MPPT Control Research Articles

Fuzzy logic controller of a step-up boost converter applied to a photovoltaic system

the constantly increasing global energy need and the polluting nature of fossil energies have increased the interest in the development of renewable energies. Among them, photovoltaic energy. The function of a photovoltaic (PV) system is to ensure the conversion of solar energy into electrical energy and the conditioning of this energy in order to adapt it to various needs. The performance of a PV system depends essentially on the quality of the conditioning of the energy, which allows the optimal transfer of the energy of the PV generator, and the management of the energy produced to meet energy needs by incorporating of converters. In this article, we present an intelligent approach to improve and optimize the performance of a photovoltaic system, by the fuzzy MPPT control. Our system consists of a photovoltaic panel, a Boost-type DC-DC converter con-sidered as an adaptation stage between the PV and the load. The IC technique was compared with FLC to evaluate the best and most accurate controller for MPPT. The results suggest that the use of FLC could improve the MPPT and increase the sTableility of the PV system. System modeling and simulation performed with Matlab/Simulink.

Design and PIL Implementation of Fuzzy Logic-based MPPT Control for Symmetrical Multilevel Boost Converter

This paper aims to introduce a fuzzy logic adaptive MPPT controller to control a symmetrical multilevel converter in a standalone PV system. The system is evaluated under fixed and variable solar radiation with 15 V as the input voltage 60 V as the required output voltage and 50 kHz as the switching frequency. Results prove that by using the controller, the system successfully tracks the MPPT point for constant and variable radiation without oscillation around the maximum power point. In addition, the overshoot and time response is reduced while the voltage ripples are eliminated. The proposed controller is verified through practical implementation in Arduino mega board to test the accuracy of results. The practical finding via a processor in the loop test validates the simulation results.

Control of a BLDC Motor of an Electric Vehicle Powered by a Photovoltaic System based on Fuzzy Logic MPPT (FLC-MPPT)

Photovoltaic Systems (PV) represent a viable solution for converting solar energy into electricity without emitting harmful gases. This technology remains highly sought-after in the market, primarily due to its renewable characteristics and the absence of movable components, which renders it a low-maintenance option. This produced energy can be stored in batteries tobe used for example in operating electric vehicles. If the generated voltage is inadequate or requires regulation, a boost converter may be employed or a buck boost To manage energy during storage and discharge to provide the energy needed to run the induction motor. In this paper, we proposed the field-oriented control as an approach for controlling the speed of BLDC Motor drive electrical vehicles to improve the overall effectiveness of the proposed system, The BLDC Motor is powered by battery storage from a photovoltaic system. Furthermore, this work also addresses the specifics of modeling the Fuzzy logic control MPPT (FLC-MPPT) used for the energy management system (PV and batteries), as well as the proposed control approach for the electric vehicle motor, which are subsequently simulated in the MatLab environment.

MPPT control for PMSG based wind turbine: Adaptive dynamic programming approach

This paper proposes an online adaptive dynamic programming-based controller for the variable-speed wind turbine system, ensuring it tracks the maximum power point under uncertain conditions. This control scheme is composed of two components: the adaptive optimal control component and the high-order disturbance observer-based adaptive sliding mode control component. The adaptive optimal control part is designed using the online adaptive dynamic programming technique, which is one of the algorithms of the reinforcement learning method. This control component has the role of achieving the optimal characteristics for the nonlinear system. Likewise, the high-order disturbance observer is established to estimate system uncertainties and external disturbances. These approximated disturbances are then fed to the adaptive sliding mode control component for system disturbance compensation. As a result, the system, which is affected by the unknown disturbances, achieves robust optimal performance. In addition, the convergence of the adaptive laws and the stability of the overall system are ensured through the Lyapunov theory. Finally, comparative simulations are conducted to validate the advantages of the proposed control scheme in comparison to the existing methods.

