Tariff based fuzzy logic controller for active power sharing between microgrid to grid with improved power quality

Nair, Divya R.; Devi, Snigdha; Nair, Manjula G.; Ilango, K.

doi:10.1109/iceets.2016.7583789

Cited by 21 publications

(3 citation statements)

References 5 publications

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“…The SAF control mechanism conceived here is based on the Icosφ algorithm. This algorithm was upgraded using a fuzzy logic controller (FLC) and rendered intelligent by accounting for diverse parameters [20] regarding the main power grid, microgrid, source current, and load demand. This concept is extensible to any number of microgrids for the management of power flow targeted to enhance power quality.…”

Section: Smart Microgrid Systemsmentioning

confidence: 99%

A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement

Nair

Thakur

2022

Energies

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The widespread popularity of renewable and sustainable sources of energy such as solar and wind calls for the integration of renewable energy sources into electrical power grids for sustainable development. Microgrids minimize power quality issues in the main grid by linking with an active filter and furnishing reactive power compensation, harmonic mitigation, and load balancing at the point of common coupling. The reliability issues faced by standalone DC microgrids can be managed by interlinking microgrids with a power grid. An artificial intelligence-based Icosϕ control algorithm for power sharing and power quality improvement in smart microgrid systems is proposed here to render grid-integrated power systems more intelligent. The proposed controller considers various uncertainties caused by load variations, state of charge of the battery of microgrids, and power tariff based on the availability of power in microgrids. This paper presents a detailed analysis of the integration of wind and solar microgrids with the grid for dynamic power flow management in order to improve the power quality and to reduce the burden, thereby strengthening the central grid. A smart grid system with multiple smart microgrids coupled with a renewable energy source with tariff control and judicious power flow management was simulated for power-sharing and power quality improvement. A hardware prototype of the artificial intelligence-based Icosϕ control algorithm with nonlinear load was also implemented successfully. Furthermore, the economic viability was investigated to ensure the feasibility of the smart microgrid system with the proposed controller design for power flow management and power quality improvement.

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Section: Smart Microgrid Systemsmentioning

confidence: 99%

A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement

Nair

Thakur

2022

Energies

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“…The FLC-based UPQC reduced the total harmonic distortion (THD) in source current down to 2.11% as compared to the conventional PI controller where the same value was recorded as 2.23%. A tariff-based FLC was developed to compensate the reactive power and current harmonics in an ac MG [47]. The controller rules were set in such a way that the MG with the lowest tariff and THD was integrated with the utility grid.…”

Section: Fl-based Reactive Power Control and Power Quality Enhancemenmentioning

confidence: 99%

Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids

et al. 2020

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The penetration of distributed generators (DGs) in the existing power system has brought some real challenges regarding the power quality and dynamic response of the power systems. To overcome the above-mentioned issues, the researchers around the world have tried and tested different control methods among which the computational intelligence (CI) based methods have been found as most effective in mitigating the power quality and transient response problems intuitively. The significance of the mentioned optimization approaches in contemporary ac Microgrid (MG) controls can be observed from the increasing number of published articles and book chapters in the recent past. However, literature related to this important subject is scattered with no comprehensive review that provides detailed insight information on this substantial development. As such, this research work provides a detailed overview of four of the most extensively used CI-based optimization techniques, namely, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) as applied in ac MG controls from 42 research articles along with their basic working mechanism, merits, and limitations. Due to space and scope constraints, this study excludes the applications of swarm intelligence-based optimization methods in the studied field of research. Each of the mentioned CI algorithms is explored for three major MG control applications i.e., reactive power compensation and power quality, MPPT and MG’s voltage, frequency, and power regulation. In addition, this work provides a classification of the mentioned CI-based optimization studies based on various categories such as key study objective, optimization method applied, DGs utilized, studied MG operating mode, and considered operating conditions in order to ease the searchability and selectivity of the articles for the readers. Hence, it is envisaged that this comprehensive review will provide a valuable one-stop source of knowledge to the researchers working in the field of CI-based ac MG control architectures.

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“…The efficient and reliable forecasting of power demand and generation has become increasingly crucial in modern energy systems. Renewable energy has significant challenge is the limited predictability of these factors in the near future, and the power output is contingent on atmospheric parameters [1]. These improvements in accurate power forecasting give advantages for electrical energy companies to have a better sight regarding resource allocation and energy trading.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Solar Power Generation Forecasting in the Eastern Coast Region of Malaysia using Artificial Neural Network (ANN) Method

Muhammad Aiqal Iskandar,

Muhammad Azfar Shamil Abd Aziz,

S. S. Sivaraju

et al. 2024

ARFMTS

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Accurate prediction of power demand and generation is crucial for modern energy systems to efficiently allocate resources and facilitate energy trading. The integration of artificial intelligence (AI) and machine learning techniques has significantly improved the precision of power forecasting. This study focuses on the application of Artificial Neural Networks (ANN) for forecasting power generation in the Eastern Coast region of Malaysia, with a specific emphasis on solar power. The research methodology involves collecting and analyzing historical power data, weather data, and relevant variables. ANN models are trained, validated, and tested on a selected power grid to assess their accuracy and predictive capabilities. The expected outcomes aim to include the development of a precise power generation forecasting model, providing valuable insights for decision-makers to optimize energy operations and seamlessly integrate renewable sources. Additionally, the study explores potential challenges, limitations, and best practices associated with ANN-based power forecasting. The dataset covers the period from 2020 to 2023, with variables such as average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed recorded at 30-minute intervals. The architecture of the ANN model, implemented using the Keras framework, is described as a Sequential model with layers utilizing the 'ReLU' activation function. Model evaluation employs metrics like root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) on the test set, offering insights into the model's overall fit, average deviation, and sensitivity to outliers. Results reveal strong correlations between PV module temperature, irradiance, and AC power generated.

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Tariff based fuzzy logic controller for active power sharing between microgrid to grid with improved power quality

Cited by 21 publications

References 5 publications

A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement

A Smart Microgrid System with Artificial Intelligence for Power-Sharing and Power Quality Improvement

Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids

Long-Term Solar Power Generation Forecasting in the Eastern Coast Region of Malaysia using Artificial Neural Network (ANN) Method

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