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The techno-economic feasibility of photovoltaic (PV) solar generation systems is greatly dependent on its operating conditions. However, significant penetration of PV sources into alternating current (AC) grids may cause lower system inertia, decreased system damping, higher frequency fluctuations when grids are subjected to fault conditions. In th...
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... Processor in the loop experimentation is a useful method for validating the control algorithm on a hardware processor, where the plant is a software model [14]- [15]- [16]. It helps in testing the control algorithm in real time by creating the code that an embedded card (ARDUINO MEGA in this case) will load and run. ...
... The theory of variable structure control is frequently employed in both linear and nonlinear systems. The classical Sliding Mode Control (SMC) approach is resilient against system uncertainties with established upper bounds, but its major limitations include high frequency chattering and implementation incompatibility [17][18][19][20]. ...
This paper describes a new method for maximizing power extraction from a wind energy conversion system (WECS) by using a doubly fed induction generator (DFIG) that operates below nominal wind speed. To maximize the collected power of a wind turbine (WTG) exposed to actuator failure, a fault-tolerant high-order sliding mode observer (HOSMO) and Seagull Optimization Algorithm with a model predictive controller (MPC) technique is proposed. Evaluate both the real state and the sensor error simultaneously using a higher-order sliding-mode observer. Active fault tolerant controllers are designed to regulate wind turbine rotor speed and power in the presence of actuator defects and uncertainty. With the growing interest in employing wind turbines (WTGs) as the primary generators of electrical energy, fault tolerance has been seen as essential to improving efficiency and reliability. This research focuses on optimal fault-tolerant pitch control, which is used to modify the pitch angle of wind turbine blades in the event of sensor, actuator, and system failures. A Seagull Optimization Algorithm (SOA) is proposed to tune controller parameters to improve the performance of WT. The proposed method has achieved 92% of power tracking performance when compared to existing method.
... In addition, the process of parameterization for metaheuristic algorithms can be time-consuming and requires expert knowledge to achieve optimal results [20]. The last MPPT category emphasizes advanced techniques like sliding mode control (SMC) [21][22][23], backstepping control [24], predictive control [25] and Takagi-Sugeno (T-S) fuzzy model approach. ...
This paper presents a novel maximum power point tracking control for a stand-alone photovoltaic (PV) system based on a robust polynomial static output feedback control law subject to input saturation. In detail, a DC/DC boost converter is used to regulate the load and extract the maximum power from the photovoltaic panel. First, a polynomial fuzzy model is used to represent the photovoltaic system. Then, as this control method is based on a reference model, a regression plane is used to generate the desired trajectory representing the optimal dynamics where the PV system supplies maximum power. Then, in order to reduce the number of required sensors, a polynomial output feedback controller was developed, in which the problem of converter performance degradation resulting from duty cycle saturation was avoided by using a saturated control approach. The controller gains have been obtained by solving a sum-of-squares optimization problem, where the H∞\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {H}}_\infty $$\end{document} performance criterion is applied to guarantee the stability of the closed-loop system while achieving an optimal rejection level of external disturbances. To evaluate the performance of the suggested controller, a number of simulations and comparisons were carried out in MATLAB/Simulink environment and under various scenarios of weather conditions.
... Many researchers have analyzed the development and efficiency of photovoltaic technologies [73,74]. For example, energy storage is very important. ...
The objective of this study was to evaluate changes in the number of small renewable energy sources (RES) power plants and the volume of generated energy in the years 2016–2020, with an outlook to year 2025. The study covered the area of Poland, including the division into provinces and different sources of renewable energy. Absolute values of electric power production and sale were presented, in addition to calculated structure indices. Moreover, the number and structure of small power plants using different renewable energy sources was determined for every Polish province. A classification of the provinces was made, where four classes were distinguished depending on the number of RES plants operating in the provinces. The research results allowed us to diagnose the current situation and make a prognosis for the future, which may translate into support for the development of particular types of installations, depending on the natural and economic characteristics of each area. The added value of the study stems from the fact that previous reports focused mainly on micro or large power plants and the time span covered data before and during the pandemic. This made it possible to assess the impact of the pandemic on the development of small renewable energy sources.
... Motahhir et al. verified the maximum power point tracking algorithms using a V-cycle development process [45]. Ulah et al. tested a three-phase photo-voltaic fault-tolerant control scheme with the PIL method [46]. However, according to the author's review, research on the design and implementation of SASS with PIL verification for control algorithm validation is still missing in the literature. ...
