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The problem of extracting maximum power from a photovoltaic (PV) system with negligible power loss is concerned with the power generating capability of the PV array and nature of the output load. Changing weather conditions and nonlinear behavior of PV systems pose a challenge in tracking of varying maximum power point. A robust nonlinear controlle...
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Introduction. With the advancements in the variable speed direct drive design and control of wind energy systems, the efficiency and energy capture of these systems is also increasing. As such, numerous linear controllers have also been developed, in literature, for MPPT which use the linear characteristics of the wind turbine system. The major lim...
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In this paper, a new Maximum Power Point Tracking (MPPT) control for a Photovoltaic (PV) system is developed based on both backstepping and terminal sliding mode approaches. This system is composed of a solar array, a DC/DC boost converter, an MPPT controller, and an output load. The Backstepping Terminal Sliding Mode Controller (BTSMC) is used via...
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... However, this constant N (step-size) value is unable to adapt to change in irradiation. Furthermore, intelligent techniques such as fuzzy logic control (FLC) [15], sliding mode control [16], neural network (NN) [17], particle swarm optimization (PSO), ant-colony optimization (ACO), firefly algorithm (FA), differential evolution (DE), and artificial bee colony (ABC) [18] as well as different bio-inspired algorithms [19] are implemented to extract the maximum power from SPV system. The core of these algorithms is soft computing and has the advantage of simple implementation using embedded systems. ...
... The closed loop structure of the system is presented in Figure 8.The converter loop gain is written as follows: G(S) = G C (S)G pwm (S)G dvout (S)H(S) (16) Where G C (S) denotes the controller transfer function and H(S) denotes the feedback transfer function. PWM generator transfer function is designated asG pwm (S) = 1 Carrier amplitude ⁄ . ...
This paper proposes a dynamic maximum power point tracking (MPPT) controller for a solar photovoltaic (SPV) system with a battery charging circuit. The voltage and current, and consequently the maximum available power of SPV panels vary based on environmental conditions. To operate SPV system at maximum power point under different weather conditions, a cascaded (PI-PD) controller with PSO gain scheduling is suggested in this paper. Also, the FOPI control is applied to an accurate dynamic model of the buck converter to function as a charge controller. For tuning the FOPI controller parameters, a stochastic inertia weight GWO algorithm is employed which maintains an appropriate balance between detection and hunting strategies, and gives the fittest wolf position during iterations. The proposed algorithm is compared with the original GWO algorithm to show its superiority. The accuracy of the proposed cascaded controller used in the SPV system to find MPP ranges from 96.05% to 98.87%. The goal of this study is to operate the SPV panel at maximum power point under variable atmospheric conditions to increase efficiency at a lower cost. It also provides appropriate current and voltage for faster battery charging, thereby increasing the life span of the battery. The system is implemented and analyzed in MATLAB/Simulink, and results are validated.
... The speed sensors in the control system models may end up being perfectly replaced by ANNs. [15] To build an effective system and minimize disturbance, the PID based ANFIS and NARMA-L2 controllers are supervised to reduce the peak overshoot as well as less rising and settling time. ...
Wind turbine performance is affected by wind speed, which can cause fluctuations in the frequency and voltage levels of the grid. Therefore, the turbines need to be regulated to avoid generating excessive power, which can cause damage to the turbines and the electrical grid. The study aims to design a sensor-less speed controller for variable-speed wind turbine control, utilizing the generator system's complete transfer function employing artificial neural network methods to ensure that they operate safely, efficiently, and reliably. In this paper, a comparative study of ANFIS (Adaptive neuro fuzzy inference system) controller and NARMA-L2 (Nonlinear Auto Regressive Moving Average) neuro-controller is implemented to control variable speed in a wind generator system, regarding their overshoot value, settling time, and rise time. The proposed controller's performance has been verified in MATLAB®/Simulink. The simulation results indicate the greater performance of the NARMA-L2 neuro-controller in terms of robustness and the fastest dynamic response.
... The diverse converter's cycle work helps in extracting the maximum current needed for the extraction of maximum power. This was done by the operation of the module with high power voltage (Ahmed et al. 2020). Moreover, Wind energy is a famous renewable energy that relies on the basis of inexhaustible and free pollution (Wang et al. 2021). ...
