Error Power Research Articles

Modeling the Role of Testing Coverage in the Software Reliability Assessment

To assure the reliability and quality of the final product, testing is an essential and crucial part in the software development cycle. During this process, fault correction/detection activities are carried out to increase the reliability of the software. The non-homogeneous Poisson Process (NHPP) is the basis of the investigated software reliability growth models (SRGMs), which are based on the supposition that the number of faults found is affected by the amount of code covered during testing and that the amount of code covered during testing depends on the testing effort expended. This research takes into consideration several testing coverage functions: exponential, delayed S-shaped and logistic distributions, to propose three SRGMs that are based on testing efforts. For testing effort expenditure Weibull distribution has been employed. Two real failure datasets have been utilised to validate the proposed models, and their performance is evaluated using four goodness-of-fit metrics, including predictive ratio risk (PRR), coefficient of determination (R^2 ), predictive power (PP) and mean square error (MSE). Sensitivity analysis of cost requirement-based release time of software for exponential function has been done by using a genetic algorithm, which minimized the overall cost of the software subject to the requirement for reliability.

Least Mean Mixed Norm Square/Fourth Adaptive Algorithm With Optimized FOPID Gains for Voltage Power Quality Mitigation

The conventional adaptive Least Mean Square (LMS) algorithm employs Mean Square Error (MSE) as a cost function based on second-order power error. This limits the extraction of fundamental signals from the noise and suffers from performance degradation in certain dynamic scenarios. The proposed approach addresses this problem with a mixed norm-based step size adaptive algorithm which is devised from the second and fourth-order error optimization. The proposed Mixed-Norm Constraint-based Improved Proportionate Normalized Least Mean Square Fourth (MNC-IPNLMS/F) control strategy extracts the fundamental weight quantity from the polluted grid voltage and generates the reference load voltage. This method exploits the enhancement of Dynamic Voltage Restorer (DVR) performance in terms of convergence and stability. The DC and AC-link voltage is regulated by Fractional Order Proportional Integral Derivative (FOPID) controller. The coefficients of FOPID are self-tuned by several optimizations namely, JAYA algorithm, Accelerated Particle Swarm Optimization (APSO), and Biogeography Based Optimization (BBO). This reduces manual tuning and computational complexity. The major improvements of the proposed approach are less overshoot (6.6%), undershoot (3.3%), and fast settling time (0.14s). The comparative study reveals that the FOPID control based on JAYA Optimization (JO) outperforms the others. The efficacy of the MNC-IPNLMS/F is investigated through the performance results.

Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM

Recently, solar energy has been gaining attention as one of the best promising renewable energy sources. Accurate PV power prediction models can solve the impact on the power system due to the non-linearity and randomness of PV power generation and play a crucial role in the operation and scheduling of power plants. This paper proposes a novel machine learning network framework to predict short-term PV power in a time-series manner. The combination of nonlinear auto-regressive neural networks with exogenous input (NARX), long short term memory (LSTM) neural network, and light gradient boosting machine (LightGBM) prediction model (NARX-LSTM-LightGBM) was constructed based on the combined modal decomposition. Specifically, this paper uses a dataset that includes ambient temperature, irradiance, inverter temperature, module temperature, etc. Firstly, the feature variables with high correlation effects on PV power were selected by Pearson correlation analysis. Furthermore, the PV power is decomposed into a new feature matrix by (EMD), (EEMD) and (CEEMDAN), i.e., the combination decomposition (CD), which deeply explores the intrinsic connection of PV power historical series information and reduces the non-smoothness of PV power. Finally, preliminary PV power prediction values and error correction vector are obtained by NARX prediction. Both are embedded into the NARX-LSTM-LightGBM model pair for PV power prediction, and then the error inverse method is used for weighted optimization to improve the accuracy of the PV power prediction. The experiments were conducted with the measured data from Andre Agassi College, USA, and the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the model under different weather conditions were lower than 1.665 kw, 0.892 kw and 0.211, respectively, which are better than the prediction results of other models and proved the effectiveness of the model.

