Maximum Power Tracking Research Articles

Maximum Power Tracking for Centralized Temperature Difference Power Generation System Based on Elman Neural Network Combined With Improved Sparrow Search

Maximum Power Tracking for Centralized Temperature Difference Power Generation System Based on Elman Neural Network Combined With Improved Sparrow Search

Efficient maximum power tracking technique in grid connected PV-wind system

• This paper develops a design and analysis of the hybrid system with a DC-DC converter controlled by hybrid approach. • This hybrid approach is a combination of Adaptive Neuro Fuzzy Interference System- Honey Badger Algorithm (ANFIS-HBA) for extracting maximum power and power compensation. • The hybrid system is designed with PV and WT for compensating load demand in the grid side. • The main objective of the proposed methodology is to extract the maximum power of the system (PV and WT). The sustainable energy resources are considered to generate the power without consumption of fuel. From that, the Photovoltaic (PV) and Wind Turbine (WT) are selected because of advantages such as pollution free and low computational cost. In the sustainable energy source, the partial shading is a main issue in the generating power. In literature, some of the methods are reviewed and it is not provided efficient results. This paper develops a design and analysis of the hybrid system with a DC-DC converter controlled by hybrid approach. This hybrid approach is a combination of Adaptive Neuro Fuzzy Interference System- Honey Badger Algorithm (ANFIS-HBA) for extracting maximum power and power compensation. The hybrid system is designed with PV and WT for compensating load demand in the grid side. The main objective of the proposed methodology is to extract the maximum power of the system (PV and WT). The proposed methodology is executed in the MATLAB and performances are evaluated with the performance metrices. The proposed method is achieved efficient results which concluded with the comparison analysis such as Adaptive Neuro Fuzzy Interference System -Genetic Algorithm (ANFIS-GA) and ANFIS- Particle Swarm Optimization (PSO) at various partial shading conditions. The proposed method is achieved the efficient maximum tracking in PV and WT systems under partial shading conditions.

A Novel Cooperative Control Technique for Hybrid AC/DC Smart Microgrid Converters

This study proposes a novel technique of cooperative control for a distributed hybrid DC/AC Microgrid (MG) by designing a digital Infinite Impulse Response (IIR) filter-based Proportional-Resonant (PR) current controller. This controller adopts an Adaptive Neuro Fuzzy Inference System (ANFIS) trained by Particle Swarm Optimization (PSO) to control inverter output current while tracking Maximum Power Point (MPP). A hybrid ANFIS-PSO extracts maximum power from both inverter and boost converter-based solar Photovoltaics (PVs) systems quickly and with zero oscillation tracking. The proposed PR controller cancels harmonics while achieving high gain at the resonant frequency (grid frequency). The PR controller offers quick reference signal tracking, grid frequency drift adaptation, easy system design, and no steady-state error. Moreover, this investigation features a PR controller frequency-domain analysis. The proposed technique smooths voltage and improves steady-state and transient responses. Cooperative control is implemented on an IEEE 14-bus MG with distributed communication. The findings indicate that the proposed control technique can regulate MG voltage to obtain a more stable voltage profile. The adopted MG, made up of dispersed resources, is crucial for assessing power flow and quality indicators in a smart power grid. Finally, numerical simulation results are utilized to verify the recommended technique.

Review of the Modern Maximum Power Tracking Algorithms for Permanent Magnet Synchronous Generator of Wind Power Conversion Systems

Wind energy conversion systems (WECSs) are considered green generators, environmentally friendly, and fully suitable energy sources to replace fossil energy sources. WECS’s output power is hugely dependent on the random nature of the wind. There are many solutions to improve the output power for WECSs, such as adjusting the profile of turbine blades, locating installation places, improving generators, etc. Nevertheless, maximum power point tracking (MPPT) algorithms for WECSs are optimal and the most effective because they are flexible in controlling different variable wind speeds and match all types of WECS. The parameters on the generator side control or the grid side control will be adjusted when MPPT algorithms are used, allowing the output power of WECSs to be maximized while maintaining stability in variable-speed wind. There are various MPPT algorithms, but the current problem is their efficiency and whether it requires deep knowledge to select the best MPPT solutions because each method has different advantages and disadvantages. This study has implemented an overview of modern maximum power tracking algorithms applied to permanent magnet synchronous generators in WECS with MPP methods based on speed convergence, efficiency, self-training, complexity, and measurement of wind parameters.

