International Journal of Applied Power Engineering (IJAPE)
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Investigating the effects of corrosion parameters on the surface resistivity of transformer’s insulating paper using a two-level factorial design
Get PDFThe integrity of the insulation in oil-filled power transformers, shunt reactors, and high voltage bushings can be affected when copper dissolves in the insulating oil and then deposits onto the paper insulation. The presence of dissolved copper in the oil increases dielectric losses, while copper deposition significantly improves the conductivity of the paper insulation. Various factors, including temperature, oxygen, sulfur groups, passivators, and ageing time, have been found to contribute to the acceleration of corrosion activity in transformer insulating oils. Unfortunately, there is a lack of extensive research focused on systematically analysing and measuring the impact of corrosion-related factors on the dissolution of copper in transformer insulating oils and the deposition of copper onto solid insulation surfaces (Kraft paper). Therefore, this study aims to thoroughly examine the effects of corrosion factors on copper and sulfur deposition on Kraft paper insulation when it is submerged in transformer mineral oil (TMO). Using a two-level (2k) factorial design, we investigated three crucial factors: i) oil temperature, ii) elemental sulfur concentration, and iii) ageing time. It is worth mentioning that the results obtained from the two-level factorial design indicate that the surface resistivity is primarily affected by the temperature of the oil. This factor alone explains a significant 38.68% of the observed variation. In order to improve predictability, a regression model was created to estimate the surface resistivity of TMO-impregnated paper insulation. This model takes into account factors such as oil temperature, elemental sulfur concentration, and ageing time
Coupled inductor interleaved boost converter with ANN and RNN based MPPT algorithm for PV system
Get PDFAn efficient machine learning approach for accomplishing the maximum power point tracking (MPPT) system for photovoltaic (PV) systems is proposed in this work. PV system is one of the most suitable renewable energy sources (RES) for electric vehicles (EV) based operations due to its ubiquitous availability and speed of installation. The deployment of PV-powered EVs reduces the quantity of carbon dioxide emitted into the atmosphere substantially. The primary objective of this research is to integrate a PV system with an EV load and to provide a constant power supply to the EV load with no distortions. A coupled inductor interleaved boost converter is used to raise voltage level of the PV because it has a wide conversions range with low leakage reaction times. Furthermore, the converter produces a consistent output with no fluctuations and high voltage gain. With the application of artificial neural network (ANN) based MPPT and recurrent neural network (RNN) based MPPT, the converter operational performance enhanced with steady dc link voltage is obtained. Consequently, it is stated that the employment of ANN and RNN-based MPPT controllers in PV-based systems offers improved DC link voltage regulation and lower power losses, thereby boosting system effectiveness. The MATLAB platform is used to test every component of the system's performance, and the findings show that the proposed system is more efficient than alternative approaches
Reduction of torque ripples using the DTC-SVM method in PMSM with extended Kalman filter
Get PDFA detailed analysis has been conducted on two motor control algorithms: direct torque control (DTC) and field-oriented control (FOC). There are two ways that a voltage source inverter (VSI) can regulate a permanent magnet synchronous motor (PMSM). When using the PMSM and voltage source inverter (VSI), dead time is employed to turn off both the upper and lower switches to prevent short circuits. However, by supplying the PMSM with unexpected polarity voltages at the VSI output voltage, this switching technique reduces distortion. It is challenging to utilize the sensor to directly detect the fault voltage that results in an open circuit. This work examines the nonlinearity of the electric power controller during dead time during PMSM operation using the DTC algorithm to increase control stability. The stress distribution is estimated using an extended Kalman filter (EKF). Ultimately, the model presented in this study verified the increase in stator current and torque output through simulations and testing
A non-isolated PFC bridgeless SEPIC-Cuk converter with adaptive PI controller for induction motor
Get PDFIn general, the induction motor (IM) is extremely nonlinear in nature and frequency dependent. In most cases, the power generated by the IM has a low power factor (PF), which exhibits detrimental effect on the extent to which the whole transmission and distribution system functions. Since there exists more current harmonics as an outcome of minimized PF, the efficiency of the power system suffers due to transmission line heating and voltage distortion characteristics. Therefore, this paper proposes a power factor correction (PFC) method to overcome the aforementioned issues. Here, by the utilization of AC-DC bridgeless SEPIC-Cuk converter, the power quality is improved by reducing reactive power consumption and enabling better control of voltage and current outputs. To maintain the stable DC link voltage with reduced ripples, the adaptive proportional-integral (PI) controller is used in this work. The three-phase voltage source inverter (VSI) transitioning function is controlled by cascaded fuzzy logic (CFL) controller, which is also utilized for regulating the speed of the three-phase IM. Implementing the proposed control strategy improves power quality significantly by reducing total harmonic distortion (THD). The proposed system is simulated in the MATLAB platform and the attained outcomes, it is clear that the proposed system is highly effective
Reliability analysis of an automated radial distribution feeder for different configurations and considering the effect of forecasted electrical vehicle charging stations
Get PDFIn the future, the expansion of electrical vehicles is becoming more prevalent, which requires electric vehicle charging stations (EVCS), and at the same time, distribution automation and smart grid technology will be implemented as part of the reforms in the distribution system. This paper reviews the effect of the increased EVCS, which causes an increase in the magnitude of current and moderates the average failure rate of feeder sections. The implementation of distribution automation and a smart grid reduces the average restoration time, thereby increasing the reliability of the distribution system. The number of electrical vehicles (EVs) for the years 2025 and 2030 is forecasted using Holt's model, and the corresponding average failure rate of feeder sections is calculated. The average switching time for adopting distribution automation and smart grid technology is taken as 5 seconds and 20 milliseconds, respectively. The voltages, power losses, and reliability indices are calculated assuming the EV charging points are located with equal capacity at all load buses for different configurations of radial feeders. The results are compared with the reliability indices of the feeder of all the configurations in the absence of EV charging station loads, automation, and smart grid technology. This work is validated on a standard IEEE 33 test bus system
