Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions

publishedVersio

Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically.publishedVersio

A Novel Artificial Neural Network (ANN) Using The Mayfly Algorithm for Classification

acceptedVersio

Resource efficient PV power forecasting: Transductive transfer learning based hybrid deep learning model for smart grid in Industry 5.0

This paper presents an innovative approach for enhancing power output forecasting of Photovoltaic (PV) power plants in dynamic environmental conditions using a Hybrid Deep Learning Model (DLM). The hybrid DLM employs a synergy of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Bidirectional LSTM (Bi-LSTM), effectively capturing spatial and temporal dependencies within weather data crucial for accurate predictions. To optimize the DLM’s performance efficiently, a unique Kepler Optimization Algorithm (KOA) is introduced for hyperparameter tuning, drawing inspiration from Kepler’s laws of planetary motion. By leveraging KOA, the DLM attains optimal hyperparameter configurations, elevating power output prediction precision. Additionally, this study integrates Transductive Transfer Learning (TTL) with the deep learning models to enhance resource efficiency. By leveraging knowledge gained from previously learned tasks, TTL enables the DLM to improve its forecasting capabilities while minimizing resource utilization. Datasets encompassing environmental parameters and PV plant-generated power across diverse sites are employed for DLM training and testing. Three hybrid models, amalgamating KOA, CNN, LSTM, and Bi-LSTM techniques, are introduced and evaluated. Comparative assessment of these models across distinct PV sites yields insightful observations. Performance evaluation, focused on short-term PV power forecasting, underscores the hybrid DLM’s superiority over individual CNN and LSTM models. This hybrid approach achieves remarkable accuracy and resilience in predicting power output under varying weather conditions, showcasing its potential for efficient PV power plant management

Hybrid General Regression NN Model for Efficient Operation of Centralized TEG System under Non-Uniform Thermal Gradients

The global energy demand, along with the proportionate share of renewable energy, is increasing rapidly. Renewables such as thermoelectric generators (TEG) systems have lower power ratings but a highly durable and cost-effective renewable energy technology that can deal with waste heat energy. The main issues associated with TEG systems are related to their vigorous operating conditions. The dynamic temperature gradient across TEG surfaces induces non-uniform temperature distribution (NUTD) that significantly impacts the available output electrical energy. The mismatching current impact may lower the energy yield by up to 70% of extractable thermal energy. As a solution, a hybrid general regression neural network (GRNN) orca predation algorithm (OPA) is proposed; backpropagation limitations are minimized by utilizing the stochastic optimization algorithm named OPA. The conclusions are evaluated and contrasted with highly improved versions of the conventional particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawk optimization (HHO). A detailed analytical and statistical analysis is carried out through five distinct case studies, including field stochastic data study, NUTD, varying temperature, and load studies. Along with statistical matrix errors such as MAE, RMSE, and RE, the results are assessed in terms of efficiency, tracking, and settling time. The results show that superior performance is achieved by the proposed GRNN-OPA based MPPT by 35% faster tracking, and up to 90–110% quicker settling time which, in turn, enables the 4–8% higher energy accumulation over a longer period of operation. A low-cost experimental setup is devised to further validate the practicality of the proposed techniques. From such comprehensive analysis, it can be safely concluded that the proposed GRNN-OPA successfully undertakes the drawbacks of existing classical MPPT methods with higher efficiency

A Novel MPPT Controller Based on Mud Ring Optimization Algorithm for Centralized Thermoelectric Generator under Dynamic Thermal Gradients

Most industrial processes generate raw heat. To enhance the efficiency of industrial operations, this raw heat is recovered. Thermoelectric generators (TEG), as solid state devices, provide an excellent application of heat recovery in the form of most manageable electrical power. This work presents a novel MPPT controller based on the Mud Ring Optimization algorithm for a centralized Thermoelectric Generator (TEG) under dynamic thermal gradients. The existing stochastic optimization algorithm for Maximum Power Point Tracking (MPPT) control in renewable energy systems exhibits several limitations that affect its performance in MPPT control. The convergence speed, local minima trap, hyper parameters’ sensitivity toward the population size, acceleration coefficients, and the stopping criterion all impact the convergence stability. In addition to these limitations, sensor noise sensitivity in measurement fluctuates the control system leading to reduced performance. Therefore, the careful design and implementation of the MRO algorithm is crucial to overcome these limitations and achieve a satisfactory performance in MPPT control. The results of this study contribute to developing more efficient MPPT control of TEG systems and implementing renewable energy technologies. The algorithm effectively tracks the maximum power point in dynamic thermal environments and increases the power output compared to conventional MPPT methods. The findings illustrate the efficacy of the proposed controller providing a higher power output (Avg. 99.95%) and faster response time (220 ms) under dynamic thermal conditions achieving 38–70% faster tracking of the GM in dynamic operating conditions

Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically

Linear Gain Controller Aided Iterative Soft Sequential Acquisition for Primitive Polynomials

In 5G cellular communication systems, achieving low latency is crucial to support the ‘tactile’ Internet with response times of less than one millisecond. Traditional initial synchronization methods face challenges due to high delays. This manuscript presents a novel approach using an EXtrinsic Information Transfer (EXIT) chart-aided method to investigate the concurrence of $m$ -sequences using Iterative Soft Sequence Estimation (ISSE) in a communication channel. The ISSE technique leverages the concept of an Automatic Gain Controller (AGC), which gradually increases the gain as the number of chips in the $m$ -sequence generator grows, both at the transmitter and the receiver. Our ISSE method stands out by achieving sequence synchronization at the receiver with as few as $F$ successive chips for a sequence of ( $2^{F}-1$ ) chips. We base our work on the EXIT chart, eliminating the need for interleavers, which introduce transmission delays. To address the delay issue, we exploit the inherent interrelationship of the $m$ -sequence generator’s concern chips, which have a duration of ( $2^{F}-1$ ), as influenced by the Linear Feedback Shift Register (LFSR) in our ISSE model. We observe that low-order Primitive Polynomials (PPs) exhibit lower Erroneous Loading Probability ( $P_{e}$ ) than higher-order PPs at a specific Signal-to-Noise Ratio (SNR). PPs with identical order but fewer connection taps outperform those with more connection taps. The EXIT chart analysis reveals that lower-order PPs exhibit a larger opening tunnel between the outer and inner curves, resulting in higher achievable rates in our proposed system. Moreover, PPs with lower order achieve higher Mutual Information (MI) more efficiently with the assistance of our ISSE system compared to higher-order PPs

Training Deep Neural Networks with Novel Metaheuristic Algorithms for Fatigue Crack Growth Prediction in Aluminum Aircraft Alloys

Fatigue cracks are a major defect in metal alloys, and specifically, their study poses defect evaluation challenges in aluminum aircraft alloys. Existing inline inspection tools exhibit measurement uncertainties. The physical-based methods for crack growth prediction utilize stress analysis models and the crack growth model governed by Paris’ law. These models, when utilized for long-term crack growth prediction, yield sub-optimum solutions and pose several technical limitations to the prediction problems. The metaheuristic optimization algorithms in this study have been conducted in accordance with neural networks to accurately forecast the crack growth rates in aluminum alloys. Through experimental data, the performance of the hybrid metaheuristic optimization–neural networks has been tested. A dynamic Levy flight function has been incorporated with a chimp optimization algorithm to accurately train the deep neural network. The performance of the proposed predictive model has been tested using 7055 T7511 and 6013 T651 alloys against four competing techniques. Results show the proposed predictive model achieves lower correlation error, least relative error, mean absolute error, and root mean square error values while shortening the run time by 11.28%. It is evident through experimental study and statistical analysis that the crack length and growth rates are predicted with high fidelity and very high resolution

Symptom Analysis of Confirmed Covid-19 Patients Managed at Allied Hospitals of Rawalpindi Medical University, Pakistan

Introduction Coronavirus can cause respiratory disease ranging from mild upper respiratory tract illness to severe pneumonia, severe acute respiratory distress syndrome, and death. The purpose of this research was to study the symptoms of confirmed Coronavirus disease (COVID-19) cases and their relationship with gender and age groups. Materials and Methods This observational cross-sectional study was conducted at Rawalpindi Institute of Urology and Transplantation (RIUT) that is the COVID-19 management center of Rawalpindi Medical University, Rawalpindi during the month of March 2020. Consecutive sampling methodology was used, and all real-time polymerase chain reaction (RT-PCR) confirmed patients of COVID-19 were included. Data regarding age, gender, and symptoms with onset was recorded and analyzed.  Results Thirty-five patients, 22 (62.9%) males, and 13 (37.1%) females were included. Seven (20%) patients were ≥60 years old, and 12 (34.8%) ≥40 years old. 21 (60%) were symptomatic and the rest of them were asymptomatic. The mean duration of symptoms was 2.8±1.1 days. Fever (13, 61.9%), persistent cough (12, 57.1%), sputum (6, 28.6%), shortness of breath (4, 19%), anorexia (3, 14.3%), fatigue (3, 14.3%), myalgia (1, 4.8%), were presenting symptoms. Cough, anorexia, and fatigue were significantly more frequent in the patients ≥40 of age. Anorexia and fatigue were common in the age groups ≥40 and ≥60 years. Myalgia was significantly frequent in patients aged ≥60 years. Conclusion Fever, persistent cough and shortness of breath are commonest symptoms of COVID 19 patients. COVID-19 can be asymptomatic in many cases