The grid connected photovoltaic system (GCPVS) faces several difficulties due to their intermittent natures such as solar irradiance variations, grid voltage distortions and unbalanced load distributions. These complications interrupt the functioning of power system to attain maximum probable power output. Therefore, to address these problems, this article proposes a robust super twisting sliding mode-incremental conductance-instantaneous power theory (STSM-IC-IPT) based control mechanism for 3-phase GCPVS. The super twisting sliding mode (STSM) controller is introduced to reduce the difference between actual inverter current and reference inverter current of GCPVS when it is subject to various uncertainties and nonlinearities. The modified incremental conductance (IC) maximizes the solar power output generated from the photovoltaic panels. In addition, the instantaneous power theory (IPT) is applied to enhance the active power and reactive power transmission between grid-connected PV systems. The proposed STSM-IC-IPT method is examined under two different experimental procedures namely simulation setup and experimental setup. The efficiency of the proposed STSM-IC-IPT control mechanism is evaluated by comparing its performance with existing ASMC-IC-IPT control scheme. The experimental results illustrate that the proposed STSM-IC-IPT control mechanism achieves superior performance efficiency under dynamic loading, steady and transient conditions.
... In addition to its connection to the use of system parameters, which requires knowing the mathematical model of the system accurately, it increases the degree of complexity, and difficulty of implementation, and makes control affected if the system parameters change, which is undesirable. To remove this phenomenon, the super-twisting algorithm (STA) has become an effective control method (Ahmed et al., 2020), especially for sensitive systems under external disturbances and/or static error. As it removes the chattering problem as well as retains the same performance as the first-class SMC (Menaga and Sankaranarayanan, 2021), this is what we will address in this study, but the problem lies in determining the gains of this algorithm. ...
... Through the works mentioned above, determining parameters using artificial intelligence is of great importance in improving results compared to using traditional methods such as simulation or relying on experience to determine these values. Moreover, the STA technique mentioned in the works (Ahmed et al., 2020;Pan and Fei, 2022;Gasmi et al., 2023aGasmi et al., , 2023b is characterized by the presence of a small number of parameters, which allows the use of artificial intelligence easily to determine these parameters and apply the resulting strategy in the control field. In addition to the ease, durability, simplicity, and lack of attachment to the mathematical model, all of these features make the STA controller one of the most prominent solutions that can be focused on in any field or control system. ...
This study proposes an intelligent robust controller to improve the performance of the multifunctional grid-connected photovoltaic (PV) system using the phase-locked loop (PLL). The novelty of this research is the use of a new strategy, called super-twisting algorithm (STA) based on genetic algorithm (GA) to improve the performance of the direct power control (DPC) with PLL technique and maximum power point tracking (MPPT), where DPC-STA-GA is used to control the active power filter. This proposed nonlinear technique is used to increase the power quality of the PV system under different conditions (variation of the nonlinear load, random nature of the irradiance solar and temperature,…etc). On the other hand, the STA-GA controller is successfully applied to control the DC link voltage of the multi-functional voltage source inverter. Durability and ease of implementation are among the most prominent features of the DPC-STA-GA with PLL technique to reduce the reactive and active power ripples compared to the DPC technique. The suggested intelligent nonlinear control ensures the harmonic current compensation and the transmission of PV energy to the electric grid and or the nonlinear load with high quality. To implement and verify the proposed controls with the PLL technique, Matlab software is used for this purpose, with the study of the behavior of the proposed controls based on STA-GA controllers with the PLL compared with the DPC in different conditions. The simulation results demonstrated the contribution of the DPC-STA-GA with the PLL in improving the performance, efficiency, effectiveness, and durability of the system under normal and abnormal conditions, where the proposed control reduced overshoot value in DC bus voltage by 100% compared to proportional-integral controller. In addition to reducing the value of THD of current compared to DPC and DPC-STA by 18.82% and 43.33%, respectively, in the case of solar radiation equal 1000 W/m2.
... Furthermore, to better the dynamic performance of the controller. According to papers [26,27], ST-SMC is designed to control a variety of applications, and it takes into consideration switching control continuously. To achieve the required control objectives, ST-SMC has been suggested in this work. ...