Photovoltaic Maximum Power Point Tracking Technology Based on Improved Perturbation Observation Method and Backstepping Algorithm

Photovoltaic power generation systems mainly use the maximum power tracking (MPPT) controller to adjust the voltage and current of the solar cells in the photovoltaic array, so that the photovoltaic array runs at the maximum power point (MPP) to achieve the purpose of maximum power output. At present, photovoltaic power stations mainly adopt the traditional method to track the maximum power point, but this fixed step method easily causes output power oscillation of the photovoltaic array when tracking the maximum power point, and it easily falls into the local extreme point under partial shadow conditions. In order to solve these problems, this paper proposes an improved perturbation observation method and backstepping method (IP&O-backstepping) to replace the traditional method applied to the MPPT controller to optimize the operating state of the solar cell, thereby improving the output power point of the photovoltaic array and increasing the output power of the photovoltaic array. The algorithm first uses the improved perturbation and observation (IP&O) method to search the maximum power point of the photovoltaic array and output the reference voltage. Secondly, the reference voltage is input into the backstepping algorithm for voltage tracking. Finally, the algorithm tracks the reference voltage and makes the photovoltaic array operate at the maximum power point. The simulation is carried out by using MATLAB/Simulink. The IP&O-backstepping algorithm is compared with the intelligent algorithm and the traditional method, and the results show that compared to the above algorithm, the IP&O-backstepping algorithm can not only track the maximum power point of the photovoltaic array, but also has a faster tracking speed, and the output power has almost no oscillation when the photovoltaic array runs at the maximum power point.

Performance evaluation of fuel cell based on a DC-DC boost converter through optimized MPPT controller

The current electric vehicle domain is increasingly focused on fuel cell technologies due to its flexibility, steady supply of power, low atmospheric pollution, increased startups, and rapid responses. Fuel cells exhibit nonlinear power versus current characteristics, making it challenging to extract maximum peak power from the fuel stack. To address this, this work introduces an adaptive Coati Optimization algorithm combined with a Tilt-integral-derivative (TID) controller (TID-ACOA) to find the maximum power point (MPP) of the fuel stack systems, ensuring maximum power extraction. The proposed MPPT controller is compared with other MPPT controller, including PI, TID, and TID-COA. Comprehensive evaluations are conducted on tracking current, voltage, maximum power extraction, MPPT controller efficiency, converter voltage settling time, and oscillations. The fuel stack’s low output voltages are enhanced using a boost DC-DC converter, and the entire fuel stack-fed boost converter systems is modeled using MATLAB/Simulink. Simulation result show that the TID-ACOA MPPT controller achieves higher MPP tracking efficiency compared to conventional controllers.

Supervisory control strategy for dual battery assisted solar water pumping using a 3-stage interleaved boost converter

This article proposes effective power management and battery protection strategies for a solar photovoltaic (SPV) powered dual-battery supported standalone water pumping approach propelled by a permanent magnet brushless direct current (PMBLDC) motor. A drift-avoidance perturb and observe MPPT algorithm is employed in conjunction with a 3-stage interleaved boost converter (TSIBC) and proposes a switching control scheme to enable double-stage solar photovoltaic generation system (SPGS) to operate at its maximum accessible power point (MPP) irrespective of any variation in climatic condition. The proposed dual battery control scheme (DBCS), which integrates a primary battery with an auxiliary battery, is connected to the 310 V DC link. This dual battery setup utilizes a parallel-active configuration facilitated by two bidirectional DC-DC converters (BDCs). The core idea behind dualization aims to maintain the state of charge (SoC) of the primary battery within upper and lower limits with the help of an auxiliary battery unit and regulate the DC link voltage at 310 V during fluctuations generated in the protection period. This scheme significantly extends the battery's lifespan, achieving an estimated 800 to 1000 cycles by maintaining its depth of discharge (DoD) at a level of 70 %. A centralized power handling unit (CPHU) is integrated for both the BDCs to facilitate the operating mode selection and regulate DC link voltage by alternatively charging/discharging both batteries. This approach enhances overall system efficiency by eliminating the need to oversize the solar array by up to 46 %, which would have been necessary to fully charge both batteries simultaneously. Previously, the DC link voltage regulation relied solely on MPPT control, resulting in significant deviations (up to ±38.43 %) during battery overcharging/deep-discharging (SoC ≥ 90 %/SoC ≤ 20 %). This work proposes a solution using an auxiliary battery controller within the DBCS, achieving near-zero (±0.5 %) DC link voltage deviation under all operating conditions. The effectiveness of the suggested DBCS controller is validated through a time-domain simulation conducted in MATLAB/Simulink, alongside real-time hardware-in-the-loop (HIL) digital simulator using the OPAL-RT platform. The acquired results corroborate enhanced performance for the standalone water pumping system (WPS) and demonstrate a seamless shift from the normal operational mode to battery protection mode, while ensuring steady regulation of the DC link voltage.

