An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems
TL;DR: In this article, a fuzzy logic based algorithm for varying the step size of the incremental conductance (INC) maximum power point tracking (MPPT) method for PV is proposed, where a variable voltage step size is estimated according to the degree of ascent or descent of the powervoltage relation.
Abstract: Recently, solar energy has been intensively employed in power systems, especially using the photovoltaic (PV) generation units In this regard, this paper proposes a novel design of a fuzzy logic based algorithm for varying the step size of the incremental conductance (INC) maximum power point tracking (MPPT) method for PV In the proposed method, a variable voltage step size is estimated according to the degree of ascent or descent of the power-voltage relation For this purpose, a novel unique treatment is proposed based on introducing five effective regions around the point of maximum PV power To vary the step size of the duty cycle, a fuzzy logic system is developed according to the locations of the fuzzy inputs regarding the five regions The developed fuzzy inputs are inspired from the slope of the power-voltage relation, namely the current-voltage ratio and its derivatives whereas appropriate membership functions and fuzzy rules are designed The benefit of the proposed method is that the MPPT efficiency is improved for varying the step size of the incremental conductance method, thanks to the effective coordination between the proposed fuzzy logic based algorithm and the INC method The output DC power of the PV array and the tracking speed are presented as indices for illustrating the improvement achieved in MPPT The proposed method is verified and tested through the simulation of a grid-connected PV system model The simulation results reveal a valuable improvement in static and dynamic responses over that of the traditional INC method with the variation of the environmental conditions Further, it enhances the output dc power and reduce the convergence time to reach the steady state condition with intermittent environmental conditions
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TL;DR: In this article, an adaptive neuro-fuzzy inference system (ANFIS) is proposed for blade pitch control of wind energy conversion systems (WECS) instead of the conventional controllers.
Abstract: Wind speed fluctuations and load demand variations represent the big challenges against wind energy conversion systems (WECS). Besides, the inefficient measuring devices and the environmental impacts (e.g. temperature, humidity, and noise signals) affect the system equipment, leading to increased system uncertainty issues. In addition, the time delay due to the communication channels can make a gap between the transmitted control signal and the WECS that causes instability for the WECS operation. To tackle these issues, this paper proposes an adaptive neuro-fuzzy inference system (ANFIS) as an effective control technique for blade pitch control of the WECS instead of the conventional controllers. However, the ANFIS requires a suitable dataset for training and testing to adjust its membership functions in order to provide effective performance. In this regard, this paper also suggests an effective strategy to prepare a sufficient dataset for training and testing of the ANFIS controller. Specifically, a new optimization algorithm named the mayfly optimization algorithm (MOA) is developed to find the optimal parameters of the proportional integral derivative (PID) controller to find the optimal dataset for training and testing of the ANFIS controller. To demonstrate the advantages of the proposed technique, it is compared with different three algorithms in the literature. Another contribution is that a new time-domain named figure of demerit is established to confirm the minimization of settling time and the maximum overshoot in a simultaneous manner. A lot of test scenarios are performed to confirm the effectiveness and robustness of the proposed ANFIS based technique. The robustness of the proposed method is verified based on the frequency domain conditions that are driven from Hermite–Biehler theorem. The results emphases that the proposed controller provides superior performance against the wind speed fluctuations, load demand variations, system parameters uncertainties, and the time delay of the communication channels.
79 citations
TL;DR: In this article, the authors proposed a new IoT architecture based on utilizing machine learning techniques to suppress cyber-attacks for providing reliable and secure online monitoring for the induction motor status, in which advanced machine learning technique are utilized here to detect cyberattacks and motor status with high accuracy.
Abstract: In recent years, the internet of things (IoT) represents the main core of Industry 4.0 for cyber-physic systems (CPS) in order to improve the industrial environment. Accordingly, the application of IoT and CPS has been expanded in applied electrical systems and machines. However, cybersecurity represents the main challenge of the implementation of IoT against cyber-attacks. In this regard, this paper proposes a new IoT architecture based on utilizing machine learning techniques to suppress cyber-attacks for providing reliable and secure online monitoring for the induction motor status. In particular, advanced machine learning techniques are utilized here to detect cyber-attacks and motor status with high accuracy. The proposed infrastructure validates the motor status via communication channels and the internet connection with economical cost and less effort on connecting various networks. For this purpose, the CONTACT Element platform for IoT is adopted to visualize the processed data based on machine learning techniques through a graphical dashboard. Once the cyber-attacks signal has been detected, the proposed IoT platform based on machine learning will be visualized automatically as fake data on the dashboard of the IoT platform. Different experimental scenarios with data acquisition are carried out to emphasize the performance of the suggested IoT topology. The results confirm that the proposed IoT architecture based on the machine learning technique can effectively visualize all faults of the motor status as well as the cyber-attacks on the networks. Moreover, all faults of the motor status and the fake data, due to the cyber-attacks, are successfully recognized and visualized on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization, thereby contributing to enhancing the decision-making about the motor status. Furthermore, the introduced IoT architecture with Random Forest algorithm provides an effective detection for the faults on motor due to the vibration under industrial conditions with excellent accuracy of 99.03% that is significantly greater than the other machine learning algorithms. Besides, the proposed IoT has low latency to recognize the motor faults and cyber-attacks to present them in the main dashboard of the IoT platform.
