Solar photovoltaic (PV) systems continue to be the most prevalent renewable energy resource despite the presence of numerous limitations. A power discrepancy between PV modules on a large scale may result in power dissipation throughout the entire PV system. This particular paper proposes an efficient multi-port converter for distributed maximum power point tracking operation (D-MPPT) for a solar PV system. The operation details of the proposed multi-port converter along with analytical waveforms are presented in this paper. To implement the D-MPPT approach in the proposed multi-port converter, a detailed analysis of mathematical modeling of solar PV systems with a mismatch of PV power and voltage stabilization approach is done. In addition, the proposed approach eliminates the need for additional current sensors and semiconductor components to overcome the effect of mismatched power in the PV system. To validate this, the prototype has been built and integrated with the real environment of the solar PV system. To verify the operation, a detailed simulation study and experimental investigation have been carried out and presented in this paper which reveals that the proposed system offers 24% improved power extraction compared to the centralized converter and MPPT method under partially shaded conditions. After a detailed investigation and discussion of measured results and analysis, it is concluded that the proposed multi-port DC-DC converter is the most suitable solution for solar PV applications.

Background: Microgrid is the recent decade terminology that surpasses the long-run issues associated with the public and utility grids. Among the renewable energy sources, solar PV units have gained greater importance owing to their huge potential availability and laidback operating characteristics on technological grounds. Conversely, it offers pollution-free electricity and perhaps the dependability is volatile in most situations. The literature study accumulates the foresaid setback and presents the fluctuation-less and controlled standard quality of power outputs. Objective: The aim of this particular research is to propose an assessment of Power Quality enhancement in a Grid-tied photovoltaic (PV) network via ANN-based UPQC. The novel idea behind this proposed approach is the UPQC component which deliberately regulates and controls the power system to achieve higher levels of power quality, ultimately meeting the recent IEEE standards. Method: This particular research enhances the performances of UPQC employed in the microgrid unit by replacing the traditional PI controller with a multi-layered feed-forward-type ANN controller for the current regulation of the series active filter. Additionally, a training algorithm for the ANN controller is built, trained and simulated via MATLAB/Simulink platform. The ANN-based UPQC is proposed to alleviate the power quality challenges like sag and swell in voltage, harmonic distortion, the time required for voltage compensation, and power factor. Therefore, UPQC is equipped to enrich the standard of power transfer at the point of common coupling inside the power frameworks, respectively. Result: Finally, the simulation results are presented to validate the operation of the grid-tied PV network via an ANN-based UPQC system. To show the enriched performance of the proposed topology, a comparative analysis is made with PI controller-based UPQC, and outcomes infer to be in agreement with the theoretical discussions. Also, the ANN-based proposed approach reduces the restoration time and THD as well under both sag and swell conditions, respectively. Conclusion: In this articulated work, a PV power system network with a DC-DC converter and three-phase inverter is employed for grid integration. The peak power extraction is ensured via a DC-DC converter with an incremental conductance algorithm. Both UPQCs are analysed and experimented via MATLAB/Simulink platform with inconstant nonlinear loads to investigate the indices mentioned above and corroborate the same within the operating regions.

Small‐signal stability is an important task and key research in electrical engineering for networks. This research article focuses on the implementation of a multi‐objective approach for choosing an optimal location for Phasor Measurement Units (PMUs) to quantify a power system's small‐signal stability by maximizing the signal‐to‐noise ratio (SNR) in the system. The novelty of this research lies in the implementation of Dingo Optimization (DOX) technique along with the Prony Analysis (PA) approach for the assessment of small‐signal stability in standard grid networks. The voltage angle, amplitude and the range of frequencies are measured by the optimal placement of PMUs, which primarily focus on the multi‐signal PA. To achieve the objective of this research, DOX integrated with the multi‐signal PA approach is used to determine the ideal position for PMU placement by considering maximum redundancy and optimizing the signal to noise ratio to a maximum level. The effectiveness of the DOX strategy is established with improved accuracy and fewer disturbances by optimizing the electromechanical oscillations of the system. The implementation of the DOX approach for attaining the best value of the maximized SNR is obtained by analyzing a wide set of conditions, perturbations, and additive noise, which provides an accurate assessment of damping ratio (DR) and frequency ( f ) of electromechanical oscillations. Numerical results obtained from the standard IEEE test systems (14, 39, 57, 118, and 300 bus systems) are compared with the existing methods in the literature. The statistical indices demonstrate that under the highly limited optimization context selected, the intended optimizer functions satisfactorily.

