Least Mean Fourth Research Articles

Stochastic‐gradient‐based control algorithms for power quality enhancement in solar photovoltaic interfaced three‐phase distribution system

AbstractHere, stochastic‐gradient‐based adaptive control algorithms have been discussed and employed for power quality enhancement in a Photovoltaics (PV) integrated distribution system. Least mean square (LMS), least mean fourth (LMF), sign‐error LMS, and ‐normalised LMS (‐NLMS) have been implemented as control algorithms for the estimation of fundamental load current. The performances of these adaptive algorithms are compared under steady‐state and dynamic conditions under the non‐linear load conditions in a closed‐loop three‐phase system. The main aim of implementing these algorithms is reactive power compensation, power quality enhancement, and load balancing in a single‐stage three‐phase grid‐tied PV system. The hysteresis current control (HCC) technique is used to generate switching pulses for the three‐phase Distribution Static Power Compensator (DSTATCOM). An MPPT is also employed to ensure maximum power delivery from the solar PV array. PV integrated three‐phase single‐stage distribution system with adaptive control algorithms is implemented in MATLAB/Simulink environment as well as in experimental environment to achieve the goals per standard IEEE‐519.

Robust variable step size least mean fourth with quotient form-based control scheme for DVR operation

ABSTRACT A robust variable step size least mean fourth with a quotient form control scheme (RVSSLMFQ) is implemented for addressing power quality concerns and delivering clean voltage to sensitive loads through a dynamic voltage restorer (DVR). This method estimates the fundamental components from distorted grid voltages, featuring an automated process with adaptive time-varying step sizes and utilizing a quotient structure to reduce mean squared error (MSE) effectively. The suggested control scheme overcomes the stability and performance issues of the least mean fourth (LMF) algorithm by improving convergence and adapting well to varying step sizes. Furthermore, RVSSLMFQ achieves remarkable results with reduced settling time and fewer peak overshoots compared to the least mean square (LMS) and LMF control algorithms. For tuning the load voltage and DC-link voltage proportional-integral (PI) controllers, the improved grey wolf optimization technique (I-GWO) is applied. The results are evaluated through MATLAB simulations.

Improved performance of a shunt hybrid active power filter by a robust exponential functional link network-based nonlinear adaptive filter control to enhance power quality

This research article details the design and implementation of a nonlinear adaptive filtering (NAF) technique using an exponential functional link network (EFLN) for a shunt hybrid active power filter (SHAPF) control to solve the current-associated power quality issues on the utility side at the distribution level of electrical power systems. Separation of the fundamental component from the harmonics, achieving unity power factor operation, reducing the reactive power drawn from the source, balancing the currents during transients, and reduction of total harmonic distortion (THD) of the source current are the issues considered to resolve. The proposed technique solves these issues by generating the sinusoidal reference current and separating the fundamental current from the harmonics. When compared to conventional and existing adaptive filtering techniques such as least mean square (LMS), least mean fourth (LMF), and variable step size LMS (VSS-LMS), the proposed EFLN-NAF method excels in terms of speedy convergence, adaptability in noise-specific environments and reduced steady-state coefficient error. MATLAB/Simulink software is utilized to perform the simulations to examine the suggested strategy for the chosen SHAPF topology both in static and dynamic scenarios. For a 15 kW and 3kVAr requirement of the nonlinear load, simulation results proved that the designed PPF for 2kVAr is able to share the reactive power with the APF, thereby reducing its rating and cost. The proposed method of filtering has been proven to be fast converging with 0.049 s, and the THD in steady state is brought to 1.32 % in steady-state and to 3.77 % during transient conditions, which are under standard limits. A hardware prototype of the experimental setup is constructed at the laboratory scale with OPAL-RT (OP4510) as the controller. With an active and reactive power demand of 1.1 kW and 210VAr, the designed PPF supplies 110 VAr, whereas the rest is supplied by the APF. The practical THD in source current is observed to be 2.081 %, which meets the standards. The results from both simulations and experiments are validated, and the efficacy of the proposed technique in mitigating the aforementioned power quality issues is proved.