Comparative analysis of direct coupling and MPPT control in standalone PV systems for solar energy optimization to meet sustainable building energy demands

Solar energy, a prominent renewable source, has reached an installed capacity of 71.78 GW in India. This research explored the load demands of the computer center at an engineering college in Tanjore, Tamil Nadu, India. The computer center at the engineering college has an annual energy requirement of 260,552 kWh/Year. Consequently, the research focused on the planning and implementation of a standalone photovoltaic (SAPV) system, assessing it against the institution's total annual energy consumption. The performance ratio and losses of the SAPV system with both direct coupling and an MPPT charge controller was compared. This comparative analysis aimed to evaluate the efficacy of two solar photovoltaic control methods—SAPV direct coupling and Maximum Power Point Tracking control—in optimizing energy harvesting from solar panels.

Enhanced MPPT controller for partially shaded PV systems using a modified PSO algorithm and intelligent artificial neural network, with DSP F28379D implementation

Enhanced MPPT controller for partially shaded PV systems using a modified PSO algorithm and intelligent artificial neural network, with DSP F28379D implementation

A novel advanced hybrid fuzzy MPPT controllers for renewable energy systems

At present, the availability of nonrenewable sources and their usage for electric vehicle technology is reducing gradually because of their disadvantages are more environmental pollution, direct effect on human health, less reliability, and taking more time to start functioning. So, in this article, the proton exchange membrane fuel cell (PEMFC) is considered for the automotive application because of its advantages quick startup, more power density, more safety to handle, high efficiency, and capability of operating at very low operational temperature conditions. However, the drawback of PEMFC is very difficult to identify the accurate MPP position of the fuel system. Here, the improved variable step genetic algorithm is added with the adaptive neuro-fuzzy inference system for tracking the operational point of the proposed system with high efficiency. These hybrid MPPT controller features are easy to understand, more accurate, have a better dynamic response, and have low design complexity. The evaluated proposed MPPT controller operational efficiency, and settling time of the converter voltage at different fuel stack temperature conditions are 98.7402%, and 0.01607 s respectively. Finally, the boost converter is used in this work to enhance the voltage supply capability of the entire system. The proposed system is investigated by applying the MATLAB tool.

Multi-variable fuzzy adaptive time-varying sliding mode control (MVFATVSMC) of the reverse osmosis-photovoltaic desalination system

Among the different desalination systems, reverse osmosis is popular due to its specific characteristics. Two challenges in these systems are power supply and compensation for the imposed disturbances and uncertainties which affect the performance of the system. To study these two challenges all main subsystems of the photovoltaic-reverse osmosis (PV-RO) system are considered. A photovoltaic system is considered for power supply and robust controllers are proposed to deal with the uncertainties and disturbances. For these purposes, all parts of the PV-RO desalination process are considered. Maximum power is tracked in different sun radiations and temperatures using a Mamdani-type fuzzy controller optimized by the invasive weed algorithm (IWA). This new MPPT controller gets about 10 % more power, 60 % lesser fluctuations, and 20 % smaller rise time. The speed controller of the induction motor and the pressure controller are fuzzy-PID. An adaptive time-varying sliding mode control (ATVSMC) is proposed to control the RO system because this system is highly nonlinear with many uncertainties and disturbances. The controller has global robustness and regulates the system to the desired points at a specified time. In this controller, knowing the maximum limit of the system uncertainties is not essential, which is a practical advantage for the PV-RO system. The numerical simulations in different scenarios and a comparison to existing work show the superiority of the proposed controllers. This novel controller decreases the overshoot and settling time of the bypass stream velocity by about 3.5 % and 7.6 % in comparison to super-twisting sliding mode control (STSMC), respectively. The settling time of the retentate stream velocity has decreased by about 9 %. Moreover, there are no fluctuations in the control inputs and outputs, and the amplitude of the bypass stream control input has decreased by about 98 %.

