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

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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Set-Membership Quaternion Normalized LMS Algorithm

10.20944/preprints201812.0061.v1 ◽

2018 ◽

Author(s):

Hamid Reza Moradi ◽

Akram Zardadi

Keyword(s):

Wind Profile ◽

Adaptive Beamforming ◽

Lms Algorithm ◽

Mean Square ◽

Least Mean Square ◽

Step Size ◽

Set Membership ◽

Normalized Least Mean Square ◽

Update Rate ◽

Normalized Lms

In this paper, we propose the set-membership quaternion normalized least-mean-square (SM-QNLMS) algorithm. For this purpose, first, we review the quaternion least-mean-square (QLMS) algorithm, then go into the quaternion normalized least-mean-square (QNLMS) algorithm. By having the QNLMS algorithm, we propose the SM-QNLMS algorithm in order to reduce the update rate of the QNLMS algorithm and avoid updating the system parameters when there is not enough innovation in upcoming data. Moreover, the SM-QNLMS algorithm, thanks to the time-varying step-size, has higher convergence rate as compared to the QNLMS algorithm. Finally, the proposed algorithm is utilized in wind profile prediction and quaternionic adaptive beamforming. The simulation results demonstrate that the SM-QNLMS algorithm outperforms the QNLMS algorithm and it has higher convergence speed and lower update rate.

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New feedforward filtered-x least mean square algorithm with variable step size for active vibration control

Journal of Low Frequency Noise Vibration and Active Control ◽

10.1177/1461348418812326 ◽

2018 ◽

Vol 38 (1) ◽

pp. 187-198 ◽

Cited By ~ 1

Author(s):

Yubin Fang ◽

Xiaojin Zhu ◽

Zhiyuan Gao ◽

Jiaming Hu ◽

Jian Wu

Keyword(s):

Vibration Control ◽

Active Vibration Control ◽

Lms Algorithm ◽

Variable Step Size ◽

Mean Square ◽

Least Mean Square ◽

Step Size ◽

Large Step ◽

Variable Step ◽

Active Vibration

The step size of least mean square (LMS) algorithm is significant for its performance. To be specific, small step size can get small excess mean square error but results in slow convergence. However, large step size may cause instability. Many variable step size least mean square (VSSLMS) algorithms have been developed to enhance the control performance. In this paper, a new VSSLMS was proposed based on Kwong’s algorithm to evaluate the robustness. The approximate analysis of dynamic and steady-state performance of this developed VSSLMS algorithm was given. An active vibration control system of piezoelectric cantilever beam was established to verify the performance of the VSSLMS algorithms. By comparing with the current VSSLMS algorithms, the proposed method has better performance in active vibration control applications.

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Efficient adaptive noise cancellation techniques in an IOT Enabled Telecardiology System

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.17.11562 ◽

2018 ◽

Vol 7 (2.17) ◽

pp. 74 ◽

Cited By ~ 1

Author(s):

Asiya Sulthana ◽

Md Zia Ur Rahman

Keyword(s):

Health Care ◽

Monitoring System ◽

Mean Squared Error ◽

Lms Algorithm ◽

Frequency Resolution ◽

Variable Step Size ◽

Mean Square ◽

Least Mean Square ◽

Step Size ◽

Health Care Monitoring

An increasing number of elderly and disabled people urge the need for a health care monitoring system which has the capabilities for analyzing patient health care data to avoid preventable deaths. Medical Telemetry is becoming a key tool in assisting patients living remotely where a “Real-time Remote Critical Health Care Monitoring System” (RRCHCMS) can be utilized for the same. The RRCHCMS is capable of receiving and transmitting data from a remote location to a location that has the capability to diagnose the data and affect decision making and further providing assistance to the patient. During the cardiac analysis, several artifacts solidly affect the ST segment, humiliate the signal quality, frequency resolution, and results in large amplitude signals in ECG that simulate PQRST waveform and cover up the miniature features that are useful for clinical monitoring and diagnosis. In this paper, several leaky based adaptive filter structures for cardiac signal improvement are discussed. The Circular Leaky Least Mean Square (CLLMS) algorithm being the steepest drop strategy for dropping the mean squared error gives a better result in comparison with the Least Mean Square (LMS) algorithm. To enlarge the filtering ability some variants of LMS, Normalized Least Mean Square (NLMS), CLLMS, Variable Step Size CLLMS (VSS-CLLMS) algorithms are used in both time domain (TD) and frequency domain (FD). At last, we applied this algorithm on cardiac signals occurred due to MIT-BIH database. The performance of CLLMS algorithm is better compared to LLMS counterparts in conditions of Signal to Noise Ratio Improvement (SNRI), Excess Mean Square Error (EMSE) and Misadjustment (MSD). When compared to all other algorithms VSS-CLLMS gives superior SNRI. These values are 13.5616dB and 13.7592dB for Baseline Wander (BW) and Muscle Artifact (MA) removal.