Adaptive filtering under multi-peak noise

In this paper, we assume the multi-peak noise in adaptive filtering follows a multimodal probability distribution (MPD), and model it as a Gaussian mixture model (GMM). Then a new gradient ascent algorithm is proposed based on the maximum likelihood function. Under the MPD assumption, multiple means and variances of the modes can be estimated by the expectation-maximization (EM) algorithm, and a corresponding closed-form solution can be obtained. Since the GMM can exactly approximate multi-peak probability density function (PDF), the closed-form solution of the proposed MPD algorithm can approximate the optimal solution of the adaptive filtering problem under multi-peak additive noise. According to the interaction between the distance and magnitudes of adjacent peaks, multi-peak noise can be divided into significant multi-peak (SMP) noise, hidden multi-peak (HMP) noise (e.g., Rayleigh), and invisible multi-peak (IMP) noise (e.g., Uniform). Simulations demonstrate that the proposed MPD algorithm outperforms the maximum correntropy criterion (MCC) and minimum error entropy (MEE) under SMP and HMP noise, and outperforms the least mean fourth (LMF) under IMP noise. The experimental steady-state errors align well with the theoretical values.

Volterra LMS/F Based Control Algorithm for UPQC With Multi-Objective Optimized PI Controller Gains

This paper describes Volterra expansions filter with Least Mean Square (LMS) and Least Mean Fourth (LMF) control algorithm on Unified Power Quality Controller (UPQC). The adaptive models based on the LMS provide a simplified structure to approximate the necessary parameters for a particular PQ interruption. However, these algorithms are ineffective in the case of time-varying perturbations. Besides that, LMF is a comparatively better solution for multiple PQ disruptions with high computational complexity than LMS. So the LMS/Fourth (LMS/F) algorithm is a mixture of LMS and LMF to improve the error convergence quality of a given filter. The Volterra expansions filter has the advantageous characteristic of estimating fundamental components at any PQ conditions without any delay and less computational calculation. Furthermore, an optimization approach named Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to calculate Proportional integral (PI) controller gains in the Volterra LMS/F control algorithms. The PI controller gains of dc link voltage control loop and ac terminal voltage control loop are optimized in a coordinated manner using the multi-objective NSGA-II. The applied algorithms deliver Pareto’s set of optimal global solutions. This complete algorithm is implemented through MATLAB in four-wire UPQC for mitigation of power quality problems.

A variable step size robust normalized least mean absolute third‐based control scheme for a grid‐tied multifunctional photovoltaic system

SummaryThis paper develops a variable step size robust normalized least mean absolute third (VSSRNLMAT)‐based control scheme for a three‐phase grid‐tied multifunctional photovoltaic system (GTMPVS). The GTMPVS delivers PV power to grid‐connected loads and feeds excess power to the grid through a voltage source converter (VSC) when PV power is available. It enhances power quality (PQ) by mitigating current harmonics and compensating for an unbalanced load and reactive power simultaneously. The proposed control scheme accurately extracts the fundamental active and reactive weight components of nonlinear load current. The power exchange at the grid side occurs with balanced and sinusoidal grid currents. During the absence of PV power, the VSC continues its operation as a distribution static compensator (DSTATCOM), thus increasing the effective utilization of the power electronic switches employed in the VSC. The GTMPVS model is created in MATLAB/Simulink. The response of GTMPVS is observed under steady‐state and dynamic operating conditions while feeding power to a nonlinear load. The dynamic operating conditions are created by a change in solar irradiation and an unbalanced nonlinear load. Further, the proposed VSSRNLMAT control scheme is compared with some of the other adaptive filtering‐based control schemes, including least mean square (LMS), least mean fourth (LMF), least mean absolute third (LMAT), and normalized LMAT (NLMAT) in terms of convergence, oscillations, mean square deviation (MSD), computational complexity, and grid current total harmonic distortion (THD). It is found that the VSSRNLMAT control scheme offers THD of grid current as per IEEE‐519 standard. The proposed control scheme is implemented to operate the laboratory‐developed GTMPVS to verify the simulation results.

Design and Analysis of the Shunt Active Power Filter with the ε-NSRLMMN Adaptive Algorithm for Power Quality Improvement in the Distribution System

In this article, an adaptive normalized Sign Regression Least Mean Mixed Norm (ε-NSRLMMN) algorithm-based control technique is developed for the Shunt Active Power Filter (SAPF) to compensate for the grid current harmonics and reactive power under balanced and unbalanced loading conditions. The adaptive ε-NSRLMMN control algorithm improves power quality and makes the source current sinusoidal and power factor (PF) close to unity. It is used for load compensation in a three-phase distribution network. The fundamental active component is extracted from the distorted load current using an adaptive technique. This control algorithm has the advantages of improved convergence, better stability, lower harmonic distortion, less complexity and less steady-state error than the Sign regressor least mean mixed norm (SRLMMN), Least mean square (LMS) and least mean fourth (LMF). Adaptive ε-NSRLMMN is used for learning the fundamental active weight components of the load current. The parameters of the proposed algorithm are trained in real-time. A traditional PI controller has been used to regulate the dc-link voltage of the Voltage Source Converter (VSC). The performance of the proposed algorithm has been justified by simulation and experimental results.