Renewable energy sources are being incorporated into power networks to guarantee dependable and inexpensive electricity for the urban and industrial sectors. Recent research approximates microgrids powered by renewable energy and controlled by smart grids may provide more reliable and efficient energy systems economically. Its natural interaction among renewable energy sources, electric demands, and energy storage devices makes DC microgrid a contemporary electrical grid technology. In this paper, a nonlinear control approach with outstanding precision, robustness, and simplicity of tuning and implementation, is made to deal with the control of the DC microgrid system to bring out the efficacy of the system at higher points. A closed-loop continuous time sliding mode controller is proposed for a boost converter with a cascade filter. DC microgrid system with sliding mode controller is simulated and the analysis has been carried outs with motor load to check the adequacy of the proposed system.

Nowadays, the modern world has more concern about increasing population as well as environmental changes.Hence using the Renewable Energy Sources (RES), power generation systems have actively developed in many countries.One of the main sources of RES is solar energy.In solar energy system, emitted energy from the sun is directly converted to electricity in Photovoltaic (PV) system.This research focuses on the advanced techniques based Maximum Power Point Tracking (MPPT) on grid connected and standalone solar PV applications.In this presents the MPPT of standalone solar PV system with Luo converter.Here, to authenticate the performance of the MPPT system with Luo converter, an incremental conductance technique of SPWM and SVM are used.The comparison of maximum power point tracking of integrated PV system with Sinusoidal Pulse-Width Modulation (SPWM) and Space Vector Pulse Width Modulation (SVPWM) techniques.The SVM performance is greater than the SPWM technique and also SVM technique provides better result than the SPWM.Here, Man of League Algorithm (MLA) is presented for the purpose of preserving the dc voltage of the system.

Direct Current microgrid has emerged as one of the major research fields in recent years due to the higher level of benefits of the DC system when compared to the AC system, such as energy storage, flexibility with modernized loads, and renewable energy sources. Many studies have been conducted on the DC microgrid system in order to improve its efficiency. In this work, by using conventional PI controllers and advanced controllers like fractional order proportional integral derivative (FOPID) and proportional resonance (PR) controllers, a DC microgrid system is controlled. A closed-loop DC micro-grid containing sustainable power sources, stockpiled components, and transients is introduced. The PI controller is the most commonly used in power frameworks for assisting with the control of microgrids. Aside from PI, there are a slew of independent and hybrid regulators that can serve significantly better than PI in terms of microgrid framework relevance. The closed-loop DC microgrid framework systems with FOPID-FOPID and PR-PR-based DC microgrid structures are investigated and re-enacted utilising Simulink in MATLAB, and their outcomes are introduced. This paper also looks at advanced solar cell material technology, which has been used as an energy source in the DC microgrid system for higher power delivery. In this work, the performance of several solar cell types is compared along with an overview of recent developments in solar cell technology.

PurposeCurrently, more renewable energy resources with advanced technology levels are incorporated in the electric power networks. Under this circumstance, the attainment of optimal economic dispatch is very much essential by the power system as the system requires more power generation cost and also has a great demand for electrical energy. Therefore, one of the primary difficulties in the power system is lowering the cost of power generation, which includes both economic and environmental costs. This study/paper aims to introduce a meta-heuristic algorithm, which offers an solution to the combined economic and emission dispatch (CEED).Design/methodology/approachA novel algorithm termed Levy-based glowworm swarm optimization (LGSO) is proposed in this work, and it provides an excellent solution to the combined economic and emission dispatch (CEED) difficulties by specifying the generation of the optimal renewable energy systems (RES). Moreover, in hybrid renewable energy systems, the proposed scheme is extended by connecting the wind turbine because the thermal power plant could not control the aforementioned costs. In terms of economic cost, emission cost and transmission loss, the suggested CEED model outperforms other conventional schemes genetic algorithm, Grey wolf optimization, whale optimization algorithm (WOA), dragonfly algorithm (DA) and glowworm swarm optimization (GSO) and demonstrates its efficiency.FindingsAccording to the results, the suggested model for Iteration 20 was outperformed GSO, DA and WOA by 23.46%, 97.33% and 93.33%, respectively. For Iteration 40, the proposed LGSO was 60%, 99.73% and 97.06% better than GSO, DA and WOA methods, respectively. The proposed model for Iteration 60 was 71.50% better than GSO, 96.56% better than DA and 95.25% better than WOA. As a result, the proposed LGSO was shown to be superior to other existing techniques with respect to the least cost and loss.Originality/valueThis research introduces the latest optimization algorithm known as LGSO to provide an excellent solution to the CEED difficulties by specifying the generation of the optimal RES. To the best of the authors’ knowledge, this is the first work that utilizes LGSO-based optimization for providing an excellent solution to the CEED difficulties by specifying the generation of the optimal RES.

