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We present an algorithm for evaluating analytic nuclear energy gradients of the state-averaged density matrix renormalization group complete-active-space self-consistent field (SA-DMRG-CASSCF) theory based on the newly derived coupled-perturbed (CP) DMRG-CASSCF equations. The Lagrangian for the conventional SA-CASSCF analytic gradient theory is extended to the SA-DMRG-CASSCF variant that can fully consider a whole set of constraints on the parameters of multi-root canonical matrix product states formed at all the DMRG block configurations. An efficient algorithm to solve the CP-DMRG-CASSCF equations for determining the multipliers was developed. The complexity of the resultant analytic gradient algorithm is overall the same as that of the unperturbed SA-DMRG-CASSCF algorithm. In addition, a reduced-scaling approach was developed to directly compute the SA reduced density matrices (SA-RDMs) and their perturbed ones without calculating separate state-specific RDMs. As part of our implementation scheme, we neglect the term associated with the constraint on the active orbitals in terms of the active–active rotation in the Lagrangian. Thus, errors from the true analytic gradients may be caused in this scheme. The proposed gradient algorithm was tested with the spin-adapted implementation by checking how accurately the computed analytic energy gradients reproduce numerical gradients of the SA-DMRG-CASSCF energies using a common number of renormalized bases. The illustrative applications show that the errors are sufficiently small when using a typical number of the renormalized bases, which is required to attain adequate accuracy in DMRG's total energies. [ABSTRACT FROM AUTHOR]

Abstract The article introduces a new model of a Reference Signal Tracking (RST) controller for the fractional order quantities. The controller is applied to an indirect field‐oriented control (IFOC) system used for the speed control of an asynchronous motor powered by a photovoltaic (PV) generator with a maximum power point tracking (MPPT) algorithm. This model utilizes two fractional order controllers: the fractional order proportional‐integral (FOPI) regulator and the fractional‐order integral‐proportional (FOIP) regulator. These controllers are used with the generalized predictive control (GPC) technique. The first step in the approach is to derive the equivalent digital RST controller's model from the FOPI and FOIP controller's transfer functions. The GPC technique converts the continuous‐time FOPI (and FOIP) controller into a discrete‐time version. This conversion ensures a fast response and effective disturbance rejection. Simulation tests are conducted to analyze the rotor speed and stator current ripples to evaluate the performance of the proposed method. The results demonstrate the effectiveness of the introduced scheme in achieving improved control performance in terms of response speed and disturbance rejection. The article presents a modified RST controller model based on the fractional order approach applied to an IFOC system for motor speed control driven by a photovoltaic generator. Using FOPI, FOIP controllers, and GPC contributes to enhanced control performance, as evidenced by the simulation results.

Abstract This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT.

Using a particle swarm optimization algorithm (a population-based stochastic optimization technique) combined with 3D finite-difference time-domain simulations, we inverse design periodic arrays of metallic nanoparticles on indium-tin-oxide electrodes and nanoholes in metallic thin films working as electrodes in P3HT (Poly(3-hexylthiophene-2,5-diyl)):PCBM ([6,6]-Phenyl C61 butyric acid methyl ester) organic solar cells to achieve the maximum short-circuit currents (J s c ). Nanohole-array electrodes have large optical losses, leading to a net reduction of J s c compared to a reference solar cell. On the other hand, nanoparticle arrays can lead to a significant enhancement of J s c of up to 20%. Detailed simulations show that this enhancement is caused by the grating coupling of the incident light to surface plasmon polaritons at the interface of the metal electrode and the hole transport layer, leading to the enhancement of the electromagnetic field in the organic blend. [ABSTRACT FROM AUTHOR]

Efficient photovoltaic devices must be efficient light emitters to reach the thermodynamic efficiency limit. Here, we present a promising prospect of perovskite photovoltaics as bright emitters by harnessing the significant benefits of photon recycling, which can be practically achieved by suppressing interfacial quenching. We have achieved radiative and stable perovskite photovoltaic devices by the design of a multiple quantum well structure with long (∼3 nm) organic spacers with oleylammonium molecules at perovskite top interfaces. Our L-site exchange process (L: barrier molecule cation) enables the formation of stable interfacial structures with moderate conductivity despite the thick barriers. Compared to popular short (∼1 nm) Ls, our approach results in enhanced radiation efficiency through the recursive process of photon recycling. This leads to the realization of radiative perovskite photovoltaics with both high photovoltaic efficiency (in-lab 26.0%, certified to 25.2%) and electroluminescence quantum efficiency (19.7 % at peak, 17.8% at 1-sun equivalent condition). Furthermore, the stable crystallinity of oleylammonium-based quantum wells enables our devices to maintain high efficiencies for over 1000 h of operation and >2 years of storage. Efficient radiation is essential to reach thermodynamic limit of photovoltaic efficiency. Here, authors design thick quantum barriers to suppress interfacial quenching and boost photon recycling in perovskite cells, achieving high radiation and photovoltaic efficiencies and long device stability. [ABSTRACT FROM AUTHOR]