Least Mean Fourth Theory-based Dynamic Voltage Restorer for the Mitigation of Voltage Sag and Harmonics

In the present paper, Dynamic Voltage Restorer (DVR) controller using the Least Mean Fourth (LMF) algorithm is presented. The proposed DVR is used for protecting various voltage-sensitive loads from voltage-related power quality issues (i.e. balanced voltage sag, unbalanced voltage sag and voltage harmonic distortion, balanced and unbalanced swell, etc.) in a distribution system. The active and reactive power components of distorted source voltage are extracted using an adaptive LMF-based control algorithm for the calculation of reference load voltage for the voltage source converter (VSC) of DVR. The control algorithm doesn’t require a phase-locked loop (PLL) and thus is less complex, robust and easy to implement. The adopted control algorithm for DVR is verified in MATLAB by simulation studies. The adaptive LMF-controlled DVR produces excellent dynamics response as compared to conventional adaptive Least Mean Square (LMS) and Synchronous reference frame theory (SRFT)-controlled DVR. The convergence rate of the source voltage fundamental active weight component is higher for the proposed adaptive LMF-controlled DVR. Various power quality issues such as balanced sag, unbalanced sag, balanced and unbalanced swell and a combination of these power quality issues are mitigated with the help of the LMF-controlled DVR. The simulation results are verified through real-time experimentation using an OPAL-RT under an RT lab environment.

Removal of multiple artifacts from ECG signal using cascaded multistage adaptive noise cancellers

Although cascaded multistage adaptive noise cancellers have been employed before by researchers for multiple artifact removal from the ElectroCardioGram (ECG) signal, they all used the same adaptive algorithm in all the cascaded multi-stages for adjusting the adaptive filter weights. In this paper, we propose a cascaded 4-stage adaptive noise canceller for the removal of four artifacts present in the ECG signal, viz. baseline wander, motion artifacts, muscle artifacts, and 60 Hz Power Line Interference (PLI). We have investigated the performance of eight adaptive algorithms, viz. Least Mean Square (LMS), Least Mean Fourth (LMF), Least Mean Mixed-Norm (LMMN), Sign Regressor Least Mean Square (SRLMS), Sign Error Least Mean Square (SELMS), Sign-Sign Least Mean Square (SSLMS), Sign Regressor Least Mean Fourth (SRLMF), and Sign Regressor Least Mean Mixed-Norm (SRLMMN) in terms of Signal-to-Noise Ratio (SNR) improvement for removing the aforementioned four artifacts from the ECG signal. We employed the LMMN, LMF, LMMN, LMF algorithms in the proposed cascaded 4-stage adaptive noise canceller to remove the respective ECG artifacts as mentioned above. We succeeded in achieving an SNR improvement of 12.7319 dBs. The proposed cascaded 4-stage adaptive noise canceller employing the LMMN, LMF, LMMN, LMF algorithms outperforms those that employ the same algorithm in the four stages. One unique and powerful feature of our proposed cascaded 4-stage adaptive noise canceller is that it employs only those adaptive algorithms in the four stages, which are shown to be effective in removing the respective ECG artifacts as mentioned above. Such a scheme has not been investigated before in the literature.

Multi-Objective Grasshopper Optimization Based MPPT and VSC Control of Grid-Tied PV-Battery System

