316 research outputs found

    Nonlinear control of two-stage single-phase standalone photovoltaic system

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    This paper presents a single-phase Photovoltaic (PV) inverter with its superior and robust control in a standalone mode. Initially, modeling and layout of the Buck-Boost DC-DC converter by adopting a non-linear Robust Integral Back-stepping controller (RIBSC) is provided. The controller makes use of a reference voltage generated through the regression plane so that the operating point corresponding to the maximum power point (MPP) could be achieved through the converter under changing climatic conditions. The other main purpose of the Buck-Boost converter is to act like a transformer and produce an increased voltage at the inverter input whenever desired. By not using a transformer makes the circuit size more compact and cost-effective. The proposed RIBSC is applied to an H-bridge inverter with an LC filter to produce the sinusoidal wave in the presence of variations in the output to minimize the difference between the output voltage and the reference voltage. Lyapunov stability criterion has been used to verify the stability and finite-time convergence of the overall system. The overall system is simulated in MATLAB/Simulink to test the system performance with different loads, varying climatic conditions and inverter reference voltages. The proposed methodology is compared with a back-stepping controller and Proportional Integral Derivative (PID) controller under rapidly varying climatic conditions. Results demonstrated that the proposed technique yielded a tracking time of 0.01s, a total harmonic distortion of 9.71% and a root means square error of 0.3998 in the case of resistive load thus showing superior control performance compared to the state-of-the-art control techniques

    Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications

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    Purpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result of rising demand for electricity, rapid fossil fuel exhaustion and the threat of global warming. However, due to their inherent intermittency, these sources offer low system reliability. So, a hybrid energy system that encompasses wind/photovoltaic/battery is implemented in order to obtain a stable and reliable microgrid. Both solar and wind energy is easily accessible with huge untapped potential and together they account for more than 60 % of yearly net new electricity generation capacity additions around the world. Novelty. A KY converter is adopted here for enhancing the output of the photovoltaic system and its operation is controlled with the help of a cascaded an adaptive neuro fuzzy interface system controller. Originality. Increase of the overall system stability and reliability using hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Practical value. A proportional integral controller is used in the doubly fed induction generator based wind energy conversion system for controlling the operation of the pulse width modulation rectifier in order to deliver a controlled DC output voltage. A battery energy storage system, which uses a battery converter to be connected to the DC link, stores the excess power generated from the renewable energy sources. Based on the battery’s state of charge, its charging and discharging operation is controlled using a proportional integral controller. The controlled DC link voltage is fed to the three phase voltage source inverter for effective DC to AC voltage conversion. The inverter is connected to the three phase grid via an LC filter for effective harmonics mitigation. A proportional integral controller is used for achieving effective grid voltage synchronization. Results. The proposed model is simulated using MATLAB/Simulink, and from the obtained outcomes, it is noted that the cascaded adaptive neuro fuzzy interface system controller assisted KY converter is capable of maintaining the stable operation of the microgrid with an excellent efficiency of 93 %.Мета. У цій статті пропонується нова стратегія управління перетворювачем KY (підвищення напруги постійного струму). Пропонована гібридна енергетична система, що живиться перетворювачем KY, використовується разом із контролером системи адаптивного нейро-нечіткого інтерфейсу. Відновлювані джерела енергії останнім часом набули величезного значення внаслідок зростання попиту на електроенергію, швидкого виснаження викопного палива та загрози глобального потепління. Однак через властиву їм уривчастість ці джерела забезпечують низьку надійність системи. Таким чином, гібридна енергетична система, що включає енергію вітру/фотоелектричних елементів/акумулятору, реалізована для отримання стабільної і надійної мікромережі. Як сонячна, так і вітрова енергія доступні з величезним невикористаним потенціалом, і разом вони забезпечують понад 60 % щорічного чистого приросту нових потужностей з виробництва електроенергії в усьому світі. Новизна. Перетворювач KY використовується тут для підвищення вихідної потужності фотоелектричної системи, і його робота керується за допомогою каскадного контролера системи з адаптивним нейро-нечітким інтерфейсом. Оригінальність. Підвищення загальної стабільності та надійності системи за допомогою гібридної енергетичної системи, що живиться перетворювачем KY і використовується разом з контролером системи з адаптивним нейро-нечітким інтерфейсом. Практична цінність. Пропорційний інтегральний контролер використовується в системі перетворення енергії вітру на основі асинхронного генератора з подвійним живленням для управління випрямлячою роботою з широтно-імпульсною модуляцією для забезпечення регульованої вихідної напруги постійного струму. Акумуляторна система накопичення енергії, в якій використовується акумуляторний перетворювач для підключення до кола постійного струму, зберігає надмірну потужність, що виробляється з відновлюваних джерел енергії. Залежно від стану заряду акумулятора, процес його зарядки і розрядки контролюється за допомогою пропорційного інтегрального контролера. Керована напруга кола постійного струму подається на трифазний інвертор джерела напруги для ефективного перетворення постійної напруги змінну. Інвертор підключений до трифазної мережі через LC-фільтр для ефективного придушення гармонік. Пропорційний інтегральний регулятор використовується для досягнення ефективної синхронізації напруги мережі. Результати. Запропонована модель змодельована з використанням MATLAB/Simulink, і з отриманих результатів випливає, що каскадний адаптивний нейро-нечіткий інтерфейс із системним контролером та перетворювачем KY здатний підтримувати стабільну роботу мікромережі з чудовим ККД 93 %

