Measurement, Control, and Automation

Combination of Analytical Model and Finite Element Method for Designing High Voltage Shunt Reactors

2023-12-25T09:05:11+07:00

As we have known, shunt reactors (SRs) play a pivotal role in addressing various power quality challenges. These SRs are specifically engineered to address a range of issues in electrical systems, such as mitigating reactive power, controlling high-voltage grid fluctuations, suppressing power frequency variations, regulating overvoltage conditions, eliminating generator excitation problems, dynamically compensating for transmission line power losses, suppressing secondary arc currents, and dampening system resonance. To reduce harmonic currents appearing in the electrical power system, and minimize losses. the choice of core material, the deliberate introduction of air gaps within the reactor core, and the proper sizing of the reactor are all critical factors. In this research, an analytical design is present to define the main dimensions, and magnetic field quantities (such as inductance, fringing flux, current, voltage) of the SRs with the variation of air gap. Then, a finite element approach is developed to simulate and verify the obtained results from the analytical model. In addition, the impact of air gap dimensions within the core on the magnetic field and inductance values is also determined, providing valuable insights into the reactor's performance. The development of the methods is validated on the SR of 16 MVar, and a voltage of 500/√3.

Start-up Procedure for the Three-Phase Four-Leg Inverter in an AC Battery application

2023-11-02T21:32:29+07:00

Three-Phase Four-Leg Active Rectifier is getting so much attention due to its ability to deal with unbalanced AC voltage sources that can be caused by grid/load faults. Recently, the flexibility of this converter to connect both the 1-phase and 3-phase grid systems in a home battery application has further concern. A vast amount of literature that deals with this topology is exclusively focused on various modulation methods and control structures aimed at optimizing different aspects of its steady-state and/or transient performance. However, no literature is available on its start-up procedure which is an indispensable part of the control design of any practical circuit. To fill this gap, in this paper, a three-step start-up procedure for a three-phase four-leg active rectifier in an AC Battery is proposed. The primary goal is to ensure that the inrush currents stay below a specific level while the output voltage increases to its reference value with a small overshoot. The proposed start-up procedure is illustrated via Hardware-in-the-loop (HIL) simulation results, which improves the feasibility of future experiments.

A Comparative Study of Machine Learning–based Models for Short-Term Multi-step Forecasting of Solar Power: An Application for Nhi Ha Solar Farm

2023-11-22T10:22:08+07:00

Over the past few decades, the utilization of solar power has gained immense significance in the power grid, gradually taking over the responsibilities of fossil fuel-based power. Therefore, accurate short-term forecasting of photovoltaic power output is crucial for making informed decisions regarding power generation, transmission, and distribution. Consequently, many machine-learning models were used to reliably forecast solar power. In this study, four machine learning models have been studied which are Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long short-term memory (LSTM) and Extreme learning machine (ELM). They have been used to forecast the solar power of Nhi Ha solar farm in short-term. First, data from Nhi Ha solar farm were collected and underwent preprocessing before being utilized by distinct machine learning models. The Root Mean Squared Error (RMSE) and normalized RMSE (N-RMSE) obtained from the models will be analyzed to determine the most effective model for short-term solar power forecasting. Following a comprehensive analysis, it has been determined that all four models have produced favorable outcomes, with low values of RMSE and N-RMSE indicating high levels of reliability and accuracy. Of the models considered, the LSTM and ELM models have demonstrated superior performance, making them the optimal choice for precise and dependable short-term solar power forecasting.

Lyapunov-based Design of a Model Reference Adaptive Control for Half-Car Active Suspension Systems

2023-12-25T09:10:29+07:00

This paper proposes a model reference adaptive control (MRAC) design for vehicle active suspension systems with unknown spring stiffness and damping coefficients with the aim of improving the performance of suspension systems. A mathematical model of a half-car active suspension system is first presented, then a reference model of a half-car suspension system that utilizes the skyhook damping concept is proposed. The control law and adaptation laws for the MRAC that guarantee the dynamic responses of the active suspension system follow well the dynamic responses of the reference model, were derived based on the Lyapunov stability criteria. To evaluate the advantages of the proposed reference model as well as the efficiency of the designed MRAC, the dynamic responses of the reference model, passive, and active suspension systems were analyzed and evaluated both in the frequency and time domains. The obtained results demonstrated that the active suspension system with MRAC provides better performance in terms of ride comfort and suspension deflection compared with the passive suspension system

Parameter Optimization of Sliding Mode Controller for Tower Crane Using Particle Swarm Optimization Algorithm

