This article presents a simple and practical method for determining the optimal day-ahead (DA) schedule of electric vehicle charging stations (EVCSs) based on dynamic pricing signals for a renewable energy source integrated smart distribution system. The objective function of the proposed problem formulation is to minimize the real power loss payment, subject to real and reactive power flow operational constraints, bus voltage limits, and dispatching and storage constraints of EVCSs. The proposed problem formulation incorporates the modelling of demand response and uncertainties of electricity prices and loads forecasting. The proposed problem is solved by mixed-integer nonlinear programming, where the distribution network operator (DNO) responsible for conducting secure and efficient daily operations makes use of single-agent as well as multi-agent system control strategies for dispatching EVCSs, based on the security of data sharing between DNO and EVCSs. The simulations have been performed on a modified 12-bus radial distribution network, and the benefits of the presented approach are highlighted. The numerical results clearly demonstrate that the proposed method significantly optimizes the use of system and EVCSs compared to available conventional techniques.

The power density of electrical machines has increased significantly over the last century due to tremendous advancements in material science, particularly in the electrical insulation systems (EIS) domain. However, both industry and academia have limited experience with the design and production of electrical machines operating at winding temperatures above 240°C, owing to the application threshold temperature of EISs and other challenges associated with design, manufacturing, and testing. This article examines the key considerations for addressing these challenges and surpassing the prevailing threshold temperature. The design approach for these specialized machines, capable of continuous operation at 350°C, is presented. Additionally, the impact of temperature on various motor performance metrics is examined using finite element analysis (FEA) and further validated through an experimental study. Surpassing the temperature rise limit of conventional polymer-based EIS will enable higher power density, thereby reducing the consumption of critical and precious materials and helping the motor industry better align with green manufacturing initiatives. Furthermore, as components and systems related to the cooling circuit become redundant, system reliability and availability will improve, which is essential for the nuclear, aerospace, medical, and transportation industries.

Partial shading conditions cause suboptimal power output and intensify voltage differentials, leading to mismatch losses and localized potential degradation in solar cell performance. However, the impact of partial shading can be mitigated to a large extent through the reconfiguration of PV arrays. The proposed method group sequence rotation array (GSRA), a generalized reconfiguration strategy for total-cross-tied arrays applicable to both square and rectangular configurations. GSRA operates without additional MPPT hardware, sensors, or complex switching circuits, offering a low-cost and scalable solution. Simulations performed on 9 × 9 and 5 × 5 arrays, along with experimental validation on a 6 × 4 prototype, demonstrate that GSRA increases power extraction and reduces shading-induced losses. Compared with the conventional TCT topology, GSRA achieves approximately 40

Unlike conventional reconfiguration methods, the Cross-Diagonal Module Rearrangement Approach (CDMRA) proposed in this article provides a generalized and scalable solution for photovoltaic (PV) arrays of any dimension and configuration. The module rearrangement is performed once during system commissioning without altering terminal electrical connections, eliminating the need for additional sensors or switching networks. In CDMRA, PV modules are repositioned within their respective columns, reducing wiring complexity while enhancing shade tolerance. The effectiveness of the proposed approach is evaluated using key performance indicators, including power boost (PB), mismatch power loss (MPL), system efficiency (SE), row current discrepancy minimization (RCDM), fill factor (FF), and execution ratio (ER). CDMRA is tested under four distinct inconsistent irradiance scenarios (IISs) (Type-I, Type-II, Type-III, and Type-IV) and benchmarked against state-of-the-art reconfiguration techniques, including total-cross-tied (TCT), chaotic baker map (CBM), OpSuDoKu (OPSUD), Parquet SuDoKu (PRS), and chessboard game methodology (CBGM). Furthermore, the robustness of CDMRA against time-varying shading patterns is validated under dynamic cloud-drift conditions. Extensive experiments on diverse PV arrays validate the superiority of the proposed approach over established methods. By mitigating row current imbalance, the proposed scheme prevents multiple peak formations in the output power curve and maximizes global peak power (GPP).