    Solar Energy Dependent Supercapacitor System with ANFIS Controller for Auxiliary Load of Electric Vehicles

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    Innovations are required for electric vehicles (EVs) to be lighter and more energy efficient due to the range anxiety issue. This article introduces an intelligent control of an organic structure solar supercapacitor (OSSC) for EVs to meet electrical load demands with solar renewable energy. A carbon fibreȬreinforced polymer, nano zinc oxide (ZnO), and copper oxide (CuO) fillers have been used in the development of OSSC prototypes. The organic solar cell, electrical circuits, converter, controller, circuit breaker switch, and batteries were all integrated for the modelling of OSSCs. A carbon fibre (CF)Ȭreinforced CuOȬdoped polymer was utilised to improve the concentration of elecȬ trons. The negative electrodes of the CF were strengthened with nano ZnO epoxy to increase the mobility of electrons as an nȬtype semiconductor (energy band gap 3.2–3.4 eV) and subsequently increased to 3.5 eV by adding 6%ȱΔȬcarbon. The electrodes of the CF were strengthened with epoxyȬ filled nanoȬCuO as a pȬtype semiconductor to facilitate bore/positive charging. They improve the conductivity of the OSSC. The OSSC power storage was controlled by an adaptive neuroȬfuzzy inȬ telligent system controller to meet the load demand of EVs and auxiliary battery charging. MoreoȬ ver, a fully charged OSSC (solar irradiance = 1000 W/m2) produced 561 Wȉh/m2 to meet the vehicle load demand with 45 A of auxiliary battery charging current. Therefore, the OSSC can save 15% in energy efficiency and contribute to emission control. The integration of an OSSC with an EV battery can minimise the weight and capacity of the battery by 7.5% and 10%, respectively

    Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications

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    Purpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result of rising demand for electricity, rapid fossil fuel exhaustion and the threat of global warming. However, due to their inherent intermittency, these sources offer low system reliability. So, a hybrid energy system that encompasses wind/photovoltaic/battery is implemented in order to obtain a stable and reliable microgrid. Both solar and wind energy is easily accessible with huge untapped potential and together they account for more than 60 % of yearly net new electricity generation capacity additions around the world. Novelty. A KY converter is adopted here for enhancing the output of the photovoltaic system and its operation is controlled with the help of a cascaded an adaptive neuro fuzzy interface system controller. Originality. Increase of the overall system stability and reliability using hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Practical value. A proportional integral controller is used in the doubly fed induction generator based wind energy conversion system for controlling the operation of the pulse width modulation rectifier in order to deliver a controlled DC output voltage. A battery energy storage system, which uses a battery converter to be connected to the DC link, stores the excess power generated from the renewable energy sources. Based on the battery’s state of charge, its charging and discharging operation is controlled using a proportional integral controller. The controlled DC link voltage is fed to the three phase voltage source inverter for effective DC to AC voltage conversion. The inverter is connected to the three phase grid via an LC filter for effective harmonics mitigation. A proportional integral controller is used for achieving effective grid voltage synchronization. Results. The proposed model is simulated using MATLAB/Simulink, and from the obtained outcomes, it is noted that the cascaded adaptive neuro fuzzy interface system controller assisted KY converter is capable of maintaining the stable operation of the microgrid with an excellent efficiency of 93 %

    Wind Turbine Active Fault Tolerant Control Based on Backstepping Active Disturbance Rejection Control and a Neurofuzzy Detector

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    © 2023 The Author(s). Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Wind energy conversion systems have become an important part of renewable energy history due to their accessibility and cost-effectiveness. Offshore wind farms are seen as the future of wind energy, but they can be very expensive to maintain if faults occur. To achieve a reliable and consistent performance, modern wind turbines require advanced fault detection and diagnosis methods. The current research introduces a proposed active fault-tolerant control (AFTC) system that uses backstepping active disturbance rejection theory (BADRC) and an adaptive neurofuzzy system (ANFIS) detector in combination with principal component analysis (PCA) to compensate for system disturbances and maintain performance even when a generator actuator fault occurs. The simulation outcomes demonstrate that the suggested method successfully addresses the actuator generator torque failure problem by isolating the faulty actuator, providing a reliable and robust solution to prevent further damage. The neurofuzzy detector demonstrates outstanding performance in detecting false data in torque, achieving a precision of 90.20% for real data and 100%, for false data. With a recall of 100%, no false negatives were observed. The overall accuracy of 95.10% highlights the detector’s ability to reliably classify data as true or false. These findings underscore the robustness of the detector in detecting false data, ensuring the accuracy and reliability of the application presented. Overall, the study concludes that BADRC and ANFIS detection and isolation can improve the reliability of offshore wind farms and address the issue of actuator generator torque failure.Peer reviewe