In a methylammonium‐free (MA‐free) composition, the uncontrollable crystallization process between Cs and formamidine (FA) currently hinders its efficiency enhancement, especially in inverted perovskite solar cells (PSCs). Here, a dual‐interface modification of perovskite films is proposed by simultaneously introducing additives and surface passivators. In particular, (aminomethyl)phosphonic acid (AMP) is introduced into the precursor solution to balance crystallization by inducing the preferential crystallization of FA through the specific formation of strong hydrogen bonds with FA. In addition, AMP spontaneously sinks and anchors to the buried interface to fill the voids of the self‐assembled monolayer (SAM) via the covalent bonds formed by ─PO3H2 and FTO. Subsequently, by the sequential modification of perovskite surface with 2‐(3‐fluorophenyl)ethylamine iodide (mF‐PEAI) and piperazine diiodide (PDI), a uniform surface potential is achieved and recombination losses at the interface are minimized. Notably, the dual‐interface‐modified inverted MA‐free PSCs achieve a state‐of‐the‐art power conversion efficiency (PCE) of 25.35% (certified: 24.87%) with a satisfactory Voc of 1.17 V based on the bandgap of 1.52 eV. Importantly, the unencapsulated devices maintain 92.8% and 91.7% of the initial efficiency after 1000 h of maximum power output (MPP) tracking and >800 h of heating at 85 °C, respectively, confirming excellent operational and thermal stability. [ABSTRACT FROM AUTHOR]

To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper. [ABSTRACT FROM AUTHOR]

In this paper, single-diode model (SDM) and double-diode model (DDM) parameters of the French RTC solar cell and the Photowatt PWP 201 photovoltaic (PV) module were extracted by combining five metaheuristic algorithms with three simulation current calculation methods (i.e., approximation method, Lambert W method and Newton–Raphson method), respectively. It was found that the parameter-extraction accuracies of the Lambert W (LW) method and the Newton–Raphson (NR) method are always approximately equal and higher than that of the approximation method. The best RMSEs (root mean square error) obtained by using the LW or the NR method on the solar cell and the PV module are 7.72986 × 10−4 and 2.05296 × 10−3 for SDM parameter extraction and 6.93709 × 10−4 and 1.99051 × 10−3 for DDM parameter extraction, respectively. The latter may be the highest parameter-extraction accuracy reported on the solar cell and the PV module so far, which is due to the adoption of more reasonable DDM parameter boundaries. Furthermore, the convergence curves of the LW and the NR method basically coincide, with a convergence speed faster than that of the approximation method. The robustness of a parameter-extraction method is mainly determined by the metaheuristic algorithm, but it is also affected by the simulation current calculation method and the parameter-extraction object. In a word, the approximation method is not suitable for application in PV-model parameter extraction because of incorrect estimation of the simulation current and the RMSE, while the LW and NR methods are suitable for the application for accurately calculating the simulation current and RMSE. In terms of saving computation resources and time, the NR method is superior to the LW method. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV)-based power generation systems are becoming increasingly popular as a due to its high performance and cleanliness. Several factors influence the performance of a PV system, including shadowing effects. PV systems employ MPPT methodologies to obtain the power from PV array. Conventional MPPTs works well under normal conditions when there is no shadow effects or partial shading. The presence of partial shading affects the system performance and generates several power peaks. This complicates the process of finding out of the global peak (GMPP) with improved tracking efficiency and reduced settling time including conversion efficiency. This work proposes three hybrid MPPT techniques: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), and two conventional MPPT techniques WCO and P&O have been implemented. The proposed system utilizes interleaved boost converter with three phase. The performances of proposed hybrid MPPTs strategies were compared in terms of output voltage, output current and extracted power. The comparison also includes conversion efficiency and average settling time. To analyse the performances, four different cases have been used to test the efficacy of hybrid MPPTs under changing climatic conditions. The MATLAB/Simulink tool has been used to analyze the PV system performances. In the three hybrid MPPT techniques, WCO-PO has performed better when compared to other two hybrid MPPTs in terms of conversion efficiency (99.56%) and settling time (1.4 m). [ABSTRACT FROM AUTHOR]

Renewable energy is undergoing significant advancements through the utilization of new technologies. Among these technologies, fixed solar panels serve as a fundamental type of solar photovoltaic energy generator. Monitoring and optimizing the performance of these panels is achieved through the implementation of controller algorithms. However, there is a growing emphasis on axis tracking systems for solar panels, as that has the feasible to extract more power compared to fixed panels. In this context, the integration of the Internet of Things (IoT), big data analytics, deep learning, and cloud computing presents a contemporary solution for addressing various challenges associated with monitoring renewable solar energy systems. By employing sensors and actuators grouped within an IoT framework, real-time data can be extracted and stored in a cloud server, allowing remote access from anywhere. Furthermore, the use of meta-heuristic optimization algorithms, such as the Whale Optimization Algorithm, has become commonplace in solving engineering issues. In this proposed system, the performance of horizontal axis trackers is analyzed and compared with fixed panels using the WOA-based axis tracking analysis. Through the application of a control algorithm, optimal values can be obtained under typical weather conditions. The system's simulation and performance are thoroughly assessed and evaluated. [ABSTRACT FROM AUTHOR]