Voltage stability index: a review based on analytical method, formulation and comparison in renewable dominated power system
Get PDFIn an interconnected complex power system network voltage stability evaluation is indispensable to guarantee secure power system operation. To further increase the system performance and to make the system safer assessment, the voltage stability improvement is obligatory. In the various literature different voltage security assessment method using the voltage stability index has been presented. Different lists proposed in literature can be utilized to realize the weak buses and weak transmission lines to enacting the countermeasures against issues of voltage insecurity. Additionally, the arrangement and measuring of inexhaustible assets, on the web and disconnected observing of force framework and the measure of load to be shed at whatever point essential. This paper shows a survey on the different voltage stability index from various perspectives. The audit results on various record gives a far and wide logical to perceive the impending works in this field and to choose the best index for variety of applications such as voltage security assessment, renewable energy integration, distributed generation (DG) placement and sizing, online monitoring of the power system, and shedding of load
Performance analysis of CKF algorithm for battery SoC estimation with its aging effect
Get PDFThe penetration of electric vehicle (EV) in automobile market is very much dependent on the battery technology. Its size, weight, and cost are issues of concern. To effectively utilize the battery expertise, precise estimate of state of charge (SoC) is vital which greatly depends on the battery model. Current models lack consideration for variations in battery capacity over their lifespan. This paper develops a battery model which depicts the depletion of battery capacity with its life. Subsequently, this model has been utilized for estimation using advanced Kalman filtering (KF) algorithms. For the developed model, the design and effectiveness of the cubature Kalman filter (CKF) is applied as a proposed robust state-estimator for this problem. Moreover, a comparative analysis was undertaken with existing non-linear KFs based on performance metrics. The optimal choice of estimator is identified, through the results obtained from the Octave/MATLAB simulation. The outcomes show CKF algorithm based SoC estimator is superior to others in ensuring high accuracy, strong robustness even under changes in initial conditions (i.e., initial SoC, process and sensor noise levels), system's ability to converge quickly while ensuring that the maximum error in state of charge (SoC) estimation remains within 1% after convergence
Phase-locked loop based synchronization schemes for three-phase unbalanced and distorted grid: a review
Get PDFThe rapidly growing dispersion of distributed generation systems into the utility grid needs appropriate control techniques to stay interconnected even under abnormal and distorted grid conditions to ensure the overall grid stability. To avoid the loss of renewable energy sources (RES) based power generation, the disintegration of RES with respect to synchronization issues must be prohibited for efficient operation. RES control highly relies on the synchronization technique as it is faster and accurate enough to detect the utility grid variables in terms of amplitude, phase, and frequency. Mostly, the phase-locked loop (PLL) synchronization schemes are utilized for control of RES and monitoring of grid voltage. The dynamics of the grid side converter (GSC) is directly influenced by the performance and design criteria of the PLLs. This paper proposes an overall review of the performances of three phase PLL based grid synchronization methods under diverse weak grid conditions
Maximizing energy efficiency in drones through accurate state of charge estimation using extended Kalman filter
Get PDFThis paper delves into the critical aspect of managing energy consumption in drone operations to achieve the utmost range and ensure accurate state of charge (SoC) estimation. Effective energy management is pivotal in determining the operational range of drones, allowing for longer distances and heavier payloads. The integration of precise energy estimation algorithms into operational planning extends the range of drones, facilitating swift, environmentally-conscious missions for sustainable and efficient logistics solutions. The paper introduces a mathematical model to understand energy consumption and battery behavior in drones, utilizing the hybrid pulse power characterization test and recursive least square with forgetting factor for parameter identification. To overcome the limitations of linear filters, the paper employs the accurate extended Kalman filter (EKF) in the nonlinear filter section. The EKF significantly enhances the battery management system by furnishing precise SoC data. The study evaluates two SoC estimation techniques: SoC-AH (ampere-hours) and SoC_EKF, using root mean square error for comparison. The SoC_EKF technique demonstrates higher accuracy, boasting a lower errors value of 0.78%, thus making it superior for precise drone battery SoC estimation. These findings contribute to the improved performance, reliability, and overall safety of drones
ANFIS-based optimisation for achieving the maximum torque per ampere in induction motor drive with conventional PI
Get PDFThis research presents an innovative approach to controlling the speed of an induction motor drive by utilizing a combination of neural networks and fuzzy inference systems (ANFIS). The study focuses on computing the rotor's magnetic flux while considering different overshoot and settling criteria for torque and motor speed. The goal is to optimize torque per ampere and generate the necessary torque. The proposed ANFIS-based torque-per-ampere control technique offers a distinctive method applicable to a static induction motor model. This method allows for an increase in stator current while maintaining flexibility and individuality in motor control strategies. It compares various motor vector control methods, specifically focusing on strategies to reduce torque ripple. These strategies include adaptive ANFIS, fuzzy logic control (FLC), and proportional-integral (PI) control. The research highlights the effectiveness of an adaptive ANFIS controller in achieving the most significant reduction in torque ripple within the induction motor system. This proposed problem identification sets the stage for exploring and developing solutions to enhance the performance and efficiency of induction motor drives