Search

• Detailed on various optimization techniques used in wind farm modelling. • Detailed on several modelling approaches used in wind farm technology. • Various performance parameters were compared; cell technologies, convergence speed, computational domain, complexity etc. • Detailed cost statistics are presented based on the Turbine installation, foundation concept, maintenance and operations. • Future trends of wind farm optimisation techniques research are also discussed in this review paper. The important interest and efforts devoted by industries and academic research institutions to electricity production from renewable and clean energy with the maturity of existed technologies justify the biggest exploitation of wind energy over the recent years. With the reduction in oil prices, renewable energy is way forward for efficient and environment friendly energy generation. Out of all existing available renewable energy, Wind Energy is the front runner owing to its ability of efficient power generation and to produce energy at large scale. Due to the non linear nature of wind energy, Optimization techniques are extremely critical as they are solely responsible for building an effective wind farm. Layout optimization is performed by using soft computing techniques and are extensively studied in the available literature. Therefore, this review paper highlights the significant research works of wind farm modelling using optimization techniques. This work also addresses the new approaches used in wind farm modelling. Further, it also presents a critical evaluation of existing research methodologies used for wind farm layout optimization. Hence, the objective of this work is to benefit scientists and new entrants in the field of modelling and layout optimization of the wind farm. [ABSTRACT FROM AUTHOR]

The array configurations have gained a higher prominence and application in the solar PV system for reducing power losses during partial shading. However, additional wires can increase the vulnerability of these configurations to electrical faults affecting the performance and lifespan of the modules. Hence, this paper investigates the reliability of these configurations under various electrical faults in the MATLAB and prototype experiment platforms. Additionally, a powerline communication-based PV monitoring system along with fault detection through optimal sensors placement and a user-friendly web application are developed to monitor the performance, detect the presence of the fault and access the data during normal and faulty operation of the PV array respectively. The investigation is conducted for 3x3 array configurations, compared using power curves, and electrical parameters, and the performance along with detection is studied using the real-time data from the proposed monitoring system under normal and faulty conditions. From the analysis, the series-parallel has a higher average tolerance of 59.48% and 48.38% than honeycomb (54.16% and 47.43%), bridge-linked (52.56% and 43.96%), and total-cross-tied (53.66% and 37.48%). The proposed low-cost PV monitoring system has effectively monitored the system performance and is capable of detecting various types of faults in PV arrays and notifying the system with an alarm for proper diagnosis. [ABSTRACT FROM AUTHOR]

• Proposing a novel reconfiguration technique based on the Ancient Chinese Magic Square (ACMS) puzzle. • It shows higher shade dispersion factor over other methods. • Tested in comparison with TCT and Su Do Ku techniques. • Prototype has been built and tested with real time scenarios. • Various test cases considered to test the superiority of method. Non-uniform irradiation levels also have a negative impact on solar photovoltaic (PV) systems, forcing them to increase their power losses. Each PV array row has different current generation levels due to the effect of partial shading conditions (PSCs) and eventually has multiple power maxima, e.g. global maximum power point (GMPP) and local maximum power point (LMPP) on the power-voltage (P-V) characteristics. The presence of multiple power maxima as a result of non-uniform irradiances always causes the maximum power point tracking (MPPT) device to be mislead. To address the aforementioned issue, the reconfiguration scheme depicts the physical relocation as well as the unaltered electrical connections in the PV array. In this context, a new physical relocation alternative solution based on the Ancient Chinese MagicSquare (ACMS) puzzle demonstrates efficient behavior under PSCs. The results show that the ACMS based reconfiguration offers higher shade dispersion over the entire PV array system relative to the existing total-cross tied (TCT), Su-do-Ku, and consequently decreases the power mismatch loss and GMPP locations through P-V characteristics observation. In the MATLAB/Simulink study, power values at GMPP are observed for the ACMS based configuration as 324.9 W, 340.9 W, 327.6 W, and 382.5 W under all four shading scenarios. Real-time experimental study of 9 × 9 size PV array configurations demonstrates and validates the higher side performance of the proposed reconfiguration approach under the considered shading scenario. [ABSTRACT FROM AUTHOR]

