Search Results (24)

The steady-state energy flow calculation (EFC) of multi-energy systems (MESs) is a fundamental foundation for MES planning and operation. However, most of the existing MES models are designed case-specifically, making them incapable of modelling diverse scenarios. Moreover, since it involves initial value setting, the convergence of the Newton–Raphson (NR) method to solve the EFC problem of MESs is often unsatisfactory. To tackle these problems, a matrix-based EFC method of MESs is proposed in this paper. The universal matrix formulations of heat and gas subnetworks are first constructed, where the injection of alternative gas sources and the effect of gas compressibility factor on the MES state are both considered. Due to the uncertainty of gas flow direction during the NR iteration process, the gas composition tracking equations are modified to avoid ill conditions. The Jacobian matrices for the constructed subnetwork models are then derived and expressed in matrix form. On this basis, the unified NR strategy is adopted to solve the constructed models. Finally, the performance of the proposed method is verified through case studies. The results demonstrate that the proposed models can accurately capture the MES operating state and achieve significant improvements in convergence and computational efficiency compared to traditional models. Full article

The forward kinematics of the Stewart platform is crucial for precise control and reliable operation in six-degree-of-freedom motion. However, there are some shortcomings in practical applications, such as calculation precision, computational efficiency, the capacity to resolve singular Jacobian matrix and real-time predictive performance. To overcome those deficiencies, this work proposes a hybrid strategy for forward kinematics in the Stewart platform based on dual quaternion neural network and ARMA time series prediction. This method initially employs a dual-quaternion-based back-propagation neural network (DQ-BPNN). The DQ-BPNN is partitioned into real and dual parts, composed of parameters such as driving-rod lengths, maximum and minimum lengths, to extract more features. In DQ-BPNN, a residual network (ResNet) is employed, endowing DQ-BPNN with the capacity to capture deeper-level system characteristics and enabling DQ-BPNN to achieve a better fitting effect. Furthermore, the combined modified multi-step-size factor Newton downhill method and the Newton–Raphson method (C-MSFND-NR) are employed. This combination not only enhances computational efficiency and ensures global convergence, but also endows the method with the capability to resolve a singular matrix. Finally, a traversal method is adopted to determine the order of the autoregressive moving average (ARMA) model according to the Bayesian information criterion (BIC). This approach efficiently balances computational efficiency and fitting accuracy during real-time motion. The simulations and experiments demonstrate that, compared with BPNN, the R² value in DQ-BPNN increases by 0.1%. Meanwhile, the MAE, MAPE, RMSE, and MSE values in DQ-BPNN decrease by 8.89%, 21.85%, 6.90%, and 3.3%, respectively. Compared with five Newtonian methods, the average computing time of C-MSFND-NR decreases by 59.82%, 83.81%, 15.09%, 79.82%, and 78.77%. Compared with the linear method, the prediction accuracy of the ARMA method increases by 14.63%, 14.63%, 14.63%, 14.46%, 16.67%, and 13.41%, respectively. Full article

AC/DC hybrid distribution networks with power electronic transformers (PETs) as distribution hubs are in line with the future development direction of the AC/DC hybrid distribution network. Unlike traditional transformers, power electronic transformers introduce new node types and may transform the network topology from radial to ring structures. These changes render traditional power flow calculation methods inadequate for achieving satisfactory results in AC/DC hybrid networks. In addition, existing commercial power flow calculation software packages are mainly based on the traditional AC power flow calculation method, which have limited support for the DC network. Especially when the DC network is coupled with the AC network, it is difficult to achieve a unified calculation of its power flow. To address these challenges, this paper proposes a novel power flow calculation method for ring AC/DC hybrid distribution networks with power electronic transformers. The proposed method is based on the alternating iterative method to ensure compatibility with mature AC power flow calculation programs in commercial software, thereby improving the feasibility of engineering applications. Firstly, the steady-state power flow calculation model of PET is constructed by analyzing that the working principle and control modes of power electronic transformer are proposed based on the source-load attributes of its connected subnetworks. According to the characteristics of the power electronic transformer, AC distribution network, and DC distribution network, a hybrid alternating iteration method combining the high computational accuracy of the Newton–Raphson (NR) method with the high efficiency of the Zbus Gaussian method in dealing with ring networks is proposed. On this basis, the power flow calculation model of the AC/DC hybrid distribution network with power electronic transformers is established. Finally, the simulation of the constructed 44-node ring AC/DC hybrid distribution network example is carried out. The simulation results show that the proposed method can not only converge reliably when the convergence accuracy is 1 × 10⁻⁶ p.u., but also ensure that the voltage magnitudes of all nodes are above 0.96 p.u. whose maximum offset value is 0.789% when the outputs of the connected distributed generations fluctuate, which verifies the effectiveness and accuracy of the proposed method. Full article

