Optimal Power Output Research Articles

Maximizing Solar Energy Efficiency: Optimized DC Power Conversion for Resistive Loads

This paper investigates the integration of photovoltaic (PV) energy systems with a DC power converter based on a boost converter designed to optimize the power output for resistive loads such as heat elements for heat generation applications. Emphasizing the role of boost converters in increasing the output voltage of PV systems to efficiently supply resistive loads, the performance and efficiency of this integration is evaluated. The work also addresses the basic principles, control strategies and efficiency considerations associated with the fusion of solar PV systems with synchronous boost converters for resistive load applications. The results demonstrate a peak efficiency of 97%, which decreases to 90.5%, 87.5%, and 84% for resistive loads of 10Ω, 15Ω, and 20Ω, respectively at 80W and for a switching frequency of 60KHz. This indicates that efficiency declines as the value of the resistive load increases. Additionally, the results exhibit a notable efficiency increase of 4.6% by simply raising the switching frequency from 20KHz to 100KHz. Through extensive testing, we have substantiated the effectiveness of employing synchronous boost converters to optimize power output and enhance the overall performance of PV systems when supplying resistive heat elements.

An enhanced broadband piezoelectric energy harvester via elastic amplification structure for multidirectional vibration

Abstract Vibration energy harvesting using a piezoelectric mechanism has significant potential for powering wireless sensors. However, most current vibration energy harvesters face limitations such as bidirectionality, narrow bandwidth, and high operating frequencies. To address these issues, we propose an enhanced broadband piezoelectric energy harvester utilizing an elastic amplification structure for multidirectional vibration (EB-PVEH). By utilizing the multidirectional rotation capacity of the excitation block and the amplified foundation excitation provided by springs, the EB-PVEH effectively captures broadband vibrations in 2D space under low-frequency excitation. Additionally, its design features long-term durability, as the piezoelectric beams are smoothly excited by the pendulum-induced motion of the block without a tip mass. The practical feasibility and the impact of structural parameters on the output behavior of EB-PVEH were investigated through theoretical analysis and experimental testing. The results revealed that the introduction of springs dynamically amplified the harnessed electrical power output. Moreover, EB-PVEH could harvest the multidirectional vibration, and it exhibited different power-generating characteristics in various directions. Furthermore, the resonance frequency could be efficiently tuned by adjusting the flexible arm length and proof mass, with different optimal arm lengths identified for each vibration direction to maximize working bandwidth. The harvester achieved an optimal output power of 3.98 mW. Practical applications, such as charging a capacitor by driving an e-bike or a bike, demonstrate the potential of the proposed harvester to provide power for micro-electrical devices.

Multi-directional and multi-modal vortex-induced vibrations for wind energy harvesting

A conventional vortex-induced vibration (VIV)-based energy harvester is typically restricted to capturing wind energy from a very limited range of wind directions, making it inefficient in varying wind conditions. This Letter proposes a tri-section beam configuration for VIV-based piezoelectric energy harvester to enable harnessing wind energy from varying incident angle with different vibration modes being triggered. The finite element analysis investigates the tri-section beam harvester's mode shapes and natural frequencies. A wind tunnel experiment is conducted for a comparative study of the energy output performance of the harvesters with straight and tri-section beams. The findings show that the proposed harvester with the tri-section beam can efficiently capture wind energy from a much wider range of incident angles, as opposed to the specific limited directions of its counterpart with the straight beam. The proposed harvester can also widen the lock-in speed range with a higher bending mode being triggered and achieve the optimal output power of 1.388 mW when the proposed harvester works in the second mode at a higher natural frequency, superior to that of its counterpart (0.386 mW) that can only work in the first mode. The proposed configuration sheds light on developing multi-directional and multi-modal VIV-based energy harvesters adapted to wind conditions in natural environments.

Optimizing power output in hybrid photovoltaic/wind systems: a nonlinear back-stepping approach for enhanced efficiency and stability

Abstract This paper investigates the challenge of controlling hybrid renewable energy systems (HRES), specifically those combining wind energy and photovoltaic sources, under varying environmental conditions such as fluctuating wind speeds and partial shading. The primary objective is to develop a robust backstepping control strategy that enhances the system’s stability and energy efficiency while ensuring seamless grid integration through the use of dual-fed induction generators. The study uses advanced modeling techniques, including maximum power point tracking for wind turbines and particle swarm optimization for photovoltaic systems, to optimize energy capture. A detailed simulation framework was designed to validate the effectiveness of the control strategy under different climatic scenarios. Quantitative results show that the wind turbine achieved over 95% power recovery, the DC link voltage remained stable within 0.5% of the reference, and photovoltaic energy extraction was optimized with 98% accuracy, even under partial shading. These findings indicate that the proposed control strategy significantly enhances the performance, reliability, and adaptability of the HRES. This work offers a promising contribution to the integration of renewable energy sources into the electrical grid, supporting a more sustainable energy future.