This article presents the control of a three-phase three-wire (3P-3W) dual-stage grid-tied PV-battery storage system using a multi-objective grass-hopper optimization (MOGHO) algorithm. The voltage source converter (VSC) control of the presented system is implemented with adaptive kernel width sixth-order maximum correntropy criteria (AKWSOMCC) and maximum power point tracking (MPPT) control is accomplished using the variable step-size incremental conductance (VSS-InC) technique. The proposed VSC control offers lower mean square error and better accuracy, convergence rate and speed as compared to peer adaptive algorithms, i.e., least mean square (LMS), least mean fourth (LMF), maximum correntropy criteria (MCC), etc. The adaptive Gaussian kernel width is a function of the error signal, which changes to accommodate and filter Gaussian and non-Gaussian noise signals in each iteration. The VSS-InC based MPPT is provided with a MOGHO based modulation factor for better and faster tracking of the maximum power point during changing solar irradiation. Similarly, an optimized gain conventional PI controller regulates the DC bus to improve the power quality, and DC link stability during dynamic conditions. The optimized DC-link generates an accurate loss component of current, which further improves the VSC capability of fundamental load current component extraction. The VSC is designed to perform multi-functional operations, i.e., harmonics elimination, reactive power compensation, load balancing and power balancing at point of common coupling during diverse dynamic conditions. The MOSHO based VSS-InC, and DC bus performance is compared to particle swarm optimization (PSO) and genetic algorithm (GA). The proposed system operates satisfactorily as per IEEE519 standards in the MATLAB simulation environment.

A novel LMSRE-based adaptive PI control scheme for grid-integrated PMSG-based variable-speed wind turbine

This article proposes a novel adaptive control scheme based on proportional-integral (PI) controllers and new adaptive filtering algorithm called the least mean square root of exponential (LMSRE) algorithm. Firstly, the proposed LMSRE algorithm is applied to unknown system identification then it is compared with other benchmarks algorithms such as least mean square (LMS), least mean fourth (LMF), and continuous mixed p-norm (CMPN). Secondly, the LMSRE is applied to self-tune the parameters of the cascaded PI controllers to improve the maximum power point tracking (MPPT) and fault ride-through ability (FRTA) of grid-integrated permanent magnet synchronous generator (PMSG) based wind turbines. Then, the robustness of LMSRE-PI and LMS-PI controllers are tested under real measured wind speed data and severe fault conditions. The simulation results revealed that the LMSRE-PI achieved better dynamic and transient responses than the LMS-PI controllers. With the proposed control scheme, the performance of variable speed wind turbines can be further enhanced.

A COMPARISON ON VARIANTS OF LMS USED IN FIR ADAPTIVE NOISE CANCELLERS FOR FETAL ECG EXTRACTION

Fetal electrocardiogram (FECG) non-invasively obtained through abdominal recordings serves as a promising diagnostic tool for fetal health monitoring during pregnancy. However, in the abdominal ECG (AECG) signal, FECG overlaps with maternal ECG (MECG) in both temporal and spectral domains in addition to interference from various sources like electromyogram, electrogastrogram, motion artifacts and other noises. The objective of this paper is to eliminate MECG components from AECG signal to extract FECG signal through FIR adaptive noise canceller (ANC) with filter coefficients updated using adaptive algorithms. Adaptive filters are suitable for current problem of interest and Least Mean Square (LMS) and its variants are analyzed for the problem of FECG extraction. We have compared the four variants of LMS such as normalized LMS (NLMS), sign-error algorithm, least mean fourth (LMF) algorithms for FECG extraction. The algorithms are evaluated using real-time abdominal ECG recordings acquired from daisy database. The performance of each algorithm is evaluated using various parameters like sensitivity, accuracy, positive predictive values and [Formula: see text] score. Further, the convergence rate for different algorithms are plotted and analyzed. From the simulation results, it is observed that the LMF algorithm outperforms its counterparts by providing an accuracy and positive predictive value of 73.3%, sensitivity of 100% and [Formula: see text] measure of 84.5%. The convergence plots obtained justify that LMF algorithm has a faster convergence rate compared to the other variants of LMS.

A multiobjective grid interactive solar photovoltaic DSTATCOM system using cascaded observer-based DFLL approach

