Estimation Of Power Output Research Articles

A Novel Strategy for Multitype Fault Diagnosis in Photovoltaic Systems Using Multiple Regression Analysis and Support Vector Machines

This study focuses on analyzing common fault types in photovoltaic (PV) modules, employing fault diagnosis methods based on machine learning technology to enhance the accuracy and efficiency of diagnosing faults in solar power systems. Initially, we collected relevant data from the solar power system and used data analysis techniques to identify system faults, designing a human-machine monitoring interface for practical application. Furthermore, the experimental results proved that the system could accurately identify eight major types of faults, including solar panel output circuits, energy storage batteries, maximum power point tracking (MPPT) controllers, inverters, dust accumulation, loosening of mounting rack screws, damage to the mounting rack foundation, and deformation of the mounting rack structure. Particularly in the detection of dust accumulation, we developed a new method of estimating power generation from multiple regression analysis (MRA), which closely aligns the estimated power output with the actual power output, highlighting the significant impact of dust accumulation on the efficiency of solar power systems. Next, by integrating voltmeters and support vector machines (SVM) into the solar PV array modules, we are able to quickly and accurately measure and locate short-circuit and open-circuit faults in bypass diodes. Ultimately, the proposed PV fault diagnosis strategy includes diagnostics for dust accumulation and mounting frame faults, making it particularly suitable for areas with severe air pollution and frequent earthquakes, providing a comprehensive fault diagnosis solution.

Design, Assessment, and Modeling of Multi-Input Single-Output Neural Network Types for the Output Power Estimation in Wind Turbine Farms

Design, Assessment, and Modeling of Multi-Input Single-Output Neural Network Types for the Output Power Estimation in Wind Turbine Farms

Turbine-Level Possible Power Prediction using Farm-Wide Spatial Information through Similarity-Based Multivariate Gaussians

Wind farms usually comprise several turbines of the same type in proximity to one another. Therefore, similarities exist between the power production of specific turbines within the wind farm over time. Considering this, it is possible to find a way to express the similarity between turbines and exploit their properties to find a formulation of the expected behavior of a turbine. With this estimation of possible power output, one can analyze the losses generated by curtailments or transients with a higher precision. Based on this, a probabilistic model is proposed that can be used to calculate energy losses due to maintenance or environmental reasons. On top of that, heavy deviations in the behavior of specific wind turbines can be detected on high-frequency (1-second) data. Overall, the goal of this work is to predict possible power on high-frequency SCADA data using a statistical white-box modeling approach. The presented method is based on a probabilistic framework, constructing a system of linear combinations that permits analytically tracking the expected behavior of a turbine, even if it is non-operational for a specific amount of time. The methodology includes two parts, the first one is the use of data-driven power curves, and the second one consists of an inferential framework based on the environmental conditions of the farm. Results show that the method presented performs better than the manufacturer’s power curves under specific wind speeds and wind directions.

Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach

Artificial neural networks (ANNs) have gained prominence among contemporary computing techniques due to their capacity to handle complicated stochastic datasets and nonlinear modelling in combined gas and combined cycle power (COGAS) plants. Researchers, academicians, and stakeholders have been unable to predict, ensure effective operation, and prevent power outages in COGAS due to the nonlinearity. The first implementation of the simultaneous adoption of three types of ANNs using Levenberg–Marquardt (LM), Bayesian regularisation (BR), and scaled conjugate gradient (SCG) configurations for training and assessing a combined cycle power plant output is presented. The dataset used in this research is a 9568-unit full combined cycle power plant basis load dataset, accessible through the public UCI Machine Learning Repository. It incorporates ambient temperature, exhaust vacuum, ambient pressure, and relative humidity as input parameters to predict the electric output power. The most accurate and dependable electric power predictions could be identified for 70% of the total data, of which 6698 were trained, 15% were tested, and 15% were validated (2870). By using the three training techniques, namely, LM, BR, and SCG, the parameterized networks are studied, increasing the number of hidden layers from 20 to 500. The lowest root-mean-square error value for a multilayer perceptron (MLP) architecture is 3.631%, which is lower than the values of 4.17%, 4.35%, and 4.63% for comparable MLP structures (20 to 500), documented in the literature. The LM and BR algorithms outperform SCG. These adopted algorithms could be a cutting-edge application in the power plant industry and other real-world applications for reliable solutions, to satisfy emerging societal needs with environmental benefits.