Offline economic dispatch for multi-area power system via hierarchical reinforcement learning

In this paper, a novel hierarchical reinforcement learning (HRL) architecture is put forward to resolve the multi-area economic dispatch (MAED) problem in a fully off-line optimization manner, while the focus of MAED is to minimize the total generation cost of the entire multi-area power system by determining output power within area and the power transfer among areas. The original MAED problem is transformed into an augmented sub-MAED problem by exploring the relationship between power balance constraint and tie line capacity constraint in each area. This architecture first calculates the scheduled area load in the top layer through the primal–dual consensus-based distributed Q-learning, and then calculates load-power table assembled by the optimal output power within single area according to the multi-stage decision algorithm in the bottom layer. Acting as an underlying framework, the assigned load-power table is implemented with the combination of off-line calculation and on-line inquiry. It is capable of parallel and asynchronous optimization in each area while satisfying various constraints. Furthermore, a power-triggered algorithm is proposed to satisfy the power balance among areas, and its convergence is theoretically analyzed, which illustrates that the area power error will converge to zero in the limited iteration. Finally, the proposed approach is validated in three different cases: 20-unit, 40-unit, and 120-unit. The comparative results confirmed the superiority of the proposed HRL architecture with high real-time performance and less computation burden. Specifically, the HRL architecture reduces the fuel cost in the range of 0.01656%–0.19173% and accelerates the speed more than 10 times than others.

An Enhanced Probabilistic-Shaped SCMA NOMA for Wireless Networks

The future digital evolution poses challenges that need to be spectral and energy-efficient, as well as highly reliable and resilient. The non-orthogonal multiple access (NOMA) accomplishes massive connectivity, spectral efficiency, effective bandwidth utilization, and low latency. The proposed work involves the code domain NOMA scheme called Sparse Code Multiple Access (SCMA) which provides shaping gain through multi-dimensional constellation and the best performance in terms of bit error rate (BER). It achieves overloading of users through the non-orthogonal allocation of resources which enhances the spectral efficiency and serves more users. The shaping gain can be further improved by reducing the BER and enhancing the capacity of the channel through constellation shaping. This work employs a probabilistic-shaped (PS) constellation where each symbol is transmitted with different probabilities which achieves a reduction of average symbol power and forward error correction (FEC) through channel coding using polar codes which aid in energy efficiency. The output is two-dimensionally spread over Orthogonal Frequency Code Division Multiplexing (OFCDM) subcarriers to achieve a flexible transmission rate through a variable spreading factor. Computer simulations showed better BER performance under AWGN and Rayleigh channels with remarkable gain in SNR which paves the way for future applications in Fifth Generation (5G) beyond networks.

Linearity Performance of Derivative Superposition in GaN HEMTs: A Device-to-Circuit Perspective

A significant disconnect exists between the linearity metrics used by device and circuit engineers, degrading the maximum achievable performance in gallium nitride (GaN) technology. A detailed linearity analysis is performed using both device and circuit metrics on GaN devices that employ derivative superposition (DS) to evaluate the amplifier performance under modulated conditions of four different device variants. The limitations of conventional linearity metrics used at the device level, such as the output third-order intercept point ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{OI}\text{P}_{\text{3}}$</tex-math> </inline-formula> ) and output 1-dB compression point ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{OP}_{\text{1}~\text{dB}}$</tex-math> </inline-formula> ), are examined and compared with communication standard-based metrics, such as the adjacent channel power ratio (ACPR) and error vector magnitude (EVM) to provide best practices for quantifying power amplifier (PA) linearity. It is found that employing more devices in the context of DS primarily improves the input-bias range of the device and not the peak amplifier performance under modulation. Ultimately, this work provides device engineers with a circuit-level perspective on linearity to help design transistors with enhanced RF performance for practical real-world deployment.

Design of Library Robot using IoT

A library robot is a smart system that helps maintain and improve the efficiency of a library. It is equipped with various controllers that allow the users to monitor and control the library services. A robot that can handle the tasks of a library efficiently. It can also save human services time and reduce the load on the staff. It can also monitor the library's activities and provide feedback on its progress. Its goal is to help the libraries cut costs and improve their efficiency. To over this problem, we had suggested this project "Robotic Library System “. In this project, we can organize the books in the racks and monitor the library activities this helps us to reduces the manual power and human errors.

A new grey wolf optimization-based parameter estimation technique of solar photovoltaic

Firstly, this paper develops a non-linear least square objective function in terms of three unknown parameters of solar photovoltaic, namely series resistance, shunt resistance, and ideality factor. Then, grey wolf optimization is used to estimate these unknown parameters. The remaining unknown parameters, namely light-induced current and diode dark saturation current, are estimated analytically. Furthermore, solar photovoltaic modules of mono-, poly-crystalline, and thin-film technologies are considered to check the accuracy and effectiveness of the proposed technique. Secondly, the outcomes of the proposed method are compared with the numerical, analytical, metaheuristics, and different existing hybrid methods. Additionally, through the various benchmarks, such as model error, absolute relative maximum power error, root mean square error, mean relative absolute error, and mean absolute error, the performance of the proposed method is evaluated. The study reveals that the model error and absolute relative maximum power error are zero in case of the proposed method. Further, among all tested methods, the minimum value of 0.21, 0.1, and 0.046 of the root mean square error, mean relative absolute error, and mean absolute error, respectively, is estimated for module KC200GT via proposed method. Thus, the study reveals that the proposed technique is more accurate than the existing methods.

