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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.

The global pandemic of COVID-19 has raised several questions and attracted researchers from all of the disciplines of scientific research. Regardless of advances in science and technology, equipped laboratories of virology, high literacy rates, and medical resources in developed countries, several nations and their health care systems completely failed to overcome the disaster. The fast spread is caused by frequent air travel for business, tourism, education, etc. COVID-19 can infect third world countries severely. United States of America has the highest per capita spending of health still 1/3 rdof the global burden of COVID-19 has consumed existing resources. The WHO has declared COVID-19 as a pandemic. More than 200 countries and territories have reported infected cases. The quarantine is the most effective way to slow the spread of disease and “Flatting of Curve” is a phenomenon to tackle the surge by health systems. To achieve good results from existing Medical Health Care Systems (MHCS), an accurate prediction for the spread of disease is crucial. This study utilizes the generalized method of SIR to accurately predict the spread of COVID-19 associated infection, recoveries, and deaths in Pakistan. The data from the National Command and Control of Pakistan (NCCP) is utilized. Through multiple cases applied on currently available data, the proposed mathematical models predict that by the end of April about more than 14553 infected and about 310 deaths are in Pakistan. The recovery rate is highest in the region up to 99.87 %.

Recently depletion of fossil fuels and environmental concerns triggered the extensive research on alternative energy resources, paricularly solar energy. The power of PV systems may be decreased by the oscillation, random fluctuation, and slow speed of their power tracking. To tackle these issues, a novel grasshopper optimization (GHO) technique is implemented to the MPPT controller under fast varying irradiance and PS conditions. A thorough comparison is made with well-established techniques, such as P&O, ABC, PSO, DFO, PSOGS, and CS optimization techniques under 5 different cases of weather conditions. The shortcomings of existing techniques are exposed under complex partial shading (CPS). An adaptive search and skip method is incorporated into GHO to enhance its robustness. Detailed qualitative and statistical analysis is made and results are presented to solidify the feasibility of GHO. The analysis confirms the effectiveness of the proposed GHO over existing bio-inspired MPPT techniques. Results show that the proposed GHO is highly robust with the tracking efficiency of up to 99.5%. The oscillation reduction of up to 85% is achieved along with 14–60% faster tracking. Experimental validation on low-cost setup further solidifies the practicality of the proposed technique in real-world applications.

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.

The need for renewable energy resources is ever-increasing due to the concern for environmental issues associated with fossil fuels. Low-cost high-power-density manufacturing techniques for the thermoelectric generators (TEG) have added to the technoeconomic feasibility of the TEG systems as an effective power generation system in heat recovery, cooling, electricity, and engine-efficiency applications. The environment-dependent factors such as the nonuniform distribution of heat, damage to the heat-transfer coating between sinks and sources, and mechanical faults create nonuniform current generation and impedance mismatch causing power loss. As a solution to this nonlinear multisolution problem, an improved MPPT control is presented, which utilizes the improvised barnacle mating optimization (BMO). The case studies are formulated to gauge the performance of the proposed BMP MPPT control under nonuniform temperature distribution. The results are compared to the grey wolf optimization (GWO), particle swarm optimization (PSO), and cuckoo search (CS) algorithm. Faster global maximum power point tracking (GMPP) within 381 ms, higher power tracking efficiency of up to 99.93%, and least oscillation ≈0.8 W are achieved by the proposed BMO with the highest energy harvest on average. The statistical analysis further solidifies the better performance of the proposed controller with the least root mean square error (RMSE), RE, and highest SR.

In PV systems, the non-uniform irradiance and diversified unpredictable weather conditions fall into the category of Partial Shading (PS). Under PS, it is challenging for PV systems to obtain the maximum output through Maximum Power Point Tracking (MPPT), i.e., the parameters of the controller are adjusted online to yield the maximum power. In the literature, various techniques have been proposed to track the MPP (Maximum Power Point) under the uniform irradiance. On the contrary, few techniques have been proposed to efficiently track MPP under PS. In this paper, a few novel MPPT techniques have been proposed, which include Adaptive Cuckoo Search Optimization Algorithm (ACOA), General Regression Neural Network GRNN) with Fruit fly Optimization algorithm (FFOA), and Dragonfly Optimization Algorithm (DFO) to track the MPP under various weather condition. The proposed techniques enhance the performance of the PV system, save the computational time and greatly reduce the oscillation around the global maximum power point. For the validation of the proposed techniques, comparative analysis of their results with the Bio-inspired Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Artificial Bee Colony Algorithm (ABC) and PSO Gravitational search Optimization (PSOGS) is presented. The comparison shows that the proposed techniques are better in term of quick power tracking, stability, and high efficiency under various weather conditions. The comparison also demonstrates that the proposed techniques can efficiently locate the GM (global maxima) under the PS and Dynamic Partial Shading (DPS) conditions. Furthermore, statistical analysis is presented to check the stability, sensitivity and robustness of the proposed techniques.