Efficient multi-objective optimization approach for solving optimal DG placement and sizing problem in distribution systems

Incorporating distributed generations (DGs) like wind turbines (WTs) and solar photovoltaic (SPV) arrays into radial distribution systems (RDSs) has become increasingly important due to their benefits in enhancing system performance. This requires determining appropriate DG locations and their optimal power output when injected into the RDS. To this end, this paper introduces a novel application of a recent multi-objective optimization technique called multi-objective grey wolf optimization (MOGWO) to allocate multiple DG units into the RDS optimally. The primary objectives are to minimize total real power losses (RPL), reduce voltage deviation (VD), and enhance the voltage stability index (VSI) across the entire system while adhering to operational constraints. Various DG operational power factor (PF) scenarios are considered, including unity-PF, fixed-PF, and optimal-PF. The proposed approach generates a Pareto-optimal set of solutions to address the multi-objective problem, after which a fuzzy decision-making method is adopted to select the best trade-off solution from the set. The technique has been implemented on 69-bus and the actual Portuguese 94-bus RDS. Numerical results are compared with other recent well-known algorithms from the literature, highlighting the efficacy of the suggested MOGWO method in handling multi-objective problems. Comparatively speaking, it provides superior solutions than other methods in selecting appropriate control parameters.

The study of active support for energy storage system in system frequency regulation

Abstract As the penetration of new energy sources increases, the proportion of traditional energy units continues to decline, reducing the inertia and primary frequency regulation capability of the power system. Therefore, exploring the frequency support capabilities of energy storage systems with low inertia is crucial. Based on this, this paper first proposes an active frequency support method for energy storage system based on a Voltage Source Virtual Synchronous Generator (VSG), aiming to enhance system inertia and reduce response delay. Based on this issue, a parameter tuning optimization selection model based on the Transient Search Optimization (TSO) algorithm is proposed to determine the optimal power output and best frequency regulation parameters for the energy storage system. Finally, the model proposed in this paper is applied to the IEEE 14-bus system to verify its effectiveness.

Multi-criteria study and machine learning optimization of a novel heat integration for combined electricity, heat, and hydrogen production: Application of biogas-fueled S-Graz plant and biogas steam reforming

The current research introduces an environmentally friendly heat design method by employing biogas fuel, aiming to yield electricity, hydrogen, and heating load simultaneously. The proposed arrangement consists of a biogas-powered S-Graz plant and a biogas steam reforming cycle. Although methane-fueled S-Graz plants for multigeneration purposes have been studied in previous studies, research on employing biogas fuel to launch a S-Graz plant and integrating a biogas steam reforming cycle with such a plant has yet to be examined. The model is simulated using the engineering equation solver software, and the study includes thermodynamic, exergoeconomic, and sustainability assessments to show the potential of the suggested configuration. By conducting a sensitivity study, a machine learning optimization method within MATLAB is implemented to exhibit the final optimal solution for the proposed arrangement. This optimization uses artificial neural networks and a non-dominated sorting genetic algorithm-II algorithm in a triple-objective framework based on energy efficiency, sustainability index, and products’ specific cost. The optimization demonstrates that the mentioned objectives reach optimal values of 58.26 %, 4.56, and 15.56 $/GJ, respectively. Also, the optimal net output power and hydrogen production rate equal 5746 kW and 1.45 m3/s, respectively. Besides, the process determines the optimal exergy efficiency, total net present value, and payback period as 52.70 %, 50.3 M$, and 8.96 years, respectively. The total investment cost rate for this system also is found to be 219.8 $/h.