On the application of symbolic regression in the energy sector: Estimation of combined cycle power plant electrical power output using genetic programming algorithm

This paper focuses on the estimation of electrical power output (Pe) in a combined cycle power plant (CCPP) using ambient temperature (AT), vacuum in the condenser (V), ambient pressure (AP), and relative humidity (RH). The study stresses accurate estimation for better CCPP performance and energy efficiency through responsive control to changing conditions. The novelty lies in applying genetic programming (GP) on a publicly available dataset to generate Symbolic Expressions (SEs) for high-accuracy Pe. To address the challenge of numerous GP hyperparameters, a random hyperparameter values search method (RHVS) is introduced to find optimal combinations, resulting in SEs with higher accuracy. SEs are created with varying input variables, and their performance is evaluated using multiple metrics (coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), Kling–Gupta Efficiency (KGE), and Bland–Altman (B-A) analysis). A key innovation involves combining the best SEs through an Averaging ensemble (AE), leading to a robust estimation accuracy. Notably, the AE YVE−2 achieves the highest (Pe) accuracy, including R2=0.9368, MAE=3.3378, MSE=18.4800, RMSE=4.2985, MAPE=0.7354%, and KGE=0.9479. The investigation highlights AT as the most influential variable, underscoring the importance of choosing inputs aligned with physical processes. This paper’s outlined procedure, combining GP, hyperparameter optimization, and ensemble techniques, offers an efficient method for estimating Pe in CCPP. It promises simplicity and effectiveness in real-world applications. B-A analysis proves valuable for SE selection, enhancing the proposed methodology.

Estimate of Power Output from Hydraulic Jumps Generated Downstream from Barrages

Hydropower is an affordable, sustainable way to generate electricity. Research on hydraulic jumps focuses only on determining head loss across the jump, but there are no studies on generating power from the jump. This research aims to utilize energy dissipated from hydraulic jumps for power-generating purposes and further use this power in real-life applications. This research simulates ideal hydraulic conditions to identify the most stable hydraulic jumps, which will be used to generate power. The Seriakos barrage in Egypt was taken as a case study to simulate energy dissipated/power generated from hydraulic jumps generated downstream. This power is then used to simulate lighting up some streets in Egypt according to Egyptian power consumption standards. An Excel spreadsheet was used to mathematically model generated hydraulic jump types, energy dissipated, and generated power. The study found that submerged flow generates maximum power values from hydraulic jumps as opposed to free flow. The research concluded that energy produced by hydraulic jumps at the Seriakos barrage could light up 78 street light bulbs. Although this is a small amount of power, Egypt could meet a significant portion of its energy needs if hydraulic jumps from all its hydraulic structures were utilized for power generation. Doi: 10.28991/HEF-2024-05-01-06 Full Text: PDF

From theory to practice: Monitoring mechanical power output during wheelchair field and court sports using inertial measurement units

An important performance determinant in wheelchair sports is the power exchanged between the athlete-wheelchair combination and the environment, in short, mechanical power. Inertial measurement units (IMUs) might be used to estimate the exchanged mechanical power during wheelchair sports practice. However, to validly apply IMUs for mechanical power assessment in wheelchair sports, a well-founded and unambiguous theoretical framework is required that follows the dynamics of manual wheelchair propulsion. Therefore, this research has two goals. First, to present a theoretical framework that supports the use of IMUs to estimate power output via power balance equations. Second, to demonstrate the use of the IMU-based power estimates during wheelchair propulsion based on experimental data. Mechanical power during straight-line wheelchair propulsion on a treadmill was estimated using a wheel mounted IMU and was subsequently compared to optical motion capture data serving as a reference. IMU-based power was calculated from rolling resistance (estimated from drag tests) and change in kinetic energy (estimated using wheelchair velocity and wheelchair acceleration). The results reveal no significant difference between reference power values and the proposed IMU-based power (1.8% mean difference, N.S.). As the estimated rolling resistance shows a 0.9–1.7% underestimation, over time, IMU-based power will be slightly underestimated as well. To conclude, the theoretical framework and the resulting IMU model seems to provide acceptable estimates of mechanical power during straight-line wheelchair propulsion in wheelchair (sports) practice, and it is an important first step towards feasible power estimations in all wheelchair sports situations.

