Error Power Research Articles

A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms

The prevalence of extreme weather events gives rise to a significant degree of prediction bias in the forecasting of photovoltaic (PV) power. In order to enhance the precision of forecasting outcomes, this study examines the interrelationships between China’s 24 conventional solar terms and extreme meteorological events. Additionally, it proposes a methodology for estimating the short-term generation of PV power based on the division of solar term time series. Firstly, given that the meteorological data from the same festival is more representative of the climate state at the current prediction moment, the sample data are grouped according to the 24 festival time nodes. Secondly, a convolutional neural network–long short-term memory (CNN-LSTM) PV power prediction model based on an Attention mechanism is proposed. This model extracts temporal change information from nonlinear sample data through LSTM, and a CNN link is added at the front end of LSTM to address the issue of LSTM being unable to obtain the spatial linkage of multiple features. Additionally, an Attention mechanism is incorporated at the back end of the CNN to obtain the feature information of crucial time steps, further reducing the multi-step prediction error. Concurrently, a PV power error prediction model is constructed to rectify the outcomes of the aforementioned prediction model. The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions.

Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials with binary and count outcomes

BackgroundThe fixed effects model is a useful alternative to the mixed effects model for analyzing stepped-wedge cluster randomized trials (SW-CRTs). It controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. While all clusters in a SW-CRT are typically designed to crossover from the control to receive the intervention, some trials can end with unexposed clusters (clusters that never receive the intervention), such as when a trial is terminated early due to safety concerns. It was previously unclear whether unexposed clusters would contribute to the estimation of the intervention effect in a fixed effects analysis. However, recent work has demonstrated that including an unexposed cluster can improve the precision of the intervention effect estimator in a fixed effects analysis of SW-CRTs with continuous outcomes. Still, SW-CRTs are commonly designed with binary outcomes and it is unknown if those previous results extend to SW-CRTs with non-continuous outcomes.MethodsIn this article, we mathematically prove that the inclusion of unexposed clusters improves the precision of the fixed effects intervention effect estimator for SW-CRTs with binary and count outcomes. We then explore the benefits of including an unexposed cluster in simulated datasets with binary or count outcomes and a real palliative care data example with binary outcomes.ResultsThe simulations show that including unexposed clusters leads to tangible improvements in the precision, power, and root mean square error of the intervention effect estimator. The inclusion of the unexposed cluster in the SW-CRT of a novel palliative care intervention with binary outcomes yielded smaller standard errors and narrower 95% Wald Confidence Intervals.ConclusionsIn this article, we demonstrate that the inclusion of unexposed clusters in the fixed effects analysis can lead to the improvements in precision, power, and RMSE of the fixed effects intervention effect estimator for SW-CRTs with binary or count outcomes.

Secure and covert communication strategy of UAV based on hybrid RF/FSO system

The hybrid radio frequency (RF)/free-space optical (FSO) system can improve communication security by using complimentary characteristics and mitigating the risks of RF links being susceptible to co-channel interference (CCI) and malicious eavesdropping. We investigate the secure and covert communication performance of unmanned aerial vehicles (UAVs) equipped with a hybrid RF/FSO system. In terms of secure communication, UAVs employ the strategy of transmitting private data concurrently through RF and FSO links, considering two modes based on active eavesdropping by a malicious user. Closed-form expressions for the secrecy outage probability (SOP) and effective secrecy throughput (EST) are derived. With respect to covert communication, the communication strategy is to utilize the RF link to generate interfering signals, thus aiding the FSO signals in accomplishing covert communication. We formulate an optimization problem that maximizes the EST while considering transmit power and user-acceptable detection error probability. A two-stage gradient-based iterative solution algorithm is proposed. The numerical results demonstrate the effectiveness of the proposed secure and covert communication strategy based on the hybrid RF/FSO system, which is expected to enhance the communication security of UAVs in the RF-challenged environment.