Balancing PEDOT:PSS conductivity for optimal power output of p-n junction based ZnO piezoelectric nanogenerator

Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonic acid) (PEDOT:PSS), has been employed in a wide range of applications owing to its good film-forming properties, high transparency, tunable conductivity, and good thermal and environmental stability. In piezoelectric nanogenerators (PENGs), p-type PEDOT:PSS has been used to form a p-n junction with n-type ZnO nanorods, which slows down the screening of the piezoelectric polarisation to increase the generated output voltage. Optimizing the conductive property of PEDOT:PSS is therefore a potential way to improve the performance of a PENG by controlling the screening effect and internal resistance of the device. In this work, ethylene glycol (EG) is added to the PEDOT:PSS layer in a ZnO PENG to modify its conductivity. Doping with EG, induces a change in conformation of the PEDOT which transform from a coil shape to expanded-coil or linear shapes as confirmed by Raman spectroscopy. The conductivity of the PEDOT:PSS layer is enhanced and the internal resistance of the PENG device is reduced, producing a higher current output. However, at the same time, devices using higher EG doping levels produce reduced voltage output, which is related to the rapid screening of the developed polarisation. Specifically, the optimal EG content was identified at 2.5%; although this resulted in a slight 4.0% reduction in voltage output compared to undoped devices, the current output increased by 41.6%, ultimately leading to a 33.1% improvement in peak power output. Therefore, we find that a balance between reducing the device resistance, which increases current output, and supressing the screening effect, which increases voltage output, is required to produce an optimum power output from the devices.

Experimental comparison of the effects of the two designed probes for exfoliation of graphene by sonication.

In the present research, comparative simulation and experimental investigation were carried out for two different probes to improve the efficiency and quality of few-layer graphene utilizing liquid-phase exfoliation. Two differently designed shaped probes are fabricated for this study, i.e., a simple probe and a stepped probe. The acoustic pressure distribution in the vessel is simulated for both probes at different output powers. In the experiment, both probes exfoliate graphite powder in a mixture of deionized water and ethanol. Different sonication times and output power were studied for different pulse modes. The graphene layers were characterized using Ultraviolet-visible spectroscopy (UV‒Vis), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Raman Microscope. The simulation shows that the total displacement on the tip of the stepped probe is 5.7% greater than that of the simple probe. Also, the pressure difference produced by the stepped probe is 9.15 × 106 pa compared to the pressure difference of the simple probe which is 8.23 × 106 pa. Consequently, the stepped probe was more effective in exfoliating graphene. The experimental results show that the absorbance peak in the stepped probe is approximately 32% greater than the absorbance peak in the simple probe with the same output power. Also, the graphene with better quality is produced with the stepped probe compared to the simple probe, which verifies the simulation findings. Additionally, the optimum output power, pulse duration, and sonication time to produce graphene with less time and energy consumption are obtained. The best graphene flakes were obtained via sonication with a pulse duration of 0.7 s, an output power of 282 W, and a sonication time of 45 min using a stepped probe. UV-Vis., SEM and TEM analysis show increases in quantity and quality of the exfoliated flakes. These results are approved by the peak ratio of I2D/IG around 1 in Raman spectra revealed the production of the few-layer graphene by the simple probe and the bilayer graphene by the stepped probe. So, this method is suitable for the production of graphene flakes from graphite in suitable quantity and quality.

Economic Load Dispatch on a 132 kV Line with Service Potential Transformer Substations: A Case Study of Juja-Rabai Line.

Power outages have created significant challenges for power system networks, particularly in developing countries where the electricity demand continues to rise without a corresponding increase in power generation or the expansion of transmission and distribution networks. In Kenya, while there is a well-established transmission line network, the distribution infrastructure remains inadequate for supplying electricity to end consumers. This paper examines the economic load dispatch (ELD) of power system networks utilizing Service Potential Transformer (SPT) substations to provide electricity to villages located near high voltage (HV) lines. The ELD analysis was conducted to identify the optimal economic power output from the Kipevu, Rabai, and Thika thermal power plants, addressing the demand for both conventional and non-conventional substations. A gradient method was employed to calculate the ELD for these three generating units, and the results were validated using the PowerWorld simulator. Findings indicated that the three generators supplied 20 MW, 37.5 MW, and 12.5 MW, respectively. The results obtained from the gradient method are consistent with those obtained from PowerWorld software. Additionally, this study projected an annual fuel cost savings of USD 17,695.20 when ELD was implemented, compared to a scenario of equal load distribution among generating units. Over a ten-year period, these savings would be sufficient to establish a conventional distribution substation to meet the power demands of villages located further away from high voltage lines.