Photovoltaic output power estimation model based on multi-meteorological variable dimension reduction and improved variational mode decomposition algorithm

ABSTRACT The instability and intermittence of photovoltaic (PV) power generation challenge the large-scale integration of PV power generation systems into the power grid and the development of energy storage systems. To accurately predict the output power of PV power generation, a power prediction model based on the optimized variational modal decomposition (OVMD)-adaptive t-distribution sparrow search algorithm (tSSA)-least squares support vector machine (LSSVM) algorithm was built. The performance of the model was then verified by three-year meteorological data and real-time PV output power data of two different climatic regions in China. The mean absolute percentage error (MAPE) and root mean square error (RMSE) of this model were less than 3.1% and 0.40, respectively, and the determination coefficient (R2) of this model was greater than 95%. Moreover, the prediction accuracy was greatly improved compared to models based on the support vector machine (SVM), LSSVM, variational modal decomposition (VMD)-LSSVM, and VMD-SSA-LSSVM algorithms. Finally, by performing dimension reduction of meteorological variables related to PV output power, the performance of the proposed OVMD-tSSA-LSSVM model was further improved.

Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power

This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models’ predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator’s rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting.

Enhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks

Power distribution grids experiences proliferation of solar photovoltaics (PV) at the system edge. However, its counterpart of sparse meter deployment provides insufficient monitoring of PVs, for which the potential violations challenge the operators for energy management and stable operation. Some previous works use satellite imagery to detect distributed PVs for the easy access of data. However, their PV localization methods rely on label-rich area with unitary background/environment to implement well; even further/harder, they do not provide precise metered-PV detection and quantification to estimate/know PV generation outputs in unobservable area, which is essential to prevent the edge from excessive two-way power flow and other violations. Thus, we combine the two steps of detecting PV existence and quantify PV amount into one classification task. To boost the classification performance in unobservable edge area, we construct a generative adversarial network that simultaneous augments the diversity of labelled PV satellite images and embed distinct PV characteristics/features for training the classifier. Furthermore, the PV localization and quantification result is combined with geographic information, historical weather conditions and neighboring generation patterns to estimate power output at the system edge. We validate the proposed approaches on PV systems in the southwest of the U.S. Experiment results show high accuracy and robustness in predicting distributed solar power without sufficient prior information.

Comparison of the dynamic wake meandering model against large eddy simulation for horizontal and vertical steering of wind turbine wakes

Accurately predicting the evolution of wake is crucial for power output and structural load estimation in wind farms. This study aims to validate the dynamic wake meandering (DWM) model, an efficient mid-fidelity wake model, against large eddy simulation (LES). The predictive capabilities of the DWM model for various wake properties, namely time-averaged wake deficit, mean wake center deflection, amplitude, and frequency spectrum of wake meandering, are comprehensively analyzed using the IEA 15MW reference wind turbine under different yaw and tilt misalignment angles. Two turbulent inflows with varying shear and turbulence intensity levels are considered. The comparison highlights the significance of the filter size (Cmeand) in DWM as a key parameter determining simultaneously the time-averaged wake deflection and meandering amplitude, with optimal values differing for horizontal and vertical wake displacements. When the appropriate Cmeand values are selected, the implementation of the DWM model in FAST.Farm demonstrates good agreement with LES data, particularly concerning time-averaged wake deficit, wake centerline deflection, and wake meandering amplitude at eight rotor diameters downstream. However, the DWM model tends to overestimate the energy in the lower frequency region with Strouhal’s number less than 0.1 and underestimate the wake oscillation induced by the shear-layer at higher frequencies, even though the wake motion standard deviation is accurately reproduced if the polar grid size is properly adjusted. Furthermore, the influence of the ground effect on downward wake deflection through tilt control is revealed. These findings clearly demonstrate the strengths and weaknesses of the current DWM model and can serve as a reference for the development of advanced wake models.

Evaluation of Horizontal Single‐Axis Solar Tracker Algorithms in Terms of Energy Production and Operational Performance

Horizontal single‐axis solar tracking systems with Astronomical tracking algorithm are commonly used in photovoltaic (PV) installations. However, different algorithms can increase the PV installation's performance without implementing new equipment or technologies. In this article, the performance of three tracking algorithms is compared to the Astronomical one. Two algorithms aim at optimizing the received irradiance focusing on the diffuse component. The third one, called Analytical, is presented as a new development in this study, defining the optimal inclination angle depending on all radiation components. The comparison focuses on different parameters like the in‐plane irradiance, DC power output from a monofacial PV system and operational aspects like the performed number of movements. High‐time‐resolution (1 min) simulations are performed with proprietary software taking into account several effects on the effective irradiance and power output estimation, such as shading effects or PV configuration. Eight locations with different climatic conditions are analyzed. All three algorithms derive in higher in‐plane irradiance and power output values than the Astronomical algorithm, although the gain depends on the climatic conditions. At locations with high diffuse fraction, the power output from the Analytical algorithm, which outperforms the others, can be up to 3% higher than the Astronomical one.