Sliding mode-based direct power control of unified power quality conditioner

The Unified Power Quality Conditioner (UPQC) is a promising solution for mitigating multiple Power Quality(PQ) issues in distribution systems, including harmonics, poor power factor, voltage sag/swell and voltage imbalance. The conventional Sliding Mode Controller (SMC) in UPQCs suffers from wide switching frequency variations, chattering problems, and inherent active and reactive power coupling. This study proposes a nonlinear control method, Sliding Mode-based Direct Power Control (SMC-DPC), for the simultaneous regulation of the shunt and series compensators in a UPQC. By optimizing voltage vector selection based on real-time power errors, the proposed method effectively mitigates chattering, and reduces switching frequency variations, and ensures precise tracking of instantaneous active and reactive powers even in the presence of coupling effects. The proposed approach simplifies the system design and improves steady state and dynamic power tracking. The simulation results in MATLAB Simulink® on a 20 kVA system demonstrate that the proposed method achieves a source current THD of 1.52%, compared to 2.31% for conventional SMC and 5.11% for linear controller. Furthermore, the method is robust to grid parameter variations and demonstrates satisfactory performance in both strong and weak grids. In weak grids, the proposed method reduces the line losses by 13.71% and 14.54% compared to SMC and linear controller respectively. The reported results comply with international standards such as IEEE-519 and IEC 61000-3-12, confirming effectiveness of SMC-DPC for enhancing PQ in distribution system.

Applying Gaussian Process Regression for Machine Learning-Assisted Reactor Simulations

Abstract This study explores the integration of machine learning, specifically Gaussian Process Regression (GPR), into traditional reactor core simulations. Building upon previous work on Boiling Water Reactors (BWR), GPR is implemented to predict and correct errors in lower-fidelity simulation outcomes. The findings demonstrate significant improvements in prediction accuracy when GPR is coupled with the diffusion-based core simulator, exhibiting remarkable reductions in both keff and nodal power errors. The comparison reveals that the GPR-enhanced core simulation model significantly outperforms both the standalone simulation and a combination of simulation with Multivariate Linear Regression. It also competes effectively with the performance of a Deep Neural Network-enhanced model. Importantly, this methodology enhances simulation accuracy while maintaining low computational costs. The research emphasizes the vast potential of machine learning, particularly GPR, in progressing nuclear reactor simulations, highlighting the immense value of combining traditional simulation methods with advanced statistical learning techniques.

Backstepping control of multilevel modified SVM inverter in variable speed DFIG-based dual-rotor wind power system

The backstepping control (BC) scheme has been successfully used in a variety of high-effectiveness industrial AC drives. This study presents the application of the BC technique based on multilevel modified space vector modulation (BC-MSVM) to get better the energy performance of a dual-rotor wind turbine system (DRWT). Two techniques are suggested to command the stator power of a doubly-fed induction generator (DFIG) driven by a DRWT. This work addresses the problems of the DRWT-based energy production system, such as stream fineness, power overshoot, and torque ripples. The use of a multilevel inverter in BC-MSVM led to an enhancement in the competence of the multilevel BC-MSVM technique and the system as a whole, and this is proven by the results performed using MATLAB software on a 1500 kW DFIG-DRWT. The use of a seven-level inverter led to a minimization in the rates of overshoot, ripples, steady-state error, and response time of active power by 26.66%, 53.84%, 2.09%, and 9.09%, respectively. Regarding the reactive power, the ratios were estimated at 18.12%, 34%, 25%, and 1.41%, respectively. These ratios prove that using a higher-level inverter significantly improves the voltage quality and characteristics of the DFIG-DRWT.

The Most Powerful Member of the Power-Divergence Family for the Independence Model in Contingency Tables

The family of power-divergence (PD) test statistic contains many well-known test statistics used in the analysis of the contingency tables under the independence model. In this work, we compare the various test statistics for the independence model. The type-I and type-II errors of the test statistics are obtained and compared via simulation study considering the different degree of freedoms and sample sizes. According to the simulation results, we recommend the PD(0.4) test statistic for the small sample size based on its power and type-I error rates. Two applications are given to demonstrate the usefulness of the PD(0.4) test statistic over the chi-square test statistic {contingency tables}.