This work presents an improvement of the incremental conductance technique by adding PI controllers optimized by the recent equilibrium optimization algorithm. It addresses the main weaknesses of the incremental conductance algorithm consisting of the choice of step-size and the elimination of oscillations around the MPP. The proposed approach makes the MPPT control more dynamic and self-adaptive against the atmospheric conditions change. It has been applied for a photovoltaic generator, the thermoelectric generator and for the hybridization of the two generators. The simulation results, and the comparative study with the optimized step-size P&O and Incremental conductance methods, prove the efficiency and superiority of the proposed technique in terms of energy extraction and speed. The introduced work is developed in the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

This study proposes a control method for transferring power generated by a solar photovoltaic system. The control algorithm, called MVF-DSOGI-PLL or Multi-Variable Filter Dual Second-Order Generalized Integrator Phase-Locked Loop), accurately estimates symmetrical components, phase angle, and positive sequence voltages of the Common Point of Interface (CPI) in distorted grid conditions. The approach creates a compensatory current to reduce PQ difficulties by extracting the size of the basic current load undergoing operation employing the SOGI method. These systems can operate in either modes of PV-DSTATCOM or DSTATCOM, depending on solar insolation's availability. The simulation models of the grid-interfaced SPV systems are developed in Matlab/Simulink to evaluate the performances of the suggested control method. The results of simulation demonstrate that the suggested control systems efficiently transfers the power generated by PV arrays to a grid while performing tasks like load balancing, power factor correction (PFC), and harmonics reduction. Furthermore, the MVF-DSOGI-PLL-based control algorithm successfully mitigates PQ issues in distribution networks with weak grids, ensuring that PQ levels remain between a limited set by the IEEE-519 standard. A laboratory prototype of the proposed system is implemented to assess its steady states and dynamic efficient under fluctuating solar insolation, and load imbalance distortions. [ABSTRACT FROM AUTHOR]

To ensure that grid-connected currents are of high quality, it is crucial to optimize the dynamic performance of grid-connected inverters and their control. This study suggests using a combination of reduced-order linear active disturbance rejection control (LADRC) and a Proportional-Integral (PI) controller. By applying this control strategy to a single-phase photovoltaic grid-connected system, the system's ability to suppress grid harmonics is significantly improved. The validity and effectiveness of this control approach have been confirmed through simulations and experiments. The results show that the LADRC-based control system is robust and capable of rejecting disturbances, resulting in a significant reduction in the Total Harmonic Distortion (THD) of grid-connected currents. Comparative analysis with traditional control methods demonstrates the superior performance of the proposed approach. [ABSTRACT FROM AUTHOR]

For the problem that traditional droop control cannot maintain the maximum output power of photovoltaic (PV) units, this work proposes a self‐adaptive communication‐free control scheme for the islanded PV‐storage AC microgrids. The proposed control enables the maximum power point tracking‐based output active powers of PVs by adaptively adjusting P‐f/Q‐V droop coefficients. It also facilitates adaptive allocations of reactive powers based on the available capacities of PVs and storage modules. The key characteristics of the proposed control strategy are summarized as follows: 1) PV units are controlled as voltage sources, which could participate in the voltage/frequency regulation to a certain extent; 2) maximum power utilization of PVs is obtained; 3) adaptive allocations of reactive powers are realized based on the maximum available capacity of PVs and storage modules. Subsequently, the stability analysis of the proposed self‐adaptive communication‐free control strategy is verified. Finally, the validity of the proposed self‐adaptive communication‐free control method is validated through simulations conducted using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Actuators utilizing snap‐through instabilities are widely investigated for high‐performance fast actuators and shape reconfigurable structures owing to their rapid response and limited reliance on continuous energy input. However, prevailing approaches typically involve a combination of multiple bistable actuator units and achieving multistability within a single actuator unit still remains an open challenge. Here, a soft actuator is presented that uses shape memory alloy (SMA) and mixed‐mode elastic instabilities to achieve intrinsically multistable shape reconfiguration. The multistable actuator unit consists of six stable states, including two pure bending states and four bend‐twist states. The actuator is composed of a pre‐stretched elastic membrane placed between two elastomeric frames embedded with SMA coils. By controlling the sequence and duration of SMA activation, the actuator is capable of rapid transition between all six stable states within hundreds of milliseconds. Principles of energy minimization are used to identify actuation sequences for various types of stable state transitions. Bending and twisting angles corresponding to various prestretch ratios are recorded based on parameterizations of the actuator's geometry. To demonstrate its application in practical conditions, the multistable actuator is used to perform visual inspection in a confined space, light source tracking during photovoltaic energy harvesting, and agile crawling. [ABSTRACT FROM AUTHOR]