Improving the power output estimation for a tidal power plant: a case study

The aim of this study is to analyse the importance of considering the variation in the discharge coefficient (Cd) of a sluice passage and the tidal current speed (i.e. the flow speed approaching the passage) in improving the estimation of power output for a tidal power plant. The simulation results, using a commercial computational fluid dynamics software package, Flow 3D, have shown that the Cd value of a sluice passage varies with the upstream (i.e. on the sea side of the impounded area) water level. In addition, the results of a case study carried out in the Taedong bay (Democratic People's Republic of Korea), using a zero-dimensional model, highlight the importance of considering variations in the tidal current speed as well as Cd when estimating the power output for a tidal power plant. In this study, a detailed scheme is proposed to consider the approach speed at each stage of filling, holding, generating and holding for the mode of ebb generation. It is hoped that the results of this study will help scientists, engineers and decision makers to accurately evaluate the tidal power output of tidal barrages and lagoons in many regions of the world.

Optimization of Wind Farm Design Layout based on the Non-Uniform Spacing Algorithm between Wind Turbines

The optimization of wind turbine layout is an important step during the design phase of wind farms, which directly influences the overall power performance and the profitability of the wind plants. The spacing distance between the wind turbines, which is often inconsistent in the prevailing wind direction and other directions, is the critical factor of wind turbine array layout optimization. With the considerations of the wind turbines’ safety in operation and the maximum utilization of wind energy resources comprehensively, an elliptic-constrained wind turbine layout optimization model is proposed in this paper. The stochastic optimal algorithm including the constraint checking and random searching processes is established to conduct the iterative calculation. It is proved that the annual energy production of the elliptic-constrained model with non-uniform spacing has increased in comparison with the conventional method. The proposed approach provides an effective engineering solution for improving the space utilization and the power output estimation of the wind farm.

Estimation of generator electrical power output and turbine torque in modelling and field testing of OWC wave energy converters

Estimation of generator electrical power output and turbine torque in modelling and field testing of OWC wave energy converters

Study of a solid-state transmitting device with a phase correction system, which increases the summing efficiency

Modern radar is a complex system, built on the basis of almost all the achievements of such areas as radio electronics, computer engineering and, of course, microwave devices. One of the most important units of radar, which determines the potential characteristics of the system as a whole, is the transmitting device. Nowadays the task of signal transmission with minimal losses in transmission lines and obtaining required power levels at the output of the transmitting device is relevant. However, existing methods may be insufficient to accomplish this task. In this regard, it may be necessary to improve or develop new systems and methods using the latest advances in microwave and antenna technology, radio and microelectronics, the totality of which will provide devices with these parameters. One such way to increase the stacking efficiency and output power of the transmitting device is to correct the phase of the amplification channels of the transmitting device in the range of operating frequencies. In this article the advantages of using solid-state transmitting devices in radar stations over electric-vacuum amplifying devices are considered. The most common ways of summation the power of microwave devices are described. An algorithm of the phase correction system is presented. The results of an experimental study of the system of gain channel phase correction for a transmitting device with eight gain channels, operating in a relatively wide frequency range, are presented. The proposed amplification channel phase correction system compensates the existing difference in the electrical lengths of the elements of the divider-adder path and amplifying modules in accordance with the algorithm, by means of controlled phase shifters (integrated into each amplifying module). The change in the phases of the microwave signal in (n-1) amplification channels is performed based on the calculated phase corrections obtained on the basis of successive estimates of the total output power of the reference amplifying module with each of the (n - 1) modules, where n is the total number of amplifying modules (channels amplification) in the transmitter. The output power is estimated by the ADC according to the level of the microwave power envelope at the lower, middle and upper frequencies of the operating range, taken from the adder output by a directional coupler. The performance of the phase correction algorithm is ensured by the control device of the amplifying module, the control and monitoring device of the transmitting device, the automatic monitoring and control system from the processing and indication system, as well as the appropriate interface at the operator's workplace. The proposed algorithm corrects the phases of the amplification channels according to the values obtained by the calculation method based on the data measured in the process of phase correction, which made it possible to reduce the maximum required number of phase shifter adjustments to 3 for each of n and each frequency, thereby ensuring a threefold increase in device performance. It was experimentally confirmed that the application of the proposed technical solution increases the efficiency of power summation of the considered transmitter from 70% to 94% - 99% (excluding active losses in the divider-adder and RMSD of the output powers of the amplifying modules) and an increase in the output power of the transmitter by 30% was achieved. The obtained results can be used to improve the algorithmic-software support of developed or modernized transmitting devices of radar stations.