This study presents an implementation of a proportional–integral–derivative (PID) controller utilizing particle swarm optimization (PSO) to enhance the compromise on road holding and ride comfort of a quarter car semi-active suspension system (SASS) through simulation and experimental study. The proposed controller is verified with a processor-in-the-loop (PIL) approach before real-time suspension tests. Using experimental data, the magnetorheological damper (MR) is modeled by an artificial neural network (ANN). A series of experiments are applied to the system for three distinct bump disturbances. The algorithm performance is evaluated by various key metrics, such as suspension deflection, sprung mass displacement, and sprung mass acceleration for simulation. The phase plane method is used to prove the stability of the system. The experimental results reveal that the proposed controller for the SASS significantly improves road holding and ride comfort simultaneously.
... The importance of these innovations cannot be overstated. PV technologies have undergone rapid advancements, enhancing solar cell efficiency, reducing manufacturing costs, and increasing their applicability in various environments [5,6]. These developments have opened up new avenues for large-scale solar power generation and enabled the integration of solar energy into our everyday lives [7]. ...
The goal of this review is to offer an all-encompassing evaluation of an integrated solar energy system within the framework of solar energy utilization. This holistic assessment encompasses photovoltaic technologies, solar thermal systems, and energy storage solutions, providing a comprehensive understanding of their interplay and significance. It emphasizes the importance of solar energy as a renewable resource and its role in addressing global energy demand and mitigating climate change. The review highlights the significance of advancements in various solar energy technologies, focusing on their environmental benefits, including greenhouse gas emissions reduction and air and water pollution mitigation. It explores the evolution of photovoltaic technologies, categorizing them into first-, second-, and third-generation photovoltaic cells, and discusses the applications of solar thermal systems such as water heaters, air heaters, and concentrators. The paper examines key advancements in energy storage solutions for solar energy, including battery-based systems, pumped hydro storage, thermal storage, and emerging technologies. It references recent published literature to present findings on energy payback time, carbon footprint, and performance metrics. Challenges to widespread adoption are discussed, including cost and economic viability, intermittency, environmental impacts, and grid integration. Strategies to overcome these challenges, such as cost reduction, policy support, energy storage integration, and sustainable practices, are presented based on published literature. By bridging gaps in existing literature, this comprehensive resource aims to equip researchers, policymakers, and industry professionals with insights into forging a sustainable and renewable energy future.
... For instance, the presence of certain faults like aging [14], cracks [15], delamination [16] and corrosion [17] can degrade the lifespan of PV modules, thereby elevating the energy costs involved [18]. PV module faults can be broadly classified into visual and electrical faults; visual faults pave the way for the occurrence of electrical faults [19]. Fault diagnosis and detection in PV modules were initially performed through manual inspections. ...
The present study introduces a novel approach employing weightless neural networks (WNN) for the detection and diagnosis of visual faults in photovoltaic (PV) modules. WNN leverages random access memory (RAM) devices to simulate the functionality of neurons. The network is trained using a flexible and efficient algorithm designed to produce consistent and precise outputs. The primary advantage of adopting WNN lies in its capacity to obviate the need for network retraining and residual generation, making it highly promising in classification and pattern recognition domains. In this study, visible faults in PV modules were captured using an unmanned aerial vehicle (UAV) equipped with a digital camera capable of capturing RGB images. The collected images underwent preprocessing and resizing before being fed as input into a pre-trained deep learning network, specifically, DenseNet-201, which performed feature extraction. Subsequently, a decision tree algorithm (J48) was employed to select the most significant features for classification. The selected features were divided into training and testing datasets that were further utilized to determine the training, test and validation accuracies of the WNN (WiSARD classifier). Hyperparameter tuning enhances WNN's performance by achieving optimal values, maximizing classification accuracy while minimizing computational time. The obtained results indicate that the WiSARD classifier achieved a classification accuracy of 100.00% within a testing time of 1.44 s, utilizing the optimal hyperparameter settings. This study underscores the potential of WNN in efficiently and accurately diagnosing visual faults in PV modules, with implications for enhancing the reliability and performance of photovoltaic systems.
... In many essential projects related to people's livelihood, such as smart energy management [32], techno-economic feasibility of photovoltaic solar generation systems [33], and permanent magnet synchronous motor [34], many advanced control algorithms have been used to obtain more satisfactory results. For the calibration system, to control the cost, the control computer in the system can often only provide the classical PID control method [35,36]. ...
