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A dynamic reconfiguration method based on neuro-fuzzy control algorithm for partially shaded PV arrays

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartB期52卷

作者： Solis-Cisneros, H. I. Sevilla-Camacho, P. Y. Robles-Ocampo, J. B. Zuniga-Reyes, M. A. Rodriguez-Resendiz, J. Muniz-Soria, J. Hernandez-Gutierrez, C. A. Univ Politecn Chiapas Master Program Renewable Energies Carretera Tuxtla Gutierrez Portillo Zaragoza Km 2 Suchiapa 29150 Mexico Univ Politecn Chiapas Energy & Sustainabil Acad Grp Carretera Tuxtla Gutierrez Portillo Zaragoza Km 2 Suchiapa 29150 Mexico Univ Politecn Chiapas Mechatron Engn Program Carretera Tuxtla Gutierrez Portillo Zaragoza Km 2 Suchiapa 29150 Mexico Univ Ciencias & Artes Estado Chiapas Inst Res & Innovat Renewable Energies Libramiento Norte Poniente 1150 Tuxtla Gutierrez 29039 Mexico Univ Autonoma Queretaro Fac Engn Cerro Las Campanas Queretaro 76010 Mexico Univ Nacl Autonoma Mexico Renewable Energies Inst Priv Xochicalco S-N Temixco 62580 Mexico Inst Tecnol Tuxtla Gutierrez Optomecatron Grp Tecnol Nacl Mexico Inst Tecnol Carretera Panamericana Km 1080 Tuxtla Gutierrez 29050 Mexico

The partial shading on PV arrays causes power decrease, hot spots, and damage to its components. In addition, the performance of the PV array is reduced for the internal mismatch. This work proposes a novel dynamic, on line, low costs, and automatic method to mitigate these effects. The method considers the mismatch due to partial shading and cell variability. In this method, the entire PV array is fully dynamic, and auxiliary PV modules are not required. The PV modules are electrically rearranged while the same interconnection scheme is maintained. The method is based on the temperature, the global voltage, and the global current of the entire PV array. These global measurements reduce the number of sensors, signal processing, computing time, electrical connections, and implementation cost. The electrical rearrangement of the PV modules is controlled by a neuro-fuzzy algorithm, a connection control, and a switch matrix. The proposed approach was implemented in hardware and validated experimentally in a real PV array. The results show that the method has 97% effectiveness in distinguishing the cause of power decrease, a 100% effectiveness in locating the shaded PV module, and a 10% increase in the PV array output power.

关键词： Partial shading Photovoltaic array Dynamic reconfiguration Power increase neuro-fuzzy algorithm

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Finding and evaluating suitable contents to recognize depression based on neuro-fuzzy algorithm 10

Finding and evaluating suitable contents to recognize depres...

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10th International Conference on Information and Communication Technology Convergence (ICTC) - ICT Convergence Leading the Autonomous Future

作者： Kim, Minwoo Lim, Joon S. Gachon Univ Dept Comp Engn Seongname Si Gyeonggi Do South Korea

ISBN: (纸本)9781728108933

Using heart rate variability (HRV) and a ne uro-fuzzy algorithm, this experiment determined certain content that show the largest difference between depres sion patients and normal subjects. To investigate the c ontent environment in which the depression patients an d normal subjects were effectively classified, the HRV extracted from test subjects was divided by the content of multimodal affective contents (MAC) stimulation sce narios. From the divided HRV, we extracted 22 feature s by using frequency domain methods, time domain m ethods, wavelet transformed methods, and Poincare tra nsform methods. A feature selection method, non-overl ap area distribution measurement, provided in the neur o-fuzzy algorithm was used to select the features with high importance from the 22 HRV features correspond ing to their respective contents. As a result of learning by using the selected features of the respective conten ts as input values, it was found that, out of 14 conten ts of MAC simulation scenarios, the Funniest Video a nd the Meditation & Nature Sound contents were effec tive for classifying the depression patients and normal subjects. In the results of conducting the leave-one-out cross-validation test 100 times with the neuro-fuzzy alg orithm by using the signals of two contents, the mean accuracy was 86.6% and maximum accuracy was 86. 9%.

