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Adaptive Learning-Based Controller to Mitigate Energy Losses in Internet of Things Devices and Power Quality Improvement Sharma Anuj1, Goswami Garima1 1Electrical Engineering Department, Teerthanker Mahaveer University, Moradabad, India Online Published on 13 October, 2023. Abstract A rapid, remote access and fine control to commercial and governmental application are made possible by the Internet of Things (IoT) technology convenient remote access eliminates system ambiguities as a problem caused by the existence of features of hybrid loads. A variety of undesirable power quality concerns were resolved by using nonlinear loads and high-performance converters built on power electronics. Among other performance objectives, distortion of harmonic analysis is given great main concern in smarter electrical equipment. In order to identify quality of power issues and show how well a sophisticated learning of self-adaptive approach works, this study studies the features of load(hybrid) systems in intelligent IoT type equipment. The model proposes an approach to handle nonlinear load disturbances in smart IoT devices using the Adaptive Fuzzy Neural Interface System (ANFIS) learning. Hysteresis current control is using to adjust the production of the reference current in categorizes to turn on the Shunt category Active Power Filter (SAPF) and lessen harmonic distortions in the distribution supply. An experimental setting is used to validate the data set for error deviation used in training neural networks and adaptive control. The estimated Adoption of the neuron’s empirical weight using a number of learning layers, lowering the total THD to an amazing rate of 82.58 % to 0.79%. This demonstrates the great feasibility of the control mechanism for a range of instantaneous smart IoT apps that adhere to with standard of 519(IEEE). Top Keywords THD, FLC, NN, IoT, PQ, ANFIS. Top | |
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