International Journal of Applied Power Engineering (IJAPE)
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Metaheuristic algorithms for parameter estimation of DC servo motors with quantized sensor measurements
Get PDFManufacturing, aviation, and robotics have increased servo motor use due to their precision, reliability, and adaptability in various applications. This study compares three metaheuristic techniques for servo motor model parameter estimation with sensor measurement quantization, focusing on their accuracy and efficiency. Armature resistance, back electromotive force (EMF) constant, torque constant, coil inductance, friction coefficient, and rotor-load inertia are crucial to servo motor behavior prediction, significantly impacting overall system performance. Each approach was rigorously tested and analyzed to evaluate its effectiveness in predicting servo motor characteristics. The results revealed that particle swarm optimization and the firefly algorithm delivered comparable performance, particularly excelling in scenarios where sensor measurement quantization introduced noise or imprecision in the data. These methods demonstrated strong resilience and accuracy under such challenging conditions. In contrast, the genetic algorithm did not perform as well, falling short when compared to the other two techniques in handling noisy or imprecise data, indicating its relative inefficiency in such environments. These findings give servo motor designers and engineers across industries a powerful tool for performance prediction
Optimal allocation of PV units using metaheuristic optimization considering PEVs charging demand
Get PDFThe distribution system is seeing a dramatic shift as a result of the increasing use of distributed generators (DGs) and plug-in electric vehicles (PEVs), or plug-in hybrid electric cars. The research endeavors to optimize the allocation of photovoltaic (PV) based DGs within radial distribution systems (RDS) while accommodating the load demand stemming from PEVs. A weighted-sum based multiobjective (WMO) technique is employed in this study to optimize three fundamental technical metrics of the distribution network: achieving the best possible voltage stability index (VSI) while reducing real power loss and total voltage variation to a minimum. Initially, the study investigates the impact of both conventional and PEVs load demand, considering PEVs load demand on distribution system performance under three charging scenarios: a situation involving peak charging, scenario involving off-peak charging, and scene of random charging. Subsequently, PV units are strategically planned, taking into account the PEVs demand within the distribution system utilizing an innovative weighted multiobjective electric eel foraging optimization (WMOEEFO) algorithm, its effectuality is validated with weighted multiobjective differential evolutionary (WMODE) and weighted multiobjective grey wolf optimization (WMOGWO) algorithms on standard test system IEEE 33-bus
State-augmented adaptive sliding-mode observer for estimation of state of charge and measurement fault in lithium-ion batteries
Get PDFEstimating the state of charge (SoC) in lithium-ion batteries (LiB) encounters challenges due to model uncertainties and sensor measurement errors. To solve this issue, this study introduces an estimator based on an innovative adaptive augmented sliding mode approach. This approach incorporates measurement faults as additional state variables to minimize the impacts of uncertainties effectively. Furthermore, based on the sliding mode framework, the design of this estimator addresses resistance to model uncertainties. However, sliding estimators commonly face the chattering issue. To counteract this, the paper suggests employing adaptive dynamics to determine the estimator's gain. This adaptive approach allows the gain calculation to minimize estimation errors across all time steps, effectively reducing chattering and enhancing estimation accuracy. The performance of the proposed method is validated through simulations using two practical data sets. Results demonstrate superior accuracy compared to conventional sliding methods, with improvements in SoC and terminal voltage estimation
Optimization and dimensioning of stand-alone systems: enhancing MPPT efficiency through DLGA integration
Get PDFThis paper explores optimizing and sizing stand-alone solar power systems using an intelligent maximum power point tracking (MPPT) method, enhanced by artificial neural networks (ANN). The study focuses on both system sizing and energy optimization, integrating genetic algorithms (GA) with deep learning (DL) to optimize the architecture of the ANN for improved performance in predicting solar energy output. The hybrid method, deep learning genetic algorithms (DLGA), efficiently reduces computational complexity and enhances flexibility through parameter tuning, significantly improving the performance of multi-layer perceptron networks. Additionally, a precise sizing methodology based on solar irradiance data was implemented to ensure the system is neither oversized nor undersized. The system's performance was tested and validated using MATLAB/Simulink simulations, which demonstrated superior predictive accuracy, faster convergence, and optimized energy capture. This combined approach of intelligent MPPT and accurate sizing presents a highly effective solution for improving the efficiency and reliability of stand-alone solar energy systems under varying environmental conditions
Voltage profile enhancement in grid system using expert system
Get PDFFrequent and severe blackouts are been attributed to insufficient voltage stability, resulting in voltage collapse. To mitigate this issue and ensure adequate voltage stability and damping in power systems, this study explores smart grid solutions. The proposed control strategies are applied to a distribution static synchronous compensator (DSTATCOM) within a multi-machine system. The recommended approach, radial basis function neural network (RBFNN)-DSTATCOM with support vector machine (SVM), incorporates a PI controller to minimize system deviations. The damping performance of the RBFNN-DSTATCOM controller is analyzed against a fixed-parameter proportional-integral (PI)-DSTATCOM controller. Simulation analysis indicates that the proposed RBFNN-DSTATCOM controller effectively enhances power system stability under various disturbances and operating conditions. Critical bus graphs are provided for scenarios both with and without the DSTATCOM. A parametric evaluation is conducted using the 'powergui' toolbox based on the system's standard ratings. Finally, a comparative analysis is presented, utilizing the results from both systems, with all graphs plotted against time using the power system analysis toolbox (PSAT) in MATLAB