In the power system, the economic dispatch (ED) problem is the key issue, while fossil fuels cause environmental pollution. The allocation of power generation is included in the actual economic load dispatch issue of power generation for reducing the operating cost. This creates the economic load dispatch issue, a large-scale, highly nonlinear controlled optimization issue. The major issue in the power systems is the loss, fuel cost, and emission. The existing algorithms can optimize the parameters mentioned above, but it is not much better. Hence, this paper presents the multi-objective multi-verse optimization (MOMVO) for the dynamic load dispatch problem. The dynamic load dispatch issue is evaluated by cost and emission evaluation with hybrid renewable energy Sources (RES). Here, the proposed algorithm generates the thermal, photovoltaic (PV) and wind power values, reducing the cost and emission values. The unit's power generation is the foremost aim of dynamic economic load dispatch (DELD) to meet the load demand while sustaining various operational constrictions; the generation's total cost is reduced. The MVO algorithm is applied to nonlinear DELD issues and is a reliable and robust optimization algorithm. The introduced scheme is implemented and tested over three test systems, such as 6, 10, and 11 generating units. The implementation is performed on the MATLAB R2016a platform, and the performance results are evaluated based on with and without valve-point loading (VPL). Finally, VPL produced better solutions than the without VPL case.

The optimization field has grown tremendously, and new optimization techniques are developed based on statistics and evolutionary procedures. Therefore, it is necessary to identify a suitable optimization technique for a particular application. In this work, Black Widow Optimization (BWO) algorithm is introduced to minimize the cost functions in order to optimize the Multi-Area Economic Dispatch (MAED). The BWO is implemented for two different-scale test systems, comprising 16 and 40 units with three and four areas. The performance of BWO is compared with the available optimization techniques in the literature to demonstrate the strategy???s efficacy. Results show that the optimized cost for four areas with 16 units is found to be 7336.76$/h, whereas it is 121,589$/h for four areas with 40 units using BWO. It is also noted that optimization algorithms other than BWO require higher cost value. The best-optimized solution for emission is achieved at 9.2784e+06 tones/h, and it is observed that there is a considerable difference between the worst and the best values. Also, the suggested technique is implemented for large-scale test systems successfully with high precision, and rapid convergence occurs in MAED.

Past studies, current conditions and future energy needs lead to a man's move to renewable energy sources, especially solar energy due to its benefits such as high availability, pollution, low gas emissions, low maintenance costs, limited site selection, no machine noise etc. Solar systems, however, have their drawbacks, which work well with different temperature and solar irradiance conditions.To get high energy in those conditions, following the point of high energy is very important in Photovoltaic systems.To date many alternatives to high power was proposed and successfully implemented.In this paper the best perturb strategies were proposed and look at the genre with the MATLAB / SIMULINK Environment where there are solar panels to serve as good reference in upcoming users of the direct of great energy in Photovoltaic systems.

The world's energy offer has been beneath an incredible pressure because of the speedy depletion of fossil resources, energy security, environmental issues and therefore the ever-increasing fashionable living sophistication. The problem of persistent hikes in oil costs, climate threats and soaring energy demand has pleased the worldwide interest to exploiting and investment in renewable sorts of energy (RE), alternative energy specially. A electrical phenomenon, PV system is simple to put in, has no moving components, is sort of freed from maintenance, reduced vulnerability to power loss and is expandable. Despite these benefits, PV energy prices significantly on top of fossil fuels. This can be because of its lower effectiveness and better prices. In PV systems tracking MPPT in effective manner is still the problem. In this paper, the 1000W grid connected PV system has been taken for analysis of various MPPT techniques. Grid connected PV system modeled, tested under totally different irradiation conditions and conjointly for partial shading conditions. additional it's enforced under partial shading condition for early MPPT ways, improvement methodology,at finally adopted deep learning methodology for the system and therefore the obtained results were compared with different methods.

State estimate serves a crucial purpose in the control centre of a modern power system. Voltage phasor of buses in such configurations is referred to as state variables that should be determined during operation. A precise estimation is needed to define the optimal operation of all components. So many mathematical and heuristic techniques can be used to achieve the aforementioned objective. An enhanced power system state estimator built on the cuck search algorithm is described in this work. Several scenarios, including the influence of load uncertainty and the likelihood of false data injection as significant challenges in electrical energy networks, are proposed to analyse the operation of estimators. The ability to identify and correct false data is also assessed in this regard. Additionally, the performance of the presented estimator is compared to that of the weighted least squares, Cuckoo Search algorithm and grey wolf Optimizer. The findings demonstrate that the grey wolf Optimizer overcomes the primary shortcomings of the conventional approaches, including accuracy and complexity, and is also better able to identify and rectify incorrect data. On IEEE 14-bus and 30-bus test systems, simulations are run to show how well the method works.

Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions.

