Optimal Maximum Power Research Articles

ANN-based Maximum Power Point Tracking Technique for PV Power Management under Variable Conditions

Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology.

A hybrid wave vibration energy harvester with electromagnetic double-speed and piezoelectric up-frequency driven by a rotating ball

The energy crisis and environmental pollution have driven the development of ambient energy harvesting technologies, and ocean waves usually contain abundant vibration energy, making the study of wave energy harvesting technology of profound value. This paper proposes a hybrid wave vibration energy harvester with electromagnetic double-speed and piezoelectric up-frequency driven by a rotating ball. For the electromagnetic generator (EMG), the excitation magnet and coil move simultaneously, resulting in double the flux variation rate compared with traditional structures, enhancing the output voltage. For the piezoelectric transducer (PZT), four piezoelectric cantilever beams are magnetically coupled with the EMG, generating power through bistable motion and broadening the working frequency band of the cantilever beam structure. The harvester is modeled and simulated, and its performance is tested on a simulated vibration platform, with simulation and experimental results in good agreement. Under external excitation at 0.8 Hz, the optimal load and maximum power for each EMG are 8.2 Ω and 207.2 mW, respectively, and the optimal load and maximum power for each PZT are 100 kΩ and 1.52 mW, respectively. The harvester can produce a maximum output power of 420.48 mW, demonstrating high efficiency in energy capture under low-frequency and multidirectional wave excitation.

A generalized model for a triboelectric nanogenerator energy harvesting system

A triboelectric nanogenerator (TENG) energy harvesting system involves at least three components: the mechanical source that inputs mechanical energy to drive TENGs, the TENG device itself as an energy converter, and an external electrical circuit that output the energy from TENGs. Multiple theoretical models have been developed for structural optimization design and maximum power output, yet there has been less emphasis on dynamic modelling of the entire energy harvesting system. This work presents attempts to establish such system-level models that encompass both mechanical and electrical systems. Special consideration is paid to the dynamic contact problem in this generalized model, since dynamic response of impacts can significantly affect the electric outputs. To address contact constraint problems, the penalty function method is utilized to handle non-smoothness and discontinuity. Subsequently, this research specifies how to improve the maximum energy conversion efficiency, and suggest optimal executive strategies for maximizing the energy output through a thorough parametric study. The anticipated outcome is that the generalized model will not only guide optimization design and predict the dynamic characteristics of the energy harvesting system but also assess the potential feasibility of mechanical energy harvesting technology across diverse application domains.

Optimizing Photovoltaic Systems: A Decision Matrix Approach for Selecting the Optimal Maximum Power Point Tracking Technique

Numerous strategies exist for improving maximum power point tracking (MPPT) techniques, which vary in terms of tracking speed, accuracy, sensor usage, complexity, and cost. Due to the importance of sustainable energy use and related challenges, it is essential to choose an appropriate algorithm that can reliably provide energy without exhausting resources. While much research has examined the benefits and drawbacks of different MPPT algorithms, guidance on selecting the best suited algorithm for a particular solar system application is lacking. For a fair and thorough comparison, this study analyzes four MPPT algorithms, including two artificial intelligence-based techniques, an adaptive neuro-fuzzy inference system, and a conventional technique. A decision matrix model and requirement analysis are used to determine their suitability for a standalone PV application, using particularly ranked evaluation criteria, which include tracking efficiency, implementation costs, rise time, settling time, tracking error and variance. Simulation results show that the perturb and observe technique has the fastest settling and rise times and high tracking efficiency for varying irradiance and temperature levels, with little implementation effort required for a stand-alone photovoltaic application.

Maximizing power density in proton exchange membrane fuel cells: An integrated optimization framework coupling multi-physics structure models, machine learning, and improved gray wolf optimizer

This study proposes an innovative optimization framework to optimize channel structure and maximize power density by coupling multi-physics structure models, machine learning, and swarm intelligence algorithms. First, proton exchange membrane fuel cells (PEMFC) imitated water-drop block channels are employed for constructing multi-physics structure models. A database of the PEMFC output performance under various structural parameters of imitated water-drop block is established. Then, a machine-learning-based surrogate model is constructed based on the adaptive boosting (AdaBoost) ensemble algorithm to predict the output performance under different channel parameters. Finally, the improved gray wolf optimizer (IGWO) fitness function is calculated using a surrogate model to establish an optimization framework for effectively optimizing the channel structure. Results show that the AdaBoost ensemble surrogate model predicts the PEMFC polarization curves with extremely high accuracy and efficiency within one second. The optimization framework is capable of swiftly predicting both the optimal channel structure and maximum power density in under two minutes. The predicted values are returned to the physical model for validation with an error of only 3.96%. Simultaneously, the optimal channel structure can effectively enhance the PEMFC performance. The proposed optimization framework will guide the performance prediction and channel structure multivariate optimization.