Jet impingement is a promising cooling mechanism to increase the rate of heat transfer in solar air collector. However, the effects of an impinging air jet on the exergetic performance of a bifacial photovoltaic-thermal with different packing factors are entirely unclear. In this research, a jet impingement bifacial photovoltaic-thermal solar air collector was developed and its exergetic performance was assessed. Jet plate reflectors with different geometric configurations are proposed to enhance the cooling and light absorption at the rear part of the bifacial photovoltaic module. Additionally, the link between exergetic performance and each variable's design and operational characteristics was examined through indoor experiments. The results showed that the maximum exergy efficiency of the bifacial photovoltaic-thermal with packing factor of 0.66 and 36-hole jet plate reflector has 11.88% under solar irradiance of 900 W/m2, and mass flow rate of 0.025 kg/s. The maximum exergy input, exergy destruction, and improvement potential of the proposed system are 402.81 W, 345.62 W, and 304.78 W, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a control strategy for grid-following inverter control and grid-forming inverter control developed for a Solar Photovoltaic (PV)–battery-integrated microgrid network. A grid-following (GFL) inverter with real and reactive power control in a solar PV-fed system is developed; it uses a Phase Lock Loop (PLL) to track the phase angle of the voltages at the PCC and adopts a vector control strategy to adjust the active and reactive currents that are injected into the power grid. The drawback of a GFL inverter is that it lacks the capability to operate independently when the utility grid is down due to outages or disturbances. The proposed grid-forming (GFM) inverter control with a virtual synchronous machine provides inertia to the grid, generates a stable grid-like voltage and frequency and enables the integration of the grid. The proposed system incorporates a battery energy storage system (BESS) which has inherent energy storage capability and is independent of geographical areas. The GFM control includes voltage and frequency control, enhanced islanding and black start capability and the maintenance of the stability of the grid-integrated system. The proposed model is validated under varying irradiance conditions, load switching, grid outages and temporary faults with fault ride-through (FRT) capability, and fast frequency response and stability are achieved. The proposed model is validated under varying irradiance conditions, load switching, grid outages and line faults incorporating fault ride-through capability in GFM-based control. The proposed controller was simulated in a 100 MW solar PV system and 60 MW BESS using the MATLAB/Simulink 2023 tool, and the experimental setup was validated in a 1 kW grid-connected system. The percentage improvement of the system frequency and voltage with FRT-capable GFM control is 69.3% and 70%, respectively, and the percentage improvement is only 3% for system frequency and 52% for grid voltage in the case of an FRT-capable GFL controller. The simulation and experimental results prove that GFM-based inverter control achieves fast frequency response, and grid stability is also ensured. [ABSTRACT FROM AUTHOR]

Voice conversion is the task of changing the speaker characteristics of input speech while preserving its linguistic content. It can be used in various areas, such as entertainment, medicine, and education. The quality of the converted speech is crucial for voice conversion algorithms to be useful in these various applications. Deep learning-based voice conversion algorithms, which have been showing promising results recently, generally consist of three modules: a feature extractor, feature converter, and vocoder. The feature extractor accepts the waveform as the input and extracts speech feature vectors for further processing. These speech feature vectors are later synthesized back into waveforms by the vocoder. The feature converter module performs the actual voice conversion; therefore, many previous studies separately focused on improving this module. These works combined the separately trained vocoder to synthesize the final waveform. Since the feature converter and the vocoder are trained independently, the output of the converter may not be compatible with the input of the vocoder, which causes performance degradation. Furthermore, most voice conversion algorithms utilize mel-spectrogram-based speech feature vectors without modification. These feature vectors have performed well in a variety of speech-processing areas but could be further optimized for voice conversion tasks. To address these problems, we propose a novel wave-to-wave (wav2wav) voice conversion method that integrates the feature extractor, the feature converter, and the vocoder into a single module and trains the system in an end-to-end manner. We evaluated the efficiency of the proposed method using the VCC2018 dataset. [ABSTRACT FROM AUTHOR]