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DSTATCOM with Improved LMS based IRP theory

E3S Web of Conferences ◽

10.1051/e3sconf/20198701013 ◽

2019 ◽

Vol 87 ◽

pp. 01013

Author(s):

D. Suresh ◽

G. Sravanthi ◽

R. Chander

Keyword(s):

Computer Simulation ◽

Reactive Power ◽

Active Power ◽

Lms Algorithm ◽

Mean Square ◽

Least Mean Square ◽

Pass Filter ◽

Power Theory ◽

Instantaneous Reactive Power Theory ◽

Instantaneous Reactive Power

In this paper, grid connected DSTATCOM with three phase three wire has been proposed for harmonics elimination, reactive power compensation and active power injection into the grid. An improved instantaneous reactive power theory based on the least mean square is computed. The least mean square (LMS) algorithm is combined with instantaneous reactive power theory (IRPT) to improve its dynamics performance and eliminates the use of low pass filter requirement for computation of reference current. The harmonics component of the active power is estimated from LMS algorithm generated load harmonics currents. The performance characteristic of DSTATCOM is estimated with control scheme using computer simulation. The technique is based on the instantaneous transformation of instantaneous signals and taking advantage of the subsequent calculation of power from supply side to the loads. The neural network based method to estimate the harmonic online, DSTATCOM can compensate the harmonics. The computer simulation is carried out with MATLAB Simulink power system block sets.

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Smart VRP-NLMS algorithm for estimation of power system frequency

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-06-2018-0263 ◽

2019 ◽

Vol 38 (1) ◽

pp. 362-381

Author(s):

Seyed Reza Aali ◽

Mohammad Reza Besmi ◽

Mohammad Hosein Kazemi

Keyword(s):

Convergence Rate ◽

Frequency Estimation ◽

Lms Algorithm ◽

Positive Sequence ◽

Mean Square ◽

Least Mean Square ◽

Size Parameter ◽

Step Size ◽

Content Type ◽

Nlms Algorithm

Purpose The purpose of this paper is to study variation regularization with a positive sequence extraction-normalized least mean square (VRP-NLMS) algorithm for frequency estimation in a three-phase electrical distribution system. A simulation test is provided to validate the performance and convergence rate of the proposed estimation algorithm. Design/methodology/approach Least mean square (LMS) algorithms for frequency estimation encounter problems when voltage contains unbalance, sags and harmonic distortion. The convergence rate of the LMS algorithm is sensitive to the adjustment of the step-size parameter used in the update equation. This paper proposes VRP-NLMS algorithm for frequency estimation in a power system. Regularization parameter is variable in the NLMS algorithm to adjust step-size parameter. Delayed signal cancellation (DSC) operator suppresses harmonics and negative sequence component of the voltage vector in a two-phase Î ± Î² plane. The DSC part is placed in front of the NLMS algorithm as a pre-filter and a positive sequence of the grid voltage is extracted. Findings By adapting of the step-size parameter, speed and accuracy of the LMS algorithm are improved. The DSC operator is augmented to the NLMS algorithm for more improvement of the performance of this adaptive filter. Simulation results validate that the proposed VRP-NLMS algorithm has a less misalignment of performance with more convergence rate. Originality/value This paper is a theoretical support to simulated system performance.

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Stability Conditions for the Leaky LMS Algorithm Based on Control Theory Analysis

Archives of Acoustics ◽

10.1515/aoa-2016-0070 ◽

2016 ◽

Vol 41 (4) ◽

pp. 731-739 ◽

Cited By ~ 7

Author(s):

Dariusz Bismor ◽

Marek Pawelczyk

Keyword(s):

Point Of View ◽

Lms Algorithm ◽

Theoretic Approach ◽

Mean Square ◽

Least Mean Square ◽

Theory Analysis ◽

Step Size ◽

Practical Applications ◽

The Mean ◽

Points Of View

AbstractThe Least Mean Square (LMS) algorithm and its variants are currently the most frequently used adaptation algorithms; therefore, it is desirable to understand them thoroughly from both theoretical and practical points of view. One of the main aspects studied in the literature is the influence of the step size on stability or convergence of LMS-based algorithms. Different publications provide different stability upper bounds, but a lower bound is always set to zero. However, they are mostly based on statistical analysis. In this paper we show, by means of control theoretic analysis confirmed by simulations, that for the leaky LMS algorithm, a small negative step size is allowed. Moreover, the control theoretic approach alows to minimize the number of assumptions necessary to prove the new condition. Thus, although a positive step size is fully justified for practical applications since it reduces the mean-square error, knowledge about an allowed small negative step size is important from a cognitive point of view.

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An Effective Adaptive Combination Strategy for Distributed Learning Network

Applied Sciences ◽

10.3390/app11125723 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5723

Author(s):

Chundong Xu ◽

Qinglin Li ◽

Dongwen Ying

Keyword(s):

Minimum Variance ◽

Iteration Scheme ◽

Estimation Problem ◽

Lms Algorithm ◽

Unbiased Estimation ◽

Mean Square ◽

Least Mean Square ◽

Combination Strategy ◽

Adaptive Combination ◽

Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

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