2024-01-21T20:11:45+07:00

Tower cranes find extensive application in the construction, ports, and industrial sectors for efficiently managing the transportation of heavy loads. However, operators face potential risks due to oscillations that occur during load movements. This not only diminishes operational efficiency but also poses significant hazards. Consequently, the control of tower cranes becomes a formidable challenge. To address this issue, various studies have been proposed, with particular attention given to the use of Sliding Mode Control (SMC). Vibrations caused by tower cranes have been mitigated by these studies. However, with SMC controllers for tower cranes, the problem of optimal parameter selection has not been adequately addressed by existing research. In this paper, a Particle Swarm Optimization (PSO) algorithm is used in conjunction with an SMC controller to determine the optimal parameter set for tower crane systems. A hierarchical sliding mode controller (HSMC) is utilized to control the position and minimize load oscillations. The PSO algorithm is applied to optimize the position settling time and angular deviation of the load. The SMC controller with the obtained optimal parameters achieves superior performance in tower crane systems, as demonstrated in simulations and experiments.

Online Inductances Estimation of the Permanent Magnet Synchronous Machines based on Deep Learning and Recursive Least Square Algorithms

2024-03-07T20:27:31+07:00

This paper presents a novel method to identify in real time d- and q- axes inductances of the permanent magnet synchronous machines (PMSM), which are normally vary during the operation due to the saturation of the magnetic fields. The proposed method is based on the combination of deep learning and recursive least square algorithms. The deep learning model is trained offline in order to compensate the non-linearity effect of the voltage source inverter, while the recursive least square algorithm was employed to estimate online d- and q- axes inductances based on the compensated d- and q- axes stator voltages, measured d- and q- axes stator currents and the operating speed. The proposed methods can overcome the problems associated with the existing methods known as effect of noise, unavailability of accurate information of the inverter. Extensive experimental studies were conducted to evaluate the estimation accuracy and the robustness of the proposed method in the critical operating conditions including the variation of load torque, operating speed and field-weakening conditions.

Vibration Suppression Control for Overhead Crane using Inverse Notch Filter

2023-12-27T20:29:53+07:00

Overhead cranes play an important role in many fields such as industry, transportation, etc. However, payload vibration is a common issue that can occur with overhead cranes. To overcome this problem, various control approaches have been applied including feedforward and feedback control approaches. One practical approach is feedforward control where the controller works as a filter to eliminate the vibration. The feedforward controller can exist in three structures including the filter is outside the trolley position control loop, in the feedforward path of the trolley position control loop, and in the feedback path of the trolley position control loop. In this paper, the inverse Notch filter in the feedback path of the trolley position control loop is considered as a vibration suppression controller for the overhead crane. This proposed controller can suppress vibration caused by both reference trolley position input and disturbance in the feedback loop. The effectiveness of the proposed controller is verified via simulation and experiments.

Improving Efficient Smart Management of Power Transmission Network Using BIM Technology

2024-02-28T21:58:47+07:00

The advent of Building Information Modeling (BIM) technology has revolutionized infrastructure projects, including electrical systems.This research explores the transformative impact of Building Information Modeling (BIM) technology on infrastructure projects, with a specific focus on optimizing critical components within power grids, such as transmission lines and substations. The study advocates for the seamless integration of BIM, creating a digital modeling that encompasses various input data, from geometry to costs. This integrated approach enhances real-time collaboration, mitigates errors during design, and streamlines construction processes by aiding scheduling, resource allocation, and progress monitoring. Additionally, the research extends its scope to the integration of BIM in MicroGrids (MGs), highlighting the potential to enhance efficiency and reliability in these decentralized power networks. By bridging the gap between macro and micro perspectives, the study aims to contribute to the advancement of SmartGrid (SG) frameworks for more sustainable and efficient energy distribution

Stability analysis of I-f startup method for PMSM drive systems

2024-01-18T08:55:00+07:00

In low-cost permanent magnet synchronous motor (PMSM) drive systems, the rotor position is usually estimated based on the back-electromotive force. However, such estimation methods have the common drawback of having large errors in low-speed regions. In order to solve that drawback in the start-up phase, the I-f start-up method is studied as its compatibility with the FOC control structure. The purpose of such a method is to make the motor accelerate and overcome the low-speed threshold. The process of implementing the I-f startup algorithm is divided into two stages, the first one is acceleration stage and the second one is smooth transition stage. During the acceleration phase, the motor speed is controlled using a constant-magnitude current vector located on a virtual rotating reference frame. Analyzes of possible oscillations and causes of system instability during the start-up phase are clarified in this study. The requirement of the parameters of the virtual rotating reference frame and the current vector for ensuring the acceleration process is under control is presented. The smooth transition stage is added so that large peak current pulses do not occur when switching from start-up mode to closed-loop control mode. The algorithm for reducing the virtual current vector magnitude by a simple linear ramp function is shown in this paper. The analysis is verified through the simulation results.