This paper presents a dynamic homotopy technique that can be used to calculate a preliminary result for a power flow problem (PFP). This result can then be used as an initial estimate to efficiently solve the PFP using either the classical Newton-Raphson (NR) method or its fast decoupled version (FDXB) while still maintaining high accuracy. The preliminary stage for the dynamic homotopy problem is formulated and solved by employing integration techniques, where implicit and explicit schemes are studied. The dynamic problem assumes an initial condition that coincides with the initial estimate for a traditional iterative method such as NR. In this sense, the initial guess for the FPF is adequately set as a flat start, which is a starting for the case when this initialization is of difficult assignment for convergence. The static homotopy method requires a complete solution of a PFP per homotopy pathway point, while the dynamic homotopy is based on numerical integration methods. This approach can require only one LU factorization at each point of the pathway. Allocating these points properly helps avoid several PFP resolutions to build the pathway. The hybrid technique was evaluated for large-scale systems with poor conditioning, such as a 109,272-bus model and other test systems under stressed conditions. A scheme based on the implicit backward Euler scheme demonstrated the best performance among other numerical solvers studied. It provided reliable partial results for the dynamic homotopy problem, which proved to be suitable for achieving fast and highly accurate solutions using both the NR and FDXB solvers. Full article

In this paper, single-diode model (SDM) and double-diode model (DDM) parameters of the French RTC solar cell and the Photowatt PWP 201 photovoltaic (PV) module were extracted by combining five metaheuristic algorithms with three simulation current calculation methods (i.e., approximation method, Lambert W method and Newton–Raphson method), respectively. It was found that the parameter-extraction accuracies of the Lambert W (LW) method and the Newton–Raphson (NR) method are always approximately equal and higher than that of the approximation method. The best RMSEs (root mean square error) obtained by using the LW or the NR method on the solar cell and the PV module are 7.72986 × 10⁻⁴ and 2.05296 × 10⁻³ for SDM parameter extraction and 6.93709 × 10⁻⁴ and 1.99051 × 10⁻³ for DDM parameter extraction, respectively. The latter may be the highest parameter-extraction accuracy reported on the solar cell and the PV module so far, which is due to the adoption of more reasonable DDM parameter boundaries. Furthermore, the convergence curves of the LW and the NR method basically coincide, with a convergence speed faster than that of the approximation method. The robustness of a parameter-extraction method is mainly determined by the metaheuristic algorithm, but it is also affected by the simulation current calculation method and the parameter-extraction object. In a word, the approximation method is not suitable for application in PV-model parameter extraction because of incorrect estimation of the simulation current and the RMSE, while the LW and NR methods are suitable for the application for accurately calculating the simulation current and RMSE. In terms of saving computation resources and time, the NR method is superior to the LW method. Full article

Renewable energy sources provide an environmentally sustainable solution to meet growing energy demands. Consequently, photovoltaics (PV) is regarded as a promising form of green distributed generation (GDG). The penetration of PV-GDG into distribution networks (DNs) is crucial, presenting a significant opportunity to improve power grid quality and reduce power losses. In this study, a comprehensive investigation was conducted to determine the optimal location, number, and capacity of PV-GDG penetrations with DN to achieve these objectives. Therefore, employing the Newton–Raphson (NR) technique and particle swarm optimization (PSO) approach for case studies, the analysis focused on the IEEE 33 bus test system as a benchmark test and the Iraq–Baghdad DN at 11 kV and 0.416 kV as a real case study. The outcomes revealed that integrating 4 × 1 MW PV-GDG units in a centralized configuration at bus 13 of the 11 kV Rusafa DN in the first scenario significantly reduced power losses and alleviated voltage drops across the network. In contrast, the second scenario entailed the utilization of dispersed PV panels with a capacity of 10 kW installed on rooftops at all 400 consumer load points with a cumulative capacity of 4 MW. This approach exemplified the enhancement of DN performance by significantly maximizing the power loss reduction and minimizing the voltage drops across the buses, exceeding the results achieved in the first scenario. The software applications employed in the practical implementation of this study included the CYMDist 9.0 Rev 04 program, PVsyst 7.2.20 software, and MATLAB R2022b. Full article