Purpose A cascaded observer-based transfer delay frequency locked loop (CODFLL) algorithm is developed to control the distribution static compensator (DSTATCOM) to address various power quality (PQ) issues arise because of distorted grid and load conditions. Moreover, frequency locked loop is included along with the observer to take care of the frequency drift from nominal value and to improve its performance during steady state and transient conditions. During daylight, the proposed system works as photovoltaic (PV) DSTATCOM and performs multiple functions for improving PQ whilst transferring power to grid and load. The system under consideration acts as DSTATCOM during night and bad weather condition to nullify the PQ issues. Design/methodology/approach CODFLL control algorithm generates reference signal for hysteresis controller. This reference signal is compared with an actual grid signal and a gate pulse is produced for a voltage source converter. The system is made frequency adaptive by transfer delay adaptive frequency locked loop (FLL). Peak power is extracted from a PV source using the perturb and observe technique irrespective of disturbances encountered in the system. Findings The PV system’s performance with the proposed controller is studied and compared with conventional control algorithms such as least mean fourth (LMF), improved second-order generalized integrator frequency locked loop (ISOGI-FLL), synchronous reference frame phased lock loop (SRF-PLL) and frequency adaptive disturbance observer (DOB) for different cases, for example, steady-state condition, dynamic condition, variable insolation, voltage sag and swell and frequency wandering in the supply side. It is found that the proposed method tracks the frequency variation faster as compared to ISOGI-FLL without any oscillations. During unbalanced loading conditions, CODFLL exhibits zero oscillations. Harmonics in system parameters are reduced to the level of IEEE standard; unity power factor is maintained at the grid side; hassle-free power flow takes place from the source to the grid and load; and consistent voltage profile is maintained at the coupling point. Originality/value CODFLL control algorithm is developed for PV-DSTATCOM systems to generate a reference grid current.

Modified leaky LMS‐based control strategy for reliable operation of single‐stage three‐phase grid‐tied PV system

This study presents development of a modified leaky least mean square (MLLMS)-based control strategy for a single-stage three-phase grid-tied photovoltaic (PV) system. In the proposed MLLMS-based control scheme, the authors employ an incremental conductance maximum power point tracking algorithm for maximum power extraction, MLLMS algorithm for extraction of fundamental active and reactive components of the load current, delivering PV power to the grid, balancing the grid current and compensating harmonics of the connected loads at the point of common coupling. By virtue of a leakage factor and selection of sum of the exponential of the adaptation error in the cost function, the MLLMS algorithm overcomes the problems of drifting, low convergence and oscillations in weights encountered by some popular adaptive algorithms, e.g. least mean square (LMS) and least mean fourth (LMF) algorithms. The proposed control scheme is simulated in MATLAB/Simulink environment under different load and environmental conditions. Subsequently, the proposed control scheme is realised on a prototype grid-tied PV system developed in the laboratory. From both the simulation and experimental results, it is observed that the proposed MLLMS algorithm outperforms LMS and LMF algorithms in terms of mean square error, oscillation in weights and total harmonic distortions of the grid currents.

FPGA implementation of NN based LMS–LMF control algorithm in DSTATCOM for power quality improvement

Neural Network (NN) based Least Mean Square (LMS)–Least Mean Fourth (LMF) control technique is developed and implemented in Field Programmable Gate Array (FPGA) for Shunt Active Power Filter (SAPF) to enhance Power Quality (PQ) in three-phase four wire distribution system. The PQ enhancement comprises the improvement of power factor, mitigation of harmonics and compensation of reactive power. The proposed control strategy is simulated in MATLAB/Simulink to determine the reference source currents and weights of active and reactive components. The simulation results of LMS–LMF control are compared with the other three control strategies. The NN control is developed as individual modules in FPGA. Thus it can be configured to any other research area which has identical requirements. The FPGA implementation overcomes the drawback of conventional controllers with a fast and accurate response. The FPGA based proposed control strategy for SAPF is developed as a laboratory prototype and the results obtained are analyzed. The results attained adhere to power quality standards.

A complex filter-based adaptive integral grid synchronisation algorithm for a PV system

ABSTRACT The issues such as deterioration of power quality owing to nonlinear loads, sudden changes in load demand, switch to dynamic loads, the occurrence of fault at the load or grid side is addressed using proposed DQ frame Complex Filter-based Adaptive Integral Algorithm (DCFAIA). The objective of the proposed control algorithm is to maintain the frequency at the desired level irrespective of electrical disturbances and also to remove the DC offset present in the synchronising parameters due to nonlinear load, lower the harmonic content in the grid parameters and maintain the power factor unity by compensating the reactive power. The performance of the proposed algorithm is compared with that of conventional algorithms such as Improved Second-Order Generalised Integrator (ISOGI), Third Order Generalised Integrator (TOGI), Least Mean Fourth (LMF). From the comparative assessment, it is observed that the proposed algorithm exhibits superior performance in terms of precision, settling time, steady and transient errors.