The effects of geometric factors on power generation performance in solar chimney power plants

This research aims to create an admissible source for specifying the relation between the plant's output power and specified geometrical parameters unique to each simulated chimney dimension. In the current study, the solar chimney output power has been studied considering 49 solar chimneys, including different collector diameters, chimney heights, and chimney diameters. The study employs the Ansys-CFX to simulate the numerical model for the unsteady k-ε turbulent flow for a vast number of geometries. The obtained results indicate that the velocity as the main factor in generating the output power has significantly improved by elevating the collector radius. The output power increased up to 50 % by elevating the collector radius and around 30 % by enhancing the chimney heights which cause an increase in airflow, temperature differentials. Moreover, increasing the chimney height has generally ascended the power plant system. Among all geometrical parameters, chimney radius had the least impact on solar chimney performance. The results indicate that the best ratio between collector radius and chimney height is 6 %–10 % to achieve the optimized output power. Our findings were verified with the available experimental setups.

Self-powered piezoelectric sensing system for rotational speed detection of AUV propellers

To realize high-precision self-supplied speed measurement of Autonomous Underwater Vehicle (AUV) propellers, a self-powered piezoelectric speed sensing and detecting system is developed, which consists of a self-powered piezoelectric detection module and energy management and sensing circuits. The system uses three piezoelectric cantilevers for power and sensing detection. The proposed speed detection system is based on the piezoelectric effect and realizes self-powered sensing by utilizing the kinetic energy generated during the rotation of the propeller into electrical energy. The comprehensive model of the proposed piezoelectric sensing system under periodic toggled excitation is established and analyzed. The optimal output power of the system is investigated through theoretical analysis and simulation. The experimental system of the self-powered speed sensing system is constructed and the effect of speed measurement is tested. Experimental results show that the maximum output power of the piezoelectric self-powered sensing system developed in this work is 218 μW, which is much higher than that of the purely triboelectric sensing system. And the average detection error of the self-powered sensing system is 1.4% and the linearity is 1.09% when the propeller speed ranges from 0 to 300 r/min. Compared with similar self-powered systems, the proposed piezoelectric self-powered system is more advantageous in terms of measurement accuracy and can provide more accurate propeller speed measurement.

Comparison of entransy loss analysis and the first law of thermodynamics in the optimization of organic Rankine cycle coupled with parabolic trough collector

Due to the numerous environmental issues associated with fossil fuel power plants, using solar energy to generate electricity is a viable alternative. The organic Rankine cycle (ORC) is a thermodynamic process used to convert low- and medium-temperature heat sources into electricity, often utilizing organic fluids as the working medium. Entransy is a relatively new concept that many readers may not be familiar with. Moreover, entransy loss (Ġloss) is derived from the entransy concept, which quantifies the inefficiency in transferring thermal energy through a system. In this study, (Ġloss) is used for the first time when designing an ORC cycle coupled with parabolic trough collectors. The entransy loss relations were driven with assumption that the heat capacity is a function of temperature. A genetic algorithm is a search heuristic inspired by natural selection. It is used to find optimal or near-optimal solutions to complex problems by evolving a population of candidate solutions. Two scenarios utilized the genetic algorithm in MATLAB to optimize the system (scenario 1: maximizing the output power and scenario 2: maximizing the Ġloss). In addition, the optimization parameters included turbine inlet temperature (Ttur), boiler pressure (Pboil), condenser pressure (Pcond), and the temperature of the collector fluid at the boiler outlet (Thf,out). This optimization was performed for the temperature of the collector fluid at the boiler inlet (Thf,in) in the range 310–400 °C at 10 °C intervals with four working fluids (i.e., toluene, cyclohexane, MM, and water). The land area and the beam solar radiation were considered to be 100 hectares and 800 W/m2, respectively. The results indicated that according to scenario one, at temperatures of 310–320 °C, the maximum power was obtained for the case of toluene fluid with values 59.8 and 63.5 MW. For the collector fluid temperature from 330 to 400 °C, water had the most optimal power with values ranging from 66.2 to 88.2 MW. Furthermore, toluene exhibited superiority to two other organic fluids in the 330–400 °C temperature range after water, with net power values ranged between 65.7 and 76.3 MW. The results indicated that the maximum entransy loss does not correspond to the maximum output power because the application preconditions of the entransy loss concept are not all satisfied. Across all working fluids and Thf,in, scenario 2 resulted in lower optimal output power, cycle efficiency, and system efficiency compared to scenario 1.