Solar, Wind and Their Hybridization Integration for Multi-Machine Power System Oscillation Controllers Optimization: A Review

Massive growth in global electrical energy demand has necessitated a genuine exploration and integration of solar and wind energy into the electrical power mix. This incorporation goes a long way in improving the cumulative generated power capacity of the power system. However, wind and solar photovoltaic (PV) are intermittent in nature, making the provisioning of a good maximum power tracking (MPPT) scheme necessary. Furthermore, the integration is characterized by synchronization challenges and introduces various modes of power system oscillations as it is converter-driven. This greatly affects the overall stability of the integrated power mix. Consequently, various technological models have been designed to address these challenges ranging from MPPT schemes, phase-lock loop (PLL), virtual synchronous generator (VSG), power system stabilizers (PSS), flexible AC transmission system (FACTS), coordinated control and artificial intelligence (AI). In this work, a multi-machine power system model is reviewed for integration stability studies. Various technical solutions associated with the integration are also reviewed. MPPT, PLL, VSG, PSS, FACTS, coordinated control, and various optimization technique schemes used for damping controller design are discussed.

Optimal dynamic frequency regulation of renewable energy based hybrid power system utilizing a novel TDF-TIDF controller

ABSTRACT Insertion of intermittent natured renewable energy resources in the microgrid to decline global warming and due to highly inconsistent propulsion load, frequency control of the isolated hybrid micro-grid system (HMGs) is the key point of attraction and necessitates a highly competent and robust control strategy. Hence, in this paper, an efficient frequency control mechanism for HMGs is proposed by utilizing an efficient tilt derivative filter-tilt integral derivative filter (TDF-TIDF) controller. HMGs are composed of solar, wind, fuel cell, freezer, diesel engines, heat pumps, and loads. A recently appeared dragonfly algorithm (DFA) is employed to optimize the suggested controllers’ design variables. The dynamic analysis is exhibited with different types of energy storage units under various conditions. The conditions incorporate perturbations in demanded load and power generation through renewable energy sources. In this study, solar thermal and wind power systems are not contributing to frequency control owing to the plan of their maximum power tracking approach. The dynamic performance of different controllers such as PIDF, TIDF, PDF-PIDF, and TDF-TIDF controllers are evaluated with their design variables tuned by employing genetic algorithm, particle swarm optimization, gravitational search algorithm, quasi harmony search algorithm, grasshopper algorithm and dragonfly algorithm. The output graphs of transient responses and collected statistical data corroborate that the suggested DFA-based TDF-TIDF scheme provides least objective function value (8.1e‒ 07) compared to PDF-PIDF (4.011e‒ 06), TIDF (3.14e‒ 05) and PIDF (5.03e‒ 05), hence, more capable in reducing the oscillations quickly for the system with non-linearities. Moreover, statistical data yielded by DFA is observed superior when evaluated with other existing optimization methods.