The PV system encounters severe power loss due to the existence of unavoidable partial shading that is mainly overcome by reconfiguration techniques. Besides the advantage of power enhancement, reconfiguration techniques deal with drawbacks such as limited to symmetrical arrays, higher switches and sensors requirements, lower convergence to optimal solution, and improper monitoring. In this paper, a gorilla troop reconfiguration-power line communication (GTR-PLC) approach is proposed for performance enhancement and monitoring of PV arrays during partial shading. The proposed approach uses a considerably lower switch count along with a low-cost simple architecture for loss reduction and smooth operation of the PV array during partial shading. The proposed approach is validated for 5 × 5 and 9 × 9 arrays under time-domain dynamic shading scenarios in simulation whereas real-time hardware setup-based analysis is conducted for 2 × 3 and 12 × 4 arrays using numerous artificial shading cases. The performance of the proposed system is compared with twelve existing static and dynamic techniques and found to have zero misleading power loss with an average power increment of 38.37 %. Also, a power extraction efficiency of 98.99 % and an average calculation time of 0.09 s in the proposed system during partial shading is recorded. [Display omitted] • A GTR-PLC approach for partial shading mitigation and monitoring is proposed. • Tested in 5 × 5, 9 × 9, 2 × 3 and 12 × 4 arrays in simulation and real-time environments. • Compared with 11 existing reconfiguration techniques under partial shading. • Power increment of 38.37 % with power extraction efficiency of 98.99 % is achieved. • An average calculation and operation time of 0.09 s during shading is recorded. [ABSTRACT FROM AUTHOR]

• A novel enhanced MPA algorithm has been proposed for effective PV modelling. • Static and dynamic PV model parameters have been estimated. • The estimated parameters have been validated via experimental data-sets. • The statistical analysis has been performed for effectiveness of the EMPA method. • The results confirm the EMPA efficiency comparing with state-of-the-art algorithms. Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301 e - 04 and 5.9135 e - 07 . Similarly for double diode are 7.4396 e - 04 and 3.1849 e - 05 , respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505 ± 1.0971 e - 17 , which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505 ± 9.6235 e - 14 and 0.0084505 ± 2.5235 e - 9). The results show the proposed model's superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563. [ABSTRACT FROM AUTHOR]

Solar Photovoltaic is a widely used renewable energy resource, and hence, the accurate and effective modeling of the PV system is crucial in real-time. The accurate PV modeling helps to predict the performance of the PV plant. In this paper, authors have proposed a novel optimization algorithm named Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO) to model solar PV modules accurately. This article focused on the modeling of single, double, and three diodes models based on experimental data under different environmental conditions. In FC-EPSO, a new approach in the meta-heuristic algorithms is proposed, where fractional chaos maps are incorporated into the algorithm to enhance its accuracy and reliability. FC-EPSO variants performance is evaluated based on three-different experimental datasets, in which two are widely utilized for commercial applications, while the third is measured in the laboratory under four different irradiance and temperature levels. For validation purposes, several statistical analyses and comparisons are carried out with the recent state-of-the-art algorithms. The statistical measures and comparative studies illustrate the accuracy and consistency of the proposed algorithm. The introduced technique is capable of emulating the experimental datasets with less deviation, a fast convergence rate, and short execution time. • Parameters of single, double and three diodes photovoltaic models are identified. • Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO) is introduced. • FC-EPSO is tested based on several experimental measured data at laboratory. • Several comparisons and statistical metrics are carried out. • Experimental results show the superiority of FC-EPSO for the three models. [ABSTRACT FROM AUTHOR]

Partial shading in the arrays reduces the power output, creates hotspots and damages the modules affecting the performance of the system. The modules bypass diodes create non-convexity in the power curves causing additional power losses. Hence, this work proposes a black widow reconfiguration (BWR) with reduced switch counts for mitigating the effects of partial shading in the arrays. The methodology effectively reduces the current difference between rows of the PV array within a short period with a faster calculation rate and electrically reconnects the modules. The reconnection ensures reduced mismatch among modules, higher power output and smoother power curves during partial shading. To show the effectiveness of the BWR in arbitrary-sized arrays, three arrays of 2 × 4, 5 × 5, and 9 × 9 sizes are considered and tested under static and dynamic partial shadings. The results are then compared with three conventional, six static, and three dynamic techniques using power curves and various parameters. Besides validation in the MATLAB environment, the experimental setup and OPAL-RT platform are considered to show the application in real-time environment. From the investigation, an average power improvement of 25.49%, 15.47%, and 9.29% in BWR compared to the existing conventional, static, and dynamic techniques with 99.60% efficiency have been observed. • Black Widow Reconfiguration (BWR) with reduced switches count and higher power output is proposed. • Analysed in MATLAB simulation, experimental setup and OPAL-RT environment under partial shading. • BWR has power improvement of 25.49%, 15.47%, and 9.29% than conventional, static, and dynamic techniques. • BWR efficiently converts 99.60% of the available power from PV arrays during partial shading. [ABSTRACT FROM AUTHOR]