To meet the needs of a large number of high-altitude meteorological detections, we need to perform fast, high-precision, and high-reliability calibrations of the sensors in the atmospheric detection system (ADS). However, using the traditional method to calibrate the sensor with high precision often takes a lot of time and increases the cost of workforce and material resources. Therefore, a method for realizing fast sensor calibration under the current system hardware conditions is required. A physical field model of Tube–Air–ADS is proposed for the first time, and the transfer function is obtained by combining the system identification, which provides the possibility for dynamic analysis of the calibration system. A Multi-Criteria Adaptive (MCA) PID controller design method is proposed, which provides a new idea for the parameter design of the controller. It controls the amplitude and switching frequency of the controller’s output signal, ensuring the safe and stable operation of the calibration system. Combined with the hardware parameters of the system, we propose the Variable Precision Steady-State Discrimination (VPSSD) method, which can further shorten the calibration time. Comparing and analyzing the current simulation results under Matlab/Simulink, the proposed MCA method, compared with other PID controller design methods, ensures the stable operation of the calibration system. At the same time, compared with the original system, the calibration time is shortened to 47.7%. Combined with the VPSSD method, the calibration time further shortens to 38.7 s.
... Algorithms and functions are often created on a PC in a development environment. More details about processor-in- Ullah et al. (2021b). PiL tests are run to ensure that the built code also runs on the target CPU. ...
Given greater penetration of wind power, the impact of wind generators on grid electricity reliability imposes additional requirements. One of the most common technologies in wind power generating schemes is the permanent magnet synchronous generator (PMSG) converter. However, the controller calculation is difficult due to the nonlinear dynamical and time-varying characteristics of this type of conversion system. This study develops a unique intelligent controller approach based on the passivity notion that tracks velocity and keeps it functioning at the optimum torque. To address the robustness issues encountered by traditional generator-side inverters (MSC) strategies such as proportional-integral (PI), this suggested scheme integrates a passivity-based procedure with a fuzzy logic control (FLC) methodology for a PMSG-based wind power converter. The suggested controller is distinguished by the fact that the nonlinear features are compensated in a damped manner rather than canceled. To achieve the required dynamic, the Fuzzy controller is used, which ensures quick convergence and global stability of the closed-loop system. The development of the maximum power collected, the lowered fixed gains, and the real-time application of the control method are the primary contributions and novelties. The primary objectives of this project are to manage DC voltage and attain adequate reactive power levels in order to provide dependable and efficient electricity to the grid. The proposed scheme is being used to regulate the MSC, while the grid-side employs a traditional PI method. The efficiency of the suggested technique is investigated numerically using MATLAB/Simulink software.
... To verify the CMPC capability, the results of the CMPC under grid fault were compared to the PI controller. Moreover, the results were compared with our previously proposed sliding mode control (SMC) [35]. The rest of the paper is organized as follows: Section 2 describes the grid connected PV system, section 3 derives the converter controller, and section 4 discusses the simulation results. ...
This paper investigates the performance of the current model predictive control (CMPC) for controlling a two-stage transformer less grid-connected photovoltaic (PV) system under grid fault conditions. A maximum power point tracking (MPPT) controller was used to extract the maximum power of the PV panel. To stabilize the DC link and generate the reference current values, a proportional-integral (PI) controller was used. The CMPC strategy was implemented to control the output current of the inverter that connects the PV system to the utility grid. The system and control strategy were simulated via a MATLAB/Simulink environment. The performance of the proposed control strategy was investigated under fault conditions between the three-phase two-level inverter and the grid. Moreover, to validate the capability of the CMPC, comparative case studies were conducted between CMPC, PI, and sliding mode control (SMC) under grid fault. Case studies' results showed that under grid fault, CMPC did not introduce any overshoot or undershoot in the PV output DC current and power. However , PI and SMC produced undershoots of almost 15 kW for the output power and 45 A for the output current. Under the fault conditions, the active output power and three-phase current recovery time of the inverter was 50 ms using CMPC, compared to PI and SMC with recovery times of 80 ms and 60 ms, respectively. Moreover, a voltage dip of 75 V at the DC link voltage was recorded with CMPC under faulty conditions, while the voltage dips for PI and SMC were around 180 V.