This paper discusses the extensive research in hybrid renewable energy system (HRES) that demands the increasing need for eco-friendly and sustainable energy worldwide. The extensively used renewable energy resources mainly includes solar photovoltaic (PV) and wind. Though, due to their multi-distributed character do not favor stable and reliable grid-connected power generation. In this study, the integrated windphotovoltaic power system is model for grid support purposes. The proposed concept utilizes a voltage source converter (VSC) interfacing to the grid and encloses a wind turbine and solar panel with an associated photovoltaic array combined through a shared DC link. DC link voltage is regulated and both active power (P) and reactive power (Q) are controlled by synchronizing technology and pulse width modulation (PWM) in the VSC. The results of simulation shows that the power combination controller can perform the stable running, reasonable distribution power for sources and specified capacity in constant wind speed and solar irradiance. This hybrid scheme emerges as a reliable and efficient unfolding for smart grid systems and sustainable grid integration.

Mismatch among solar cells induces a thermal imbalance between shaded and unshaded cell(s), ultimately causing premature failure of the PV module. Bypass diodes (BPD) have conventionally been used to resolve this issue; however, this study demonstrates the incompetence of BPD in mitigating hotspots when the shaded PV module is in array interconnect. In this study, three different $3\times 3$ array topologies-Series-Parallel (SP), Bridge Linked (BL), and Total-Cross-Tied (TCT) are experimentally analyzed to assess BPD behavior. Results reveal that BPD fails to conduct across all interconnection topology, leading to hotspot formation, corroborated via thermographic pictures. notably, although a TCT-linked PV array exhibits minimal mismatch loss under partial shading, it suffers the highest misled loss and inferior thermal performance compared to other topologies. To address these challenges, this paper presents an improved hotspot detection and protection circuit (IHDPC), that enhances both electrical and thermal response of the PV systems by improving the sensitivity of BPD conduction towards shading. The efficacy of IHDPC is compared with the conventional BPD. Results indicate a substantial reduction in misled loss and maximum temperature of shaded module by up to 60.48% and 35.07%, respectively. Furthermore, IHDPC improves power generation, previously obstructed by hotspot protection circuits.

Partial shading in a building-mounted photovoltaic (PV) systems leads to decreased energy yield, and reduced overall system reliability. To address this, an adaptive PV layout is proposed via a novel reconfiguration strategy termed Ladder Circular Column Transformation (Ladder.CCT), which enhances power extraction under a wide range of partial shading conditions (PSCs). The Ladder.CCT approach is systematically evaluated through both MATLAB-based simulations and real-time experiments on a 5 & times; 5 PV array. To demonstrate its scalability and robustness, the method is further validated using extensive simulations on a larger 9 & times; 9 PV array. Performance assessment is carried out using prominent key indicators, including Global Maximum Power Point (GMPP), Power Enhancement (PE), Fill Factor (FF), Execution Ratio (ER), and Mismatch Loss (ML). Results indicate that Ladder.CCT achieves up to 44.64 % energy yield improvement while reducing the row current variance and standard deviation by up to 75 % and 79.3 % respectively, compared to the conventional Total-Cross-Tied (TCT) configuration. Further, it reduces ML by up to 33.9 %, significantly higher than other popular state-of-the-art-methods. Additionally, it surpasses several state-of-the-art reconfiguration techniques in terms of payback period (PP) and return on investment (ROI), while improving PP and ROI by up to 34.78 % and 88.27 % respectively.

ABSTRACT The significance of photovoltaic (PV) plants among renewable energy systems is unparalleled due to their ozone‐friendly features. However, their inherent nonlinear behavior for various uncertainties (due to change in irradiation) renders the suboptimal performance. Thus, there is a need for robust controllers. On the basis of daily operation curve of PV plant, parametric uncertainty is incorporated in nonlinear PV time delay models. Further, an intelligent H ∞ loop shaping controller is designed to improve the performance of PV plant subjected to parametric variations. The uncertain PV plant output with H ∞ loop shaping controller is compared with conventional PID controller and Smith Predictor control techniques. Simulation results show degraded performance with PID controller whereas Smith Predictor performs better. However, the best dynamics are obtained with H ∞ loop shaping controller for all operating points. The robust control design yields in significant reduction in rise time, settling time, rapid disturbance dismissal with a short peak time. Further, sensitivity and complementary sensitivity analysis is also carried out to validate the stability and robustness of plant with proposed H ∞ loop shaping controller under all operating points. Thus, proposed H ∞ loop shaping controller enhances the performance, stability and robustness of system manifolds for all operating points under uncertainty and nonlinearity.