Design and Development of an Automatic Unlocking System to Help during Medical Emergency using Mechatronics

In this study, an advanced unlocking mechanism was developed and automated using the concepts of mechatronics for the betterment in medical sciences by providing emergency lifesaving services. The goal of minimizing the time required to provide medical help was achieved after various observations. Equipping the Arduino board with a constant 12 V power supply, a trial and error analysis was conducted to make the system more efficient, reliable with minimum time delay. Finally, the observed changes with respect to expected changes were noted and discussed.

Modular fuzzy control for nonlinear multipoint based core model of TMI PWR under variable fuel cycle conditions

This paper provides design of modular fuzzy controller based on reactor power for multipoint kinetic model of TMI PWR, under transients and variable operational fuel cycle conditions. The validated four-point kinetic model is developed considering six groups of delayed neutron precursors, reactivity feedbacks and diffusion among selected nodes. Fuel burn-up effects and power variations are modeled using the variable thermal coefficients. The ability of adaptive multi-module fuzzy controller for reactor power is evaluated and compared at start and end of burn-up cycle. The power dependant nonlinearity of reactor core and time dependant variations of reactivity characteristics are handled with modular approach of TS fuzzy control. The performance monitoring and control improvements are achieved by online parametric identification of process model. The four modules are distributed on the power base line using the triangular mapping. Design of fuzzy controller is based on the optimized gain requirement at different power levels, error and error change. Control output is mapped as biased linear function of selected input variables using TS type fuzzy logic inference. Safety analysis is performed with Lyapunov function criteria for multipoint model. Transient analysis is performed for power tracking and accident handling ability of proposed control design and compared with the PID controller. The power variation among the adjacent nodes is reduced up to 5% under load following transients with the proposed model. Modular fuzzy approach found proficient in handling the changing operational conditions and variable dynamics of the system.

Thermoelectric Field Analysis of Trapezoidal Thermoelectric Generator Based on the Explicit Analytical Solution of Annular Thermoelectric Generator

The geometrical configuration is one of the main factors that affect the thermoelectric performance of a device. Research on the trapezoidal thermoelectric generator (TTEG) with varied cross section is mainly based on finite element simulation and experiment. In this paper, an explicit analytical solution of the maximum output power of annular thermoelectric generators (ATEG) is proposed, which has been proved to have high accuracy. Then, the maximum output power between ATEG and TTEG is compared. Results show that, for the appropriate geometric parameter δ, the relative error of maximum output power between explicit analytical ATEG and the simulated solution of TTEG can reach the order of 10−3. When the hot end is at the a side, the high temperature and θ is 510 K and 10°, respectively. For Bi2Te3 material and PbTe material, the relative error of maximum output power between the explicit analytical and simulated solution is 0.0261% and 0.074%, respectively. Under suitable working conditions, the explicit analytical results of ATEG can provide some reference for the performance optimization of TTEG.

A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules

In this work, a multi-objective Hybrid Bald Eagle Search Simulated Annealing (Hybrid BESSA) parameter extraction technique for photovoltaic (PV) modules is discussed. First, the efficacy of the Hybrid BESSA was proved via testing on unimodal functions, multimodal functions, and fixed dimensional multimodal functions and the results were compared with the Bald Eagle Search (BES) and other recently proposed optimization techniques. Then, a multi-objective Hybrid Bald Eagle Search Simulated Annealing (Hybrid BESSA) parameter extraction technique was devised for photovoltaic (PV) module parameter extraction. The Hybrid BESSA parameter extraction technique was simulated and analyzed in the MATLAB/SIMULINK environment and in a practical experimental setup for the PV Module AS-M3607-S (G1 CELLS). It was found that the Hybrid BESSA possessed better exploration and exploitation capabilities as compared to the BES and other state-of-the-art techniques. It was found that the fitness function value derived by the Hybrid BESSA technique was less than that of the BES technique when tested under various weather conditions. The percentage error for open circuit voltage, output power, and short circuit current was lower when derived by the Hybrid BESSA in comparison with the BES technique. From the results obtained from modeling the PV Module AS-M3607-S (G1 CELLS) based on Hybrid BESSA-based extracted parameters and BES-based extracted parameters, it was seen that percentage improvement in the combined objective function for the condition of keeping irradiance fixed at 1000 W/m2 at a temperature varying from −30 °C, 0 °C, 25 °C, 30 °C, 50 °C, and 70 °C were 0.9%, 8.5%, 29.2%, 0.03%, 5.7%, and 0.5%, respectively. When the temperature was kept fixed at 250 °C and irradiance varied from 1000 W/m2, 800 W/m2, 600 W/m2, and 400 W/m2, the percentage improvement in combined objective function was found to be 0.5%, 8.1%, 0.5%, and 0.8%, respectively. By analyzing the simulation as well as the experimental results, it was established that the PV model parameter extraction method based on the Hybrid BESSA is more accurate than the BES technique. This analysis is based on a single-diode PV module. A double-diode PV module analysis still needs to be explored.

