A New Control Algorithm for Power Quality Improvement in a Stand-alone Synchronous Reluctance Generator with Energy Storage

Nayar, Priya; Singh, Bhim; Mishra, Sukumar; Niwas, Ram

doi:10.1080/15325008.2015.1122105

Cited by 1 publication

(1 citation statement)

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“…Study and implementation of seventeen of the best known LMS algorithms and their comparison on the practical aspects based on the study is carried out in [4]. The LMS algorithm is applied to an isolated hydropower station for the regulation of voltage and frequency, then a leaky factor is introduced and a further modified version is used to control the behavior of a Synchronous Reluctance Generator and in active noise control [5][6][7][8][9]. A new model is introduced to tackle the issue of the low convergence rate of the leaky algorithm [10].…”

Section: Introductionmentioning

confidence: 99%

An ALO Optimized Adaline Based Controller for an Isolated Wind Power Harnessing Unit

Kodakkal

Rajagopal

Raju

et al. 2021

Designs

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A power generating system should be able to generate and feed quality power to the loads which are connected to it. This paper suggests a very efficient controlling technique, supported by an effective optimization method, for the control of voltage and frequency of the electrical output of an isolated wind power harnessing unit. The wind power unit is modelled using MATLAB/SIMULINK. The Leaky least mean square algorithm with a step size is used by the proposed controller. The Least Mean Square (LMS) algorithm is of adaptive type, which works on the online modification of the weights. LMS algorithm tunes the filter coefficients such that the mean square value of the error is the least. This avoids the use of a low pass filter to clean the voltage and current signals which makes the algorithm simpler. An adaptive algorithm which is generally used in signal processing is applied in power system applications and the process is further simplified by using optimization techniques. That makes the proposed method very unique. Normalized LMS algorithm suffers from drift problem. The Leaky factor is included to solve the drift in the parameters which is considered as a disadvantage in the normalized LMS algorithm. The selection of suitable values of leaky factor and the step size will help in improving the speed of convergence, reducing the steady-state error and improving the stability of the system. In this study, the leaky factor, step size and controller gains are optimized by using optimization techniques. The optimization has made the process of controller tuning very easy, which otherwise was carried out by the trial-and-error method. Different techniques were used for the optimization and on result comparison, the Antlion algorithm is found to be the most effective. The controller efficiency is tested for loads that are linear and nonlinear and for varying wind speeds. It is found that the controller is very efficient in maintaining the system parameters under normal and faulty conditions. The simulated results are validated experimentally by using dSpace 1104. The laboratory results further confirm the efficiency of the proposed controller.

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