Centrifugal pumps are an inseparable part of the sustainable life of the population across geographies, agriculture, energy, and all industrial sectors. Hence, pumps and their drives account for a significant portion of global energy consumption, and improving their energy efficiency is critical for sustainability and energy security. One approach to optimizing energy consumption is variable-speed operation of the driving motor with variable-frequency drives (VFDs), rather than the traditional method of throttling the pump valve, which wastes much of the energy when system demand fluctuates. However, deployment of this approach remains limited in scale, especially in non-industrial applications, due to a lack of full understanding of its technical and economic aspects. In the present paper, the impacts on various performance parameters of an 18.5 kW, 4-pole induction motor have been studied under variable-speed operation using Finite Element Analysis (FEA). The deviations in various performance parameters from the theoretical estimates are quantified, and their impact on pump operation is explored. The economic and environmental analyses showed that variable-speed control of the considered machine has an annual energy-saving potential of 35,400 kWh and a potential reduction in carbon emissions of 30.5 tons.

Unpredictable and irregular shading on solar photovoltaic (PV) panels are very common in reality that significantly degrades system performance by causing higher power loss, reduced energy yield, creating hotspots, and multiple local maxima in power–voltage (P–V), and current–voltage (I–V) characteristics. This paper proposes a novel triangular PV array configuration (T-PVAC) as a shading-resilient and highly efficient alternative to conventional PV array topologies. MATLAB-based simulation studies on various PV configurations are experimentally validated across six pragmatic shading scenarios: short-narrow, long-wide, diagonal, non-uniform row, non-uniform column, and random shading. Data collected over one week using 4 × 4 PV arrays at an average solar irradiation of 950 W/m2 and ambient temperature of 23 °C are analyzed through data envelopment analysis (DEA) to compare the relative efficiencies and cross-efficiency score of the six different PV panel configurations—series (S), SP, TCT, HC, 3CT, and T-PVAC configurations. The comparative assessments reveals that the proposed T-PVAC panels provide 24.6

Two new copper(II) compounds [Cu-2(2-NH2BDC)(2)(bipy)(2)(H2O)(4)] (1) and [Cu-2(mu (2)-OH)(2)(5-aip)(bipy)(2)(H2O)].5H(2)O (2) have been synthesized and characterized by elemental analyses, IR, thermogravimetric analysis, and electronic absorption spectroscopy. Molecular structures of compounds 1 and 2 were determined crystallographically. Single crystal X-ray studies show that the coordination network 1 is a centrosymmetrical dimeric unit with the 2-aminoterephthalate ligand bridging the two copper atoms. Each CuII atom is hexacoordinated, residing in an octahedral environment, surrounded by one oxygen and one nitrogen atoms from 2-aminoterephthalate ligand and two nitrogen atoms from one bipy ligand and two water molecules to give a CuO3N3 segmentand generate the an infinite three-dimensional (3D) supramolecular structure resulting from C-H center dot center dot center dot O interactions between adjacent dimer molecules running parallel to the a axis. In compound 2, each CuII atom is pentacoordinated, residing in a square pyramidal geometry and generate an infinite two-dimensional (2D) supramolecular structure resulting from C-H center dot center dot center dot O, N-H center dot center dot center dot O and O-H center dot center dot center dot O hydrogen bond interactions. The coordination network 1 and 2, on pyrolyzing, yielded copper oxide nanoparticles, which have been characterized by TEM and powder X-ray diffraction. The catalytic properties of the resulting copper oxide nanoparticles have also been studied for C-N cross-coupling reactions with aryl halides. The C-N coupling products were obtained in 70-99 % yields.

Shading remains a persistent issue that critically undermines the operational efficacy of photovoltaic (PV) systems by inducing substantial power loss. Among the numerous countermeasures, PV array reconfigurations have emerged as a reliable solution for mitigating shading effects. This article proposes a generalized static reconfiguration method, inspired by the odd-even approach for improved shade dispersion. This methodology is validated via both simulation and experimental execution on a 5 x 5 PV array, examined for various conventional and random shading patterns. A comprehensive simulation study is also performed on 9 x 9 PV array, to validate the effectiveness of proposed method on large PV arrays under dynamic shading scenario as well, demonstrating its potential for real-world applications. The performance of the proposed method is assessed against Series-Parallel (SP), Total-cross-tied(TCT), Index-based-matrix (IBM), Chaos-Map (CMap), and Tom-Tom (TT), Twisted-two step algorithm (TTSA), Parquet Sudoku (PRS.Sud), and Hyper Sudoku (Hyp.Sud), and Modified Chess Knight Method (Mod.CKM). The results obtained show a substantial reduction of up to 35.27 % in mismatch loss (ML), with significant improvements of nearly 150 % in Fill-factor (FF), Power Enhancement (PE), and Execution Ratio (ER) respectively with respect to TCT, outperforming other compared approaches. Further, this study pioneers the incorporation of a prominent statistical metric, standard deviation (SD), to assess inter-row current uniformity among compared approaches. The findings reveal a significant reduction in SD by up to 100 % compared to TCT configuration.