Power maximization in standalone photovoltaic system: an adaptive PSO approach

A control strategy for power maximization which is an important mechanism to extract maximum power under changing environmental conditions using Adaptive Particle Swarm Optimization (APSO) is proposed in this paper. An Adaptive Inertia Weighting Factor (AIWF) is utilized in the velocity update equation of traditional PSO for the improvement in speed of convergence and precision in tracking Maximum Power Point (MPP) in standalone Photovoltaic system. Adaptation of weights based on the success rate of particles toward maximum power extraction is the most promising feature of AIWF. The inertia weight is kept constant in traditional PSO for the complete duration of optimization process. The MPPT in PV system poses a dynamic optimization problem and the proposed APSO approach paves way not only to track MPP under uniform irradiation conditions, but also to track MPP under non-uniform irradiation conditions. Simulations are done in MATLAB/Simulink environment to verify the effectiveness of proposed technique in comparison with the existing PSO technique. With change in irradiation and temperature, the APSO technique is found to provide better results in terms of tracking speed and efficiency. Hardware utilizing dSPACE DS1104 controller board is developed in the laboratory to verify the effectiveness of APSO method in real time.

Maximum Power Tracking Method of Photovoltaic Sequence Based on Nonlinear Particle Swarm Optimization Algorithm

Maximum Power Tracking Method of Photovoltaic Sequence Based on Nonlinear Particle Swarm Optimization Algorithm

ANALYSIS OF PERFORMANCE COEFFICIENTS IN MAXIMUM ELECTRICAL POWER EXTRACTION FROM STAND-ALONE WIND ENERGY CONVERSION SYSTEM

Increasing performance and improving efficiency in maximum power extraction from Wind Energy Conversion Systems (WECS) is a quite important research topic. Today, in the large-scale WECS, it is widely aimed to extract the maximum mechanical power from the wind turbine using the Maximum Power Point Tracking (MPPT) unit. Similarly, it can also be targeted to achieve maximum mechanical power in small-scale WECS applications. However, losses occur in structural subsystems and electrical subunits located in WECS. Due to these losses, the overall system efficiency decreases and the characteristic of the system is also affected. The operation of these systems can also be performed via maximum electrical output power extraction, which is one of the most up-to-date ideas. Thus, the overall WECS rather than the wind turbine can be optimally controlled. Eventually, maximum electrical power tracking (MEPT) based designs can provide higher power extraction with higher efficiency than MPPT-based ones. In this paper, considering the system operating concepts with MPPT and MEPT for a stand-alone Permanent Magnet Synchronous Generator (PMSG) based WECS, the changes in performance coefficients at defined focus points in terms of system efficiency are evaluated. Technical and theoretical comparative analyzes are also made for each specific wind speed between 8m/s and 12m/s.

Study Comparison Between Enhanced Firefly and Differential Evolution to Solve the Maximum Power Point Tracking Problem

The penetration of photovoltaic (PV) in electric power generation is continually increasing. On the other side, the load will receive the actual power, which is a part of power supplied by the photovoltaic. Therefore, it is necessary to extract maximum power from PV. One of a defy problem, is the tracking maximum power point (MPPT) in photovoltaic frameworks and it is a significant task. It can be a reproducer of maximum power from a photovoltaic system, which it depends on the adjusting of duty cycle of DC-DC converter. In order to produce a maximum power transfer, the impedance between the source and the load should be coincide by using of a buck boost converter. In this work, the proposed methods; Firefly algorithm (FA), Enhanced Firefly (EFA), Differential Evolution Scheme1, Differential Evolution Scheme2, and Differential Evolution Scheme 3 were tested for their performances in different conditions. Finally, the simulation results confirm that the second scheme of DE outperforms the others. Visual Basic. Net has been used to simulate the results and proceed with algorithms.

The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems

This paper presents a new heuristic method for maximum power point tracking (MPPT) in PV systems under normal and shadowing situations. The proposed method is a modification of the original queen honey bee migration (QHBM) to shorten the computation time for the maximum power point (MPP) in PV systems. QHBM initially uses random target locations to search for targets, in this case, MPP. So, we adjusted it to be able to do MPP point quests quickly. We accelerated the mQHBM learning process from the original randomly. We had fairly compared the mQHBM with several heuristics. Simulations were carried out with 2 scenarios to test the mQHBM. Based on the simulation results, it was found that mQHBM was able to exceed the capabilities of other methods such as original QHBM, particle swarm optimization (PSO) and perturb and observe (P&O), ANN, gray wolf (GWO), and cuckoo search (CS) in terms of MPPT speed and overshoot. However, the accuracy of mQHBM cannot exceed QHBM, ANN, and GWO. But still, mQHBM is better than PSO and P&O by about 15% and 18%, respectively. This experiment resulted in a gap of about 2% faster in speed, 0.34 seconds better in convergence time, and 0.2 fewer accuracies.

