Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)systems.In light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment.To achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the system.The localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the GCPV.To develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation signals.Further,the developed algorithm is combined with the PMCU implemented with the experimental setup of GCPV.The results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

Electromagnetic and Thermal Simulations of Human Neurons for SAR Applications

The impact of the electromagnetic waves (EM) on human neurons (HN) has been under investigation for decades, in efforts to understand the impact of cell phones (radiation) on human health, or radiation absorption by HN for medical diagnosis and treatment. Research issues including the wave frequency, power intensity, reflections and scattering, and penetration depths are of important considerations to be incorporated into the research study. In this study, computer simulation for the EM exposure to HN was studied for the purpose of determining the upper limits of the electric and magnetic field intensities, power consumption, reflections and transmissions, and the change in temperature resulting from the power absorption by human neurons. Both high frequency structural simulators (HFSS) from ANSYS software, and COMSOL multi-physics were used for the simulation of the EM transmissions and reflections, and the temperature profile within the cells, respectively. For the temperature profile estimation, the study considers an electrical source of 0.5 watt input power, 64 MHz. The EM simulation was looking into the uniformity of the fields within the sample cells. The size of the waveguide was set to be appropriate for a small animal model to be conducted in the future. The incident power was fully transmitted throughout the waveguide, and less than 1% reflections were observed from the simulation. The minimum reflected power near the sample under investigation was found to be with negligible reflected field strengths. The temperature profile resulting from the COMSOL simulation was found to be near 0.25 m°K, indicating no change in temperature on the neuro cells under the EM exposure. The paper details the simulation results for the EM response determined by HFSS, and temperature profile simulated by COMSOL.

OBSO Based Fractional PID for MPPT-Pitch Control of Wind Turbine Systems

In recent times,wind energy receives maximum attention and has become a significant green energy source globally.The wind turbine(WT)entered into several domains such as power electronics that are employed to assist the connection process of a wind energy system and grid.The turbulent characteristics of wind profile along with uncertainty in the design of WT make it highly challenging for prolific power extraction.The pitch control angle is employed to effectively operate the WT at the above nominal wind speed.Besides,the pitch controller needs to be intelligent for the extraction of sustainable secure energy and keep WTs in a safe operating region.To achieve this,proportional–integral–derivative(PID)controllers are widely used and the choice of optimal parameters in the PID controllers needs to be properly selected.With this motivation,this paper designs an oppositional brain storm optimization(OBSO)based fractional order PID(FOPID)design for sustainable and secure energy in WT systems.The proposed model aims to effectually extract the maximum power point(MPPT)in the low range of weather conditions and save the WT in high wind regions by the use of pitch control.The OBSO algorithm is derived from the integration of oppositional based learning(OBL)concept with the traditional BSO algorithm in order to improve the convergence rate,which is then applied to effectively choose the parameters involved in the FOPID controller.The performance of the presented model is validated on the pitch control of a 5 MW WT and the results are examined under different dimensions.The simulation outcomes ensured the promising characteristics of the proposed model over the other methods.

Position Control of Flexible Joint Carts Using Adaptive Generalized Dynamics Inversion

This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion(AGDI)to track the position of a Linear Flexible Joint Cart(LFJC)system along with vibration suppression of the flexible joint.The proposed AGDI control law will be comprised of two control elements.The baseline(continuous)control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control objectives.The control law is realized by inverting the prescribed dynamics using dynamically scaledMoore-Penrose generalized inversion.To boost the robust attributes against system nonlinearities,parametric uncertainties and external perturbations,a discontinuous control law will be augmented which is based on the concept of sliding mode principle.In discontinuous control law,the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering reduction.The closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is guaranteed.Rigorous computer simulations followed by experimental investigation will be performed on Quanser’s LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.

Intelligent Dynamic Inversion Controller Design for Ball and Beam System

The Ball and beam system(BBS)is an attractive laboratory experimental tool because of its inherent nonlinear and open-loop unstable properties.Designing an effective ball and beam system controller is a real challenge for researchers and engineers.In this paper,the control design technique is investigated by using Intelligent Dynamic Inversion(IDI)method for this nonlinear and unstable system.The proposed control law is an enhanced version of conventional Dynamic Inversion control incorporating an intelligent control element in it.The Moore-PenroseGeneralized Inverse(MPGI)is used to invert the prescribed constraint dynamics to realize the baseline control law.A sliding mode-based intelligent control element is further augmented with the baseline control to enhance the robustness against uncertainties,nonlinearities,and external disturbances.The semi-global asymptotic stability of IDI control is guaranteed in the sense of Lyapunov.Numerical simulations and laboratory experiments are carried out on this ball and beam physical system to analyze the effectiveness of the controller.In addition to that,comparative analysis of RGDI control with classical Linear Quadratic Regulator and Fractional Order Controller are also presented on the experimental test bench.