Affine Projection Algorithm Based on Least Mean Fourth Algorithm for System Identification

In the field of signal processing such as system identification, the affine projection algorithm (APA) is extensively implemented. However, running such algorithms in a non-Gaussian scenario may degrade its performance, since the second-order moment cannot extract all information from the signal. To prevent performance degradation of the algorithm in system identification tasks, we propose a novel APA based on least mean fourth (LMF) algorithm. The new algorithm, namely affine projection least mean fourth algorithm (APLMFA) is based on the high-order error power (HOEP) criterion and as such, can achieve improved performance. We also provide a convergence analysis for APLMFA. Numerical simulation results verify the presented APLMFA achieves smaller steady-state error as compared with the state-of-the-art algorithms.

Novel Adaptive Filtering Algorithms Based on Higher-Order Statistics and Geometric Algebra

Adaptive filtering algorithms based on higher-order statistics are proposed for multi-dimensional signal processing in geometric algebra (GA) space. In this paper, the proposed adaptive filtering algorithms utilize the advantage of GA theory in multi-dimensional signal processing to represent a multi-dimensional signal as a GA multivector. In addition, the original least-mean fourth (LMF) and least-mean mixed-norm (LMMN) adaptive filtering algorithms are extended to GA space for multi-dimensional signal processing. Both the proposed GA-based least-mean fourth (GA-LMF) and GA-based least-mean mixed-norm (GA-LMMN) algorithms need to minimize cost functions based on higher-order statistics of the error signal in GA space. The simulation results show that the proposed GA-LMF algorithm performs better in terms of convergence rate and steady-state error under a much smaller step size. The proposed GA-LMMN algorithm makes up for the instability of GA-LMF as the step size increases, and its performance is more stable in mean absolute error and convergence rate.

Modified Variable Step Size Affine Algorithm for Grid I ntegrated PV System

This paper proposes a Modified Variable Step Size Affine (MVSA) algorithm for a single stage grid integrated photovoltaic (GIPV) system. Integration of photovoltaic (PV) system to utility grid encounters many control issues like nonlinearities, disturbances and unbalanced loading. The proposed algorithm not only improves the overall performance of the system despite all the adverse conditions but also improves the power quality as per IEEE-519 standard. The proposed algorithm is developed to eliminate harmonics caused by nonlinear loads, to transfer active power from PV source to load as well as to grid under unity power factor condition, to maintain constant voltage profile at the point of common coupling and to provide a controlled reference signal to the hysteresis controller so as to produce suitable gate pulse to voltage source converter (VSC). In this paper, a PI control is used to maintain dc link voltage and P&O method is used to extract optimum power from PV source irrespective of all disturbances in the utility grid. The system under consideration is modelled and simulated using MATLAB/Simulink software. Comparing the performances of the said variable step size control algorithm with that of fixed step size algorithm like Fixed Step Size Affine (FSA) Projection Algorithm and Least Mean Fourth (LMF), it is observed that the mean square error in MVSA is less as compared to FSA and LMF.

An Adaptive Variable Leaky Least Mean Square Control Scheme for Grid Integration of a PV System

This paper presents a new grid synchronization control scheme for integrating a 3-phase PV system to a grid. The Leaky Least Mean Square (LLMS) algorithm uses the fixed value for leak factor, which causes the weight vector to drift beyond its limit. Hence the proposed control scheme comprises of an adaptive Variable Leaky Least Mean Square (VLLMS) algorithm to generate Reference Inverter Current (RIC) which overcomes the aforesaid drawback, Reinforcement Learning (RL) algorithm for Maximum Power Point Tracking (MPPT) and a Sliding Mode approach to generate switching signals. The MPPT is designed with RL algorithm for extraction of maximum power from PV panels during varied solar insolations. Sliding Mode Controller (SMC) is designed for generation of switching signal to Voltage Source Inverter (VSI) during dynamic loading condition. The proposed VLLMS-RL-SMC control algorithm is first implemented in MATLAB/Simulink, then on a prototype PV experimental setup developed in the laboratory. The performance of the proposed VLLMS-RL-SMC algorithm is compared with that of other control schemes namely Leaky Least Mean Square-RL-SMC (LLMS-RL-SMC), Least Mean Fourth (LMF)-RL-SMC and Instantaneous Power Theory-RL-SMC (IPT-RL-SMC) in which RL is used for MPPT as in case of VLLMS-RL-SMC scheme for steady state and dynamic loading conditions. From the comparison, it is observed that the proposed VLLMS-RL-SMC algorithm outperforms amongst above three control schemes.