Maximum power tracking-based adaptive cold start strategy for proton exchange membrane fuel cell

The difficulty of cold start of proton exchange membrane fuel cells is one of the key factors limiting their commercialization. To determine the optimal cold-start load current to balance PEMFC icing and internal reaction heat generation, an adaptive cold-start strategy for fuel cells based on maximum power tracking is proposed in this paper. First, the semi-empirical model of output voltage is improved and the recursive least squares method with adaptive forgetting factor is used for model parameter identification. Subsequently, a multi-parameter evaluation model based on gray correlation analysis and technique for order preference by similarity to ideal solution is established for screening the optimal start-up time, and the corresponding load is used as the learning target of the output voltage prediction model. Finally, the output voltage model is used for maximum power point tracking, and the resulting current load is used for cold-start experiments of the PEMFC. The results of the study show that the PEMFC performs the best overall at 360 S under experimental conditions according to the evaluation model. The proposed strategy based on the new output voltage prediction model can reduce the prediction deviation of the maximum power point at 360 S by 40.64 % compared to the cold start strategy using the benchmark empirical model. The 27 A current load obtained by the proposed strategy results in a high and stable output performance of the PEMFC cold start, which verifies the feasibility of the strategy.

A hybrid adaptive-prediction maximum power point tracking method for the smart city with massive photovoltaic

The partial shadow condition seriously affects the efficiency of the photovoltaic system in the modern city with dense built and other occlusions. From this, the characteristic curve of the photovoltage system shows multi-peak, which further increases the difficulty of getting photovoltaic systems to operate at maximum efficiency. As an efficient technique, the intelligent optimized maximum power point tracking method relies on initialization information and is difficult to balance the tracking performance. Therefore, a hybrid adaptive-prediction maximum power tracking method is proposed in this paper. Firstly, the neighborhood range of the maximum power points is located by the fuzzy predicted mechanism at the upper layer. Secondly, on the bottom layer, based on improving the Cuckoo search algorithm, the proposed method uses an interpolation function fitting curve to guide the particles to converge accurately on the bottom layer. At the same time, the output voltage of the system under an open loop is directly controlled by the duty cycle of the control signal, which improves the universality of the method. Finally, the simulation results show that the proposed method is superior to other advanced methods in tracking speed and with smaller power oscillations and comparable tracking accuracy, for which the proposed method is suitable for the city with complex environments and dense buildings.

Solar Photovoltaic Based Water Electrolysis System to Generate Hydrogen and Oxygen

Abstract: In this never-ending quest for finding an alternative or a solution to the current problems related to increasing energy demands and climate change, various research, progress and development have been made in the subject of environmentally friendly energy systems. On one aspect the conversion of water into hydrogen gas and oxygen gas by using electro-chemical process is an ideal method which can produce a clean and green fuel via: Sustainable methods and fossil free path ways. Hydrogen production by means of using various renewable energy sources such as solar energy, wind energy, tidal energy and hydro energy is a way to eliminate dependency of three systems on the electric grid. In this study which is based on a technique in which a fuel cell (ox-hydrogen electrolyzer) is connected to a Photovoltaic solar panel to produce clean hydrogen and oxygen gas as a fuel. The objective of this project was to develop a strategy and make this generation process independent of other energy sources. A DC converter is used in such a sequence which can control and supply maximum power to the electrolytic cell which is connected to the solar Photovoltaic panel, which in turn we can obtain maximum hydrogen gas. Also, various catalysts which are most commonly found or easily available in the market are experimented in this research to get optimum output. The converter and voltage regulator is also responsible for operating the electrolytic cell in its optimum functioning region to avoid over heating of the cell. The DC converter and the Electrolytic cell has been tested and simulated within indoor and outdoor conditions. Analysis of this setup based on maximum power tracking showed 85% efficiency

Study and Implementation of a High-Quality True Sine Wave DC-to-AC Inverter for Solar Power Generation Systems.