关键词： Depression neuro-fuzzy algorithm Conten ts HRV

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Hardware-in-the-loop simulation to validate the fractional-order neuro-fuzzy power control of variable-speed dual-rotor wind turbine systems

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ENERGY REPORTS 2024年 11卷 4904-4923页

作者： Benbouhenni, Habib Yessef, Mourad Bizon, Nicu Kadi, Sara Bossoufi, Badre Alhejji, Ayman Nisantasi Univ Fac Engn & Architecture Dept Elect & Elect Engn TR-34481742 Istanbul Turkiye SMBA Univ Fes Fac Sci Dhar El Mahraz LIMAS Lab Fes 30000 Morocco Natl Univ Sci & Technol POLITEHN Bucharest Pitesti Univ Ctr Pitesti 110040 Romania USTHB Algiers Algeria Yanbu Ind Coll Dept Elect & Elect Engn Yanbu Ind City 41912 Saudi Arabia

New and efficient command strategies and algorithms are needed in the field of wind turbine energy generation to ensure power quality according to international standards. However, conventional linear strategies are still used to command doubly-fed induction generators (DFIGs). Thus, inadequate power quality is obtained, which can cause network-level disturbances. This Hardware-in-the loop (HIL) study presents a new command scheme for a DFIG-based dual-rotor wind turbine (DRWT) system. The new command is a combination of a neuro-fuzzy algorithm and fractional-order control that overcomes the declining power quality of the DFIG-DRWT system. The new strategy is based on using pulse width modulation to generate command pulses for the rotor-side converter of DFIG. In the designed command system, the reference value of the active power is determined using maximum power point tracking to provide efficient power conversion performance under different operating conditions. First, MATLAB software is used to implement the fractional-order neuro-fuzzy (FONF) control technique, and the behavior of the proposed strategy is studied in comparison to that of the traditional direct power command (DPC) technique. A comparison is performed in terms of the ripple minimization ratio, durability, overshoot, steady-state error, reference tracking, and current quality. The results of the comparison show the high performance of the FONF technique. Second, the FONF technique is implemented in HIL test using the dSPACE 1104 Card, where two different forms of wind speed are used to study the characteristics of the proposed strategy and confirm its effectiveness and performance in HIL test compared to DPC technique. The experimental results obtained in the two tests confirm the efficiency, effectiveness, and high performance of the proposed FONF technique compared to DPC technique in improving the energy system characteristics, and the simulation obtains the same results.

关键词： neuro-fuzzy algorithm Dual -rotor wind turbine Fractional -order control Pulse width modulation Doubly -fed induction generator

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neuro-fuzzy Based algorithm for Online Dynamic Voltage Stability Status Prediction Using Wide-Area Phasor Measurements 30

Neuro-Fuzzy Based Algorithm for Online Dynamic Voltage Stabi...

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30th International Power System Conference (PSC)

作者： Ahmadi, Ahmad Beromi, Yousef Alinezhad Semnan Univ Dept Elect Engn Semnan Iran

ISBN: (纸本)9781509027057

In this paper, a novel neuro-fuzzy based method combined with a feature selection technique is proposed for online dynamic voltage stability status prediction of power system. This technique uses synchronized phasors measured by phasor measurement units (PMUs) in a wide-area measurement system. In order to minimize the number of neuro-fuzzy inputs, training time and complication of neuro-fuzzy system, the Pearson feature selection technique is exploited to select set of input variables that have the strongest correlation with the output. Study on the network features such as phase angle and voltage amplitude has shown that among two interesting features, phase angle has maximum information about the performance of the network and solely can be used for training purposes. This is extra advantage of the proposed method that minimum data is needed to predict dynamic voltage stability status The efficiency of the proposed dynamic voltage stability prediction method is verified by simulation results of New England 39-bus and IEEE 68-bus test systems. Simulation results show that the proposed algorithm is accurate, computationally very fast and reliable. Moreover, it requires minimum data and so it is desirable for Wide Area Monitoring System (WAMS).

关键词： dynamic voltage stability prediction wide area monitoring system neuro-fuzzy algorithm feature selection technique

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Chronic Kidney Disease Prediction Using ML-Based neuro-fuzzy Model

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2024年第6期24卷

作者： Praveen, S. Phani Jyothi, Veerapaneni Esther Anuradha, Chokka VenuGopal, K. Shariff, Vahiduddin Sindhura, S. PVP Siddhartha Inst Technol Dept CSE Vijayawada Andhra Pradesh India Velagapudi Ramakrishna Siddhartha Engn Coll Dept Comp Applicat Kanuru Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Vaddeswaram Dept CSE Guntur Andhra Pradesh India Lakkireddy Bali Reddy Coll Engn Dept IT Mylavaram Andhra Pradesh India Sir CR Reddy Coll Engn Dept CSE Eluru Andhra Pradesh India NRI Inst Technol Dept CSE Vijayawada Andhra Pradesh India

