Implementation of Neural Network Based Least Mean Square Algorithm with PID-VPI Controller and Integrated Electronic Load Controller for Isolated Asynchronous Small Hydro Generation

摘要

The Hydro power is recognized as the promising and widely used renewable source of energy for power generation in large scale, it is gaining popularity due to rising rate of depletion as well as increasing cost of fossil fuels. The Hydro power is very economical in case of run-of-the-river scheme, and environmental friendly keeping in mind the harmful effect of fossil fuels on the climate change. This paper deals with neural network (NN) based least mean square (LMS) algorithm known as adaptive linear element (ADALINE) algorithm, with PID-VPI controller for isolated asynchronous generator (IAG) with integrated electronic load controller (IELC) in small hydro generation feeding three-phase four-wire nonlinear load with neutral-current compensation. The integrated electronic load controller (IELC) is based on zigzag/three single-phase transformers and a six-leg insulated-gate bipolar-transistor-based current-controlled voltage-source converter, a chopper switch, and an auxiliary load on its dc bus. The integrated electronic load controller (IELC) utilizes Adaptive linear element (Adaline) to extract the positive-sequence fundamental-frequency component of load current to obtain load balancing in integrated manner and to control the voltage and frequency of the isolated asynchronous generator (IAG). Non-linear loads are considered for critical evaluation of system, as they have the capability to introduce harmonics that are deleterious for any system. The propound system is modeled and simulated in MATLAB environment to demonstrate the effectiveness of the proposed integrated electronic load controller for the control of isolated asynchronous generator.