Featured Application: This study is mainly applied to the MRTD tester and improves the four-bar target to enhance the detection accuracy of the MRTD tester. Additionally, MRTD is an important parameter for infrared thermal imaging cameras, contributing to improving their accuracy. Furthermore, the dung beetle optimized fuzzy PID parameters can be applied to most control systems, enabling stable tracking of abrupt commands and compensating for the deficiencies of traditional PID control. With the widespread application of infrared thermal imagers in various fields, the demand for thermal imagers and their performance parameter testing equipment has increased significantly. There are particularly high demands on the detection accuracy of minimum resolvable temperature difference (MRTD) testers. Traditional MRTD testers have an issue with the four-bar target temperatures being easily affected by the external environment, resulting in non-uniform temperatures and imprecise detection results. This paper proposes an improvement to the four-bar targets by making them temperature-controllable. Temperature is controlled by installing thermoelectric coolers (TECs) and thin-film platinum resistors at the center and periphery of the four-bar targets with different spatial frequencies. The dung beetle algorithm is used to optimize fuzzy PID parameters to regulate the TEC's heating and cooling, improving the overall temperature uniformity of the four-bar targets. Temperature simulations of the four-bar targets were conducted on the COMSOL platform, with the control part simulated on the Simulink platform. The simulation results show that, compared to traditional PID, the fuzzy PID controller reduces overshoot by approximately 3.6%, although the system still exhibits mild oscillations. The fuzzy PID controller optimized by the dung beetle optimization (DBO) algorithm, in comparison to standard fuzzy PID, reduces the settling time by about 40 s and lowers overshoot by around 7%, with oscillations in the system nearly disappearing. Comparing the fuzzy PID optimized by the particle swarm optimization (PSO) algorithm with the fuzzy PID optimized by the DBO algorithm, the DBO-based controller shows shorter rise and settling times, further illustrating the superiority of the fuzzy PID control optimized by the dung beetle algorithm. This provides a theoretical foundation for improving the accuracy of MRTD detector measurements. Finally, experimental verification was carried out. The experimental results indicate that DBO (drosophila-based optimization) has significant advantages, and its optimized results are closer to the actual values. [ABSTRACT FROM AUTHOR]

To improve the workshop production efficiency of textile enterprises and balance the total operating time of all machines in each operation, this paper proposes a modified algorithm based on the combination of the ant colony optimization (ACO) algorithm and production products, which we call the product ant colony optimization (PACO) algorithm. The local pheromone update rule in the ACO algorithm is modified through the close relationship between textile machinery and production products in the textile workshop; the pheromone is then introduced into production products based on the constraints of the textile machine. A heuristic function is designed to improve the utilization rate of textile machines to increase the heuristic value of machines that are less frequently used in the algorithm iteration process. In addition, this paper combines the convergence speed and the global search ability of the algorithm with a designed variable pheromone evaporate parameter. The comparison among the initially designed PACO algorithm, the separately modified PACO algorithm, and the integratively modified PACO algorithm demonstrates that the proposed enhancement effectively addresses scheduling issues in textile flexible workshops and various workshops with similar constraint conditions. [ABSTRACT FROM AUTHOR]

Mechanical water detection is recognized as the most reliable and safe production technology for coal mines, mainly for the detection of water hazards in pre-mining operations. Intelligent water detectors are currently the main research direction in mechanical water detection, and the automatic installation of drilling bars is the key to achieving intelligent water detection. Improving the connection accuracy in the process of installing drilling bars is an important research topic for the improvement of control links. To improve the connection accuracy of the drilling bars at the time of supplying material, we used the modified Denavit–Hartenberg method to analyze the motion gestures of the supplied material device and the Lagrange equation to establish a dynamic analysis model. We aimed at better control precision by improving the sliding mode control algorithm and at increasing the convergence rate of tracking errors with a sliding controller based on an exponential approximation law and using saturated functions instead of the symbol functions in the reaching law to weaken the vibration in the control process. We then used particle swarm optimization (PSO) to find the optimum combination parameters of the sliding mode controllers and test the performance of the sliding mode controllers before and after PSO with MATLAB/Simulink. The results showed that the optimized controller has a strong resistance to parameter fluctuations, and the system responds quickly, achieves a good performance, and improves the convergence rate of tracking errors. [ABSTRACT FROM AUTHOR]

This paper focuses on enhancing the energy extraction efficiency of photovoltaic (PV) modules through the use of a straightforward power converter and control algorithm. This research delves into the electrical characteristics of PV modules, explaining the concepts of global maximum power point, and local maximum power points. By integrating maximum power point tracking algorithms and differential power processing technology, an innovative scheme for power equalization and optimization of PV modules is introduced. The scheme is based on a single-switch multi-winding forward-flyback converter. Using the STP-340-72-Vfh-type PV module as a case study, a simulation model is developed with PLECS simulation software. The simulations cover 30 different irradiance scenarios. The findings illustrate the effectiveness of the proposed PV module power optimization system in achieving maximum power output under different irradiance conditions, achieving an average efficiency of 94.61%. This efficiency rate is 13.95% greater than that of existing global maximum power tracking schemes. [ABSTRACT FROM AUTHOR]