In this article, a novel maximum power point tracking (MPPT) controller for the fast-changing irradiance of photovoltaic (PV) systems is introduced. Our technique utilizes a modified incremental conductance (IC) algorithm for the efficient and fast tracking of MPP. The proposed system has a simple implementation, fast tracking, and achieved steady-state oscillation. Traditional MPPT techniques use a tradeoff between steady-state and transition-state parameters. The shortfalls of various techniques are studied. A comprehensive comparative study is done to test various existing techniques against the proposed technique. The common parameters discussed in this study are fast convergence, efficiency, and reduced oscillations. The proposed method successfully addresses these issues and improves the results significantly by using a proportional integral deferential (PID) controller with a genetic algorithm (GA) to predict the variable step size of the IC-based MPPT technique. The system is designed and tested against the perturbation and observation (P&O)-based MPPT technique. Our technique effectively detects global maxima (GM) for fast-changing irradiance due to the adopted GA-based tuning of the controller. A comparative analysis of the results proves the superior performance and capabilities to track GM in fewer iterations.

Recently thermoelectric generation (TEG) systems are gaining popularity due to their increased power density and wide applications in industrial and energy conversion applications. This paper presents a novel optimal control technique for TEG systems under non-uniform temperature distribution (NUTD). Although centralized MPPT control can reduce the footprint and cost of MPPT of TEG systems and provide excellent advantages over the cross-tied, string, and distributive control configurations, it suffers from drawbacks caused by NUTD on the hot or cold sides of TEG modules. Specifically, the bypass diodes streamline the power flow in centralized TEG systems, which may generate multiple local maxima in electrical characteristics curves of TEG systems. To resolve such issues, this paper implements a novel MPPT control technique based on a swarm intelligence (SI)-based optimization algorithm, Equilibrium Optimization (EQO). To study the effectiveness of the proposed EQO technique, five distinct case studies were used. Additional sensitivity analysis and statistical studies were also done to analyze the comparative performance. The results show that the power tracking efficiency can be increased to 99.68% with 1.8–8% more energy harvesting. The stable voltage and current transients can be achieved with a min tracking time of 180 ms, achieving up to 46% faster GM tracking and 238% faster settling time than some conventional MPPT control techniques. In light of experimental simulations, it is safe to conclude that the proposed EQO-based technique outperforms the existing control techniques for TEG systems under all concerned operating conditions.

The need to combat the increase in global warming is well taken by solar energy lead renewable energy resources. The techno-economic feasibility of solar systems in the form of photovoltaic (PV) generation is highly dependent upon its operating conditions. The nonlinear control problem is further worsened by partial shading (PS) environment causing major power losses. Bio-inspired maximum power point tracking (MPPT) control techniques, in literature, exhibit some major common drawbacks such as high tracking and settling time, oscillations at global maxima (GM), and local maxima (LM) trapping under PS conditions. This paper presents a novel search and rescue (SRA) optimization algorithm based MPPT control of PV systems to circumvent these shortcomings. Enhancement in performance of PV systems, fast and effective tracking of GM and very low oscillations at GM are the improvements exhibited by the proposed technique. Comprehensive case study-wise comparison of the SRA technique is made with recently developed grasshopper optimization (GHO), grey wolf optimization (GWO), particle swarm optimization (PSO), Cuckoo Search (CS), and PSO-gravitational search (PSOGS) based MPPT techniques that elaborate the qualitative, quantitative and statistical viewpoints. The experimental verification and field atmospheric data of Islamabad, the capital city of Pakistan, is utilized to validate the practicality of the proposed SRA based MPPT controller in real-world applications. As compared to the above-mentioned MPPT techniques, the proposed SRA achieves up to 8% more power and 5% more energy. Furthermore, the settling time and tracking time are shortened by up to 72% and 180% respectively. The simplicity of implementation, robustness, and up to 99.93% power tracking efficiency in steady-state are the prominent features of the proposed SRA control strategy.