A new representation learning based maximum power operation towards improved energy management integration with DG controllers for photovoltaic generators using online deep exponentially expanded RVFLN algorithm

To incorporate Local Energy Management System (LEMS) based Tertiary Controller (TC) operation with multiple Photovoltaic based Distributed Generations (PV-DGs) level Primary Controller/ PC: Independent DG Controllers (IDGC) more adequately, a new representation learning (Symmetrical Input-Output weight Encoded Data Compression/ SIODC, with Exponential Expansion based Random Vector Functional Link Network towards Softmax layer’s Control Reference Estimation/ ExRVFLN-SoCRE) based MPPT controller is proposed in this paper in terms of Secondary Controllers (SCs). A new Hybrid LEMS (HyLEMS) is presented towards the proposed Deep Neural Network based SCs (i.e. DNN-SCs) in a distributed (SCdist), as well as centralized (SCcent) manner, to ease the computational, and communication requirements. To investigate that multiple PV-DGs based duty cycle (kth instant Control References/CRs estimation) controllers are considered here with auxiliary Battery Energy Storage Systems (BESS), and AC utility, integrated to Common DC feeder in terms of DC-DC converter dynamics. To avoid Control Reference Estimation Error (CREE) due to initial randomization, optimization subroutines are incorporated for SIODC by generalized semi-supervised learning, and for ExRVFLN-SoCRE by Lagrange multiplier weighted, rms based cost function. The proposed control performance is verified in MATLAB Simulink® based average modeling, and validated through dSPACE DS1104 based RTI with multi-PV (emulators) test-bench as well.

Efficient and robust nonlinear control MPPT based on artificial neural network for PV system

Efficient and robust nonlinear control MPPT based on artificial neural network for PV system

MPPT control of photovoltaic array based on improved marine predator algorithm under complex solar irradiance conditions

In practical engineering applications, factors like dust adhesion and environmental changes can cause photovoltaic arrays to exhibit multiple peaks in output power. An optimization algorithm with global optimization capability is needed to track its maximum power. In this regard, this paper proposes an improved marine predator algorithm (IMPA) to extract the maximum power point of photovoltaic system under complex solar irradiation conditions. To overcome the issues in the traditional marine predator algorithm (MPA), the opposition-based learning(OBL) strategy is introduced in IMPA, and the sine cosine algorithm (SCA) is integrated into the iteration stage to enhance the search ability of the algorithm. Furthermore, the low-order converter in the traditional MPPT control system is replaced by the Zeta converter, which increases the operating voltage range. Ultimately, simulation results demonstrate that the MPPT based on IMPA has higher tracking efficiency and shorter response time.The experimental results also indicate the practical feasibility of this method, as well as its high level of stability and robustness.

Design of type-2 fuzzy MPPT controller for optimization of a photovoltaic system

Design of type-2 fuzzy MPPT controller for optimization of a photovoltaic system

A novel development of wide voltage supply DC–DC converter for fuel stack application with PSO-ANFIS MPPT controller

The present power production companies are working on renewable energy systems because their features are more reliable for the local energy consumers, high continuity in the energy production, and less cost is required for maitainence. In this article, the proton exchange membrane fuel stack (PEMFS) renewable energy is utilized to supply energy to the automotive systems. Here, the PEMFS is selected because of its merits are high energy density, quick system response concerning the source operational temperature, and more suitable for electric vehicle application. However, the PEMFS supplied voltage is completely nonlinear which is solved by utilizing the modified particle swarm optimization with adaptive neuro-fuzzy inference system (MPSO with ANFIS) controller. This hybridization-based maximum power point tracking controller provides more accuracy, high power point identifying speed, best dynamic response at different fuel stack functioning temperature conditions, and easy maitainence. Here, the fuel stack generated current is very high which is optimized by introducing the new DC–DC converter. The advantages of this DC–DC converter are more voltage transformation ratio, low-level voltage stress appearing across the switches, and wide voltage gain. The overall system is investigated by utilizing the MATLAB/Simulink tool.

