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All Journal Journal of Fuzzy Systems and Control (JFSC)
Chatterjee, Arunava
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Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Wind Power Generation for Isolated Loads with IoT-based Smart Load Controller Chatterjee, Arunava
Journal of Fuzzy Systems and Control Vol. 2 No. 2 (2024): Vol. 2, No. 2, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i2.210

Abstract

This study presents an investigation of combining a microgrid structure for the generation of power with a wind energy conversion system. The chief purpose of the study is to provide a smart controller for such a hybrid system with a dump load for controlling the generated voltage. The smart load controller is used which is unique and it is controlled using Internet-of-Things (IoT) for easier and flexible control. Only the dump load is controlled using the IoT-based controller in a way to keeps the bus voltage at a steady value. With the proposed control, the load voltage regulation becomes better with reduced transients. The generation system can be modeled and installed in any isolated place with optimized use of renewable sources with stable power output. Simulations and experimental observations on a laboratory setup for load control authenticate the proposed method.
Wind Power Forecasting using Type-2 Fuzzy Control and its Optimization based on Artificial Neural Network for Small Scale Wind Power Chatterjee, Arunava
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.259

Abstract

Improving the efficiency and economic feasibility of variable renewable resources, wind speed forecasting can improve the quality of wind energy generation. By using the properties of wind-related factors, this work provides a new model for wind energy forecasting for electrical power generation at an onshore location in India. The model, which employs an Interval Type-2 fuzzy logic system (IT2FS), takes inputs of wind features and forecasts wind power. Further, an artificial neural network (ANN) is chosen as the adjustment model for optimization in the architecture. The neural network begins evaluating its performance using a different number of hidden-layer neurons. The ANN-based hybrid model outperforms other models according to comparisons drawn from statistical indices. The usage of this adjustment model of forecasting is shown to be quite helpful in predicting the wind power for driving fractional kW loads using wind-based generation techniques.
Analysis of a Self-excited Induction Generator with Fuzzy PI Controller for Supporting Domestic Loads in a Microgrid Chatterjee, Arunava
Journal of Fuzzy Systems and Control Vol. 1 No. 2 (2023): Vol. 1, No. 2, 2023
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v1i2.42

Abstract

This study provides a technical analysis of integrating a microgrid structure for power generation with a wind energy conversion system. This study's primary objective is to identify solutions to issues that arise when isolated induction generator is utilized in a microgrid. A closed loop electronic load connector is used to control the loads of the induction generator-based system. The load controller is adjusted using an adaptive fuzzy based proportional-integral controller for better control. Additionally, by employing a straightforward variable capacitor design to inject reactive power during voltage dips, it provides a way to sustain the voltage profile. The suggested approach and its control are investigated in MATLAB/Simulink and verified with the aid of a lab experimental setup. The outcomes clearly demonstrate the practicality of the suggested plan to be implemented in grid-isolated places.
ANFIS-Based Fault Detection in Brushed and Brushless DC Motors: A Hybrid Intelligence Approach Chatterjee, Arunava
Journal of Fuzzy Systems and Control Vol. 3 No. 2 (2025): Vol. 3, No. 2, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i2.312

Abstract

Electric motors are a key component in industrial automation and renewable energy systems. Faults like short-circuit and overload conditions may cause performance deterioration, overheating, or even permanent damage. Conventional fault detection techniques depend on threshold-based methods, which are not efficient in handling nonlinear system behavior. The following research introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) method for fault detection of short-circuit and overload faults in BLDC and DC motors. Through the assessment of input parameters like current, voltage, speed, and temperature, the model efficiently classifies fault conditions with greater accuracy than traditional methods. The outcomes affirm the capability of ANFIS in dealing with nonlinear relationships and enhancing fault detection reliability.
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