PV systems generate electrical energy from solar energy and are categorized as the cleanest and cost-effective form of electrical energy. Due to changes in temperature and irradiance conditions, PV systems fall into the category of partial shading (PS) which renders its output to be non-linear. Power losses occur due to PS conditions. Multiple bio-inspired MPPT techniques are pre-selected such as particle swarm optimization (PSO), grey wolf optimizer (GWO), cuckoo search (CS), and grasshopper optimization (GHO). Oscillations around the Global Maxima (GM), getting trapped in local maxima and in-efficient tracking of the GM, are the main drawbacks that are observed in the above mentioned techniques. This paper utilizes two swarm intelligence (SI) based novel MPPT techniques namely marine predator algorithm (MPA) and mayfly optimization algorithm (MFA). These techniques overcome the above-mentioned drawbacks. Three cases have been introduced in this paper namely fast varying irradiance, partial shading conditions, and complex partial shading condition. Improvements in the tacking time of up to 45% and efficiency greater than 99.9% has been observed in the proposed technique. It is observed that oscillations have been reduced to as low as 1 W along with extreme reduction in power loss as well. Rapid MPPT, low computational power, and high efficiency are the main features of the proposed techniques.

PV systems may suffer significant power loss caused by irregular irradiance patterns and changing temperatures. Moreover, partial shading (PS) is a major defect for existing maximum power point tracking (MPPT) controllers. This paper proposes efficient and robust MPPT controllers using novel Slime mould optimization (SMO) and improved salp swarm optimization algorithm (ISSA) to track GMPP for different PV array configurations. ISSA takes opposition based learning (OBL) and local search algorithm (LSA) to perform maximum exploration of the search space. Due to LSA, the oscillation of ISSA can be reduced to zero. Moreover, a novel SMO MPPT technique is introduced for MPPT tracking of PV systems under various weather conditions. The results of the proposed controllers are compared with those of well-known perturb and observe (P&O), particle swarm optimization (PSO), SSA, and cuckoo search optimization algorithm (CSA) MPPT techniques. Six distinct cases of varying temperature, field atmospheric data study, uniform irradiance, PS, and complex-PS are studied. Results and their analysis confirm that the proposed controllers have the highest transient and steady-state efficiency of up to 99.9% with the least tracking time of up to 130 ms while keeping the oscillation in the steady-state well below 1–2W.

Solar energy is the most promising renewable energy resource. In order to harvest maximum solar energy in PV systems, maximum power point tracking (MPPT) techniques have been investigated. In this paper, a novel MPPT technique based on the henry gas solubility optimization (HGO) is proposed. The HGO-based MPPT technique can successfully tackle two major issues of PV systems, including random oscillation caused by traditional MPPT techniques and power loss caused by partial shading (PS). The proposed HGO-based MPPT technique is compared with some state-of-the-art MPPT techniques, including perturb and observe (P&O), particle swarm optimization (PSO), dragonfly optimization algorithm (DFOA), cuckoo search algorithm (CSA), and grasshopper optimization (GHO). The comparative results confirm that the HGO-based MPPT technique can the aforementioned two major MPPT issues and outperform those existing MPPT techniques. Moreover, a study using field atmospheric data is done to estimate the impact on energy harvest during different seasons of a year. The HGO-based MPPT technique is robust and effective in improving power efficiency by 1–4% against some SI (swarm intelligence)-based techniques. Compared with a few traditional gradient-based techniques, the HGO-based MPPT technique reduces the tracking time by 15–30%, the settling time by 20%, and the oscillation by 80%.