A novel robust maximum power extraction framework for sustainable PV system using incremental conductance based MRAC technique

AbstractThis study proposes a maximum power point tracking (MPPT) approach with two components: an Incremental Conductance (INC) MPPT for reference voltage regulation and a Model Reference Adaptive Controller (MRAC) for adjusting the duty cycle of the DC‐DC converter switch. A robustness test is performed on the system considering real‐world situations that involve an abrupt change in atmospheric conditions with load uncertainties. The probabilistic load distribution analysis is accomplished through levels of uncertainty (i.e., Probabilistic DOWN and UP) to guarantee the operation of the proposed INC‐MRAC controller while generating unexpected disturbances in the system. Using MATLAB/Simulink, the performances of the novel INC‐MRAC MPPT are comparatively analyzed with PO, INC, VSSPO, and ANN under realistic case studies in seven states. After comparative analysis, it is evident that the proposed MPPT offers less tracking time, i.e., 3.8 ms, to track maximum power point (MPP) with negligible steady‐state oscillation and ripples. It is about 3, 7, 9, and 10 times faster than ANN, VSSPO, INC and PO, respectively. Moreover, the tracking efficiency of the proposed controller is up to 99.63% as well as overall efficiency of the system is more than 98%. The tracking power loss and error rate in finding MPP for the proposed controller is the lowest among all state‐of‐the‐art MPPT approaches. Finally, the effectiveness of the proposed INC‐MRAC approach is experimentally validated by employing a real‐time simulator OPAL‐RT (OP4510). The study helps in environmental protection and participation in sustainable development.

FPGA Implementation of a Linearization System for Wideband Envelope Tracking Power Amplifiers

This article focuses on the implementation of a linearization system for envelope tracking (ET) power amplifiers (PAs) in field-programmable gate array (FPGA). The ET PA linearization system includes a slew-rate reduction envelope generator, a RF leakage cancellation system in the supply envelope path and a baseband <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I - Q$ </tex-math></inline-formula> digital predistorter (DPD). This work targets the implementation of an ET PA linearization system on a radio frequency system-on-chip (RFSoC) device running under a demanding baseband sampling frequency of 614.4 MHz, which allows handling communication signals with up to 200 MHz bandwidth, considering a DPD bandwidth expansion by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> . The detail of the FPGA implementation is presented to illustrate the trade-off between hardware resources and linearization performance, i.e., adjacent channel power ratio (ACPR) and error vector magnitude (EVM), under different bit configurations for the arithmetic. The power consumption is also evaluated since it is another relevant performance indicator to be considered in the FPGA implementation of the linearization system.

IMPROVED ADAPTIVE NONLINEAR CONTROL FOR VARIABLE SPEED WIND-TURBINE FED BY DIRECT MATRIX CONVERTER

This paper proposes a robust decoupling power algorithm based on a doubly fed induction generator (DFIG) for variable speed wind-turbine (WT). The DFIG rotor circuit is fed by the direct matrix converter (DMC), which presents several features such as no need to the dc-bus voltage, sinusoidal supply, rotor side waveforms, bidirectional power flow, and adjustable input power factor. The 18 bidirectional switches are controlled using the Venturini modulation technique. On the other hand, the DFIG stator circuit is connected directly to the grid. The nonlinear control strategy based on feedback linearization is applied to control the stator power (Ps and Qs) independently using the rotor quadrature and direct currents (irq and ird), which present the images of the previous stator powers. Some limitations appear in the power algorithm using the conventional pi controller, especially in power tracking, error, and quality. In this context, the model reference adaptive controller (MRAC) presents an alternative solution, a robust and efficient controller proposed instead of the pi controllers to control stator powers. Finally, the simulation results confirm that the proposed algorithm could work under hard conditions and demonstrate that the wind energy conversion system (WECS) provides enhanced dynamic responses in transient and steady states and good power quality delivered to the grid.