Robust model-based control and stability analysis of PMSM drive with DC-link voltage and parameter variations

To ensure a stable operation of Permanent Magnet Synchronous Motor (PMSM) drive under both DC link voltage and parameter variations, a robust control technique based on a new dynamic model that includes both drive’s and motor’s specifications is proposed in this paper. In the proposed controller, the first component of the drive’s control law consists of the d-component error of the stator current, and the second one is shaped based on the error in the square value of q-component of the stator current. To further deal with the dynamic alterations of the drive system, compensators are designed to reduce the adverse effects of rotor angular frequency variations and the difference between electrical and load torque errors. Another compensator based on the drive’s output power error is also placed at the q-component of the proposed control law. Moreover, a general operation curve (GOC) for the stator current is introduced to further assess the operation of the PMSM. In the next step, a comprehensive stability analysis verifying the stable operation of both d- and q-components of the stator current is performed using two closed-loop descriptions of the proposed control strategy. Several simulation results in MATLAB/SIMULINK environment are provided to verify the validity of the proposed control technique under various dynamic scenarios. It is worth mentioning that comparative results show that the proposed control technique compared to conventional PI controller has enabled the PMSM speed and torque to reach its 50% and 95% of their desirable values with respectively 42.9% and 28.6% less time. Also, the PMSM speed and torque responses due to proposed control technique have 75% less undershoot compared to conventional PI controller.

Comparative analysis of Vedic multiplier using Vedic sutras with existing multipliers in biomedical application

In today's computer world, a lot of real-time applications require rapid processing units. Arithmetic Logic Units (ALU) and Multiply-Accumulate (MAC) are the fundamental parts of these circuits and are necessary for their effective and rapid operation. The most significant prevalent part of digital signal processing devices is multipliers. The multiplier, adder, and registers need to be changed in order to maintain accuracy and increase execution speed, which will improve the performance of the ALU and MAC. The development of greater multipliers is being given priority for application in processors because of the increasing constraints on latency. To accelerate multiplication, it is essential to develop quicker multipliers. Vedic multipliers are preferred over different current expansions due to their low power consumption, fast operation, and efficient use of space. Vedic mathematics-based algorithms are often utilized to build quick, low-power multipliers. In addition to simulation results, this section covers the four sutras of Vedic mathematics: Urdhva Tiryakbhyam, Ekadhikena Purvena, Ekanyunena Purvena, and Nikhilam. Vedic multipliers are also compared to a variety of modern multipliers, including booth, Wallace, and array multipliers. All of the sutras are evaluated according to area, speed, power, propagation delay, and mean relative error (MRE) in the current research. The results of the study will be applied in the biomedical field.

Linear Sources and Anisotropic Scattering in the Random Ray Method

An enhanced implementation of the random ray method (TRRM), a stochastic adaptation of the method of characteristics, is presented. This implementation generalizes from a traditional flat source (FS) approximation to a linear source (LS) approximation, and from the standard isotropic scattering approximation to an anisotropic approximation, up to P3. On the two-dimensional C5G7 benchmark, LS alone enables a 1.4× and 1.7× enhancement in run time for parallel and serial executions, respectively, without compromising accuracy. Further testing on the three-dimensional C5G7 Rodded B benchmark demonstrates that LS enables significant axial coarsening and reduction in ray populations. This results in a 5.3× speedup in run time while still offering comparable accuracy. On the Babcock and Wilcox 1484 Core II benchmark, incorporating anisotropic scattering in TRRM up to P3 with a FS decreased keff errors (~110 pcm) and maximum and average pin power errors. LS with anisotropic scattering (LSPN), showed similarly improved keff errors while benefiting from a coarser mesh. The linear isotropic flat anisotropic (LIFA) method, relative to LSPN, offers further run time and memory savings of 4.1× and 1.6×, respectively, for P3, while maintaining comparable accuracy to anisotropic FS on more refined meshes.