The battery electric vehicle charger offers an efficient bidirectional interface for the charging and discharging of the battery in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes respectively. The effective supervision of power flow is very complicated in any mode of operation. In this work, a super-twisting sliding mode-based controller (ST-SMC) is proposed for the control of a BEV charger power converters to track the required current and voltage in both charging (G2V mode) and discharging (V2G) processes. The chattering effect is a very serious issue in power converters, which is mitigated by the proposed controller. The Lyapunov stability criteria is used to evaluate the system stability. The safety and effectiveness of the proposed controller have been demonstrated through hardware-in-loop (HIL) based experimental verification employing the Delfino F28369D dual core micro-controller. MATLAB/Simulink is used to simulate the proposed controller.
... Nonlinear super-twisting sliding mode methods are currently a viable choice for the speed control of wind energy system such as [10] The speed sensors in the control system models may end up being perfectly replaced by ANNs. [11] To build an effective system and minimize disturbance, the super twisting sliding mode and NARMA L2 controllers are supervised to reduce the peak overshoot as well as less rising and settling time. ...
... Time derivative of equation (10): (11) .0 V S S ...
... Under unexpected weather changes, both algorithms fail to converge and oscillate around MPP. Contrarily, FLC [29,30] is lack of systematic way for generating the membership functions. A powerful alternative to FLC for handling nonlinear systems is ANN [31] since it produces output based on real-time numerical data, which eventually leads to less instability at MPP than FLC-based MPPT controller. Unluckily, the issue of enormous training data input and delayed training of large data is a significant problem for ANN [32,33]. ...
In recent era, the reduction of greenhouse gas emission and fuel consumption is accompanied by adopting photovoltaic (PV) and wind turbine-based hybrid renewable energy sources (HRES). In nature, an intermittent characteristic of the wind speed and solar irradiation makes these sources unpredictable, and hence, energy produced by wind and PV system generates uncertain conditions in operation of microgrid. In such cases, the security and reliability of microgrid are enhanced by integration of energy storage system (ESS). This work deals with an energy management in a hybrid system incorporating PV source and permanent magnet synchronous generator (PMSG)-based wind energy system. The PV output is enhanced with a help of switched trans-quasi-Z-source (TQZS) boost converter in which cuckoo search-assisted radial basis function neural network (RBFNN) approach is used as maximum power point tracking (MPPT) technique for tracking maximum photovoltaic power. The proposed approach results in high-power tracking efficiency with reduced power loss and settling time. A battery is incorporated to address an intermittent nature of RES. Artificial neural networks (ANN), which are capable of self-learning battery dynamics, keep track of the state-of-charge (SOC) of the battery. The system thus framed is implemented using MATLAB software, and promising results are generated in terms of power management with improved efficiency of 92%, gain ratio of 1:10 and total harmonic distortion (THD) value of 2.33%, respectively.
... Proper tuning of the PID controller parameters is essential to ensure optimal performance and stability in the presence of variations or disturbances [50][51][52] To avoid the pitfalls of improperly tuning the PID controller and its impact on the system performance, researchers have proposed numerous sophisticated non-linear control strategies for MPPT [53,54]. These included adaptive control [27], predictive control [28], backstepping-based control [55], and intelligent control methods rooted in ANNs and fuzzy logic systems, as well as sliding mode control (SMC) [53,54,56,57]. SMC stands out among nonlinear control techniques for its robustness, fast response, and easy implementation. ...
... The only exception is that Integral Super-Twisting (STSMC) can be chosen. The latter uses SOSMC and needs only the information about the sliding surface [56]. This helps lessen chattering and makes the system work better thanks to its continuous control action. ...
... The STSMC belongs to the SOSMC category and operates only based on the sliding surface "s", without requiring the derivative of "s". The discontinuous term of the proposed super-twisting controller can be defined by the function U ST , which is constituted by two components as shown in Equation (26) [56,129]: ...
