International Journal of Applied Power Engineering (IJAPE)
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Cost optimization of electricity in energy storage system by dynamic programming
Get PDFThis paper presents a dynamic programming solution for the cost optimization of an electric storage system. The objective is to minimize the total cost of meeting electricity demand over a specified time interval, considering energy constraints and costs. The proposed algorithm efficiently determines the optimal energy discharge and charge strategies for the storage system, resulting in reduced overall costs. The effectiveness and efficiency of the algorithm are demonstrated through various test cases, highlighting its potential for real-world applications in energy storage systems and electric grid management. It also provides an overview of different types of electrical storage systems, review recent research on optimization techniques for energy storage, and examines recent studies on the optimization of electrical storage systems for specific applications, such as peak load shaving and grid stability. Through this comprehensive analysis, we hope to shed light on the current state of the field and identify areas for further research and improvement
Exploratory data analysis for electric vehicle driving range prediction: insights and evaluation
Get PDFOne of the biggest challenges of electric vehicle (EV) users has been predicting the amount of driving time their vehicles will have on one battery charge. Planning a trip and reducing range anxiety depends on an accurate range estimate. This study aims to anticipate the EV driving range using machine learning methods. In this research, several regression models for predicting EV driving range will be developed and compared. A real-world dataset comprising various factors affecting EV range, such as power, trip distance, energy consumption, driving style, and environmental factors, is used for analysis. The dataset is preprocessed using exploratory data analysis methods to manage missing values, outliers, and categorical variables. The findings of this study contribute to the expanding area of EV range prediction and provide EV buyers, producers, and regulators with insightful information. The user experience can be improved, EV adoption can be boosted, and effective charging infrastructure design is made possible with accurate range prediction. The study also highlights the importance of model selection and data pretreatment in making accurate predictions
Fundamental frequency switching strategies of a seven level hybrid cascaded H-bridge multilevel inverter
Get PDFThis paper presents a novel hybrid cascaded H-bridge multilevel inverter (HCHB MLI) designed to address the growing importance of multilevel inverters in the context of renewable energy sources such as solar, wind, and fuel cells. The proposed topology features eight insulated-gate bipolar transistor (IGBT) switches and utilizes two distinct input direct current (DC) sources: a battery and a capacitor, making it a hybrid system. The control strategy employed in this topology is based on fundamental switching frequency techniques. Simulation results of the proposed topology are conducted using MATLAB/Simulink software, while hardware experimentation with a single-phase H-bridge inverter is also demonstrated in the paper. For pulse generation and IGBT switch control, an Arduino UNO microcontroller is utilized. The output voltage of the single-phase H-bridge inverter is verified through experimentation using a cathode-ray oscilloscope (CRO)
Optimization of controllers using soft computing technique for load frequency control of multi-area deregulated power system
Get PDFGiven the changing nature of power systems, it is challenging to optimize the controller for controlling load frequency problems. Distributed power generating sources and power system reorganization with multi-sources and multi-stakeholders make traditional load frequency control approaches unsuitable for current power systems. This research provides the comparative analysis of regulation of the load frequency in a multiple-area deregulated electricity system with the help of soft computing. In a reorganized electrical system, the major objectives of load frequency control (LFC) are to set up system frequency into acceptable limit, swiftly return the frequency to the setpoint, reduce tie-line power flow fluctuations across adjacent control zones, and track load demand agreements. To achieve LFC's goals, proportional integral derivative (PID) gain values must be tuned, for optimization purpose, soft computational methods are used in this present work. MATLAB/Simulink simulation results show that soft computing controllers can keep tie line power interchange within contracted constraints and frequency variation within the allowed range. This article compares auto tuned PID, genetic algorithm (GA), and particle swarm optimization (PSO) controllers in unregulated circumstances, load frequency regulation of two-area power systems
A simple method for controlling buck-boost SEPIC H-bridge inverter
Get PDFOver the past decade, much research and development has gone into the use of electric power converters, and the trend is upward. Inverters are employed when converting DC voltage to AC voltage. Typically, inverters perform functions such as voltage boost (to compensate for voltage decrease) or both voltage buck and boost. Other issues to consider include the structure of the inverter topology and the control method. Based on the problem, a study was conducted on a buck-boost inverter that integrates an H-bridge inverter and a single ended primary inductor converter (SEPIC). The H-bridge inverter is widely recognized for its simplicity of operation and always runs in buck mode. The SEPIC converter always runs in buck-boost mode. Since it is unipolar, it can operate as a buck or boost when combined with SEPIC AC-AC. The output voltage is significantly improved because it has several filters to enhance the signal. This hybrid topology is controlled by sinusoidal pulse width modulation, resulting in a straightforward control technique with outstanding performance. The H-bridge inverter operates in index modulation 0-100%, and the SEPIC converter more than 50%. In the lab, a computational and implementation procedure is used to test the effectiveness of the hybrid topology and control method under consideration. The test results show that the hybrid architecture can function within the desired parameters. The proposed inverter has 4.531% THD_V, 4.531% THD_I, and 97.85% efficiency under simulation
A novel reverse and forward directional relaying scheme in six phase overhead transmission lines using adaptive neuro-fuzzy inference system