Due to the various peaks that are produced, partial shadowing (PS) is one of the key environmental elements that affect the maximum power (MP) output of photovoltaic (PV) systems. The current research describes a unique reconfiguration repeated application of a derived fundamental integer method based on the recursive addition approach protecting PV systems and abstract maximum power under partial shading conditions. This novel approach attempts to maintain a consistent row current by physically shifting the panels while maintaining the electrical connections. It enhances power by making sure the least number of shaded panels are put in the same row. A 9×9 PV array with short narrow, short wide, and long wide shading patterns is used in this study to test the effectiveness of the suggested approach using software models. The efficacy of the suggested method is also compared to other static reconfiguration approaches that have been proposed in the past, and it is found to be more effective for all the shading patterns taken into consideration in the current work. The increase in output power is as high as 34

With the continual efforts by researchers and sustained investments around the globe over the last three decades, over 21 different photovoltaic technologies have been developed with technical efficiency high enough to achieve commercial viability. However, a significant chunk of the market share (97.4%) is held by crystalline silicon (c-Si) technology. Thin film technologies, despite being cheaper and less material guzzlers than c-Si technologies, constitute only about 2.5% of the market share as of date, and all other technologies mostly remain confined to research scale only. This paper critically analyses the veracity of such biases towards c-Si technology deployments. For a holistic comparative analysis in the present work, various parameters are considered that have socio-environmental impact, including water and energy consumption, the consumption and emission of ecotoxic and hazardous materials throughout the complete life cycle, and the greenhouse potential of directly consumed materials and byproducts of manufacturing processes. The findings of the analysis, particularly the critical analysis and comparison of the manufacturing process, reveal that the ecological footprint of PV technologies is significantly larger at the manufacturing stage than at the end-of-life waste management stage; however, this aspect has not been given due importance by researchers or policymakers in the past. Secondly, thin film technology, especially cadmium telluride (CdTe), is found to have several advantages over the c-Si technology with regard to ecotoxicity and overall ecological footprint. Hence, a policy intervention is advocated to promote and incentivize technologies with a lesser environmental impact, such as CdTe.

As the need for sustainable and clean energy solutions grows, solar energy systems are becoming an increasingly important part of the solution to the world's climate problems. However, operational uncertainty and environmental unpredictability have a major impact on photovoltaic (PV) system efficiency. With an emphasis on energy forecasts, real-time changes, and system performance enhancements, this study investigates the integration of artificial intelligence (AI) for solar energy system optimization. To forecast energy generation, maximize energy storage, and improve operational efficiency, artificial intelligence (AI) approaches like Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks, and hybrid AI models are used. Through AI-integrated numerical weather forecasting, the suggested AI-embedded solar energy framework reduces absolute forecasting error by 51.7%, hourly prediction variations by 9-20%, and Root Mean Square Error (RMSE) by 18-20%. IoT-enabled real-time monitoring systems and cloud-based artificial intelligence platforms allow for dynamic energy modifications in response to variations in power generation and demand. The findings show notable advancements in cost reduction, energy balancing, and predictive maintenance, guaranteeing sustainable energy supply. Additionally, forecasting powered by AI improves grid dependability and makes it easier to integrate dispersed energy sources. This study demonstrates AI's revolutionary potential to improve PV system efficiency, lower transmission losses, and lower energy expenses. Future research highlights how crucial it is to overcome privacy issues, data standardization, and ethical considerations in order to achieve broad use of AI technologies in renewable energy systems.