58 citations
02 Apr 2021
TL;DR: The findings show a high closeness between the estimated power–voltage (P–V) and current–voltages (I-V) curves achieved by the proposed TFWO compared with the experimental data as well as the competitive optimization algorithms, thanks to the effectiveness of the developed T FWO solution mechanism.
Abstract: Recently, the use of diverse renewable energy resources has been intensively expanding due to their technical and environmental benefits One of the important issues in the modeling and simulation of renewable energy resources is the extraction of the unknown parameters in photovoltaic models In this regard, the parameters of three models of photovoltaic (PV) cells are extracted in this paper with a new optimization method called turbulent flow of water-based optimization (TFWO) The applications of the proposed TFWO algorithm for extracting the optimal values of the parameters for various PV models are implemented on the real data of a 55 mm diameter commercial RTC France solar cell and experimental data of a KC200GT module Further, an assessment study is employed to show the capability of the proposed TFWO algorithm compared with several recent optimization techniques such as the marine predators algorithm (MPA), equilibrium optimization (EO), and manta ray foraging optimization (MRFO) For a fair performance evaluation, the comparative study is carried out with the same dataset and the same computation burden for the different optimization algorithms Statistical analysis is also used to analyze the performance of the proposed TFWO against the other optimization algorithms The findings show a high closeness between the estimated power–voltage (P–V) and current–voltage (I–V) curves achieved by the proposed TFWO compared with the experimental data as well as the competitive optimization algorithms, thanks to the effectiveness of the developed TFWO solution mechanism
48 citations
TL;DR: A novel swarm-based algorithm called coyote optimization algorithm (COA) for finding the optimal parameter of PEM fuel cell as well as PEM stack is proposed and the final estimated results and statistical analysis show a significant accuracy of the proposed method.
Abstract: In recent years, the penetration of fuel cells in distribution systems is significantly increased worldwide. The fuel cell is considered an electrochemical energy conversion component. It has the ability to convert chemical to electrical energies as well as heat. The proton exchange membrane (PEM) fuel cell uses hydrogen and oxygen as fuel. It is a low-temperature type that uses a noble metal catalyst, such as platinum, at reaction sites. The optimal modeling of PEM fuel cells improves the cell performance in different applications of the smart microgrid. Extracting the optimal parameters of the model can be achieved using an efficient optimization technique. In this line, this paper proposes a novel swarm-based algorithm called coyote optimization algorithm (COA) for finding the optimal parameter of PEM fuel cell as well as PEM stack. The sum of square deviation between measured voltages and the optimal estimated voltages obtained from the COA algorithm is minimized. Two practical PEM fuel cells including 250 W stack and Ned Stack PS6 are modeled to validate the capability of the proposed algorithm under different operating conditions. The effectiveness of the proposed COA is demonstrated through the comparison with four optimizers considering the same conditions. The final estimated results and statistical analysis show a significant accuracy of the proposed method. These results emphasize the ability of COA to estimate the parameters of the PEM fuel cell model more precisely.
45 citations
TL;DR: The enhanced operation and control of DC microgrid systems, which are based on photovoltaic modules, battery storage systems, and DC load, are presented and it is illustrated that the grid-tied mode of operation regulated by voltage source converter control offers more stability than the islanded mode.
Abstract: Recently, the penetration of energy storage systems and photovoltaics has been significantly expanded worldwide. In this regard, this paper presents the enhanced operation and control of DC microgrid systems, which are based on photovoltaic modules, battery storage systems, and DC load. DC–DC and DC–AC converters are coordinated and controlled to achieve DC voltage stability in the microgrid. To achieve such an ambitious target, the system is widely operated in two different modes: stand-alone and grid-connected modes. The novel control strategy enables maximum power generation from the photovoltaic system across different techniques for operating the microgrid. Six different cases are simulated and analyzed using the MATLAB/Simulink platform while varying irradiance levels and consequently varying photovoltaic generation. The proposed system achieves voltage and power stability at different load demands. It is illustrated that the grid-tied mode of operation regulated by voltage source converter control offers more stability than the islanded mode. In general, the proposed battery converter control introduces a stable operation and regulated DC voltage but with few voltage spikes. The merit of the integrated DC microgrid with batteries is to attain further flexibility and reliability through balancing power demand and generation. The simulation results also show the system can operate properly in normal or abnormal cases, thanks to the proposed control strategy, which can regulate the voltage stability of the DC bus in the microgrid with energy storage systems and photovoltaics.