Research on Reactive Power Optimization of Synchronous Condensers in HVDC Transmission Based on Reactive Power Conversion Factor

With the rapid development of high-voltage direct current (HVDC) transmission systems, the coupling between AC and DC grids is becoming increasingly close. Voltage disturbances in the grid can easily cause commutation failures in the DC system, threatening its safe and stable operation. The new generation of synchronous condensers (SCs) and modified synchronous condenser units are powerful reactive power support devices widely used in large-capacity DC transmission systems. To maximize the voltage support and commutation failure suppression of SCs, this paper proposes improvements in the initial operating state of SCs, using the Shanxi–Wuhan HVDC receiving end in the Hubei power grid as an example, to better support the HVDC commutation process. Additionally, a reactive power output optimization strategy for SCs is proposed, considering the reactive power equivalent factor of electrical connections between grid nodes. This strategy determines the optimal reactive power output limit of SCs near the converter station to suppress DC commutation failures. Simulation results show that this strategy effectively utilizes the dynamic support capabilities of SCs, prevents DC commutation failures, improves HVDC transmission capacity, and enhances the safety and stability of the receiving end power grid, providing theoretical guidance for reactive power output control.

A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach

AbstractThe uncertainty of distributed energy resources (DERs) and loads in distribution networks poses challenges for reactive power optimization and control timeliness. The computational limitations of the traditional algorithms and the development of artificial intelligence (AI) based technologies have promoted the advancement of hybrid model‐data‐driven algorithms. This article proposes a two‐stage reactive power optimization method for distributed networks (DNs) based on a hybrid model‐data‐driven approach. In the first stage, based on the topology and line parameters of the DN, as well as forecasts of loads and renewable energy outputs, a mixed‐integer second‐order cone programming (MISOCP) algorithm is used to control the on‐load tap changer (OLTC) positions on an hourly day‐ahead basis. In the second stage, leveraging deep learning technology, the real‐time reactive power output of photovoltaics (PV) and wind power units is controlled at a 5‐min time scale throughout the day. Specifically, using traditional solvers, the global optimal reactive power output for PV and wind power units is determined first, corresponding to various load and renewable energy output scenarios. Then, neural networks are trained to map node power to the optimal reactive power outputs of renewable energy units, capturing the complex physical relationships. For the second stage, a transformer network framework with a self‐attention mechanism and multi‐head attention for deep learning training is applied to uncover the intrinsic and physical spatial relationships among high‐dimensional features. The proposed method is tested on a modified IEEE 33‐bus system with multiple distributed renewable energy sources. The case study results demonstrate that the proposed hybrid model‐data‐driven algorithm effectively coordinates day‐ahead and real‐time controls of various devices, achieving real‐time model‐free optimization throughout the day. Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results.

Optimizing power output in direct formic acid fuel cells using palladium film mediation: experimental and DFT studies

Low-temperature hydrogen production from environmentally friendly liquid fuels such as methanol, ethanol, and formic acid for miniature fuel cells used in powering portable electronic devices is attracting reasonable attention. In this study, we present the findings from the decomposition of formic acid and subsequent power generation within a microfluid fuel cell. The fuel cell system was engineered to incorporate a palladium membrane at the anode and a modified carbon Torray paper at the cathode. To assess fuel cell performance, we employed chronopotentiometry and cyclic voltammetric electro­chemical techniques. This simple fuel cell could achieve a current peak of 3.17 µW by utilizing 2.50 M HCOOH solution and 2.26 µW with 0.1 M solution, all at room temperature. Finally, to provide a deeper understanding of the reactivity and HCOOH decomposition pathways on various palladium surfaces, we conducted density functional theory (DFT) studies. Our DFT investigations revealed that the Pd(111) surface exhibited more negative adsorption energy compared to other surfaces, suggesting its propensity for a more isomeric crystal morphology. This research underscores the promising potential of low-temperature hydrogen production using safe liquid fuels in microfuel cell applications for portable electronic devices.

