International Journal of Power Electronics and Drive Systems (IJPEDS)
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Enhanced speed regulation using separate P and I gain controllers in a fuzzy-PI framework
Get PDFThis paper explores an enhanced method for regulating the speed of brushless DC (BLDC) motors using field-oriented control. Conventionally, a proportional-integral (PI) controller is employed to adjust output speed and current FOC method. While the PI controller is effective in many scenarios, it exhibits limitations including poor performance when the speed reference changes rapidly. To address these limitations, a fuzzy-PI control scheme is proposed in this study with the aim of improving the speed control performance of BLDC motors, especially under rapidly changing speed reference. The proposed two separate fuzzy logic controllers adaptively adjust the proportional and integral gains so that it combines the robustness of fuzzy logic with the steady-state error of PI control. Simulation and experimental results demonstrate that the fuzzy-PI control significantly outperforms the conventional PI controller in terms of BLDC stability, response time, and accuracy. The proposed approach ensures more reliable and efficient speed regulation for BLDC motors, making it a reliable solution for applications where speed reference fluctuate frequently
Design and analysis of brushless permanent magnet motor for light electrically powered two-wheeler vehicle
Get PDFThis study provides a comprehensive process of designing an electric motor that will be used for a small two-wheeled electric vehicle. Due to high performance capability in term of power and torque, brushless permanent magnet topology is chosen so that a compromise between size constraint and performance can be met. For an accurate motor design sizing, the design process is initially carried out by determination of power rating that derived from vehicle dynamic calculation. Based on winding factor calculation, fractional-slot 12-slot/10-pole and 9-slot/10-pole motors equipped with non-overlapping winding are chosen and analyzed using finite element analysis (FEA) software. For an optimum electromagnetic performance, parametric optimization is included, mainly on the stator dimension. Despite the performance of both designs improved, only 9-slot motor results a convincing performance as the rated torque is 18% higher than the 12-slot design. For verification purpose, 1-D analytical solution is also included and compared with results deduced by the FEA. According to the analysis, the proposed motor designs are adequately reliable for a light electrically powered electric vehicle application
Approach to self-synchronization of a group of static power converters
Get PDFThis study examines the control and synchronization of an orderly connected network of three-phase bidirectional power converters, serving as the grid interface for an energy storage system. The primary objective is to ensure stable operation under single-phase and non-symmetrical three-phase grid conditions. The control employs independent phase voltage regulation for compatibility. To achieve seamless coordination of an unlimited group of converters, the paper proposes a synchronization method based on a modified Kuramoto model. This method is designed to be compatible with independent phase control during asymmetric grid states. The proposed approach utilizes a structured connection graph, defined by phase shift magnitude, to synchronize the converter group. A brief overview of the tools for synchronizing oscillator groups is provided. A computer model was developed to study the operating modes of this converter class under both symmetrical and asymmetrical loads. Simulation studies confirmed the viability of the synchronization method. Furthermore, the research results were successfully applied in the design and implementation of a physical 10 kW grid - connected uninterruptible power supply prototype, demonstrating practical feasibility
Optimizing slow-charging EV loads with a two-layer strategy to enhance split-phase voltage quality and mitigate issues in PDNs
Get PDFPower distribution networks (PDN) were mostly affected by the voltage imbalances created by the slow charging of electric vehicles (EV), were there random load into the PDN system, causing split-phase voltage quality (SPVQ) issues. Hence, to mitigate the problems associated with EVs’ slow charge in distributed phases of the power system, a multi-layer charging strategy is proposed considering the following constraints in the system: voltage deviation (VD) and voltage harmonics (VH) in split phase (SP). Further multi-layer control is associated with an inner layer equipped with hybrid non-dominated sorting genetic algorithm (NSGA-II) to select the optimal phase for charging the EV and send it to the output layer where a SP current algorithm is utilized so that voltage quality can be fed in loop to inner layer so that iterations were performed to satisfy the convergence condition. Simulation results in MATLAB demonstrate a voltage unbalance (VU) reduction of up to 32.81%, a maximum VD reduction of 9.11%, and a VH reduction of 6.25% at key grid nodes. The proposed method significantly enhances PDN efficiency and maintains voltage quality within national standards across 1,000 to 5,000 EV connections. The generated results reflected the optimal improvement in SPVQ, and the harmonics content reduced further; PDN operational efficiency also improved to a greater extent
Development of a PEM fuel cell equivalent circuit model with PINN-based parameter identification
Get PDFThis paper presents a novel equivalent electrical circuit model for proton exchange membrane fuel cells (PEMFCs) and introduces a physics-informed neural network (PINN) algorithm for parameter identification. The proposed model provides a more accurate representation of the fuel cell’s dynamic behavior while maintaining computational efficiency. Unlike conventional methods, the PINN framework integrates physical constraints with data-driven learning, ensuring physically consistent parameter estimation. To validate its effectiveness, the proposed model is compared with the widely used RC equivalent circuit and a generic PEMFC model. Experimental data from a 1.2 kW PEMFC test bench serve as a benchmark for evaluating the transient and steady-state performance of each modeling approach. Results demonstrate that the proposed circuit, combined with PINN-based identification, yields enhanced accuracy in predicting voltage response under various operating conditions. Additionally, the model exhibits improved adaptability to transient phenomena compared to conventional equivalent circuits. These findings highlight the potential of physics-informed machine learning for advancing fuel cell modeling and control strategies