The susceptibility of the PV array towards partial shading has raised a major reliability concern for efficient power generation. The partial shading forces the arrays to generate lower power along with forming non-convex curves causing complicated operation of power tracking algorithms. Hence, to reduce the losses, various conventional configurations and reconfiguration techniques exist with vulnerabilities in terms of reliable power enhancement and complexity. In this paper, a highly reliable, less complex, and fast array reconfiguration based on the Dragonfly algorithm (DA) optimization with a higher power enhancement capability, lower computational time, and hasty convergence is proposed for unwanted shading losses reduction in arrays. The effectiveness of the proposed reconfiguration is evaluated against conventional configurations and three pre-existing reconfiguration techniques under various artificial shading cases via power generation, losses, and efficiencies using simulation and experimental analysis for 3 × 3 and 9 × 9 arrays. From the conducted analysis, it has been established that the DA reconfiguration has 22%, 10.10%, 15.36%, 5.85%, 2.95%, 2.55%, and 1.07% higher power generation than the conventional configurations, electrical reconfiguration, SD-PAR, Sudoku, GA, PSO, and EAR respectively with reduced switches counts. • Dragonfly optimization based reconfiguration for shaded PV arrays is proposed. • Compared with existing configurations in simulation and real-time scenario. • Applicable for symmetric and asymmetric arrays with reduced number of switches. • 22%, 10%, 2.95% and 1.07% higher power than static, dynamic and EAR. [ABSTRACT FROM AUTHOR]

• The paper proposes a single phase virtual inertia emulation technique. • The emulation technique is based on the concept of virtual two phase space vector mechanism. • The gain tuning is achieved through the Self-tuned PIC. • Prototyped in ARM Cortex A-72 based architecture. The fast frequency stability limit is generally low in a short distribution system with a high penetration of renewables. The study proposes a single-phase virtual inertia emulation technique for residential slow charging systems to improve transient performance in islanded networks serving as an additional vehicle to the grid scheme. The proposed emulation technique is based on the novel notion of using a virtual two-phase space vector to ride over the frequency excursion propensity in a single-phase architecture. For the computation of the instantaneous active power reference to correctly alter network behavior through the control loop, the Self-Tuned Fractional Order Proportional Integral (ST-FOPI) controller has been proposed. The suggested ST-FOPI-based virtual inertia emulation technique's performance was tested on an ARM Cortex-A72 processor-driven hardware platform. The real-time performance gain is remarkable when compared to the typical emulation technique. [Display omitted] [ABSTRACT FROM AUTHOR]

The susceptibility of solar photovoltaic (PV) modules towards partial shading is one of the major demerits encountered by PV arrays installed in the field. The partial shading among the modules leads to a serious reduction in the array power generation as it introduces several losses due to mismatch among modules. These power losses are mainly countered by implementing various electrical interconnecting configurations such as bridge-linked (BL), honeycomb (HC), and total-cross-tied (TCT) rather than using the conventional series-parallel (SP) connection. However, the configurations fail to generate maximum power during all partial shading scenarios. Hence, in this paper, a new module electrical reconfiguration technique is proposed to disperse the effect of partial shading power generation improvement and reduce the losses in the PV arrays. The technique is a one-time fixed electrical reconnection strategy for modules based on the algorithm proposed and requires no sensors and switches. The efficacy of the proposed electrical reconfiguration is investigated for two array sizes in MATLAB/Simulink and a real-time experimental environment whereas, the algorithm is programmed in the Python language. Also, a comparison is done with the conventional electrical configurations under various shading scenarios using the characteristics curves analysis, power generation, losses, efficiencies and performance ratio. The proposed reconfiguration enhances the power output of 26.92% than SP, 24.07% than BL, 25.17% than HC, and 22.96% than TCT during shading. [ABSTRACT FROM AUTHOR]