Control of Three-level Four Wire Shunt Active Power Filter in Grid Connected PV-Fuel Cell System Employing Model Predictive Current Control Strategy

In this paper a Model Predictive Current Control (MPCC) of Four Wire Shunt Active Power Filter (4WSAPF) with Photo Voltaic (PV) system is proposed. The shunt active power filter requires an energy source to correct distortions in the current, and it utilizes a photovoltaic array with a DC-DC boost converter as a source of DC power. this study presents the application of a Particle Swarm Optimization (PSO) maximum power point tracking (MPPT) technique to the DC-DC boost converter, aiming to obtain the highest possible power from the photovoltaic array. The reference signal for the SAPF is obtained from the pqr theory. To fulfil the requirement of the active power filtering function under balanced, unbalanced, and distorted (including 3rd and 5th harmonics) power supply voltages, the inverter switching state control has been achieved based on MPCC. The effectiveness of the proposed configuration is verified through results obtained from simulation studies conducted in the MATLAB/Simulink environment.

Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system

This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimally selected using AOA. To accomplish this study, a 100-kW benchmark PV system connected to a medium distribution utility is constructed and analyzed employing MATLAB/SIMULINK. The optimization framework seeks to minimize four standard benchmark performance indices, then select the best of the best among them. To verify the efficacy of the recommended methodology, a comprehensive comparison is conducted between AOA-based PI-IC-MPPT, modified incremental conductance MPPT (MIC), grey wolf optimization (GWO), genetic algorithm (GA), and particle swarm optimization (PSO)-based MPPT. The proposed control approach has achieved a reduction of 61, 3, 4.5, and 26.9% in the rise time and a decrease of 94, 84.7, 86.6, and 79.3% in the settling time compared with MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively.

A piezoelectric energy harvester with inner beam adapting to low and high wind speeds: modeling, simulation and experiment

Traditional vortex-induced vibration energy harvesters could transform wind or water energy into electricity at low flowing speeds. However, it has the disadvantage of narrow working speed band, which limits wide application in velocity-changing environments. A piezoelectric harvester with an inner beam for harvesting wind energy at both low and high wind speed regions is presented. A comprehensive nonlinear distributed fluid–solid–electric governing equations for vortex-induced vibration piezoelectric energy harvesting are derived and the theoretical results show that dimensions of outer beam and diameter of attached cylinder can affect optimal wind speed and maximum power output at both low and high wind speeds. In contrast, the dimensions of the inner beam and mass block only have impacts at high wind speeds. The equivalent circuit modeling method is utilized to analyze energy harvesting output characteristics. Analogies between mechanical and electrical domains are built, and the governing equations are converted to circuit equations. Then the circuit equations are settled in electrical software for time-varying analysis. The electrical circuit simulation results show that the optimal load resistance is 400 kΩ at low wind speed and 500 kΩ at low wind speed, which is consistent with theoretical results. The prototypes were fabricated and experiments were carried out in a wind tunnel. Experimental results indicate that energy harvester could generate power at both low and high speeds. Mass block has great impact on optical speed and working wind speed band. The energy harvester with 7.06 g mass block could output 127.36 μW at 2.65 m s−1 and 63.63 μW at 4.4 m s−1. Numerical and circuit simulation results are consistent with experimental results on optical load resistances and optical wind speeds. This design provides a feasible method for broadening wind speed region for energy harvesting.

A Distributed Multimode Control Strategy for the Cascaded DC–DC Converter Applied to MVAC Grid-Tied PV System

The cascaded dc–dc converter is a popular topology for large-scale medium-voltage photovoltaic (PV) system with the advantages of high voltage conversion ratio, high efficiency, great flexibility, and independent control capability. However, the absence of optimal string maximum power point tracking (MPPT) operation under power mismatch is a big issue in common centralized control. Furthermore, the cost and time delay are unacceptable resulting from the distance among PV strings in the large-scale PV station and the cascaded dc–dc converter needs to adopt the distributed control method without real-time communication. To this end, by introducing other necessary constraints including voltage constraints, power constraints, and gain constraints on the basis of the distributed MPPT control strategy, a distributed multimode control scheme is proposed in this article. Each submodule (SM) can be switched flexibly between multiple operation modes to maximize the extracted solar energy. Individual string MPPT and autonomous distribution of output voltage are achieved without communication under the proposed distributed control strategy. In this way, optimal operation range is wider, mode switching is simpler, and applicability and robustness are improved. A downscaled prototype and a 35-kV/6-MW simulation system are constructed to verify the effectiveness of the proposed control strategy.