A novel fast MPPT strategy with high efficiency for fast changing irradiance in PV systems
Get PDFThis paper discusses about the photovoltaic (PV) system novel non-iterative maximum power point tracking algorithm with faster converging speed under varying solar irradiation level. PV system is a scattered renewable energy resource and a safe environmental energy source. However, the PV power oscillates around MPP value due to the fluctuations of temperature and insolation effects, leading to nonlinear maximum power tracking issues. For each change in atmospheric condition, output of the PV system changes necessitating the need to search for new maximum power conditions. An efficient maximum power point tracking (MPPT) device that improves the power transmitting efficiency along with a suitable high frequency direct current (DC) to DC power converter device are required for efficient operation. Finally, a comparison is made between existing MPPT algorithms and proposed novel non-iterative MPPT algorithm. The proposed MPPT system show that the overall tracking speed of the proposed MPPT is 5.6 times, 3.8 times faster than perturb and observe (P&O) method and INC method respectively. During the variation of irradiance, the power loss is reduced by 18.84% and 11.29% in comparison with P&O and INC method. The proposed method also minimizes the steady state oscillations
Performance enhancement using sensor and sensorless control techniques for a modified bridgeless Ćuk converter-based BLDC motor in EV applications
Get PDFThis work proposes a solar photovoltaic (PV)-powered, modified bridgeless Ćuk converter tailored for electric vehicle applications. It overcomes limitations such as high ripple, reduced power density, significant switching losses, and complex circuit structures in traditional designs. The system integrates a boost converter with a bridgeless Ćuk topology to ensure a reliable and efficient direct current (DC) power output. Performance evaluation includes sensor-based and sensorless speed control techniques-pulse width modulation (PWM), proportional integral derivative (PID), back electromotive force (EMF), and spider controllers-under both no-load and full-load scenarios. Key parameters such as rise time, overshoot, settling time, and steady-state error are analyzed. MATLAB/Simulink simulations indicate that the spider controller delivers superior dynamic behavior and stability. A 48 W, 1500 rpm hardware prototype confirms the simulation outcomes, demonstrating the practical viability and effectiveness of the proposed converter
Integration and optimization of grid through ANN-based solar MPPT and battery
Get PDFIntegration of solar energy into the grid is the most important aspect for achieving sustainable energy systems. This paper presents an artificial neural network-based maximum power point tracking (ANN-MPPT) system with battery storage to enhance grid efficiency. The proposed ANN-MPPT is dynamically adapted to the varying irradiance and temperature, hence ensuring optimal power extraction from the photovoltaic system. Excess energy is stored in batteries during high solar radiation and discharged when solar generation is low or grid demand is high, maintaining a stable power supply. This system enhances the grid performance in terms of supporting real-time energy exchange, load balancing, and grid stability. Efficient management of the energy fluctuations ensures reliability even at times of grid failures. Further, integration of ANN-based MPPT with battery storage reduces dependence on non-renewable sources and harmonizes solar energy utilization. It can be achieved through enabling smarter energy management and thus contributing to the resilience and efficiency of a grid for better integration of renewable energies. The proposed system can tolerate fluctuating grid demands apart from supporting the features of smart grid, hence viable for increasing stability and sustainability in the grid
Solar-powered bidirectional charging of electric vehicle
Get PDFSolar-powered bidirectional charging of an electric vehicle has three different modes of operation. The first mode of operation is “solar-powered electric vehicle charging” in which the vehicle is charged with solar energy. The second mode of operation is “grid-powered electric vehicle charging” which charges the vehicle in the absence of solar energy. The third mode of operation is “vehicle supplying to the grid” and in this mode, the vehicle energy is transferred back to the grid when there is demand to charge the other electric vehicles connected to the same grid. The system uses maximum power point tracking (MPPT) to improve power extraction from solar panels under standard test cell conditions, allowing for effective charging of electric cars. It also uses a proportional-integral (PI) controller to continually monitor the battery's state of charge (SOC). This controller modulates the duty cycle of pulse width modulation (PWM), which regulates the charging current. The charging system includes a buck-boost converter, which functions as a buck converter while supplying grid voltage to the vehicle, and a boost converter in supplying excess voltage of the vehicle to the grid. For three different modes of operation, the battery parameters such as voltage, current, and charging state are presented. The grid voltage and current are observed for the last two modes of operation
Powering the future of electrical load forecasting using a regression learner in machine learning
Get PDFThe primary intent of the present research was to design and execute an electrical load forecasting system using machine learning (ML) techniques. The implementation of an advanced predictive method, specifically an ML algorithm, helped in accurate load forecasting, which is crucial for efficient power grid management, and optimizing resource allocation. Electricity load fluctuates due to various complex factors, making traditional forecasting methods struggle. This is where ML shines. ML algorithms can learn from historical data, identifying intricate patterns and relationships that influence electricity demand. This allows them to make more accurate predictions than static models. In this work, regression learning models in ML are used with the MATLAB platform. Three years of real-time data from the Wavi substation in India are used. Considering day, date, hour of day, max and min temperature of the day, and voltage and current are taken as input parameters to test fourteen different models of assorted regression algorithms. The performance of these models is evaluated using commonly used metrics, root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE), along with a few other parameters. The optimized trained model is then tested with real data to obtain the forecasted load. The correlation between the Actual load and forecasted load is found to be 0.999962