A comprehensive analysis of the optimal GWO based FOPID MPPT controller for grid-tied photovoltaics system under atmospheric uncertainty

Recent years have seen an increase in the deployment of renewable energy sources on power system networks as a result of the gradual development of renewable energy sources. In order to utilize these intermittent sources, grid integration is crucial, since weather conditions are hard to predict accurately and do not match the pattern of generation. As a result, controlling energy becomes an unavoidable challenge when the alternative energy source is connected to the grid in a distributed manner. A number of factors contribute to the problem, including sporadic sources, daytime expenses, and limitations concerning the specifications of solar panels. Therefore, a more robust control system is needed to enhance the reliability and efficiency of the renewable energy integrated power network. Thus, this manuscript presents the design, implementation, and performance of an improved fractional order PID (FOPID) controller for proposed grid-connected photovoltaic systems. To maximize the power from the photovoltaic source, the controller is designed to extract as much energy as possible. In addition to having the adaptive nature of a PID controller, the proposed FOPID controller is capable of optimizing its gain parameter according to the generator and grid side parameters being considered. The proposed study utilized grey wolf optimization (GWO) to tune the FOPID controller for tracking the quadrature axis model, DC link voltage and current regulation, and maximizing the maximum power point. Further, FOPID is used to perform the system's current control functions, and each time the error is measured, the regulating parameters are updated accordingly. This paper contrasts fuzzy logic controllers (FLC), flying squirrel search optimization (FSSO), and PSO-tuned FOPID controllers with the proposed work. The simulation's results are displayed and examined in the section on results and outcomes. The obtained results demonstrate maximum solar power output under erratic weather conditions, validating the effectiveness of the proposed controller.

Modified control approach for MPP tracking and DC bus voltage regulation in a hybrid standalone microgrid

This paper proposes modified MPPT control technique for hybrid standalone microgrid. Standalone PV based microgrid has emerged as potential solutions to electricity challenges in the region where grid is not available. The battery energy storage system (BESS) is integrated into the system to facilitate synchronized load control and power flow. Variation in solar irradiation, load demand and fluctuation in battery SOC results in DC voltage fluctuation and maximum power point tracking challenges. Hybrid PV-Battery systems therefore must require control algorithms that can both flexibly adjust power flows as well as stabilize bus voltages. Proposed control technique is successful in regulating DC bus voltage and balancing the power flow in system under various operating conditions. A bidirectional converter is employed to maintain the nominal DC voltage conditions by charging/discharging the battery due to intermittent PV generation. It regulates the voltage of the DC bus to the maximum power point of the PV array. The main goal of this research article is to implement modified MPPT technique which includes Incremental conductance algorithm with double closed loop controller to track maximum power and to regulate the DC bus voltage of the system under different type of dynamic and atmospheric conditions.

MPPT control strategy of photovoltaic based on improved zebra optimization algorithm

Abstract According to the power generation principle of solar cells, the mathematical model analysis of photovoltaic cells shows that in order to maintain the maximum output power, the work of photovoltaic cells needs to be maintained at the maximum power point. Energy issues are global concerns, and the rational utilization of renewable energy is key to solving these problems. Solar energy, the most readily available source of energy, is characterized by its cleanliness, renewability, and permanence, meeting the requirements for new energy generation. This paper, addressing the shortcomings of the traditional particle swarm algorithm, proposes an MPPT based on an improved zebra optimization algorithm. This strategy is designed to decrease the energy loss during oscillation processes and increase convergence speed. Its performance advantages are also verified through simulation.

A new adaptive Perturb & Observe controller via robust approach for PV grid-connected

Given the importance of renewable energies, several countries are integrating these energies into their energy strategies, such as their connections to the grid, in particular photovoltaic energy. In this paper, a PV grid-connected system in a two-stage configuration is studied. In this configuration, an improvement to a control MPPT is proposed. To achieve this, we propose a novel concept for MPPT control that consists of generating an adaptive step size for P&O control that is based on sliding mode control in order to minimise ripple phenomena and chattering phenomena. The proposed control approach was then tested using MATLAB/Simulink simulations under varying irradiation conditions. We compared this approach to the conventional P&O method as well as the adaptive step size of P&O and hybrid P&O-sliding mode control. The results of the test indicate that the proposed control strategy significantly minimises the two phenomena and improves overall efficiency.