-connected bidirectional converter. Moreover, special considerations of the NFO-TIDC are accomplished with an improved Chimp optimization algorithm. In addition, the enhanced seagull optimized maximum power tracking controller has been proposed for maximizing the power from PV under varying irradiance. The proposed method is implemented on MATLAB/Simulink, and the obtained results verify the superiority of the controller.

Improved climbing algorithm with variable step size in tidal energy Application of maximum power tracking control technique for power generation system

In the context of carbon neutral carbon peak, in order to cope with climate change caused by environmental pollution and energy shortage caused by excessive use of fossil energy, the vigorous development of clean, low-carbon, and efficient renewable energy has become a major strategic direction. The development and utilization of tidal energy in China is still a certain gap with the European powerhouse, the efficiency of tidal energy generation at a low flow rate is low and the scale of development and utilization is small. The feasibility of the proposed strategy is verified by simulation analysis, and the results show that the improved variable step climbing algorithm can quickly track the maximum power point and operate stably at this point.

Research on coordinated control strategy of photovoltaic energy storage system

In this paper, the modular design is adopted to study the control strategy of photovoltaic system, energy storage system and flexible DC system, so as to achieve the design and control strategy research of the whole system of “photovoltaic + energy storage + DC + flexible DC”. This realizes the flexibility and diversity of networking. Due to space reasons, this article focuses on the detailed explanation of the photovoltaic energy storage system control strategy, including the maximum power tracking control strategy of photovoltaic power generation, photovoltaic power generation boost chopper circuit control strategy, photovoltaic power generation DC/AC converter control strategy, PCS device control strategy, PCS virtual impedance control algorithm, etc. Simulation experiments are carried out according to the specific case setting parameters. The simulation results prove that the proposed flexible DC system coordinated control strategy can ensure grid frequency stability and grid voltage stability, and improve the consumption capacity of distributed new energy.

A novel hybrid image processing‐based reconfiguration with RBF neural network MPPT approach for improving global maximum power and effective tracking of PV system

SummaryThe solar photovoltaic (PV) array output is reduced significantly by the frequently occurring inevitable partial shading conditions. In consequence, the array exhibits multiple peaks in its characteristics that cause the conventional maximum power point tracking (MPPT) algorithms to get stuck at the local maximum. So, to track the global maximum power (GMP) among the multiple peaks, a novel radial basis function (RBF)‐based neural network approach has been proposed for predicting the optimal GMP. Additionally, a novel and intelligent encryption‐based ruler transform (RT) reconfiguration approach is proposed to disperse the shading effect enhancing the GMP and mitigating the multiple peaks. The effectiveness of the proposed RBF‐MPPT and novel RT‐reconfiguration strategies has been tested and analyzed for a 5 × 7 PV array under distinct dynamic, uniform, and nonuniform shading conditions. The results of the proposed RBF have been compared with the conventional incremental conductance (INC) algorithm before and after reconfiguration of the PV array. Further, the ease of GMP tracking by a simple conventional INC due to the reduction of peaks after the array reconfiguration under shading conditions has been demonstrated and discussed in detail. After reconfiguration, the GMP is enhanced by 37.35%, 31.41%, 30.86%, 21.46%, 13.69%, and 8.88%, using the proposed RBF for the considered five shading conditions. The steady‐state oscillations are also considerably mitigated by employing the proposed reconfiguration and RBF strategies.

Adaptive maximum power point tracking based on Kalman filter for hydrogen fuel cell in hybrid unmanned aerial vehicle applications

In this paper, an adaptive real-time estimation method based on Kalman filter is proposed for tracking the maximum power point (MPP) of a hydrogen fuel cell (FC) in hybrid unmanned aerial vehicle (UAV) applications. To achieve the adaptive MPP tracking (MPPT), a mathematical model for the hydrogen FC is established. Then, the recursive least square method is employed to identify the initial values of model parameters. On this basis, the MPP of the hydrogen FC under steady conditions can be derived. Furthermore, the state and observation equations based on Kalman filter are introduced to adaptively estimate the model parameters in real-time. Moreover, the real-time model parameters would be used to optimize the MPP in accordance with the operating conditions such that the adaptive MPPT can be achieved. Finally, various simulations and experiments are conducted to verify the effectiveness and accuracy of the adaptive MPPT for the hydrogen FC in hybrid UAV applications. Results show that the adaptive MPPT can not only track the MPP accurately in real-time, but also reduce the oscillation of the hydrogen FC. Compared with the MPPT methods based on perturb and observe (P&O) and particle swarm optimization (PSO), the maximum power tracking error of the adaptive MPPT can be improved by 2.83% and 1.10%, respectively.

A REVIEW ON MPPT ALGORITHMS FOR SOLAR PV SYSTEMS

In past few decades, solar energy plays a vital role in energy production among the different renewable energy resources. In shaded/unshaded photovoltaic (PV) systems, tracking of maximum power under different environmental conditions is provided by maximum power point tracking (MPPT). In recent years many works available on different types of MPPT techniques to track maximum power from PV systems with own pros and cons. This article comprehensively reviews the different traditional methods like perturb and observation (P&O), open circuit voltage (OCV), short circuit current (SCC), hill climbing (HC), incremental conductance (IC). Also recall the advanced MPPT techniques like particle swarm optimization (PSO), grey wolf optimization (GWO), cuckoo search (CS), artificial neural networks (ANN), fuzzy logic controller (FLC) available in literature. This review is conducted based on implementation, accuracy, tracking speed, cost, merits, and demerits of each technique. Traditional MPPT methods can’t able to track global maximum power point under partial shaded conditions and exhibits less efficiency when compared with advanced soft computing methods. Hybrid methods provide good efficiency and performance than traditional and advanced methods. Authors powerfully confirm that this article offers convenient information’s to enthusiastic engineers and new researchers those who are all working in solar PV systems.