Get PDFRecent power system is structurally difficult and is vulnerable to undesirable conditions like transmission faults. In this event of transmission line faults, exact fault zone detection enhances the restoration process, thus improving reliability of the complete power system. In order to solve the above problem, this paper presents an adaptive neuro-fuzzy inference system (ANFIS) based fault zone detector, which combines artificial neural network (ANN) and fuzzy logic technique (FLT) in six phase overhead transmission lines (SPOTL). To overcome the limitation of ANN and fuzzy expert system (FES) architectures and, the selection work has been formulated as an optimization method and solved using ANFIS. The inputs are the zero sequence component currents at the middle bus of the transmission line. The training data are extracted using discrete Fourier transform and collected, and then ANFIS is trained to identify the fault zone. The ANFIS based scheme reach setting has been checked for various types of faults, with a wide range of faults and transmission line parameters. Simulation study ensure that this method has a high reach setting, does not require the design of communication channel. Further, the ANFIS study shows that ANFIS is suitable for all type of faults. The ANFIS significantly outperforms other techniques proposed in the literature in terms of various evaluation metrics
Efficiency enhancement in hybrid renewable energy system using polycrystalline silicon cell
Get PDFAccessing the unelectrified rural population is currently not possible through grid expansion, as connectivity is neither economically viable nor encouraged by large companies. Additionally, conventional energy options, such as broom-based systems, are being gradually phased out of rural development programs because to growing oil prices and the unbearable effects of this energy source on consumers and the environment. A hybrid generator using solar and wind can solve this issue. Proven hybrid systems are the best choice for delivering high-quality power. Nowadays, hybrid renewable energy systems are becoming popular. The power system provides electricity to remote and isolated areas. Villages and residents in the forest area had their electricity cut off due to the forest environment. While creating a renewable energy source near the load. Solar power and wind power are renewable sources, solar power works in the morning and wind can make morning and night time to synchronize both output voltage and frequency to provide provides the ability to charge continuously, without interruption. The main objective of the project is to provide mixed renewable energy without interruption
Digital pseudo-random modulation: a key to EMI reduction in EVS boost converters
Get PDFPseudo-random position pulse modulation (RPPM) technique can be implemented either analogically using pseudo-random binary sequences (PRBS) to generate a pulse-width modulation (PWM) control signal or digitally through an Arduino Uno board. It plays a critical role in mitigating conducted electromagnetic emissions (EMI) in boost converters dedicated to electric vehicle systems (EVS) applications. The digital implementation offers a significant advantage by enabling a substantial widening of the frequency spectrum of the control signal. This expanded spectral range results in a noticeable reduction in emitted electromagnetic interference (EMI), making the digital method the preferred choice. The increased spectral bandwidth effectively mitigates EMI, which is particularly advantageous for EMI-sensitive EVS systems. In conclusion, the digital pseudo-random modulation approach, facilitated by Arduino Uno, proves to be more effective in reducing EMI in EVS boost converters. Its capability to broaden the control signal's frequency spectrum leads to a favorable reduction in emitted EMI, ultimately enhancing electromagnetic compatibility and overall system performance.
Transformation and future trends of smart grid using machine and deep learning: a state-of-the-art review
Get PDFA smart grid is a cutting-edge energy system designed to take over old-fashioned energy infrastructure in the twenty-first century. With comprehensive communication and computation capabilities, its primary objective is to increase energy distribution's dependability and efficiency while minimizing unfavorable effects. A number of approaches are needed for effective analysis and well-informed decision-making due to the massive infrastructure and integrated network of communications of the smart grid. In this study, we examine the architectural elements of the smart grid as well as the uses and methods using machine learning (ML) and deep learning (DL) with regard to the smart grid. We also clarify present research limitations and propose future directions for machine learning-driven data analytics. In order to improve the stability, reliability, security, efficiency, and responsiveness of the smart grid, this paper examines the implementation of several machine learning methodologies. This paper also covers some of the difficulties in putting machine learning solutions for smart grids into practice
An effective control approach of hybrid energy storage system based on moth flame optimization
Get PDFIn modern days, renewable sources increase the independence of urban energy infrastructures from remote sources and grids. In renewable energy systems (RES) systems, batteries are frequently used to close the power gap between the power supply and the load demand. Due to the variable behavior of RES and the fluctuating power requirements of the load, batteries frequently experience repeated deep cycles and uneven charging patterns. The battery's lifespan would be shortened by these actions, and increase the replacement cost. This research provides an effective control method for a solar-wind model with a battery-supercapacitor hybrid energy storage system in order to extend battery’s lives expectancy by lowering intermittent strain and high current need. Unlike traditional techniques, the suggested control scheme includes a low-pass filter (LPF) and a fuzzy logic controller (FLC). To begin, LPF reduces the fluctuating aspects of battery consumption. FLC lowers the battery's high current need while continuously monitoring the supercapacitor's level of charge. The moth flame optimization (MFO) optimizes the FLC's membership functions to get the best peak current attenuation in batteries. The proposed model is compared to standard control procedures namely rule based controller and filtration-based controller. When compared to the conventional system, the suggested method significantly reduces peak current and high power of the battery. Furthermore, when compared to standard control procedures, the suggested solution boosts supercapacitor utilization appreciably