Metalens integrated receiver to reduce the effect of angle of arrival jitter in free space optical communication

In this paper, we propose a metalens integrated receiver for free space optical communication, which provides robust performance in the presence of angle of arrival fluctuations jitter. As a proof of concept, we show that our proposed receiver can tolerate up to a 2.5 mrad angle of arrival fluctuations with a detector radius of 20 µm before reaching a 3 dB power penalty. We design a meta unit cell to realize the optimized phase profile and present a quasi-3D full wave simulation of the proposed architecture. The system shows steady signal power and bit error rate in the presence of angle of arrival fluctuation.

Predictive discrete‐time sliding mode based direct power control for grid‐connected energy conversion systems

AbstractA method for digital direct power control design for grid‐connected energy conversion systems was proposed in this paper. The active and reactive powers are directly controlled with constant switching frequency by selecting an appropriate switching control sequence of voltage source converter as a key part of energy conversion systems. The control design combines a discrete‐time sliding mode control and predictive control. The selection of feasible switching control vectors is a direct result of the discrete‐time sliding mode control approach. These vectors satisfy the existence conditions of the sliding mode, but each of them induces system motion with different properties. The task of predictive control is to select an appropriate switching control sequence to reduce the chattering phenomenon, steady‐state error, and overshoot. The cost function of the prediction process is defined as the average value of both power errors on the prediction horizon of three sampling periods. Simulation results, experimental results, and results of a comparative analysis are presented in order to show the performance and effectiveness of the proposed control design.

Response of Meteor Plasma Trails Observed by Meteor Radar to Geomagnetic Activity

AbstractFrom meteor radar observations typically measuring neutral winds and temperatures in the upper atmosphere, we showed that under‐dense meteor plasma trails respond to geomagnetic activity. Depending on the geomagnetic activity conditions, the parameters of the under‐dense trail echoes show substantial anomalies, and their responses are immediate. As geomagnetic activity increases, the decay time decreases up to about 10% at Kp = 8, while both the returned signal power and radial velocity error increase. In addition, the types of diffuse evolution vary with geomagnetic activity, resulting in strong geomagnetic activity that immediately affects the trail. The evolution of the trail is affected not only by the ambient mesospheric neutral atmospheric properties but also by electromagnetic effects, such as strong electric fields inside meteor trails. Furthermore, our findings suggest that the geomagnetic effect should be considered when estimating atmospheric parameters from MR during a geomagnetically active period.

Performance analysis of solar PV parameter estimation techniques

Firstly, this paper investigates the performances of the three different established parameter estimation techniques, namely multi-objective function, five-point metaheuristic, and hybrid-based approaches. Using these three different techniques, unknown model parameters, such as photovoltaic current (Ipv), dark saturation current (Io), series resistance (Rs), shunt resistance (Rsh), and Ideality factor (A), of mono-and poly-crystalline technology-based solar PV modules, are predicted. Afterwards, using the estimated unknown parameters and P-V &I-V characteristics curves, two different benchmarks, namely absolute relative maximum power error (ARMPE) and overall model error (OME), of these modules, are evaluated to reveal their best performances. In addition, various other parameter estimation techniques, such as iterative, analytical, curve-fitting, and metaheuristics-based methods, have also been considered for performance analysis. It is shown that the hybrid-based approach provides a better characterization than the other various established approaches. The value of %ARMPE is found almost zero with the hybrid approach for both modules. In addition, the smallest values of OME are obtained with the hybrid approach, i.e., 0.002 or 0.003 for poly- and 0.031 for the mono-crystalline technology-based solar PV module. Hence, a hybrid-based parameter estimation technique is suggested to achieve accurate characteristics of solar PV.

Preliminary development of machine learning-based error correction model for low-fidelity reactor physics simulation

Better prediction capability in reactor simulation procedures can result in better fuel planning, increased safety, and compliance with the Technical Specifications. Motivated by this necessity in the nuclear industry, we develop a method to improve the current reactor core simulation process using a machine learning approach. With a well-trained machine learning model, it is possible to predict the errors of the low-fidelity diffusion-based core simulator without a significant increase in complexity and computational cost. For the machine learning models, we have tested two different models based on Deep Neural Network and Extreme Gradient Boosting trained on high-fidelity Monte Carlo reactor simulation data. The proposed method has been verified in this work on simple 2x2 boiling water reactor color sets. We collected large data points that include different variations of assembly configuration, burnup, void fraction, and control blade insertion in both low-fidelity and high-fidelity data. The developed models can accurately predict errors in eigenvalue and assembly power. Utilizing the predicted errors, the machine learning-aided simulation results in a significant improvement over the conventional reactor simulation approach.