A new neuro-fuzzy controller based maximum power point tracking for a partially shaded grid-connected photovoltaic system

Today, renewable energy systems like photovoltaic system are widely used in various applications. Among the different types of microgrids, hybrid microgrids are the most used type, therefore, inverters should be used to exchange power between DC and AC sides. According to the existing economic issues, extracting the maximum possible power from these systems are an important issue. This paper presents a new neuro-fuzzy controller for achieving maximum power point tracking (MPPT) in a grid-connected PV system under partially shaded conditions. This controller uses the Gravity Search Algorithm (GSA) to track the global maximum power point (GMPP) of the presented grid-connected PV system. The method controls the grid-connected inverter at the desired voltage to achieve maximum power after receiving its required specifications from the system. The Matlab/Simulink software is used to evaluate the performance of the proposed method. The results show that the proposed method can track the maximum power point under uniform and partial shading conditions with high speed and accuracy. Specifically, the proposed algorithm improves the tracking speed and increases the power output compared to traditional methods. The neuro-fuzzy controller's adaptive capabilities allow it to respond efficiently to dynamic changes in shading, ensuring stable and optimal power output. These advantages make the proposed method a significant improvement over existing MPPT techniques.

A tunable pendulum-like piezoelectric energy harvester for multidirectional vibration

Harvesting energy from vibrations using piezoelectric mechanism has attracted much attention for powering wireless sensors over the past decade. This paper proposes a tunable pendulum-like piezoelectric energy harvester for multidirectional vibration (TP-PVEH) to enhance the power generation characteristic, durability, and environmental adaptability of energy harvester. Unlike traditional cantilevered piezoelectric vibration energy harvesters (PVEHs), which typically lowered working frequencies by adding the weight of proof mass at the end of beam or reshaping beam, TP-PVEH employed a pendulum to harness low-frequency vibrations. Moreover, in contrast to typical pendulum-like PVEHs, the pendulum in this design was not mounted at the end of beam but was attached to a radial spherical plain bearing (RSPB) structure, which avoided the irreversible beam damage caused by gravitational force. TP-PVEH utilized simple-pendulum-induced RSPB motion to smoothly pluck piezoelectric beams, subjecting the piezoelectric beams to unidirectional compressive stress only. Meanwhile, the RSPB structure's capability to facilitate multidirectional rotation enabled TP-PVEH to efficiently capture energy from various directions. Theoretical analysis, numerical analysis and experiment tests were conducted to validate the design and examine how excitation and structural parameters influenced on the output performance of TP-PVEH. The results demonstrated that the excitation amplitude, excitation angle, proof mass, and mass distance brought significant effects on the output characteristic of TP-PVEH. The working frequency, output voltage and power could be efficiently tuned by the abovementioned parameters. With an excitation amplitude of 3 mm, TP-PVEH achieved an optimal output power of 9.81 mW and an output power density of 11.37 μW/mm3, operating with a load resistance of 200 kΩ at a frequency of 12.5 Hz.TP-PVEH could power 100 blue LEDs and a calculator. Additionally, the ability of TP-PVEH to charge capacitors further demonstrated its practical power supply capabilities.

Optimization of IPOP-connected dissimilar power-rated converters: An equal incremental cost-based approach

As electric vehicles gain prominence, optimizing the efficiency of charging stations has become a critical issue. This paper explores the application of dual active bridge (DAB) converters arranged in an input-parallel-output-parallel (IPOP) configuration, offering a flexible solution for power conversion across varied power-rated loads. While previous research mainly focused on optimizing single DABs and establishing the system using identical converters, this study leverages dissimilar modules, enhancing flexibility and operational efficiency. We introduce a novel IPOP system incorporating diverse modules and corresponding switch regulators. Additionally, an efficiency optimization strategy featuring dynamic load allocation is proposed. Herein, each module or each group of modules is responsible for its corresponding power range. Based on curve-fitted power profiles, an equal incremental cost-based method is employed to formulate the path of power allocation. Moreover, a simulated annealing algorithm determines the optimal power output strategy to accommodate dynamic power flow requirements. Experimental comparisons with the conventional equal-power sharing scheme underscore the proposed method’s effectiveness and superiority.

Improved machine learning-based pitch controller for rated power generation in large-scale wind turbine

In variable speed and variable pitch large-scale wind turbines, the pitch controller plays a crucial role in optimizing power output near the rated value during wind speeds that exceed the rated threshold. Nevertheless, the erratic nature of wind speeds poses challenges to the pitch controller’s efficacy, leading to a decline in generator power. So, there has been a growing interest among researchers in the development of machine learning-based pitch controllers. This paper introduces an improved recurrent radial basis function neural network and its parameters were tuned using the modified particle swarm optimization algorithm to enhance neural network performance. The proposed controller is validated in a benchmark wind turbine and comparative analysis are conducted against existing controllers in the literature. Through a series of comprehensive studies, the proposed controller consistently outperforms its counterparts, particularly in achieving power output close to the rated value.