Global electricity demand is increasing with the rising population and rapid urbanization. Building Integrated photovoltaic (BIPV) system is a new method of renewable energy generation where solar photovoltaic (PV) modules are integrated into the building surfaces such as façade, shades, windows, roofs, and tiles. BIPV systems reduce the urban energy demand. Utilization of vertical surfaces makes the BIPV system a preferable choice where land scarcity affects the implementation of large PV systems. The economic viability depends on the maximum power generated by the BIPV array. In urban environments, the BIPV arrays experience severe partial shading conditions (PSCs). The PSCs cause mismatch losses, reducing the global maximum power of the BIPV array and efficiency. Fixed array configurations such as series-parallel (SP), total-cross-tied (TCT), triple-tied (TT), and quarter-tied (QT) are designed to solve this issue. The cross ties across the rows of the BIPV array improve the performance at the expense of more wiring. Researchers proposed various optimal array configurations with different shading patterns. This research attempts to generalize the design of the BIPV array configurations by considering the trade-off between wiring requirements and shading losses. The performance of SP, TT, QT, and TCT configurations under four different shading conditions is simulated with the proposed BIPV array design algorithm. A 9 × 8 BIPV array of 3.6 kW is considered. QT configuration reduces the wiring requirement by 10.45% compared to TCT and improves up to 8.43% maximum power than SP. The fill factor is improved to 48.49%, and the mismatch loss is limited to 34.10%. Therefore, QT and TCT are considered favorable configurations for BIPV array design. [ABSTRACT FROM AUTHOR]

Silicon (Si) photovoltaic cells and their production play a pivotal role in advancing energy utilization within the rapidly expanding solar industry, which serves as a critical driver in realizing global sustainability objectives and mitigating environmental repercussions associated with energy production. Here, we introduce a novel two-step pattern transfer technique to successfully imprint a two-dimensional plasmonic micro-square periodic array onto an Si substrate, utilizing Kapton® Tape and plasma technology. Remarkably, this represents the first-ever application of flexible and stretchable Kapton® polyimide for pattern transfer onto Si. The vacuum plasma treatment plays a pivotal role in significantly enhancing surface adhesion performance, thereby facilitating the efficient pattern transfer process. As a result, the resulting microstructure exhibits exceptional performance as a plamonic broadband absorber in the visible region, making it a highly promising candidate for enhancing efficiency in Si-based solar cells. To support our experimental findings, the finite-difference time domain method was employed for simulating the fabricated plasmonic structure and determining the electric field distribution. The simulation results unequivocally affirm the robust and intense light trapping capabilities of the microstructure. Moreover, our fabrication technique demonstrates the potential for achieving high-resolution microstructure through an innovative, straightforward, and cost-efficient approach. [ABSTRACT FROM AUTHOR]

This article presents an analysis of the performance of a 14.04 kWp grid-connected photovoltaic (PV) installation consisting of monocrystalline silicon, polycrystalline silicon and bifacial glass–glass monocrystalline silicon modules. The photovoltaic system was mounted in Poland, a location characterized by temperate climate conditions. On the basis of the obtained results, the photovoltaic parameters were determined in accordance with the international standard. The annual energy yield of the entire system was 1033 kWh/kWp, and the performance ratio achieved was 83%. The highest average daily final yield was in the range of 4.0–4.5 kWh/kWp for each photovoltaic technology under investigation. In the cold part of the year, the efficiency of the photovoltaic modules was estimated to be 15%, and it was estimated to be 7% during the warm part of the year. Array capture losses accounted for around 0.75 kWh/kWp of energy loss per day, whereas the inverter efficiency was over 95% during the months that are beneficial for energy production. [ABSTRACT FROM AUTHOR]

Grid-connected photovoltaic (PV) systems are commonly designed for maximum energy production. However, as their presence grows, revised grid regulations increasingly require these systems to partially adhere to the primary frequency regulation (PFR) by reducing their power production when the grid frequency exceeds the nominal value. Nevertheless, full participation in PFR of PV systems, without battery storage, would require operating with a dedicated active power reserve to be utilized in the event of underfrequency conditions. This paper presents a model-based (MB) flexible power point tracking (FPPT) algorithm for PV systems. Results of a microgrid simulation show that the proposed algorithm enables a PV system with a nominal power of 100 kW to maintain a 20 kW active power reserve under various irradiation and temperature conditions, with a relative error lower than 3%. Furthermore, a simulation of the PV system assisting in primary frequency regulation is presented. Thanks to the proposed strategy, when employing the algorithm, the frequency deviation is reduced both in the event of underfrequency and overfrequency conditions. By enabling operation with a constant active power reserve, as well as a quick adjustment of the produced active power, in the event of frequency imbalance, the proposed MB algorithm is an interesting candidate for achieving complete PFR capabilities of PV systems. [ABSTRACT FROM AUTHOR]