Optimal maximum power point tracking of wind turbine doubly fed induction generator based on driving training algorithm

The operation of wind power system at optimum power point is a big challenge particularly under uncertainty of wind speed. As a result, it is necessary to install an effective maximum power point tracking (MPPT) controller for extracting the available maximal power from wind energy conversion system (WECS). Therefore, this paper aims to obtain the optimal values of injected rotor phase voltage for doubly fed induction generator (DFIG) to ensure the extraction of peak power from wind turbine under different wind speeds as well as to get the optimal performance of DFIG. A new metaheuristic optimization approach; Driving Training Algorithm (DTA) is used to crop the optimal DFIG rotor voltages. Three different scenarios are presented to have MPPT, the first one is the MPPT with unity stator power factor, the second one is the MPPT with minimum DFIG losses, and the third scenario is MPPT with minimum rotor current to reduce the rating of rotor inverter. The MATLAB environment is used to simulate and study the proposed controller with 2.4 MW wind turbine. The optimum power curve of wind turbine has been estimated to get the reference values of DFIG mechanical power. The results ensured the significance and robust of the proposed controller to have MPPT under different wind speeds. The DTA results are compared with other two well-known optimization algorithms; water cycle algorithm (WCA) and particle swarm optimizer (PSO) to verify the accuracy of results.

A comparative study between deterministic and two meta-heuristic algorithms for solar PV MPPT control under partial shading conditions

• Under partial shading conditions, adequate tuning of firefly and particle swarm optimization maximum power point tracking algorithms converge to the global maximum power point irrespectively of the starting voltage value. • Under partial shading conditions, there is no guarantee that perturb and observe algorithm will converge to the global maximum power point even for different starting voltage values. • The sensitivities of the parameters of firefly algorithm in improving the performance of the algorithm are investigated to find the best possible control parameters. • Firefly algorithm based maximum power point tracking performed slightly well than particle swarm optimization based maximum power point tracking for most irradiance patterns investigated in this paper. During partial shading conditions (PSC), the PV modules in a solar PV array become reverse biased and act as a load, resulting in hotspot issues. This can significantly decrease the efficiency of the solar PV. The general way to deal with PSC is to connect bypass diodes across the non-shaded PV module. However, this will alter the uniform characteristics of the PV array, resulting in multiple power peaks i.e., a multi modal landscape. The conventional gradient-based Perturb and Observe (PnO) algorithm generally used for maximum power point tracking (MPPT) in solar PV is ineffective in finding the maximum power during PSC because it is prone to converge to local optimum due to its nature of searching in a multimodal landscape. However, in this article an attempt is made to show how the PnO behaves in a multimodal landscape considering different starting points. Robust stochastic algorithms that are based on a population of search agents are used to guarantee convergence to the global MPP. In this paper, the maximum power point under PSC was tracked using two meta-heuristic algorithms namely, particle swarm optimization (PSO) and the firefly algorithm (FA). The performances of these algorithms in tracking the maximum power point are evaluated. The efficiency, standard deviation (STD), and root mean square error (RMSE) of the PSO and FA algorithms are compared to those of the PnO algorithm. The PSO was found to have the lowest RMSE, and the FA had the lowest STD. The efficiency of the PSO and FA were relatively the same. Simulation results show that PSO and FA-based MPPT algorithms can efficiently track the global maximum power point (GMPP) irrespectively of the starting points. On the other hand, PnO is shown to be unreliable under PSC even with different starting points.

Fabrication of alginate-based multi-crosslinked biomembranes for direct methanol fuel cell application

The alginate-based multi-crosslinked biomembranes (ABMCBs) were prepared mainly with sodium alginate as matrix and self-made functionalized organosilane containing different groups as additive. The properties of ABMCBs with various additive loading were investigated as proton exchange membranes (PEMs). The results showed that higher water absorption and lower swelling were obtained simultaneously with increasing additive loading, which is very beneficial to the use of PEMs. The ABMCB-4 containing 40 wt% additive exhibited the optimal selectivity and maximum power density, which were obviously higher than that of commercial Nafion@ 117. Furthermore, ABMCB-4 possessed excellent mechanical property, methanol barrier and stability, indicating its potential adaptability as PEM for direct methanol fuel cell application.

Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review

In the recent past, the solar photovoltaic (PV) system has emerged as the most promising source of alternative energy. This solar PV system suffers from an unavoidable phenomenon due to the fluctuating environmental conditions. It has nonlinearity in I-V curves, which reduces the output efficiency. Hence, the optimum maximum power point (MPP) extraction of the PV system is difficult to achieve. Therefore, for maximizing the power output of PV systems, a maximum power point tracking (MPPT) mechanism, which is a control algorithm that can constantly track the MPP during operation, is required. However, choosing a suitable MPPT technique might be confusing because each method has its own set of advantages and disadvantages. Hence, a proper review of these methods is essential. In this paper, a state-of-the-art review on various MPPT techniques based on their classifications, such as offline, online, and hybrid techniques under uniform and nonuniform irradiances, is presented. In comparison to offline and online MPPT methods, intelligent MPPT techniques have better tracking accuracy and tracking efficiency with less steady state oscillations. Unlike online and offline techniques, intelligent methods track the global MPP under partial shade conditions. This review paper will be a useful resource for researchers, as well as practicing engineers, to pave the way for additional research and development in the MPPT field.

COMPARATIVE PERFORMANCE ANALYSIS ON ARTIFICIAL NEURAL NETWORK BASED MPPT CONTROLLER WITH CONVENTIONAL CONTROLLER FOR GRID CONNECTED WIND ENERGY CONVERSION SYSTEM

This paper focuses on the development of ANN based MPPT interfaced Permanent magnet synchronous generator (PMSG) for wind energy conversion system (WECS). It focuses on design and modeling aspects of the completely different elements of the WECS like the fundamental model of ANN based MPPT controller, MLI, wind turbine, optimum maximum power point tracking system utilizing MATLAB/Simulink. Major object is to extract the maximum energy from the wind which confirms a highest efficiency of developed system. The thesis shows the model of wind turbine together with the model of PMSG. The ANN based mostly MPPT approach used right here is predicated on Perturbation and observation (P&O). To complete the process of modeling and simulation the platform of MATLAB/Simulink is utilized. Developed ANN are most efficient technique compared to conventional methods. It achieves maximum power with more stability, precision and better performance with good dynamic response under variable wind speed conditions. It exhibits the enhancements for the developed system and control action for balance/unbalanced steady state in addition to transient, dynamic response circumstances.

Power characteristics of vibration-based piezoelectric energy harvesters: the effect of piezoelectric material nonlinearity

Piezoelectric energy harvesting has received tremendous interests in the past two decades as a viable solution to self-powered electronics and devices. Recently, significant emphasis has been given to nonlinear energy harvesters driven by the desire for broadband, high-performance energy harvesting. Numerous efforts have been devoted to the understanding and modeling of the electromechanical coupling and the effect of nonlinearities introduced by mechanical and electrical aspects of the system. However, very few works in the literature considered the effect of piezoelectric material nonlinearity on the system power performance. Nevertheless, it has been found that piezoelectric nonlinearity is significant even at low to moderate excitation level. This paper is motivated to study the power behavior of piezoelectric energy harvesters with piezoelectric nonlinearity, most importantly, the power limit and electromechanical coupling. For this purpose, an approximate model is developed from the nonlinear model in the literature to derive the closed-form expressions of important power characteristics. Analytical analysis shows that the effect of piezoelectric material nonlinearity results in a nonlinear damping term and a nonlinear stiffness term in the approximate model. The approximate solutions of optimal load resistance, maximum power, power limit, and critical electromechanical coupling are obtained and validated by numerical simulations first. The induced nonlinear damping reduces the power limit of the system compared to its linear counterpart. Interestingly, a harvester that exhibits strong electromechanical coupling under small excitation could become weakly coupled under large excitation. The analytical analysis and numerical results are validated by experiments.

Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions

ABSTRACT Photovoltaic (PV) energy is one of the most abundant energy in the world for generating huge electrical power to meet the desired load. However, the arduous task in the electrical industry is to contribute to the uninterrupted power supply by the PV system as a result of partial shading conditions (PSC). To track the global maximum peak power (GMPP) instead of local maxima peak power (LMPP), the combination of gray wolf optimization (GWO) and differential evolution (DE) algorithm is hybridized (GWO-DE) in this work. Furthermore, the proposed system is developed in the MATLAB/Simulink software. The system is investigated under distinct atmospheric conditions and compared its performance with other studied approaches. The simulation results disclose that the hybrid GWO-DE approach shows a greater performance as compared to other studied methods with respect to convergence time, accuracy, extracted power, and efficiency. Moreover, the proposed system is developed experimentally and tested in four different cases. The outcomes of the GMPP are 984.65 W at 0.08 sec for case 1, 630.39 W at 0.08 sec for case 2, 602.56 W at 0.07 sec for case 3, and 650.08 W at 0.05 sec for case 4. It is found that the suggested hybrid GWO-DE method ensures a greater performance than other studied methods.