Control of Linear Servo Carts with Integral-Based Disturbance Rejection

This paper describes a system designed for linear servo cart systems that employs an integral-based Linear Active Disturbance Rejection Control(ILADRC)scheme to detect and respond to disturbances.The upgrade in this control technique provides extensive immunity to uncertainties,attenuation,internal disturbances,and external sources of noise.The fundamental technology base of LADRC is Extended State Observer(ESO).LADRC,when combined with Integral action,becomes a hybrid control technique,namely ILADRC.Setpoint tracking is based on Bode’s Ideal Transfer Function(BITF)in this proposed ILADRC technique.This proves to be a very robust and appropriate pole placement scheme.The proposed LSC system has experimented with the hybrid ILADRC technique plotted the results.From the results,it is evident that the proposed ILADRC scheme enhances the robustness of the LSC system with remarkable disturbance rejection.Furthermore,the results of a linear quadratic regulator(LQR)and ILADRC schemes are comparatively analyzed.This analysis deduced the improved performance of ILADRC over the LQR control scheme.

Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action.But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime concerns.These problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification error.Hence to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection methodology.Based on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance requirement.Further,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a fault.The results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

A 0.5–3.0 GHz SP4T RF switch with improved body self-biasing technique in 130-nm SOI CMOS

A single-pole four-throw(SP4T)RF switch with charge-pump-based controller is designed and implemented in a commercial 130-nm silicon-on-insulator(SOI)CMOS process.An improved body self-biasing technique based on diodes is utilized to simplify the controlling circuitry and improve the linearity.A multistack field-effect-transistor(FET)structure with body floating technique is employed to provide good power-handling capability.The proposed design demonstrates a measured input 0.1-d B compression point of 38.5 d Bm at 1.9 GHz,an insertion loss of 0.27 d B/0.33 d B and an isolation of 35 d B/27 d B at 900 MHz/1.9 GHz,respectively.The overall chip area is only 0.49 mm^2.This RF switch can be used in GSM/WCDMA/LTE frontend modules.

Fractional Order Linear Active Disturbance Rejection Control for Linear Flexible Joint System

A linear flexible joint system using fractional order linear active disturbance rejection control is studied in this paper.With this control scheme,the performance against disturbances,uncertainties,and attenuation is enhanced.Linear active disturbance rejection control(LADRC)is mainly based on an extended state observer(ESO)technology.A fractional integral(FOI)action is combined with the LADRC technique which proposes a hybrid control scheme like FO-LADRC.Incorporating this FOI action improves the robustness of the standard LADRC.The set-point tracking of the proposed FO-LADRC scheme is designed by Bode’s ideal transfer function(BITF)based robust closed-loop concept,an appropriate pole placement method.The effectiveness of the proposed FO-LADRC scheme is illustrated through experimental results on the linear flexible joint system(LFJS).The results show the enhancement of the robustness with disturbance rejection.Furthermore,a comparative analysis is presented with the results obtained using the integer-order LADRC and FO-LADRC scheme.

Non-integer Order Control Scheme for Pressurized Water Reactor Core Power

Tracking load changes in a pressurized water reactor(PWR)with the help of an efficient core power control scheme in a nuclear power station is very important.The reason is that it is challenging to maintain a stable core power according to the reference value within an acceptable tolerance for the safety of PWR.To overcome the uncertainties,a non-integer-based fractional order control method is demonstrated to control the core power of PWR.The available dynamic model of the reactor core is used in this analysis.Core power is controlled using a modified state feedback approach with a non-integer integral scheme through two different approximations,CRONE(Commande Robuste d’Ordre Non Entier,meaning Non-integer orderRobust Control)and FOMCON(non-integer order modeling and control).Simulation results are produced using MATLAB■program.Both non-integer results are compared with an integer order PI(Proportional Integral)algorithm to justify the effectiveness of the proposed scheme.Sate-spacemodel Core power control Non-integer control Pressurized water reactor PI controller CRONE FOMCON.

Finite element modeling of electromagnetic properties in photonic bianisotropic structures

Given a constitutive relation of the bianisotropic medium,it is not trivial to study how light interacts with the photonic bianisotropic structure due to the limited available means of studying electromagnetic properties in bianisotropic media.In this paper,we study the electromagnetic properties of photonic bianisotropic structures using the finite element method.We prove that the vector wave equation with the presence of bianisotropic is self-adjoint under scalar inner product,we propose a balanced formulation of weak form in the practical implementation,which outperforms the standard formulation in finite element modeling.Furthermore,we benchmark our numerical results obtained from finite element simulation in three different scenarios.These are bianisotropy-dependent reflection and transmission of plane waves incident onto a bianisotropic slab,band structure of bianisotropic photonic crystals with valley-dependent phenomena,and the modal properties of bianisotropic ring resonators.The first two simulated results obtained from our modified weak form yield excellent agreements either with theoretical predictions or available data from the literature,and the modal properties in the last example,i.e.,bianisotropic ring resonators as a polarization-dependent optical insulator,are also consistent with the theoretical analyses.