Closed-loop DC Micro-Grid containing sustainable power sources, stockpiling components and burdens are introduced. The regulators for helping the control of mico-grid are numerous in the power-frameworks, yet the most normally utilized one is PI regulator. Aside from PI there are, an entire bundle of independent regulators and hybrid regulators that can serve significantly better compared to PI for microgrid framework relevance’s. Closed-loop DC microgrid framework system with FOPID-FOPID (Fractional-order-Proportional-Integral Derivative controller) and PR—PR (Proportional-Resonant Controller) based DCMG structures are investigated and re-enacted utilizing Simulink in MATLAB and their outcomes are introduced. After re-enactment analyses, some preliminary eventual outcomes of FOPID-FOPID and PR-PR controlled DCMG framework are given to actually look at the sufficiency of the proposed framework.

Among Renewable Energy Sources wind and solar energy are the most prominent and favorable substitutes to meet mankind’s future electricity requirements. Typically, these resources are combined in a distribution network to provide local distribution users. The amalgamation of these upgrades into the distribution network may modify the malfunction and network topologies, which may fail due to their pre-set state. Therefore, an advanced and accurate tracking device must continuously monitor topology changes, which is the Phasor Measuring Unit (PMU). This work proposes a new technique based on the Sea Lion Optimization Algorithm to determine the Optimal PMU Placements and its employment positions, i.e. the power structure is completely perceptible. In addition, the cost of system losses can be calculated with or without wind energy to achieve energy reserves. The energy losses before and after the wind turbine connection is compared to realize energy reserves. This comparison of energy reserves is made using Realistic and Stochastic platforms. Comparison and proposed formulas offered in the IEEE 15, 33, 69, and 85 bus distribution networks were explored to prove their effectiveness. Moreover, the proposed methodology produces more trusted results than those of other methods in the literature.

The technological advancements and policy reformations make the electric power distribution system an active network. The modern power system operational regulations encourage coordinated network operations among various entities and customer participation. Unlike traditional operational environments, real-time monitoring has become a mandate for secure and reliable operations. Phasor Measurement Units (PMU) is the most reliable data acquisition tool, and their application for State Estimation (SE) has already been proven. This work extends the application of PMUs for the distribution system. A multi-objective optimization framework is proposed considering the optimal placement of PMU, network reconfiguration, and topology expansion. A novel optimization tool, the sea lion optimizer has been chosen to address the developed optimization problem. The proposed operational model is employed in IEEE RBTS-2 bus system, IEEE 33 node system and 69 node radial distribution network. Numerical results provide new insights into PMUs role in enhancing distribution network operations. The statistical indices confirm that the intended optimizer performs well in the chosen highly constrained optimization environment.

Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions.

For improving the working efficiency of sensors and for testing them under different conditions, a predictive algorithm is essential. This is possible only when deep learning methods are used, where different strategies are followed when any problem occurs on the network. The health-monitoring system has been integrated with SDAE, and the results are simulated using MATLAB encoder toolbox. The simulation setup for health monitoring using SDAE has been executed, and similar works of other researchers that include artificial neural networks have been investigated, and the results are also compared. The results are simulated by considering parameters such as path loss, cost, energy, and lifetime. The efficiency of SDAE was tested with four deep learning techniques, including SAE, LTS, K-nearest neighbor, and neural network methods, and SDAE proved very efficient in terms of sensor integration for monitoring the health of individuals when different sensor nodes are placed in the human body.

This paper elucidates a procedure for ascertaining the performance of induction generators to disseminate the inevitable role of an induction machine in an alternate energy conversion among undergraduate and postgraduate students. Initially, a circle diagram is constructed for generator mode and motor mode using a drawing tool—AutoCAD from a simplified, energy consumption predetermination test data. Later a set of data is derived and is trained using an innovative, powerful pedagogical tool-radial basis function neural network to predict the performance indices of an induction machine. The proposed technique is compared with the experimental results of a three-phase 5 HP induction machine for various loaded conditions. Further, it provides encouraging results that will help the manufacturers to become more involved in the performance strategies of induction machines. Thus, the noninvasive method avoids the manipulation of an algebraic equation of nth order polynomial equations in predicting the generator performance indices.

The electrical power generation from fossil fuel releases several contaminants into the air, and these become excrescent if the generating unit is fed by multiple fuel sources (MFS). The ever more stringent environmental regulations have forced the utilities to produce electricity at the cheapest price and the minimum level of pollutant emissions. The restriction in generator operations increases the complexity in plant operations. The cost effective and environmental responsive operations in MFS environment can be recognized as a multi-objective constrained optimization problem. The ant lion optimizer (ALO) has been chosen as an optimization tool for solving the MFS dispatch problems. The fuzzy decision-making mechanism is integrated in the search process of ALO to fetch the best compromise solution (BCS). The intended algorithm is implemented on the standard test systems considering the prevailing operational constraints such as valve-point loadings, CO2 emission, prohibited operating zones and tie-line flow limits.