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This paper presents a machine learning (ML) model designed to track the maximum power point of standalone Photovoltaic (PV) systems. Due to the nonlinear nature of power generation in PV systems, influenced by fluctuating weather conditions, managing this nonlinear data effectively remains a challenge. As a result, the use of ML techniques to optimize PV systems at their MPP is highly beneficial. To achieve this, the research explores various ML algorithms, such as Linear Regression (LR), Ridge Regression (RR), Lasso Regression (Lasso R), Bayesian Regression (BR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), and Artificial Neural Networks (ANN), to predict the MPP of PV systems. The model utilizes data from the PV unit’s technical specifications, allowing the algorithms to forecast maximum power, current, and voltage based on given irradiance and temperature inputs. Predicted data is also used to determine the boost converter’s duty cycle. The simulation was conducted on a 100 kW solar panel with an open-circuit voltage of 64.2 V and a short-circuit current of 5.96 A. Model performance was evaluated using metrics such as Root Mean Square Error (RMSE), Coefficient of Determination (R 2 ), and Mean Absolute Error (MAE). Additionally, the study assessed the correlation and feature importance to evaluate model compatibility and the factors impacting the predictive accuracy of the ML models. Results showed that the DTR algorithm outperformed others like LR, RR, Lasso R, BR, GBR, and ANN in predicting the maximum current (I m ), voltage (V m ), and power (P m ) of the PV system. The DTR model achieved RMSE, MAE, and R 2 values of 0.006, 0.004, and 0.99999 for I m , 0.015, 0.0036, and 0.99999 for V m , and 2.36, 0.871, and 0.99999 for P m . Factors such as the size of the training dataset, operating conditions of the PV system, model type, and data preprocessing were found to significantly influence prediction accuracy.

This paper applies the innovative idea of DLCI to PV array reconfiguration under various PSCs to capture the maximum output power of a PV generation system. DLCI is a hybrid algorithm that integrates multiple meta-heuristic algorithms. Through the competition and cooperation of the search mechanisms of different metaheuristic algorithms, the local exploration and global development of the algorithm can be effectively improved to avoid power mismatch of the PV system caused by the algorithm falling into a local optimum. A series of discrete operations are performed on DLCI to solve the discrete optimization problem of PV array reconfiguration. Two structures (DLCI-I and DLCI-II) are designed to verify the effect of increasing the number of sub-optimizers on the optimized performance of DLCI by simulation based on 10 cases of PSCs. The simulation shows that the increase of the number of sub-optimizers only gives a relatively small improvement on the DLCI optimization performance. DLCI has a significant effect on the reduction in the number of power peaks caused by PSC. The PV array-based reconstruction system of DLCI-II is reduced by 4.05%, 1.88%, 1.68%, 0.99% and 3.39%, when compared to the secondary optimization algorithms.

The Photovoltaic (PV) system is an eco-friendly renewable energy system that is integrated with a DC-DC buck-boost converter to generate electrical energy as per the variations in solar irradiance and outdoor temperature. This article proposes a novel Adaptive Fractional Order PID (A-FOPID) compensator with self-adjusting fractional orders to extract maximum power from a stand-alone PV system as ambient conditions change. The reference voltage is generated using a feed-forward neural network. The conventional FOPID compensator, which operates on the output voltage error of the interleaved buck-boost converter, is employed as the baseline maximum-power-point-tracking (MPPT) controller. The baseline controller is retrofitted with an online state-error-driven adaptation law that dynamically modifies the fractional orders of the controller’s integral and differential operators. The adaptation law is formulated as a nonlinear hyperbolic scaling function of the system’s state error and error-derivative variables. This augmentation supplements the controller’s adaptability, enabling it to manipulate flexibly the tightness of the applied control effort as the operating conditions change. The efficacy of the proposed control law is analyzed by carrying out customized simulations in the MATLAB Simulink environment. The simulation results show that the proposed MPPT control scheme yields a mean improvement of 25.4% in tracking accuracy and 11.3% in transient response speed under varying environmental conditions.

This work presents a nonlinear control approach to maximise the power extraction of wind energy conversion systems (WECSs) operating below their rated wind speeds. Due to nonlinearities associated with the dynamics of WECSs, the stochastic nature of wind, and the inevitable presence of faults in practice, developing reliable fault-tolerant control strategies to guarantee maximum power production of WECSs has always been considered important. A fault-tolerant fractional-order nonsingular terminal sliding mode control (FNTSMC) strategy to maximize the captured power of wind turbines (WT) subjected to actuator faults is developed. A nonsingular terminal sliding surface is proposed to ensure fast finite-time convergence, whereas the incorporation of fractional calculus in the controller enhances the convergence speed of system states and simultaneously suppresses chattering, resulting in extracted power maximisation by precisely tracking the optimum rotor speed. Closed-loop stability is analysed and validated through the Lyapunov stability criterion. Comparative numerical simulation analysis is carried out on a two-mass WT, and superior power production performance of the proposed method over other methods is demonstrated, both in fault-free and faulty situations.

This study presents a fast terminal synergetic control (FTSC) scheme to investigate the nonlinear control problem of permanent magnet vernier generator (PMVG)-based variable-speed wind energy conversion systems (WECSs). In wind turbines, better speed tracking and fast dynamic behavior is required to achieve the maximum power extraction. To do this, the FTSC method is firstly proposed to improve the dynamic performance of tracking the speedby, addressing the turbulent wind and uncertainties in the PMVG system, which improves the wind energy extraction efficiency and alleviates mechanical stress over the turbine. Next, the closed-loop FTSC with a macro variable and novel reaching law is presented to enhance the convergence of the speed error signal when it is far from equilibrium in finite time. At this time, the controller’s output is a zero chattering generator torque reference that can operate the system in both below- and above-rated wind conditions, in addition to pitch control. Then, the proposed control method is verified for its effectiveness in energy capture through numerical simulation and experimental verification of a 5 kW direct drive PMVG-based WECS. Finally, comparative results confirm the better performance of the proposed system under transients than other controllers considered in this analysis.