    Оптимізація технікоекономічних показників локальних систем електроживлення з транзактивним керуванням

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    У монографії розглянуті питання побудови та функціонування локальних систем електроживлення з транзактивним керуванням. Представлена оптимізація технікоекономічних показників. Розроблено алгоритм для оптимізації витрат, який виконує розрахунок розподілу потужностей паралельно з’єднаних генераторів, ці потужності відповідають мінімальному значенню витрат. Запропоновано забезпечення вимог щодо якості енергопостачання та електромагнітної сумісності в рамках транзактивного контролю, шляхом використання розроблених систем силової електроніки як інтерфейсу сонячних та вітрогенераторів. Розглянуті питання якості електричної енергії локальних систем, надійності та обмінних енергетичних процесів

    Brief Review on Identification, Categorization and Elimination of Power Quality Issues in a Microgrid Using Artificial Intelligent Techniques

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    Power quality is the manifestation of a disruption in the supply voltage, current or frequency that damages the utility equipment and has become an important issue with the introduction of more sophisticated and sensitive devices. So, the supply power quality issue still remains a major challenge as its degradation can cause huge destabilization of electrical networks. As renewable energy sources have irregular nature, a microgrid essentially needs energy storage system containing advanced power electronic converters which is the root cause of majority of power quality disturbances. Also, the integration of non-linear and unbalanced loads into the grid adds to its power quality problems. This article gives a compact overview on the identification, categorization and mitigation of these power quality events in a microgrid by using various Artificial Intelligence-based techniques like Optimization techniques, Adaptive Learning techniques, Signal Processing and Pattern Recognition, Neural Networks and Fuzzy Logic

    Sistem Pengisian Baterai Konstan Tegangan Berbasis Fuzzy Logic Pada Aplikasi Off Grid Rumah DC

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    Pada penelitian yang diusulkan dirancang Rumah DC dengan kapasitas daya 200 watt. Rumah DC berisikan beban – beban rumah tangga DC. Panel surya digunakan sebagai sumber dari Rumah DC. Konverter daya yaitu SEPIC terhubung dengan panel surya yang berfungsi sebagai pengisian baterai dengan tegangan keluaran sebesar 14,8 V . Luaran tegangan panel surya adalah 18 V, dari panel surya akan diturunkan tegangannya melalui SEPIC konverter sesuai dengan nilai duty cycle yang diatur pada mosfet untuk mengisi daya baterai sesuai dengan kebutuhan beban baterai lead acid 12V/140 Ah. Sistem pengisian baterai yang digunakan adalah  metode constant voltage (CV) dengan diberikan algoritma fuzzy logic. Simulasi yang akan disajikan yaitu SEPIC telah dikontrol menggunakan algoritma fuzzy dengan metode CV dengan set point 14,8 V , sehingga dengan algoritma fuzzy digunakan untuk control pengisian baterai. Sedangkan pada pengujian alat, tegangan pengisian baterai dapat dikontrol menggunakan logika fuzzy yaitu dengan rata – rata tegangan pengisian 14,8 V  dengan iradiasi yang bervariasi. Oleh karena itu desain ini dapat diaplikasikan pada Rumah DC 12 V/200 watt.

    Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology

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    The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components

    Salp Swarm Optimized Hybrid Elman Recurrent Neural Network (SSO-ERNN) based MPPT Controller for Solar PV

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    Renewable energy technologies provide clean and abundant energy that can be self-renewed from natural sources; more support from the public to replace fossil fuels with various renewable energy sources to protect the environment. Although solar energy has less impact on the environment than other renewable sources, the output efficiency is lower due to the different weather conditions. So to overcome that, the MPPT controller is used for tracking peak power and better efficiency. Some conventional methods in MPPT controllers provide less tracking efficiency, and steady-state oscillations occur in maximum power tracking due to the sudden variations in solar irradiance. Thus, in this work salp swarm optimized (SSO) based Elman recurrent neural network (ERNN) controller is proposed to track the maximum power form PV with high efficiency. The weight parameter of ERNN layer is optimized with the help of SSO, which solve the complex problems and give maximum efficiency. The proposed method is performed in MATLAB/Simulink environment, which differs from existing plans and gives a better output efficiency. Using this proposed controller, the system can achieve high tracking efficiency of 99.74% compared to conventional processes
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