Grid-connected virtual synchronous generator transient suppression based on proportional differential control and frequency low-pass filtering

Abstract The virtual synchronous generator (VSG) technology has a wide range of applications in various distributed generation units. However, sudden changes in power command at grid connection can lead to severe oscillations in the output power and frequency of virtual synchronous generators. In addition, existing oscillation suppression methods are either based on complex parameter designs or change the original inertial support characteristics and power frequency drop characteristics. In this paper, a new transient suppression method is proposed, i.e., a proportional differential controller is added behind the power error and an additional low-pass filter is placed behind the output frequency. Finally, by establishing a simulation platform, it is verified that the proposed scheme can greatly reduce the overshoot, regulation time and frequency offset of the active power.

Improvement of Wind Power Prediction by Assimilating Principal Components of Cabin Radar Observations

Abstract Data assimilation is an important approach to improve the prediction performance of near-surface wind and wind power. Based on the four-dimensional variational technique, this study proposes an approach to improve near-surface wind and wind power prediction by extracting and assimilating the principal components of cabin radar radial wind observations installed at wind turbines within a wind farm. The verification for a series of cases under strong and weak vertical wind shear conditions indicates that compared to the simulations without assimilation, the predicted ultra-short-term (0–4 h) mean absolute error of near-surface wind and single turbine wind power could be reduced by 0.09–1.17 m s−1 and 53–209 kW after the assimilation of radial wind directly and by 0.33–1.38 m s−1 and 62–239 kW after the assimilation of principal components. These illustrate that assimilating the principal components of radial wind is superior to assimilating radial wind directly and could obviously reduce prediction error. Further investigation suggests that extracting the principal components of radial wind has marginal influences on the density and distribution of observations, but could obviously reduce the fluctuation of the observations and the correlation among the observations. The prediction improvement by assimilating the principal components of radial wind is essentially due to the assimilation of low-frequency and low-correlation information involved in the observations.

Neutronic and thermal–hydraulic coupling of FCM and helium annular fuel as accident-tolerant fuel

The study aims to explore two types of Accident Tolerant Fuel (ATF) (helium annular fuel and Fully Ceramic Microencapsulated (FCM) fuel), both with Silicon Carbide (SiC) used as clad material and as a matrix material for TRISO (TRi-structural ISOtropic particle fuel) particles in FCM fuel. The safety characteristics (such as maximum fuel temperatures) were evaluated and compared against reference UO2-Zr for a Pressurised Water Reactor (PWR) with neutronic and thermal–hydraulic coupling during normal operation for fresh fuel. The results show that both annular and FCM fuels exhibit reduced maximum temperatures compared to the reference fuel, with a reduction of 479 K (25.3 %) for helium annular fuel and 798 K (42.2 %) in the FCM fuel. The annular fuel improved the burnup by 12.4 MWd/kgU (24.2 %) without the cycle length being affected significantly (−5.6 %). The FCM fuel significantly reduced in the cycle length by 44.1 % while the burnup was more than double the reference providing a 163.5 % increased burnup in comparison to the reference fuel assembly. Assembly pin power peaking factors showed no dramatic deviation from the reference and the axial power distribution of ATFs is more symmetrical than that of the reference fuels. In terms of coupling the pin models converged after eight iterations on average with maximum relative power errors per radial node below 2 %.