Artificial raindrop algorithm for control of frequency in a networked power system
Get PDFLoad frequency control (LFC) evaluates the net changes in generation by continuously monitoring tie-line flows and system frequency required relying on load changes. It adjusts generator set points to minimize the area control error's (ACE) time-averaged value. ACE is regarded as a controlled output of LFC. Previous research focused on customary power systems like hydro-hydro, thermal-thermal, and hydro-thermal configurations. This current development study introduces the hybrid PV and dual thermal system interconnected systems for LFC analysis. The research evaluates LFC performance with different controllers, considering parameters such as maximum peak overshoot (Mp), maximum undershoot (Mu), settling time (Ts), and peak time (Tp). Controllers, including proportional integral (PI), anti-windup PI, fuzzy gain scheduling PI, and A cutting-edge algorithm generating fake raindrops are used for minimize ACE. The analysis introduces various load perturbations to observe controller performance in interconnected power systems. Both PV-thermal-thermal and thermal-thermal-thermal systems exemplify innovative approaches to energy management that bolster energy efficiency and sustainability. By integrating these advanced systems, we can make significant strides towards achieving global sustainability goals and promoting a cleaner and support energy efficiency for the future
A novel temperature parametric method for rapid maximum power point detection in photovoltaic modules
Get PDFPhotovoltaic systems (PVS) exhibit variability in their maximum power point (MPP) output due to variations in irradiance and cell temperature. This can lead to reduced efficiency, as maximum power point tracking (MPPT) algorithms often have slow response times and limited ability to adapt to rapidly changing environmental conditions. New algorithms are therefore needed to capture more energy and improve the efficiency of these systems. In this context, this article presents a new method for temperature parametric (TP) and its implementation using a digital PI controller, a buck converter, and MATLAB-Simulink. This innovative approach relies on detecting the MPP by continuously measuring the cell temperature of the PV panel () and solar irradiance (S). A 3D linear regression model connects these two parameters with the maximum current (), enabling real-time monitoring of the MPP. We have applied this new method on two different types of PV (POLY-40W and BPSX330J) under a range of environmental conditions, including stable and dynamic scenarios. The results of the simulation demonstrate the superiority of our approach compared to the hill climbing (HC) for perturbation steps of HC (1%) and HC (2%). Our method achieves faster convergence time 0.009 s and high MPPT efficiency at 98.18%, fewer steady-state oscillations, and better detection
Set up of a secondary on-board charger fed by PV DC station with grid injection for fast charging the recent city cars
Get PDFIn this article, we propose a secondary high-power charger provided by a photovoltaic source with grid connection. The high-power charger is composed of a phase shift full bridge (PSFB) DC-DC converter controlled by a constant voltage constant current algorithm based on PI control. The first stage is composed of a PV panel source, controlled by a fuzzy logic using a maximum power point tracking (MPPT) algorithm, associated with a synchronous boost converter to set up the voltage at the standard common 400 V bus level. While the charger is a second stage composed of a phase shift converter for step down and adapting the voltage and the charging current for the battery of the urban electric car. We have also proposed the grid connection with a simple optimization to inject the generated power into the electrical grid when no car is connected to the power station. To achieve this goal, simulation results of the proposed configuration control techniques by using the MATLAB/Simulink environment are presented and discussed at the end of this paper
Machine learning techniques for solar energy generation prediction in photovoltaic systems
Get PDFFor photovoltaic (PV) systems to be as effective and dependable as they possibly can be, it is vital to make an accurate prediction of the amount of power that will be generated by the sun. Using machine learning, it is now much simpler to forecast the amount of solar energy that will be generated. These approaches are more accurate and are able to adapt to the ever changing conditions of the nature of the environment. We take a look at the most recent machine learning algorithms for predicting solar energy and examine their methodology, as well as their strengths and drawbacks, in this paper. Using performance metrics like root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) makes it possible to evaluate important algorithms like support vector machines, decision trees, and linear regression. The results show that machine learning could help make predictions more accurate, lower the amount of uncertainty in operations, and help people make decisions in real time for PV systems. The study also points out important areas where research is lacking and suggests ways to move forward with the use of machine learning in systems that produce renewable energy
Single stage boost cascaded multilevel inverter for photovoltaic applications
Get PDFThis article discusses a high-gain five-level SL-SC-based cascaded multilevel qSBI (qSBMLI) for photovoltaic applications. A combination of switched inductor and switched capacitor structure produces a boost at high levels. Two identical SL-SC-based qSBI modules are cascaded and powered with two stiff DC voltage sources of 18 V each. The DC voltage of 18 V obtained from two different DC voltage sources is applied to each module. An 18 V DC voltage is supplied to a single module-A, which produces a DC link voltage (VPN) of about 240 V at the inverter's input side. The modulation index (MI) is selected as 0.68, and the duty ratio is kept at 0.3. The boost factor is obtained as 13.3, and the load voltage of 150 V is achieved across the resistive load. Hence, the voltage gain is 6.9. The proposed topology delivers 337 W of power to the load at an efficiency of 73%. The complete circuit topology and its operations are analyzed in MATLAB/Simulink. The control signals for the power switches are produced using the field programmable gate array (FPGA) SPARTAN 3E Kit. When the proposed circuits are analyzed and compared with the existing classical topologies, the proposed one shows the superior performance