The extraction of PV cell and module parameters is vital for the accurate simulation of PV systems. PV cell exhibits non-linear nature of I – V and P – V curves. Hence, parameters estimated at the optimum value of the current error criterion, such as root mean square error (RMSE) of current, may not give accurate power estimation at maximum power point (MPP). In this work, the shortcomings of the single objective optimization (SOO) approach based parameter estimation methods are assessed using two different problem formulations: compound objective function and weighted sum. The results from the SOO approach indicate that weightage assigned to objectives such as RMSE and the percentage relative power Error at MPP (% RPE) has impacted the accuracy of parameter estimation. The selection of weightage for each objective is indeed a difficult choice to make. Moreover, the obtained cumulative value of RMSE and % RPE is high. In order to overcome these shortcomings of the SOO approach, a multi-objective optimization (MOO) approach based parameter estimation is formulated. Then formulated MOO problem is solved by using the proposed nondominated sorting cuckoo search optimization (NSCSO). Combining nondominated sorting, a crowded comparison technique, and Lévy flight characteristics, the NSCSO algorithm is proposed, which is inspired by both the NSGA-II and NSCS algorithms. Results indicate that the proposed parameter estimation method based on the MOO approach using NSCSO has given the optimum value for cumulative RMSE and % RPE in comparison with the SOO approach using the CSO algorithm. The proposed method is also validated on various PV models: PVM 752 (GaAs thin film) cell, STM6-40 (monocrystalline), and STP6-120/36 (polycrystalline) silicon modules. • The multi objective optimization approach based parameter estimation of PV cell and module is proposed. • A new NSCSO algorithm is proposed. • Weighted Sum method is applied to formulated objective functions for parameter estimation of PV cell and module. • The deficiency of single objective optimization based parameter estimation of PV cell is established. • The performance of the NSCSO algorithm is compared with NSCS and NSGA-II algorithms. [ABSTRACT FROM AUTHOR]

This paper provides an extensive literature review focusing on the modeling of artificial pacemakers and the various mechanisms employed for their pacing control. In this survey, we initially gone through the fundamental concept of artificial pacemakers. Subsequently, we expound on their modeling techniques. Additionally, we furnish a holistic overview of diverse control methodologies tailored for the continuous pace tracking and control of pacemaker signals. Our discussion extensively reviews and scrutinizes various control algorithms and deployment approaches. Moreover, we spotlight the application of the IMP-based Repetitive Control (RC) technique for ensuring uninterrupted pace tracking in pacemakers. Conclusively, we address the spectrum of research challenges inherent in controller design advancements, underscoring the journey towards achieving precise and accurate pace control in pacemakers. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) are environmentally friendly. Extensive progress makes EVs popularly deployed and adopted. Once EVs are connected to the smart grid, EVs can act as both variable load and energy supply systems. One major challenge in EV deployment is the management of charging stations with minimum waiting time and reduced EV customer electricity prices. Considering dynamic pricing and various EV features could provide optimum scheduling. To address this issue, we proposed dynamic pricing and optimized scheduling as constrained by a Markov decision process. The solution is obtained by a novel Multi-Agent Deep Neural Network (MADNN). A numerical experiment was conducted with real-time data using the Nissan Leaf model EV. The proposed MADNN uses queuing model and obtained the highest saving rate of 18.45% and an average profit of 340.5 $/kWh with a network convergence time of 520 s. This obtained result validates the effectiveness of the proposed EV scheduling algorithm. [ABSTRACT FROM AUTHOR]

An effective harmony between the exploration and exploitation phases in meta-heuristics is an essential design consideration to provide reliable performance on a wide range of optimization problems. This paper proposes a novel approach to enhance the exploratory behavior of the Harris hawks optimizer (HHO) based on the fractional calculus (FOC) memory concept. In the proposed variant of the HHO, a hawk moves with a fractional-order velocity, and the rabbit escaping energy is adaptively tuned based on FOC parameters to avoid premature convergence. As a result, the fractional-order modified Harris hawks optimizer (FMHHO) is proposed. The sensitivity of the algorithm performance vis-a-vis the FOC parameters is addressed, and the best variant is recommended based on twenty-three benchmarks. For validating the quality of the proposed variant, twenty-eight benchmarks of CEC2017 are tested. For evaluating the proposed variant against the other present-day techniques, several statistical measures and non-parametric tests are performed. Moreover, to demonstrate the applicability of the proposed technique, the proton exchange membrane fuel cell (PEMFC) model parameters estimation process is handled based on several measured datasets. In this series of experiments, the FMHHO variant is compared with the standard HHO and the other techniques based on intensive statistical metrics, mean convergence curves, and dataset fitting. The overall outcome shows that the FOC memory property improves the performance of the classical HHO and leads to accurate and robust solutions fitting the measured data. [ABSTRACT FROM AUTHOR]