Improvement in output power assessment by wind turbine power curve modeling based on data mining

The accurate assessment of wind turbine output power is crucial in the process of sizing wind farms. Typically, this assessment is based on the manufacturer’s characteristic power curve, which relates wind speed to power output. However, the manufacturer’s power curve is often an idealized representation that may not accurately reflect the actual power output of the turbine under real-world conditions. To address this limitation, various techniques have been employed to develop more precise power curves, including curve fitting, artificial intelligence, probabilistic models, and Gaussian processes. This paper introduces a novel method for modeling the power curve that takes into account the specific conditions at the wind turbine’s location. The method involves transforming wind speed data into a graph that resembles the phase space commonly used in statistical mechanics. By applying the k-means algorithm to this phase space, clusters of wind speeds can be identified. Furthermore, the corresponding clusters of wind turbine output power can be determined based on the identified wind speed clusters. These clusters of power data provide valuable information for constructing a more accurate power curve using an adjustment function. By utilizing this method, the authors demonstrate a significant improvement in the accuracy of power output estimation compared to relying solely on the manufacturer’s power curve. The proposed approach considers the unique characteristics of the wind speed data and incorporates them into the modeling process, resulting in a more reliable representation of the turbine’s power output. This advancement represents a significant step forward in optimizing the sizing of wind farms and ensuring their efficient operation.

Utilizing hydrogen pressure energy by expansion machines – PEM fuel cells in mobile and other potential applications

Hydrogen is an important energy carrier for a future without fossil fuels, but PEM fuel cell systems have to utilize the entire available energy as efficiently as possible. Hydrogen is typically stored at a maximum pressure of up to 700 bar and is supplied to the anode system at about 15 bar. Normally, a pressure reduction valve expands the hydrogen between 700 and 15 bar, but the pressure energy is unused during expansion. In this paper, different expansion machines are analyzed and the most attractive concepts are studied in detail to regain the expansion energy. Finally, a piston expansion system concept is designed with an estimated power output of 10 kW and a driving range increase of 2%, reflecting the high potential for efficiency improvement.

단상 멀티 레벨 인버터의 출력 전력 전향 보상

This study proposes a feedforward compensation method based on output power estimation for 2-stage single-phase multilevel inverter-based solid-state transformer. The proposed method consists of the output power estimation by phase reference of DAB (Dual Active Bridge) converter and feedforward the d-axis current reference of AFE (Active Front-End) converter. Feedback controller for AFE and DAB converters is designed based on small signal model of each converter for verifying the proposed method. Simulations and experimental results are provided to confirm the effectiveness of the proposed method.

Abstract P444: Estimated Power Output for a Distance Run and Maximal Oxygen Uptake in Young Adults

Background: Both cardiopulmonary exercise testing (CPET) and run field test are recommended by the American Heart Association for assessing maximal oxygen uptake (VO 2 max) of youth. Power output was highly correlated with VO 2 max in a CPET. However, it is unclear the correlations of time and estimated power output (EPO) for a run field test with VO 2 max obtained from a CPET in young adults. Methods: This study included 45 participants, aged 20-40 years, from a sample of 1,120 military personnel who completed a 3000-m run field test in Taiwan in 2020. The participants subsequently received a CPET with the Bruce protocol to assess VO 2 max in the same year. According to the physics rule, EPO (watts) for the run field test was defined as a product of half body mass (kg) and [distance (3000-m)/time (s) for a run field test] 2 . Pearson product-moment correlation analyses were performed. Results: The Pearson correlation coefficient (r) of time against EPO for the run field test was estimated 0.708 (p <0.001). The correlation coefficient between time for the run field test and VO 2 max (L/min) in a CPET was estimated 0.462 (p =0.001). By contrast, the correlation coefficient between time for the run field test and VO 2 max scaled to body mass in a CPET was estimated 0.729 (p <0.001). The correlation coefficient of EPO for the run field test against VO 2 max in a CPET was estimated 0.813 (p <0.001). Conclusion: In young adults, although time for a run field test was a reliable estimate of VO 2 max scaled to body mass, EPO proportionally to the mean velocity squared was found as a superior estimate of VO 2 max.