The rotor eccentricity fault is one of the weak links in the reliability chain of induction motors (IM), and the commercially available solutions for its detection are complex, costly, and, most importantly, require service interruption. Therefore, in real-world scenarios, measurements of rotor eccentricity are not common unless its effects become noticeable through increased noise, vibrations, or bearing failure. However, such practices exacerbate the severity of faults and can lead to catastrophic and cascading failures. In this work, the effects of various eccentricity faults are discussed in detail, and a search coil-based solution is explored for online detection of rotor eccentricity events without interrupting machine operation. The proposed method is based on the principle of induced voltage compensation, utilizing coils placed near the air gap to achieve better accuracy. Finite element analysis demonstrates that this technique not only quantifies the extent of eccentricity with reasonable precision but is also capable of generating metadata such as the type of eccentricity and residual bearing life. The effectiveness of the fault detection method is verified through experiments with different configurations of search coils, demonstrating their ability to identify bearing abnormalities and eccentricity faults early, proving its potential to prevent catastrophic failures, especially in critical applications such as nuclear power, chemical, aerospace, and defense.

In the context of increasing global solar Photovoltaic (PV) technology adoption for electricity generation, the performance degradation of photovoltaic panels due to the accumulation of various soil and ash types is investigated by various studies and experimental works. Eight common particulate types - coal ash, brick powder, cement, wood ash, house dust, yellow sand, construction sand, and pit sand - were tested with a set laboratory condition for three irradiance levels (900, 680, and 360 W/m(2)). Experimental outcome reveals that coal ash had the most severe impact, reducing PV efficiency by 36.0%, 34.0%, and 33.2%, respectively, due to its fine particle size, high absorption coefficient (1.5-2.0 cm(-1)), and dense surface coverage, as confirmed by microscopic and X-ray imaging. In contrast, yellow and pit sand showed less than 15% performance loss. Optical parameters such as the reflection coefficient (similar to 0.04-0.05) and refractive index (1.3-1.6) were analyzed to quantify light transmission losses. To evaluate the relative performance, Data Envelopment Analysis (DEA) was carried out, treating each test case as a decision-making unit (DMU), with irradiance and soiling weight as inputs and P-max, I-sc, V-oc as electrical outputs of PV panels. The cross-efficiency score (CES) framework is used to eliminate bias in weight selection and validate rankings. Coal ash consistently ranked lowest in DEA results, reinforcing its status as the most detrimental contaminant. This multidisciplinary approach provides an evidence-based framework for optimizing site selection, scheduling maintenance, and policy formulation for solar PV deployment in dust-prone regions, especially those near thermal power plants and civil construction work zones, enabling strong support to the sustainable PV performance enhancement.

Uneven irradiance conditions over a PV array have a profound influence on its overall energy yield and operational reliability. To address this, a novel shade resilient PV array architecture is proposed in this paper that strategically repositions PV modules with minimal shifting. The proposed method is simulated on a $9 \times 9 \text{PV}$ array, and its performance is extensively compared with other popular state-of-art-methods (i.e., Optimal SudoKu, Parquet SudoKu, Chaos Map, Index Based Matrix, and Odd-Even-Prime), based on five critical indicators, namely power improvement, mismatch loss, fill factor, execution ratio, and row current variance. Further, this study pioneers the incorporation of a prominent statistical metric, standard deviation, to assess the wiring requirements of various reconfiguration approaches. The results reveal a substantial reduction of up to 35.27% in mismatch loss, and up to 100% reduction in standard deviation and row current variance by proposed method with respect to conventional TCT configuration.

The growing competitiveness of solar photovoltaic (PV) panels as a sustainable energy option has led to an increase in the installation of PV panels in recent pass. The power output of solar panels highly depends on various environmental factors such as wind, humidity, solar radiation, and temperature. Under adverse weather conditions or unfavorable conditions, it is difficult for solar panels to provide the best efficiency. Understanding the anticipated power generation of solar panels in advance is crucial for the proper configuration and optimization of their potential. This study introduces an approach to anticipate the power output of solar panels by employing artificial neural network techniques or algorithms, while considering diverse environmental variables, such as wind, humidity, and temperature. We created artificial neural network models using the NN tool in MATLAB. Our datasets were collected from www.github.com . A comparative analysis of various artificial neural network models conducts in this article based on five environmental factors: temperature of the air, solar radiation, humidity relative to the air, and direction and speed of the wind solar radiation. The experimental findings indicate that these features serve as effective predictors for estimating the power output of solar panels.