Active Distribution Networks are Multi-Input Multi-Output (MIMO) systems with coupled dynamics, which cause interactions among the control loops of Distributed Energy Resources (DERs). This undesired effect leads to performance degradation of voltage control. To mitigate the effects of this unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction's sources. In detail, each DER is equipped with a double-loop PI to control the active and reactive power output of the voltage source converter, which connects the DER to the network's node. Furthermore, to guarantee ancillary services, the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter's pulse are designed according to a procedure with two sequential steps based on the Internal Model Control (IMC) technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in the presence of model parameter uncertainty in the MIMO plant, modeling various operating conditions of the ADN, including a step connection of large loads, renewable energy source variations, and changes in the substation transformer ratio. [ABSTRACT FROM AUTHOR]

Ice detection poses significant challenges in sectors such as renewable energy and aviation due to its adverse effects on aircraft performance and wind energy production. Ice buildup alters the surface characteristics of aircraft wings or wind turbine blades, inducing airflow separation and diminishing the aerodynamic properties of these structures. While various approaches have been proposed to address icing effects, including chemical solutions, pneumatic systems, and heating systems, these solutions are often costly and limited in scope. To enhance the cost-effectiveness of ice protection systems, reliable information about current icing conditions, particularly in the early stages, is crucial. Ultrasonic guided waves offer a promising solution for ice detection, enabling integration into critical structures and providing coverage over larger areas. However, existing techniques primarily focus on detecting thick ice layers, leaving a gap in early-stage detection. This paper proposes an approach based on high-order symmetric modes to detect thin ice formation with thicknesses up to a few hundred microns. The method involves measuring the group velocity of the S1 mode at different temperatures and correlating velocity changes with ice layer formation. Experimental verification of the proposed approach was conducted using a novel group velocity dispersion curve reconstruction method, allowing for the tracking of propagating modes in the structure. Copper samples without and with special superhydrophobic multiscale coatings designed to prevent ice formation were employed for the experiments. The results demonstrated successful detection of ice formation and enabled differentiation between the coated and uncoated cases. Therefore, the proposed approach can be effectively used for early-stage monitoring of ice growth and evaluating the performance of anti-icing coatings, offering promising advancements in ice detection and prevention for critical applications. [ABSTRACT FROM AUTHOR]

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation thus degrading their sensing accuracy. Most of the existing approaches model the impact of trajectory deviation into a polynomial phase error on the received signal, which cannot fit the space-variant motion error well. Moreover, the large trajectory deviations of UAVs introduce the unavoidable envelope error. This article proposes an autofocusing algorithm based on the back projection (BP) image, which directly estimates the trajectory deviations between the actual and measured track. Thus, the problem of the 2D space variability of the motion error can be circumvented. The proposed method mainly consists of two steps. First, we estimate the trajectory deviation in the line-of-sight (LOS) direction by exploring the underlying linear property of the wall embedded in the BP imaging result. Then, the estimated trajectory deviation in the LOS direction is compensated for to obtain an updated BP image, followed by a Particle Swarm Optimization (PSO) approach to estimate the trajectory deviation along the track through focusing targets behind the wall. Simulations and practical experiments show that the proposed algorithm can accurately estimate the serious trajectory deviations larger than the range resolution, improving the sensing robustness of UAV-borne through-wall radar greatly. [ABSTRACT FROM AUTHOR]

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm's ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications. [ABSTRACT FROM AUTHOR]

Partial shading in series-connected photovoltaic (PV) systems poses a significant challenge as it considerably restricts the system's output power and has a notable impact on the overall efficiency. Differential power processing (DPP) has emerged as a widely employed solution to mitigate this effect. However, implementing DPP architectures introduces challenges such as system cost, reliability, control implementation, and modularity. This research proposes a simplified voltage equalization (VE) algorithm, executed on a low-cost and modular DPP architecture using bi-directional flyback converters. Notably, under light and moderate partial shading conditions, the PV system's output power exhibits a significant increase compared to the traditional string configuration with bypass diodes. To further optimize the system performance, a methodology adapted to the VE algorithm is suggested, minimizing the processed power while increasing overall efficiency. The proposed solutions have undergone rigorous testing through simulations and experiments. The results validate that, under moderate shading conditions, the system efficiency reaches approximately 90%. [ABSTRACT FROM AUTHOR]

This paper experimentally and analytically investigated the damage and seismic behavior of concrete walls reinforced by low-bond ultra-high-strength (LBUHS) bars. To this end, four half-scale rectangular concrete walls were fabricated and tested under reversed cyclic loading and constant axial compression. The test variables were the shear span ratio and the axial load ratio. Based on the test results, the propagation of cracks on the wall surface, the maximum strain capacity of concrete, the hysteresis loops and envelope curves, the residual drifts, and the strain distributions of LBUHS rebars were presented and discussed. The experimental results showed that all the test walls could exhibit drift-hardening capability until at least a 2.0% drift ratio if LBUHS rebars were anchored by nuts at their ends. The test results also indicated that the maximum strain capacity of concrete was above 0.86%, much larger than the currently recommended 0.4%. After unloading from the transient drift ratios of 2.0% and 2.5% for the walls with shear span ratios of 1.5 and 2.0, respectively, the measured residual drift ratios were controlled below 0.4%, which is less than the critical drift ratio (0.5%) having 98% repairable probability recommended in the FEMA document (P-58) for general concrete structures. Furthermore, a numerical method was presented to evaluate the cyclic response of the test walls, and a comparison between the experimental and the calculated results verified the reliability and accuracy of the proposed numerical method. [ABSTRACT FROM AUTHOR]