The impact of Partial Shading Conditions (PSCs) significantly influences the output of Photovoltaic Systems (PVSs). Under PSCs, the Power-Voltage (P-V) characteristic of the PVS unveils numerous power peaks, inclusive of local maxima and a global maximum. The latter represents the optimum power point. Traditional Maximum Power Point Tracking (MPPT) algorithms struggle to track the Global Maximum Power Point (GMPP). To address this, our study emphasizes the creation of a novel algorithm capable of identifying the GMPP. This approach combines the Cuckoo Search (CS) MPPT algorithm with an Integral Super-Twisting Sliding Mode Controller (STSMC) using their benefits to enhance the PVS performance under PSCs in terms of high efficiency, low power losses, and high-speed convergence towards the GMPP. The STSMC is a second-order Sliding Mode Control strategy that employs a continuous control action that attenuates the “chattering” phenomenon, caused when the first-order SMC technique is employed. Indeed, the proposed CS-STSMC-MPPT algorithm consists of two parts. The first one is based on the CS algorithm used for scanning the power-voltage curve to identify the GMPP, and subsequently generating the associated optimal voltage reference. The second part aims to track the voltage reference by manipulating the duty cycle of the boost converter. The proposed CS-STSMC-MPPT algorithm is featured by its strength against uncertainties and modeling errors. The obtained simulation results underline a high convergence speed and an excellent precision of the proposed method in identifying and tracking the GMPP with high efficiency under varying shading scenarios. For comparative purposes, this method is set against the hybrid CS-Proportional Integral Derivative, the conventional CS, the Particle Swarm Optimization, and the Perturb and Observe algorithms under different PSCs, including zero, weak, and severe shading. Simulation conducted in the Matlab/Simulink environment confirms the superior performance of the proposed CS-STSMC-MPPT algorithm in terms of precision, convergence speed, efficiency, and resilience.
... Moreover [28], employs a nonadaptive backstepping nonlinear controller, assuming all parameters are known. The works [29,30] propose nonadaptive sliding mode controllers (SMC). Here [29], details how the power and current injection versus the voltage of photovoltage panels (PVP) depend on their characteristics curves depending on the irradiance and temperature changes. ...
... The works [29,30] propose nonadaptive sliding mode controllers (SMC). Here [29], details how the power and current injection versus the voltage of photovoltage panels (PVP) depend on their characteristics curves depending on the irradiance and temperature changes. Then, the designed SMC ensures robustness despite these variations. ...
... The DC and AC subsystems are interconnected and dynamically coupled, as detailed in [31] (Chapter 8). The work [32] considers this DC/AC coupling effect and proposes adaptive SMC instead of the nonadaptive SMC from [29,30]. As a result, it improves robustness under parameter variations. ...
Hybrid grid-connected renewable energy systems have gained significant importance in sustainably responding to an increased electrical energy demand. These are time-varying nonlinear dynamical plants, where the value of their parameters depends on changing weather conditions and the alternating grid voltage with randomly fluctuating amplitude. This paper proposes a robust cascade MRAC for nonlinear plants representing a class of these systems, which includes n renewable energy converts and a DC/AC single-phase full bridge inverter. The proposal reduces commissioning time by avoiding linearization and knowledge of the plant parameters. Moreover, it includes specific formulas for tuning the controller parameters that decrease their adjustments based on trial and error. Finally, it uses a direct adaptive method with adaptive laws having modification and an inner loop at least five times faster than the outer loop. The proposition validation includes the theoretical stability proof based on the Lyapunov stability method and Barbalat’s Lemma. Furthermore, it presents comparative simulation results with quoted cascade PI controllers for a monophasic system, including two renewable energy sources and injection. Both techniques effectively track setpoint changes of the energy sources’ currents and direct current bus voltage, showing the proposal similar or reduced ripple. At the same time, both ensure robustness against decreased photovoltage panels irradiance, increased fuel cells voltage, and grid voltage amplitude random fluctuations. However, the proposal does these things while avoiding prior linearization and unknowing the plant parameters.
... Solar energy is presently available as an infinite and clean source of energy generation. Because traditional energy sources are constantly depleted and have negative environmental consequences [1] [2].The need for renewable energy sources, specifically solar energy, which can be collected using solar panels, is increasing by the day [3]. The benefit of pv systems is that it doesn't pollute the environment and doesn't require fossil fuels. ...
