Battery state‐of‐charge‐based control and frequency regulation in the MMG system using fuzzy logic

Sahoo, Saroja Kanti; Kishore, Nanda

doi:10.1049/iet-gtd.2019.1638

Cited by 18 publications

(4 citation statements)

References 35 publications

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“…Lithium-ion batteries mostly work in complex environmental conditions. For the influence of environmental factors, the statistical characteristics of the observation noise will keep changing, which leads to the continuous change of the observation noise covariance matrix R. [44][45][46][47] To improve the convergence speed of the CKF, a fuzzy adaptive algorithm is introduced. The innovation e k and its rate of change Δe k are selected as the input of the fuzzy controller and the adjustment factor α of R as the output.…”

Section: Fuzzy Adaptive Algorithmmentioning

confidence: 99%

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

Yang

Wang

et al. 2021

Circuit Theory & Apps

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To solve the problem of the slow convergence speed for the battery stateof-charge estimation of cubature Kalman filter algorithm, the ternary lithium-ion battery is taken as the research object, and an algorithm combining the fuzzy self-adaptation and singular value decomposition cubature Kalman filtering is proposed. The algorithm takes the system innovation and its change rate as the fuzzy input and the output as the adjustment factor, which is used to adjust the process noise covariance matrix R. The Kalman gain is adjusted through the fuzzy control of R. To ensure the stability of the algorithm in the calculation process, the singular value decomposition is applied to cubature Kalman algorithm. Then, a second-order RC equivalent circuit model with double internal resistance is built and tested under different conditions to verify the rationality of the improved algorithm. The verification results show that under the simple condition, the convergence speed of the proposed algorithm in the different initial state-of-charge values increased by 40.00% and 25.00%, the maximum estimation error of the state-of-charge is 2.52% and 2.51%, the Mean Absolute Error is 0.816% and 0.880%, and the Root Mean Square Error is 1.276% and 1.380%. When the initial state-of-charge value is 0.8, the convergence speed in the complex condition is increased by about 30.00%; the maximum estimation result error, Mean Absolute Error, and Root Mean Square Error are 2.21%, 0.222%, and 1.327%, respectively. When the initial state-of-charge value is 0.6, the convergence speed in the complex condition is increased by about 10.00%; the maximum estimation result error, Mean Absolute Error, and Root Mean Square Error are 2.72%, 0.941%, and 1.327%, respectively. Without reducing the estimation accuracy, the improved algorithm can significantly increase the convergence speed of predictive value tracking, which provides a theoretical basis for the wide application of lithium-ion batteries.

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Section: Fuzzy Adaptive Algorithmmentioning

confidence: 99%

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

Yang

Wang

et al. 2021

Circuit Theory & Apps

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“…The proposed control strategy encompasses various aspects, including speed control, DC bus voltage control, active-reactive power control, and PV power management. In another study by Lawan et al [8], the authors discuss a multi-source power system based on PV-batteries and a diesel generator for micro-grid applications. The integration of solar energy with diesel generators to ensure continuous power supply in microgrids is highlighted.…”

Section: Related Workmentioning

confidence: 99%

Numerical Simulation and Analysis of Grey Wolf Optimization Based Maximum Power Point Tracking Under Complex Operational Conditions

2024

Acta Energetica

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Efficiently harnessing solar energy is pivotal in the pursuit of sustainable energy sources. Maximum Power Point Tracking (MPPT) techniques are essential for optimizing the performance of photovoltaic (PV) systems, especially under challenging operational conditions. This study presents a comprehensive numerical simulation and analysis of a novel Grey Wolf Optimization (GWO) based MPPT algorithm tailored to address complex operational scenarios, including partial shading, temperature fluctuations, and varying solar irradiance. The research begins with an in-depth exploration of the GWO algorithm, a nature-inspired optimization technique. The GWO algorithm's integration with MPPT in PV systems is thoroughly investigated. A precise mathematical model, based on the single-diode five-parameter model, is employed to emulate the nonlinear characteristics of PV panels. Numerical simulations are conducted using MATLAB/Simulink with real-world data inputs, replicating diverse operational conditions. Comparative assessments are made against traditional MPPT methods like Perturb and Observe (P&O) and Incremental Conductance (IncCond). Key performance metrics, including tracking efficiency, convergence speed, steady-state oscillations, and energy yield, are rigorously evaluated. The results demonstrate the superiority of the GWO-based MPPT algorithm in complex operational conditions, with higher tracking efficiency, faster convergence, and reduced steady-state oscillations compared to conventional approaches. This algorithm particularly excels in scenarios with partial shading and rapidly changing solar irradiance. Additionally, a sensitivity analysis is conducted to fine-tune the GWO algorithm's control parameters, enhancing its adaptability to various PV system configurations. In conclusion, this study underscores the potential of Grey Wolf Optimization as an effective tool for enhancing MPPT performance in PV systems under challenging operational conditions. The findings have significant implications for the advancement of renewable energy technologies and their seamless integration into the grid, making this research valuable for engineers and researchers in the field.

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“…Induction motor are two types, squirrel cage induction motor,slipring induction motor [1]. MCSA technique is used to monitor the condition of motor, initially this concept is proposed in 1970's [2].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Induction Motor Drive Using Motor Current Signature Analysis

Ramya¹,

Basha²,

Bharathi³

2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Asynchronous motors are widely used in transportation, mining and in all electric drive application. It is robust. Compare to other motors it is easy to control and less cost. In this paper conditioning of motor is achieved. It has become necessary to monitor the condition of motor in industries. MCSA is the best method for conditioning of motor, we need single phase current of three phase induction motor to analyze the condition of monitor. So many faults will occur in the induction motor. Weare taking open switch fault in this induction motordrive.

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Battery state‐of‐charge‐based control and frequency regulation in the MMG system using fuzzy logic

Cited by 18 publications

References 35 publications

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

Numerical Simulation and Analysis of Grey Wolf Optimization Based Maximum Power Point Tracking Under Complex Operational Conditions

Fault Diagnosis of Induction Motor Drive Using Motor Current Signature Analysis

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