This paper examines the impact of different voltage unbalance definitions on voltage control in unbalanced distribution systems. Traditional voltage regulation methods for unbalanced systems rely on a single voltage unbalance definition with inconsistent adoptions. In such a situation, voltage control performance can vary between definitions, and improper choice leads to control failures. In this study, we perform numerical analysis and simulations to investigate the impact of different definitions. In the numerical analysis, two definitions quantifying negative and zero sequence components are used as reference definitions, and the theoretical error range of the remaining definitions is identified. The results are further verified by the simulations on the IEEE 123 node test system with phase‐by‐phase controlled three‐phase step voltage regulators and smart photovoltaic inverters. The control of the devices is optimized to minimize or limit a specific definition. The results show that the values of each definition at the same voltage condition can differ significantly, and inappropriate definitions may lead to significant uncertainty in the voltage control. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Currently, various light-emitting diode (LED) lighting systems are being developed because LEDs are one of the most used lighting sources for work environments, buildings, homes, and public roads in terms of some of their applications. Similarly, they have low energy consumption, quick responses, and excellent optimal performance in their operation. However, these systems still need to precisely regulate lighting, maintain stable voltage and current in the presence of faults and disturbances, and have a wide range of operations in the event of trajectory changes or monitoring tasks regarding the desired voltage and current. This work presents the design and application of two types of robust controllers (structure-at-infinity control and geometric control) applied to an LED driver using a buck converter. The controllers aim to follow the desired trajectories, attenuate disturbances at the power supply input, and compensate for faults in the actuator (MOSFET) to keep the capacitor voltage and inductor current stable. When comparing the results obtained with the two controllers, it was observed that both present excellent performance in the presence of constant disturbances. However, in scenarios in which variable faults and path changes are implemented, the structure-at-infinity control method shows an overimpulse of output voltage and current ranging from 39 to 42 volts and from 0.3 to 0.45 A, with a margin of error of 1%, and it can generate a failure in the LED driver using a buck converter. On the other hand, when using geometric control, the results are satisfactory, achieving attenuating constant disturbances and variable faults, reaching the desired voltage (40 v to 35 v) and current (0.3 to 0.25 A) with a margin of error of 0.05%, guaranteeing a system without overvoltages or the accelerated degradation of the components due to magnetic conductivity. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a self‐powered SSHI piezoelectric energy extraction circuit combined with Modified FOCV‐MPPT technology, which analyzes the electrical characteristics of S‐SSHI operation. The paper demonstrates that the determination of the optimal operating state of S‐SSHI can be determined independently of the open‐circuit voltage. Based on the key parameters obtained from the theoretical analysis, the FOCV‐MPPT strategy is modified, and the corresponding functional circuit is designed. This strategy serves as an intermediate stage between S‐SSHI and the load, addressing the dependence of S‐SSHI output power on the load while avoiding the power supply interruption issue of the traditional FOCV‐MPPT strategy. In addition, the higher output power provided by S‐SSHI improves the utilization of MPPT technology. The correctness of the investigations is verified through the simulation and experiments. By using self‐powered S‐SSHI, the proposed circuit increases the output power of PEH by a factor of 2.46. In the open‐circuit voltage range of 2–7 V, the average maximum power tracking efficiency achieved 90.1%. The maximum tracking efficiency can reach 98%. [ABSTRACT FROM AUTHOR]

Under partial shading conditions (PSCs), photovoltaic arrays exhibit power–voltage curves with multiple peaks. This phenomenon complicates the task of traditional maximum power point tracking (MPPT) algorithms, since they often converge to local maximum power points. To tackle this challenge, a novel MPPT control strategy, termed the slime mold golden sine algorithm (SMGSA), was introduced in this paper. This strategy adeptly identifies and tracks the global maximum power point. The efficacy of the SMGSA algorithm was assessed through six test functions from IEEE CEC 2020. A comparative analysis underscored its superior performance in both convergence speed and accuracy. A simulation model for MPPT was developed in MATLAB/Simulink. Within this model, various algorithms such as particle swarm optimization (PSO), tuna swarm optimization (TSO), slime mold algorithm (SMA), and SMGSA were examined. Comparative simulations revealed that the SMGSA-based MPPT strategy showcased expedited convergence speed and heightened accuracy under scenarios of uniform irradiance intensity, partial static shading, and dynamic shading. Consequently, the implementation of an SMGSA-based MPPT system can notably enhance the power generation efficiency of photovoltaic arrays under PSCs. [ABSTRACT FROM AUTHOR]