The most cost-effective electrical energy is produced by photovoltaic (PV) systems, and with the smallest carbon footprint, making it a sustainable renewable energy. They provide an excellent alternative to the existing fossil fuel-based energy systems, while providing 4% of global electricity demand. PV system efficiency is significantly reduced by the intrinsic non-linear model, maximum power point (MPP), and partial shading (PS) effects. These two problems cause major power loss. To devise the maximum power point tracking (MPPT) control of the PV system, a novel group teaching optimization algorithm (GTOA) based controller is presented, which effectively deals with the PS and complex partial shading (CPS) conditions. Four case studies were employed that included fast-changing irradiance, PS, and CPS to test the robustness of the proposed MPPT technique. The performance of the GTOA was compared with the latest bio-inspired techniques, i.e., dragon fly optimization (DFO), cuckoo search (CS), particle swarm optimization (PSO), particle swarm optimization gravitational search (PSOGS), and conventional perturb and observe (P&amp, O). The GTOA tracked global MPP with the highest 99.9% efficiency, while maintaining the magnitude of the oscillation &lt, 0.5 W at global maxima (GM). Moreover, 13&ndash, 35% faster tracking times, and 54% settling times were achieved, compared to existing techniques. Statistical analysis was carried out to validate the robustness and effectiveness of the GTOA. Comprehensive analytical and statistical analysis solidified the superior performance of the proposed GTOA based MPPT technique.

Solar energy is a sustainable and inexhaustible renewable energy. Solar-powered PV systems contribute to the cleanest and the most cost-effective electrical energy. However, PV systems are confronted with a nonlinear maximum power point tracking (MPPT) problem caused by partial shading (PS). PS causes the loss of available power. This paper presents a novel Harris hawk optimization (HHO) based MPPT controller to effectively track maximum power under all weather conditions. The effectiveness of the proposed HHO based MPPT is supplemented by a comparative study with perturb and observe algorithm (P&O), dragonfly optimization algorithm (DFOA), particle swarm optimization (PSO), cuckoo search (CS) and grey wolf optimization (GWO). The analytical and statistical analysis is made based on 4 different cases, including fast varying irradiance, PS, complex-PS (CPS), and field atmospheric data of Hefei city of China, to solidify the effectiveness of HHO based MPPT for PV systems in real-world applications. The proposed HHO shows superior performance in tracking maximum power point and faster convergence at the global maximum power point. The improvement of 10–30% in tracking time and more than 90% in random oscillations are effectively achieved by the proposed MPPT technique. Faster maximum power point (MPP) tracking, lower computational burden and higher efficiency are the key contributions of the HHO based MPPT technique.

In recent years, solar photovoltaic power generation has been widely used in the world because of its eco-friendly and recyclable nature. It is therefore critical to extract maximum power from solar photovoltaic systems. Numerous maximum power point tracking (MPPT) techniques of solar photovoltaic systems have been proposed. Conventional MPPT techniques are usually limited to uniform weather condition. This paper presents a novel bio-inspired technique for Photovoltaic (PV) systems under various weather condition, which utilizes Salp Swarm Optimization (SSO) for effective MPPT. It makes use of the confined exploitation property of salps to track the maximum available power, especially under Partial Shading (PS), which may severely degrade the output power. Moreover, robustness and efficiency are significantly improved by the proposed SSO technique. The results of SSO in five different weather cases are tested against conventional MPPT techniques, such as Artificial Bee Colony (ABC) Optimization, Particle Swarm Optimization (PSO), PSO-Gravitational Search (PSOGS), DragonFly Optimization (DFO), Cuckoo Search (CS) Optimization, and, Perturb and Observe (P&O) algorithms. The proposed SSO technique can successfully tackle the global maxima (GM) under various weather conditions and demonstrates performance superiority in terms of efficiency, faster tracking, and stable output.

In this paper, a comprehensive review of essential components of the PV (Photovoltaic) system is elaborated, and their comparative unique features are discussed. The paper describes hardware design (power converters topologies specifically) employed in PV based energy generation systems to harvest maximum power from the available energy source. In this study, thirty different Maximum Power Point Tracking (MPPT) techniques have been critically analyzed and their response with respect to partial shading condition has been discussed. It is very difficult to say which technique is best as one must consider various factors and parameters while selecting a technique such as application, convergence speed, accuracy, efficiency, system reliability, and cost and performance of available hardware. Aiming at the complexity, hardware implementation, tracking speed, steady-state accuracy, or global maximum detection of the algorithm, an MPPT algorithm based on a rule table is proposed. In addition, the MPPT of a PV system based on bio inspired techniques is considered. The bio inspired algorithms and its application in PV system are compared for the authenticity of the review, and six different MPPT techniques are implemented on PV systems. A comparative analysis is made based on the results of four different cases of irradiance.