Nowadays, in most countries, the most dangerous and life threatening infection is Chronic Kidney Disease (CKD). A progressive malfunctioning of the kidneys and less effectiveness of the kidney are considered CKD. CKD can be a life threatening disease if it continues for longer period of time. Prediction of chronic disease in early stage is very crucial so that sustainable care of the patient is taken to prevent menacing situations. Most of the developing countries are being affected by this deadly disease and treatment applied for this disease is also very expensive, here in this paper, a Machine Learning (ML)-positioned approach called neuro-fuzzy model is used for prediction belonging to CKD. Based on the image processing technique, fibrosis proportions are detected in the kidney tissues. It also builds a system for identifying and detection of CKD at an early stage. neuro-fuzzy model is based on ML which can detect risk of CKD patients. Compared with other conventional methods such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), the proposed method of this paper (sic) ML-based neuro-fuzzy logic method (sic) obtained 97% accuracy in CKD prediction. This method can be evaluated based on various parameters such as Precision, Accuracy, Recall and F1-Score in CKD prediction. From the results, the patients having high risk of chronic disease can be predicted.

关键词： Machine learning chronic kidney disease (CKD) neuro-fuzzy algorithm random forest fibrosis conditions CKD stage identification

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Strong Convergence of neuro-fuzzy Learning With Adaptive Momentum for Complex System

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IEEE ACCESS 2019年 7卷 39362-39368页

作者： Liu, Yan Liu, Fang Li, Long Dalian Polytech Univ Sch Informat Sci & Engn Dalian 116034 Peoples R China Dalian Univ Technol Sch Math Sci Dalian 116024 Peoples R China Hengyang Normal Univ Dept Math & Computat Sci Hengyang 421002 Peoples R China

This paper studies a split-complex-valued neuro-fuzzy algorithm for fuzzy inference system, which realizes a frequently used zero-order Takagi-Sugeno-Kang system. Here, adaptive momentum is utilized to speed up the learning convergence. Some strong convergence results are demonstrated based on the weak convergence results, which expresses that the weight sequence of fuzzy parameters converges to a fixed point. Simulation results support the theoretical findings.

关键词： Strong convergence neuro-fuzzy algorithm complex adaptive momentum fuzzy inference system

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Experimental kinematic identification and position control of a 3-DOF decoupled parallel robot

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2019年第5期233卷 1841-1855页

作者： Heydarzadeh, Mohsen Karbasizadeh, Nima Masouleh, Mehdi Tale Kalhor, Ahmad Univ Tehran Human & Robot Interact Lab Fac New Sci & Technol Tehran Iran Univ Tehran Sch Elect & Comp Engn Human & Robot Interact Lab Tehran Iran Univ Tehran Sch Elect & Comp Engn Control & Intelligent Proc Ctr Excellence Tehran Iran

This paper aims at using a kinematic identification procedure in order to enhance the control of a 3-DOF fully decoupled parallel robot, the so-called "Tripteron." From a practical standpoint, manufacture errors lead to some kinematic uncertainties in the robot which cause real kinematic equations of the robot to be different from the theoretical ones. In this paper, using a white box identification procedure, the independence of degrees-of-freedom in the robot is studied. Considering the fact that the kinematic identification of a robotic manipulator requires the position of its end-effector to be known, in this paper "Kinect" sensor, which is a vision-infra red sensor, is utilized to obtain the spatial coordinates of the end-effector. In order to calibrate the Kinect, a novel approach which is based on a neuro-fuzzy algorithm, the so-called "LoLiMoT" algorithm, is used. Moreover, the results of experimentally performing the identification and calibrating approach are used to the end of implementing a closed-loop classic controller for path tracking purposes. Furthermore, the theoretical unidentified model was implemented in a sliding mode robust controller in order to compare the results with classic controller. The comparison reveals that classic controller which uses identified model leads to a better performance in terms of accuracy and control effort with respect to robust controller which is purely based on theoretical model.