Optimal aggregation and disaggregation for coordinated operation of virtual power plant with distribution network operator

Virtual power plants (VPPs) offer an effective approach for managing distributed energy resources (DERs), including microturbines, distributed generators, demand response aggregators, and energy storage systems. This technology significantly enhances the economic efficiency and flexibility of distribution network systems. This study aims to facilitate a flexible power exchange between the distribution network system and the upper-level grid by aggregating the power flexibility of heterogeneous DERs via a VPP. Additionally, it introduces a methodology for optimal aggregation and disaggregation within a coordinated operation framework between the VPP and distribution network operator (DNO). On one hand, the VPP can determine its day-ahead feasible region and real-time flexible regulation power based on operational constraints of DERs and representative data provided by the DNO, circumventing the need for detailed network information. On the other hand, day-ahead and real-time correction procedures for the DER cost functions are proposed, effectively neutralizing the impact of network operational constraints on these cost functions. Consequently, precise cost functions for both the active power and the flexible regulation power aggregated by the VPP are derived. Employing this aggregated cost function enables the determination of a cost-minimized optimal scheduling solution in real-time by solving a fundamental economic dispatch problem, significantly alleviating computational demands. Finally, case studies demonstrate that the proposed method achieves an error in aggregated power of only 0.77%, compared to the precise computation method that requires comprehensive system information. The proposed optimal VPP disaggregation scheme exhibits power discrepancies of 0.63% and cost discrepancies of 0.91% relative to the precise method. Additionally, when implementing the most cost-effective demand response plan based on the proposed cost function, the average costs for aggregators are reduced by 19.7%.

Simulating the Effects of Ammonia Crossover and Alternative Ligands in Thermally Regenerative Flow Batteries

There is a significant amount of low-temperature heat (< 100 °C) available globally from various sources such as geothermal energy, industrial processes, and thermal power plants. Electrochemical and membrane-based methods for harnessing this heat are becoming of greater interest due to their ability to utilize the low temperature energy sources and because they can be sized modularly to fit the power, energy, and spatial requirements of the application. The all-aqueous thermally regenerative ammonia battery (Cuaq-TRAB) is a leading technology in the waste heat to electrical power production space due to recent results showing operational power densities of 25 mW cm-2 with an estimated thermal energy efficiency of 7%, outperforming most technologies in this space. Previous research on the Cuaq-TRAB has shown that the battery suffers from the crossover of ammonia through the membrane which adversely impacts battery performance.To investigate these impacts, we developed a simple numerical model that simulates discharge curves using one fitting parameter to account for parasitic crossover instead of modeling species transport through the membrane. The model was able to replicate experimental data from Cuaq-TRABs with different membrane materials and for multiple applied current densities with < 5% error for average power and energy capacity. Results showed that there is a 20% loss in Cuaq-TRAB energy capacity due to ammonia crossover when compared to a discharge with no parasitic crossover losses. The model was then extended to demonstrate the impact of different changes to the electrolyte chemistry on the resultant power and energy capacities for Cuaq-TRABs. Using this model, we estimate that if chloride was the ligand for the positive electrolyte instead of bromide, it would decrease the power and energy capacity of the battery by 30%. However, considering that an economic analysis showed that chloride is 80% cheaper than bromide, it may be beneficial to consider the use of chloride ligands in this technology despite its worse electrochemical performance.

On-chip based power estimation for CMOS VLSI circuits using support vector machine

Power estimation has a major impact on the reliability of very-large-scale integration (VLSI) circuits. As a results power estimation is highly needed in VLSI circuits at the early stages. One of the evident challenges in integrated circuit (IC) industry is development and investigation of techniques for the reduction of design complexity due to the growing process variations and reduction of chip manufacturing turnaround time. Under these conditions, the higher design levels of average power estimation before the chip manufacturing process is highly essential for the calculation of power budget and to take the necessary steps for the reduction of power consumption. Over the years, most of the approaches were designed to estimate the power usage, however, most of the conventional techniques are time consuming, resource-intensive and largely manual. Machine learning techniques have received much attention in many of the engineering applications and are capable for modelling the complex systems through historical data. Hence, in this work on chip-based power estimation for complementary metal-oxide-semiconductor (CMOS) VLSI using support-vector machine (SVM) is presented to estimate the power. The SVM is employed to estimate the usage of power at runtime. The performance of this model is evaluated in terms of Power usage, delay, data accuracy and error rate.