In this work, a photovoltaic (PV) system integrated with a non-inverting DC-DC buck-boost converter to extract maximum power under varying environmental conditions such as irradiance and temperature is considered. In order to extract maximum power (via maximum power transfer theorem), a robust nonlinear arbitrary order sliding mode-based control is designed for tracking the desired reference, which is generated via feed forward neural networks (FFNN). The proposed control law utilizes some states of the system, which are estimated via the use of a high gain differentiator and a famous flatness property of nonlinear systems. This synthetic control strategy is named neuro-adaptive arbitrary order sliding mode control (NAAOSMC). The overall closed-loop stability is discussed in detail and simulations are carried out in Simulink environment of MATLAB to endorse effectiveness of the developed synthetic control strategy. Finally, comparison of the developed controller with the backstepping controller is done, which ensures the performance in terms of maximum power extraction, steady-state error and more robustness against sudden variations in atmospheric conditions.

This studyaimed at improving the performance and efficiency of conventional static photovoltaic (PV) systems by introducing a metaheuristic algorithm-based approach that involves reconfiguring electrical wiring using switches under different shading profiles. Themetaheuristicalgorithmused wasthe firefly algorithm (FA), which controls the switching patterns under non-homogenous shading profiles and tracks the highest global peak of power produced by the numerous switching patterns. This study aimed to solve the current problems faced by static PV systems, such as unequal dispersion of shading affecting solar panels, multiple peaks, and hot spot phenomena, which can contribute to significant power loss and efficiency reduction. The experimental setup focusedon software development and the system or model developed in the MATLAB Simulink platform. Athorough and comprehensive analysis was done by comparing the proposed method’s overall performance and power generation with thenovel static PVseries–parallel (SP) topology and totalcross-tied (TCT) scheme. The SP configuration is widely used in the PV industry. However, the TCT configuration has superior performance and energy yield generation compared to other static PV configurations, such as the bridge-linked (BL) and honey comb (HC) configurations. The results presented in this paper provide valuable information about the proposed method’s features with regard toenhancing the overall performance and efficiency of PV arrays.

This paper is focused on resonant vibration energy harvesters (RVEHs). In applications involving RVEHs the maximization of the extraction of power is of fundamental importance and a very crucial aspect of such a task is represented by the optimization of the mechanical resonance frequency. Mechanical tuning techniques (MTTs) are those techniques allowing the regulation of the value of RVEHs mechanical resonance frequency in order to make it coincident with the vibration frequency. A very great number of MTTs has been proposed in the literature and this paper is aimed at reviewing, classifying and comparing the main of them. In particular, some important classification criteria and indicators are defined and are used to put in evidence the differences existing among the various MTTs and to allow the reader an easy comparison of their performance. Finally, the open issues concerning MTTs for RVEHs are identified and discussed.

Solar-powered water pumping systems (SPWPS) are increasingly used in remote agricultural areas where grid integration is not feasible. The main objective of SPWPS is to extract maximum power from the solar photovoltaic array (SPVA) and deliver it to the motor-pump drive. A simplified motor control scheme for the extraction of maximum power from SPVA with the induction motor drive for partial shading conditions is not presented so far. This paper proposes a vectorial sliding mode (VSM)-based field-oriented control for a single-stage induction motor drive. The VSM control is inherited with maximum power extraction algorithm for SPVA. The improved particle swarm optimization (IPSO) method is used to achieve maximum power extraction from the SPVA, even in partial shading conditions. The IPSO algorithm generates a peak power equivalent voltage reference, which is used as a torque reference for the VSM control (VSMC). The VSMC control is developed in complex valued form to eliminate the need for Clark and Park transformations and thus reducing the order of the system. VSMC incorporates sensorless features, eliminating the need for speed and voltage sensors at the motor terminals. The proposed system is investigated for change in insolation of SPVA that works for partial shading conditions also. The IPSO algorithm is compared with the incremental conductance and particle swarm optimization algorithms, showing the superiority of IPSO method in the peak power tracking accuracy. The IPSO-VSMC algorithm through hardware prototype achieves maximum power tracking accuracy between 87.1 and 93.71% for the most possible insolation cases with partial shading conditions.

A simple photovoltaic (PV) system capable of operating in both grid-connected mode and stand-alone mode using multilevel boost converter (MBC) and line commutated inverter (LCI) has been developed for extracting the maximum power and feeding it to a single phase utility grid and stand-alone system simultaneously. Theoretical analysis of the proposed system is done and the duty ratio of the MBC is estimated for extracting maximum power from PV array. For a fixed firing angle of LCI, the proposed system is able to track the maximum power with the determined duty ratio which remains the same for all irradiations. This is the major advantage of the proposed system which eliminates the use of a separate maximum power point tracking (MPPT) controller. Experiments have been conducted on a 80 V, 9.4 A PV array feeding a 110 V single phase grid and a 230 V, 100 W DC motor. The MBC extracts maximum power from the PV array and feeds the major portion of power to the single phase utility grid via LCI and the remaining power to separately excited DC motor. It was found that the theoretical analysis, simulation and experimental results closely correlate with each other and proves the effectiveness of the proposed configuration.