An Optimized Synergetic Nonlinear Controller (OSNC) based maximum power point tracking for a standalone photovoltaic system using a boost converter

In this paper, an Optimized Synergetic Nonlinear Controller (OSNC) is proposed to efficiently determine the maximum power point of a Photovoltaic Generator matched to the load through a DC–DC boost converter. The dynamic equation of the conventional synergetic controller is modified to adjust the Manifold in such a way that the optimum maximum power point (MPP) is reached. This new approach is implemented in the Matlab/Simulink software and shows increased convergence in steady and transient states. Consequently, this new approach exhibits better performance than the conventional nonlinear synergetic controller (SNC) in standard test conditions and weather varying conditions.

Application of Effective Wind Speed Estimation and New Sliding Mode Observer in Wind Energy Conversion System

The wind speed information measured by the wind speed sensor in the wind turbine generator may differ from sufficient wind speed. Therefore, this paper proposes a new effective wind speed calculation method and applies it to the optimal maximum power point tracking (MPPT) of wind energy. After estimating the turbine torque and its rotor speed, the process reverses the turbine’s aerodynamic model. The extended state observer (ESO) based on sliding mode control is used to estimate the aerodynamic torque, which solves complex and challenging tuning of the traditional ESO parameters, and replaces the conventional PI controller, thereby improving the anti-interference and robustness of the system. The sliding mode observer (SMO) is used to estimate the rotor speed. While satisfying the conditions of Lyapunov’s inequality, the design of the SMO is discussed in detail. The wind speed estimation method proposed for evaluating permanent magnet semidirect drive wind turbine and its application performance in maximum power tracking. The simulation was carried out in MATLAB/Simulink; the simulation confirmed that the estimated wind speed under different wind conditions is accurate. Compared with the traditional PI control, the utilization rate of wind energy is increased by 2%, which can be used for the MPPT control of the wind energy conversion system.

Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed

Over the last few years, improving power extraction from the wind energy conversion system (WECS) under varying wind speeds has become a complex task. The current study presents the optimum maximum power point tracking (MPPT) control approach integrated with neural network (NN)-based rotor speed control and pitch angle control to extract the maximum power from the WECS. So, this study presents a reference model adaptive control (RMAC) for a direct-drive (DD) permanent magnet vernier generator (PMVG)-based WECS under real wind speed conditions. Initially, the RMAC-based rotor speed tracking control is presented with adaptive terms, which tracks a reference model that guarantees the expected exponential decay of rotor speed error trajectory. Then, to reduce the wind speed measurement errors, a recurrent neural network (RNN)-based training model is presented. Moreover, the asymptotic stability of the proposed control method is mathematically proven by Lyapunov theory. In addition, the pitch angle control is presented to efficiently operate the rotor speed within the allowable operating range. Eventually, the proposed control system demonstrates its effectiveness through simulation and experimentation using a prototype of 5 kW DD PMVG-based WECS. After that, the comparative results affirm the superiority of the proposed control method over existing control methods.

Artificial Intelligence of Things-Based Optimal Finite-Time Terminal Attractor and Its Application to Maximum Power Point Tracking of Photovoltaic Arrays in Smart Cities

The combination of artificial intelligence of things (AIoT) and photovoltaic power generation can save energy and reduce carbon emissions and further promote the development of smart cities. In order to obtain the maximum power output from photovoltaic (PV) arrays, we can use optimal maximum power point tracking (MPPT) technique with AIoT sensing to improve system efficiency. The optimal MPPT technique is the finite-time terminal attractor (FTTA) based on the gradient particle swarm optimization (GPSO), which can be applied to track the maximum power of a PV array system. The FTTA not only provides fast finite-time convergence but also attenuates steady-state errors, making it ideal for nonlinear system applications. The GPSO is used to search the control parameters of the FTTA, which is able to find the global best solution. This avoids unmodeled dynamic behavior of the system excited by the quiver, which slows down the control convergence and prematurely traps the system into a local optimum. The MATLAB computer software is used to simulate the proposed PV maximum power point tracking system. The results show that more accurate and better tracking control of the PV array can be produced under partial shading conditions and then improve the steady-state and transient performance.