True sine wave DC-to-AC inverters are becoming more and more important in solar power generation in order to raise the system’s efficiency. A high-quality true sine wave DC-to-AC inverter can be built with a robust intelligent control method. This robust intelligent control method is comprised of improved sliding mode reaching law (ISMRL) and particle swarm optimization (PSO)—catfish effect (CE). The sliding mode reaching law is robust and insensitive to parameter variations and external disturbances. However, it has infinite system-state convergence times and steady-state errors. In addition, solar panels are often affected by partial shading, causing the output power–voltage characteristic curve to be multi-peaked. Such a situation causes misjudgment of the maximum power point tracking with conventional algorithms, which can neither obtain the global extremes nor establish high conversion efficiency. Therefore, this paper proposes an ISMRL based on PSO-CE applied to the tracking of maximum power in the case of partial shading of a solar power generation system. The ISMRL guarantees quick terminable time convergence, making it well-suited for digital implementation. In this paper, PSO-CE is used to find the global best solution of ISMRL, rejecting steady-state errors, slow convergence, and premature trapping in local optimums. Simulation and experimental results are verified using digital implementation based on a Texas Instruments digital signal processor to produce more accurate and better tracking control of true sine wave DC-to-AC inverter-based solar power generation systems.

A Multiple Time Scales Rolling Coordinative Dispatching Method for an Island Microgrid with High Proportion Tidal Current Energy Access and Demand Response Resources

Currently, the ocean energy strategy is rapidly developing, and a high proportionate tidal current energy grid connection presents significant obstacles to the planning and secure and stable operation of an island microgrid. For an island microgrid with high proportion tidal current energy access and demand response resources, this research suggests a multiple time scales rolling coordinative dispatching method. An MPPT control based on Q-Learning algorithm is first developed for real-time maximum power tracking of tidal current energy generation after the island microgrid’s topology has been examined. Following that, a multiple time scales rolling coordinative dispatching’s fundamental architecture and implementation method are provided, with equal time intervals coordinated in a step-by-step recursive way. In the example analysis of an island microgrid, we consider the rigid demand load that does not participate in the demand side response, and the ship load and controllable load that participate in the demand side response. On sea islands, ship loads on the long timeframe achieve traffic and energy interaction, and dispatchable loads on the short timescale participate in supply and demand balancing. This is due to the multiple time scales properties of demand response resources. In addition, a multiple time scales rolling coordinative dispatching model for an island microgrid is developed. It includes day-ahead, intraday, and real-time components. Finally, example analysis is used to confirm the dispatching method’s usefulness and advancement, and we conclude that the tidal current energy consumption rate of the island microgrid is increased by 17.08%.

Maximum Power Tracking for Magnetic Field Editing-Based Omnidirectional Wireless Power Transfer

This paper proposes and implements an optimized omnidirectional wireless power transfer (WPT) system using distributed transmitter coils is proposed. Under the consideration of practical WPT applications which involve human safety concerns, such as daily home apparatus or wireless rechargeable medical implants, the proposed system can achieve the shortest charging time within the safe limits of human exposure to radiofrequency electromagnetic energy. Based on the calculation of the magnetic intensity generated by multi-coupling WPT systems, a more processable magnetic threshold model is presented and analyzed based on the common standard. A newly developed optimization model of the transmitter currents and accordant control strategy is designed and presented to realize both maximum power tracking and flux suppression at target positions. The double-resonant circuit topology with LCC compensation is adopted to reach the stable and synchronized transmitter currents. The cross-coupling problem in multi-input WPT systems will be solved as well. Full-range magnitude control is realized with a single controller and direct current power source. The hybrid frequency pacing technique is adopted for lower switching frequency and higher system efficiency under weak coupling operations. Taking practical wireless rechargeable medical implant as the research object, both simulation and experimental results validate the feasibility and the prospect of the proposed system.