45 citations
References
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TL;DR: In this article, the authors proposed a method of modeling and simulation of photovoltaic arrays by adjusting the curve at three points: open circuit, maximum power, and short circuit.
Abstract: This paper proposes a method of modeling and simulation of photovoltaic arrays. The main objective is to find the parameters of the nonlinear I-V equation by adjusting the curve at three points: open circuit, maximum power, and short circuit. Given these three points, which are provided by all commercial array data sheets, the method finds the best I-V equation for the single-diode photovoltaic (PV) model including the effect of the series and parallel resistances, and warranties that the maximum power of the model matches with the maximum power of the real array. With the parameters of the adjusted I-V equation, one can build a PV circuit model with any circuit simulator by using basic math blocks. The modeling method and the proposed circuit model are useful for power electronics designers who need a simple, fast, accurate, and easy-to-use modeling method for using in simulations of PV systems. In the first pages, the reader will find a tutorial on PV devices and will understand the parameters that compose the single-diode PV model. The modeling method is then introduced and presented in details. The model is validated with experimental data of commercial PV arrays.
3,811 citations
TL;DR: In this paper, a comprehensive review of the MPPT techniques applied to photovoltaic (PV) power system available until January, 2012 is provided, which is intended to serve as a convenient reference for future MPPT users in PV systems. But, confusion lies while selecting a MPPT as every technique has its own merits and demerits.
Abstract: This paper provides a comprehensive review of the maximum power point tracking (MPPT) techniques applied to photovoltaic (PV) power system available until January, 2012. A good number of publications report on different MPPT techniques for a PV system together with implementation. But, confusion lies while selecting a MPPT as every technique has its own merits and demerits. Hence, a proper review of these techniques is essential. Unfortunately, very few attempts have been made in this regard, excepting two latest reviews on MPPT [Salas, 2006], [Esram and Chapman, 2007]. Since, MPPT is an essential part of a PV system, extensive research has been revealed in recent years in this field and many new techniques have been reported to the list since then. In this paper, a detailed description and then classification of the MPPT techniques have made based on features, such as number of control variables involved, types of control strategies employed, types of circuitry used suitably for PV system and practical/commercial applications. This paper is intended to serve as a convenient reference for future MPPT users in PV systems.
1,584 citations
TL;DR: Evaluations among the most usual maximum power point tracking techniques, doing meaningful comparisons with respect to the amount of energy extracted from the photovoltaic (PV) panel [tracking factor) in relation to the available power, PV voltage ripple, dynamic response, and use of sensors.
Abstract: This paper presents evaluations among the most usual maximum power point tracking (MPPT) techniques, doing meaningful comparisons with respect to the amount of energy extracted from the photovoltaic (PV) panel [tracking factor (TF)] in relation to the available power, PV voltage ripple, dynamic response, and use of sensors. Using MatLab/Simulink and dSPACE platforms, a digitally controlled boost dc-dc converter was implemented and connected to an Agilent Solar Array E4350B simulator in order to verify the analytical procedures. The main experimental results are presented for conventional MPPT algorithms and improved MPPT algorithms named IC based on proportional-integral (PI) and perturb and observe based on PI. Moreover, the dynamic response and the TF are also evaluated using a user-friendly interface, which is capable of online program power profiles and computes the TF. Finally, a typical daily insulation is used in order to verify the experimental results for the main PV MPPT methods.
1,205 citations
Book•
14 Oct 2010
TL;DR: This paper presents a model for a Fuzzy Rule-Based System that automates the very labor-intensive and therefore time-heavy process of decision-making in the context of classical sets.
Abstract: Classical Sets and Fuzzy Sets.- Classical and Fuzzy Relations.- Membership Functions.- Defuzzification.- Fuzzy Rule-Based System.- Fuzzy Decision Making.- Applications of Fuzzy Logic.- Fuzzy Logic Projects with Matlab.
994 citations
TL;DR: A detailed analysis of the two most well-known hill-climbing maximum power point tracking algorithms: the perturb-and-observe (P&O) and incremental conductance (INC) reveals that there is no difference between the two.
Abstract: This paper presents a detailed analysis of the two most well-known hill-climbing maximum power point tracking (MPPT) algorithms: the perturb-and-observe (P&O) and incremental conductance (INC). The purpose of the analysis is to clarify some common misconceptions in the literature regarding these two trackers, therefore helping the selection process for a suitable MPPT for both researchers and industry. The two methods are thoroughly analyzed both from a mathematical and practical implementation point of view. Their mathematical analysis reveals that there is no difference between the two. This has been confirmed by experimental tests according to the EN 50530 standard, resulting in a deviation between their efficiencies of 0.13% in dynamic and as low as 0.02% under static conditions. The results show that despite the common opinion in the literature, the P&O and INC are equivalent.
670 citations