Study on novel control method for small wind – solar hybrid power system using photovoltaic cooperating system in parallel connection mode

Currently, a small-scale wind turbine can be connected to the Power Conditioning System (PCS) of the solar power system by simulating the technical characteristics of the solar panels to enhance the efficiency of the PCS on cloudy or rainy weather days. However, the working efficiency of the entire system has not been optimized because the use of traditional control techniques. P-I control has several problems of high starting overshoot, and slow response to sudden disturbances. Besides, P&O MPPT control has fluctuations in output power and slow response time at peak solar cell power due to weather effects. Therefore, this research proposes a novel control system including Artificial Neural Networks (ANN) MPPT control and digital slide mode control (DMSC) for the power conversion circuit to connect small-scale wind turbine power with the PCS of the solar power system in the parallel mode. The novel control solution in this study can minimize the disadvantages of PI control and P&O MPPT control. The study results showed that DSMC controller has better performance than the PI controller when controlling the PVCS in parallel mode with more 17.5 % power, no static error, 20 % less chattering of the power value of PVCS in the steady state, and 20 % less static error of power. Besides, the ANN MPPT controller also works better when changing solar radiation and has a harvested power of about 0.6 % higher than the power generated by the P&O MPPT controller.

Automatic Generation Control in Renewables-Integrated Multi-Area Power Systems: A Comparative Control Analysis

Electrical load dynamics result in system instability if not met with adequate power generation. Therefore, monitoring and control plans are necessary to avoid potential consequences. Tie-line-bias control has facilitated power exchange between interconnected areas to cope with load dynamics. However, this approach presents a challenge, as load variation in either area leads to frequency deviations and power irregularities in each of the interconnected areas, which is undesirable. The load frequency control loop method is used to address this issue, which utilizes area control errors. This study focuses on the control of inter-area oscillations in a six-area power system under the effect of renewable energy sources. It evaluates the area control errors in response to changes in load and the penetration of renewable energy into the system. To mitigate these errors efficiently, an adaptive-PID controller is proposed, and its results are compared with PI and PID controllers optimized with heuristic and meta-heuristic algorithms. The findings demonstrate the superiority of the proposed controller over traditional controllers in mitigating tie-line power errors and frequency deviations in each area of the interconnected power system, thus helping to mitigate inter-area oscillations and restore system stability.

Adaptive VSG-Based power allocation strategy for hybrid energy storage

Abstract Aiming at the stochasticity of photovoltaic output and the problem of power, voltage, and frequency fluctuation caused by the lack of system inertia and damping parameters, a photovoltaic storage microgrid is constructed as a research object. Firstly, a virtual synchronous motor VSG is used to control the grid-connected inverter and to improve the dynamic performance of the system further. The inertia and damping parameters of the VSG are adjusted adaptively. Secondly, the hybrid energy storage device composed of a supercapacitor and battery adopts a second-order filter to avoid the integrating effect of the first-order filter on the high-frequency power and improve the poor performance of the first-order filter at the cutoff frequency to improve the accuracy of power distribution. Moreover, two negative feedback links of power error are added in the power distribution link to correct the reference power of the energy storage device, which further improves the power tracking effect of the energy storage system. Finally, the effectiveness of the above strategy is verified by a Matlab simulation session.

Bayesian Estimation Improves Prediction of Outcomes after Epilepsy Surgery.

Low power is a problem in many fields, as underpowered studies that find a statistically significant result will exaggerate the magnitude of the observed effect size. We quantified the statistical power and magnitude error of studies of epilepsy surgery outcomes. The median power across all studies was 14%. Studies with a median sample size or less (n<=56) and a statistically significant result exaggerated the true effect size by a factor of 5.4 (median odds ratio 9.3 vs. median true odds ratio 1.7), while the Bayesian estimate of the odds ratio only exaggerated the true effect size by a factor of 1.6 (2.7 vs. 1.7). We conclude that Bayesian estimation of odds ratio attenuates the exaggeration of significant effect sizes in underpowered studies. This approach could help improve patient counseling about the chance of seizure freedom after epilepsy surgery.