Modeling and parameter estimation of solar photovoltaic based MPPT control using EKF to maximize efficiency

In this paper, we focus on the design, modeling and implementation of a MPPT controller based maximum power tracking of photovoltaic system. The electrical characteristic of The PV system is non-linear and changes with the solar irradiation and the ambient temperature. Therefore, the incremental conductance (IC) method control is known for its stability and robustness, and is used to extract the maximum energy from the PV source using a boost converter topology. It provides a strong basis for the improvement and optimization of control parameters of a photovoltaic system. Implementing MPPT algorithm usually need the use of a lot of sensors if accuracy of the system has to be increased. However, IC method with an extended Kalman filter (EKF) can be utilized in order to estimate some parameters to reduce the number of Sensors. The EKF is deployed in the optimal position to estimate both current and the capacitor voltage, thus allowing to eliminate two sensors devise from the entire PV system, which increases the system efficiency and reliability, simplifies the control method and decreases the system cost. The performance of the proposed technique is validated by experimental and simulation results under different operating conditions and load changes.

Comparative Analysis of a DC-microgrid Incorporating Hybrid Battery/Supercapacitor Storage System Addressing Pulse Load

Renewable energy sources (RESs) have become the primary power source of microgrids with many merits. However, RESs depend on the climate (i.e., wind speed and solar irradiance), leading to intermittent power generation. Moreover, some of the particular loads, such as the pulse load, severely impact the microgrid performance. Therefore, energy storage systems (ESSs) are considered a vital solution to improve microgrid performance and overcome microgrid challenges. This paper presents a comparative analysis of the performance of a DC-microgrid incorporating a battery storage system (BSS) and a hybrid battery/supercapacitors storage system (B-SCSS). The study addressed the fluctuations of solar radiation and wind velocity on the system performance in the presence of both energy storage technologies. In addition, the sudden load disturbance is also investigated with particular attention to the pulse load. The overall microgrid components are designed and controlled based on the PI controller to enhance the system's stability. The photovoltaic and wind energy are utilized with maximum power tracking (MPPT) to get the full benefits from RESs. The B-SCSS is proposed to maintain the DC-bus voltage, load power, and frequency constant during various disturbance events. The obtained results using Matlab software prove the effectiveness of the proposed method.

Intelligent Control for Islanded Operation of Doubly Fed Induction Generator based Wind Energy Conversion System coupled to DC Micro-Grid

A single parameter based fuzzy model for controlling the Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion System (WECS) to operate in islanded mode is discussed in this paper. The system employs Rotor Side Converter (RSC) and Stator Side Converter (SSC) connected back-to-back to a dc micro grid. The control scheme for both RSC and SSC employ cascaded control loops: Outer speed and inner current loop for RSC and Outer voltage and inner current loop for SSC respectively so as to provide better response during dynamic conditions. In both control schemes, the outer loop is processed and the error along with its derivative is found to produce a single variable for feeding the fuzzy model. As only one variable is involved, less memory occupancy, less complex control and computation is faster. The RSC control employs the speed reference obtained through power calculator based maximum power tracking method which is novel in its kind. The SSC employs the terminal voltage at PCC for the outer voltage loop. The simulation of the system under constant and variable wind speeds is carried out using Simpower Systems toolbox of MATLAB. The results indicate that the efficacious working of the system with faster response to dynamic variations.