Integration of Wind and PV Systems Using Genetic-Assisted Artificial Neural Network

The prominence of Renewable Energy Sources(RES)in the process of power generation is exponentially increased in the recent days since these sources assist in minimizing the environmental contamination.A grid-tied DFIG(Doubly Fed Induction Generator)based WECS(Wind Energy Conversion System)is introduced in this work,in which a Landsman converter is implemented to impro-vise the output voltage of PV without anyfluctuations.A novel GA(Genetic Algorithm)assisted ANN(Artificial Neural Network)is employed for tracking the Maximum power from PV.Among the rotor and grid side controllers,the for-mer is implemented by combining the statorflux with d-q reference frame and the latter is realized by the PI controller.The proposed approach delivers better per-formance in the compensation of real and reactive power along with the DC link voltage control.The controlling mechanism is verified in both MATLAB and experimental bench setupby using an emulated wind turbine for the concurrent control of DC link potential,active and reactive powers.The source current THD is observed as 1.93%and 2.4%for simulation and hardware implementation respectively.

Early Detection of Heartbeat from Multimodal Data Using RPA Learning with KDNN-SAE

Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem thus;the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed.In contrast,this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders(KDNN-SAE)that computes the disease before the exact heart rate by combining features from multiple ECG Signals.Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.This work contained Training and testing stages,in the preparation part at first the Adaptive Filter Enthalpy-based Empirical Mode Decomposition(EMD)is utilized to eliminate the motion artifact in the signal.At that point,the robotic process automation(RPA)learning part extracts the effective features are extracted,and normalized the value of the feature then estimated utilizing the RPA loss function.At last KDNN-SAE prepared training for the data stored in the dataset.In the subsequent stage,input signal compute motion artifact and RPA Learning the evaluation part determines the detection of Heartbeat.So early diagnosis of heart failures is an essential factor.The results of the experiments show that our proposed method has a high score outcome of 0.9997.Comparable to the CIF,which reaches 0.9990.The CNN and Artificial Neural Network(ANN)had less score 0.95115 and 0.90147.

The Influence of Benzophenone Substitution on the Physico-Chemical Characterizations of 8-HydroxyQuinoline NLO Single Crystals

Single crystals of 8-Hydroxyquinoline(8-HQ) and Benzophenone substituted 8-HydroxyQuinoline(B8-HQ) are grown by slow evaporation of acetone at room temperature. Coloured crystals of 8-HQ and B8-HQ with good optical quality of dimensions 54 × 3 × 1.5 mm3 and 27 × 3 × 1 mm3 are harvested. Single crystal X-ray diffractometer was utilized to measure the unit cell parameters and to confirm the crystal structure. The presence of various functional groups in the molecule was ascertained by FTIR spectral analysis. The cut-offwavelength of 8-HQ andB8-HQwas centered at 350 and 356 nm. The functional groups in the molecule are elucidated by 1H and 13C-NMR spectral analyses. Kurtz Perry test confirms the SHG in8-HQ andB8-HQ single crystals.

Harmonics Mitigation Using MMC Based UPFC and Particle Swarm Optimization

The application of non-linear loads in the power electronic device causes serious harmonic issues in the power system since it has the intrinsic prop-erty of retrieving harmonic current and reactive power from Alternating Current(AC)supply that leads to voltage instability.To maintain a reliable powerflow in the power system,an innovative Unified Power Flow Converter(UPFC)is uti-lized in this proposed approach.The conventional series converter is replaced with the Modular Multilevel Converter(MMC)that improves the power handling capability and achieves higher modular level with minimized distortions.The shunt compensator assists in minimizing the voltagefluctuations and maximizing the voltage stability under different load constraints.The Direct Quadrature(DQ)theory is utilized in this study to separate the harmonic components by generating reference frame current.The Proportional Integral(PI)controller aids in maintain-ing the direct current potential difference in the constant mode whereas the Pulse Width Modulation(PWM)generator helps in delivering optimized output to the MMC.The gain parameters of PI controller are optimized with the aid of employ-ing Particle Swarm Optimization(PSO).The entire work is validated using MATLAB simulink and the obtained outcomes proves that this new UPFC is highly beneficial in minimizing the distortions with reduced Total Harmonic Distortion(THD)of 2.21%.

Machine Learning Controller for DFIG Based Wind Conversion System

Renewable energy production plays a major role in satisfying electricity demand.Wind power conversion is one of the most popular renewable energy sources compared to other sources.Wind energy conversion has two major types of generators such as the Permanent Magnet Synchronous Generator(PMSG)and the Doubly Fed Induction Generator(DFIG).The maximum power tracking algo-rithm is a crucial controller,a wind energy conversion system for generating maximum power in different wind speed conditions.In this article,the DFIG wind energy conversion system was developed in Matrix Laboratory(MATLAB)and designed a machine learning(ML)algorithm for the rotor and grid side converter.The ML algorithm has been developed and trained in a MATLAB environment.There are two types of learning algorithms such as supervised and unsupervised learning.In this research supervised learning is used to power the neural networks and analysis is made for various hidden layers and activation functions.Simulation results are assessed to demonstrate the efficiency of the proposed system.

An Efficient Hybrid Optimization for Skin Cancer Detection Using PNN Classifier

The necessity of on-time cancer detection is extremely high in the recent days as it becomes a threat to human life.The skin cancer is considered as one of the dangerous diseases among other types of cancer since it causes severe health impacts on human beings and hence it is highly mandatory to detect the skin cancer in the early stage for providing adequate treatment.Therefore,an effective image processing approach is employed in this present study for the accurate detection of skin cancer.Initially,the dermoscopy images of skin lesions are retrieved and processed by eliminating the noises with the assistance of Gaborfilter.Then,the pre-processed dermoscopy image is segmented into multiple regions by implementing cascaded Fuzzy C-Means(FCM)algorithm,which involves in improving the reliability of cancer detection.The A Gabor Response Co-occurrence Matrix(GRCM)is used to extract melanoma parameters in an effi-cient manner.A hybrid Particle Swarm Optimization(PSO)-Whale Optimization is then utilized for efficiently optimizing the extracted features.Finally,the fea-tures are significantly classified with the assistance of Probabilistic Neural Net-work(PNN)classifier for classifying the stages of skin lesion in an optimal manner.The whole work is stimulated in MATLAB and the attained outcomes have proved that the introduced approach delivers optimal results with maximal accuracy of 97.83%.