关键词： Parallel robot system identification position control neuro-fuzzy algorithm LoLiMoT

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Prediction of Poisson's Ratio from Conventional Well Log Data: A Committee Machine with Intelligent Systems Approach

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2013年第10期35卷 962-975页

作者： Asoodeh, M. Islamic Azad Univ Dept Comp Sci & Engn Birjand Branch Birjand Iran

Quantitative formulation between conventional well logs and Poisson's ratio, the most critical geomechanical property of reservoir rocks, could be a potent tool for planning and post analysis of wellbore operations. Direct estimation of Poisson's ratio from conventional well logs makes the problem too complicated. Therefore, the present study proposes an improved multi-step strategy for making a quantitative formulation between conventional well logs and Poisson's ratio. In the first stage, shear wave slowness was predicted from conventional well logs using a radial basis neural network, Sugeno fuzzy inference system, neuro-fuzzy algorithm, and simple averaging method. Consequently, the Poisson's ratio was computed from the results of each expert, independently. Eventually, a committee machine with intelligent systems was constructed by virtue of a hybrid genetic algorithm-pattern search technique. The values of Poisson's ratio, derived from the results of a radial basis neural network, Sugeno fuzzy inference system, neuro-fuzzy algorithm, and simple averaging method, were used as inputs of the committee machine with intelligent systems. The proposed committee machine with intelligent systems combines the results of aforementioned experts for overall estimation of Poisson's ratio from conventional well log data. It assigns a weight factor to each expert, indicating its contribution in overall prediction. The proposed methodology was applied in Asmari formation, which is the major carbonate reservoir rock of Iran. A group of 1,582 data points were used to establish the intelligent model, and a group of 600 data points were employed to assess the reliability of the proposed model. The results show that the committee machine with intelligent systems method performs better than individual intelligent systems, which perform alone.

关键词： artificial neural network conventional well logs fuzzy logic genetic algorithm-pattern search technique neuro-fuzzy algorithm

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A test on a neuro-fuzzy algorithm used to reduce continuous gravity records for the effect of meteorological parameters

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PHYSICS OF THE EARTH AND PLANETARY INTERIORS 2004年第1-2期142卷 37-47页

作者： Andò, B Carbone, D Ist Nazl Geofis & Vulcanol I-95123 Catania Italy Univ Catania DEES I-95125 Catania Italy

Gravity measurements are utilized at active volcanoes to,detect mass changes linked to magma transfer processes and thus to recognize forerunners to paroxysmal volcanic events. Continuous gravity measurements are now increasingly performed at sites very close to active craters, where there is the greatest chance to detect meaningful gravity changes. Unfortunately, especially when used against the adverse environmental conditions usually encountered at such places, gravimeters have been proved to be affected by meteorological parameters, mainly by changes in the atmospheric temperature. The pseudo-signal generated by these perturbations is often stronger than the signal generated by actual changes in the gravity field. Thus, the implementation of well-performing algorithms for reducing the,gravity, signal for the effect of meteorological parameters is vital to obtain sequences useful from the volcano surveillance standpoint. In the present paper, a neuro-fuzzy algorithm, which was already proved to accomplish the required task satisfactorily, is tested over a data set from three gravimeters which worked continuously for about 50 days at a site far away from active zones, where changes due to actual fluctuation of the gravity field are expected to be within a few microgal. After accomplishing the reduction of the gravity series, residuals are within about 15 muGal peak-to-peak, thus confirming the capabilities of the neuro-fuzzy algorithm under test of performing the required task satisfactorily. (C) 2004 Elsevier B.V. All rights reserved.

关键词： gravimeters exogenous parameter compensation neuro-fuzzy algorithm

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Study on real-time measurement of nugget diameter for resistance spot welding using a neuro-fuzzy algorithm

Study on real-time measurement of nugget diameter for resist...

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Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference, From the Electrometer to the Networked Instruments: A Giant Step toward a Deeper Knowledge Volume 1

作者： Zhongqin Lin Yansong Zhang Guanlong Chen Yongbing Li School of Mechanical Engineering Shanghai Jiaotong University Shanghai China

ISBN: (纸本)078038248X

Resistance spot welding (RSW) is still the most successful sheet metal joining method in the automobile industry. However, an effective quality evaluation method has not yet been developed. Real-time quality inspection of RSW is necessary in order to deal with all kinds of problems during welding. This paper developed an experimental system using for measuring electrode displacement. Accordingly based on electrode displacement curve proposes a neuro-fuzzy algorithm to inference nugget diameter online. Inference results showed that among the total number of specimens, 88% were successfully inferred within a range of 1.5% error.

关键词： spot welding welding electrodes diameter measurement quality control automobile industry fuzzy neural nets fuzzy set theory inference mechanisms learning (artificial intelligence) gradient methods process control measurement by laser beam resistance spot welding nugget diameter real-time measurement sheet metal joining real-time quality inspection electrode displacement neuro-fuzzy algorithm electrode velocity laser displacement sensor fuzzy if-then rules learning algorithms membership functions selection gradient-descent algorithm intelligent weld quality control systems

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