Flexible alternating current transmission systems (FACTSs) have been widely incorporated in electric power systems in order to control system parameters. This paper proposes the modeling of four FACTS devices, using the Branch Flow Model (BF) as an optimization problem to reduce the complexity of the Newton–Raphson (NR) load flow code with FACTS devices. The devices are represented as variable impedances, as a function of a firing angle, and as voltage source converters (VSCs) located on the buses and transmission lines. This proposed model solves the problem associated with the selection of appropriate initial conditions of the parameters of each device that guarantee convergence. The model is validated by evaluating its percentage deviation with respect to the NR method, using the standard test systems, IEEE 5-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 57-bus systems. Full article

Due to the flexibility of the Weibull distribution and the proportional hazard (PH) model, Weibull PH is widely used in survival analysis under right censored data and interval censored data but it is seldom investigated under current status data, partially because there is less information in current status data than in right censored data and interval censored data. This paper considers the Weibull PH model under the current status data and introduces the Poisson latent variables to augment the data, then uses the expectation-maximization (EM) algorithm to obtain the maximum likelihood estimators of the model parameters. The EM algorithm is compared with the Newton–Raphson (NR) algorithm from several perspectives in the simulation studies, and the results show that the proposed method has several highlights, such as computational simplicity, improved convergence stability, and overall estimator results that are either comparable or slightly better in terms of bias. Furthermore, the performance of the Weibull PH model and the semi-parametric PH model is compared under two simulation scenarios, and two standard model selection criteria are used for model selection. The results indicate that the Weibull PH model has significant advantages when failure time follows a Weibull distribution. Lastly, the Weibull PH model along with EM algorithm is applied to lung tumor data and intraocular lens (IOL) calcification data with the aim of assessing the impact of covariates, including environmental factors and gender, on event timing and risk. Full article

Compensation Admittance Load Flow (CALF) is a power flow analysis method that was developed to enhance the sustainability of the power grid. This method has been widely used in power system planning and operation, as it provides an accurate representation of the power system and its behavior under different operating conditions. By providing a more accurate representation of the power system, it can help identify potential problems and improve the overall performance of the grid. This paper proposes a new approach to the load flow (LF) problem by introducing a linear and iterative method of solving LF equations. The aim is to obtain fast results for calculating nodal voltages while maintaining high accuracy. The proposed CALF method is fast and accurate and is suitable for the iterative calculations required by large energy utilities to solve the problem of quantifying the maximum grid acceptance capacity of new energy from renewable sources and new loads, known as hosting capacity (HC) and load capacity (LC), respectively. Speed and accuracy are achieved through a properly designed linearization of the optimization problem, which introduces the concept of compensation admittance at the node. The proposed method was validated by comparing the results obtained with those coming from state-of-the-art methods. Full article

This paper proposes a homotopy-based approach to solve the power flow problem (PFP) in islanded microgrid networks with droop-controlled distributed generation (DG) units. The technique is based on modifying an “easy” problem solution that evolves with the computation of intermediate results to the PFP solution of interest. These intermediate results require the solution of nonlinear equations through Newton–Raphson (NR) method. In favor of convergence, the intermediate solutions are close to each other, strengthening the convergence qualities of the technique for the solution of interest. The DG units are modeled with operational power limits and three types of droop-control strategies, while the loads are both magnitude voltage- and frequency-dependent. To evaluate the method performance, simulations are performed considering the proposed and classical NR methods, both departing from a flat start estimation. Tests are carried out in three test systems. Different load and DG unit scenarios are implemented for a 6-, 38-, and 69-bus test system. A base case is studied for all systems, while for the two larger models, a loading factor is used to simulate the load augmenting up to the maximum value. The results demonstrated that for the largest-size model system, only the homotopy-based approach could solve the PFP for stringent requirements such as the diversification of the load profile and hard loading operation point. Full article

The performance of human gait recognition (HGR) is affected by the partial obstruction of the human body caused by the limited field of view in video surveillance. The traditional method required the bounding box to recognize human gait in the video sequences accurately; however, it is a challenging and time-consuming approach. Due to important applications, such as biometrics and video surveillance, HGR has improved performance over the last half-decade. Based on the literature, the challenging covariant factors that degrade gait recognition performance include walking while wearing a coat or carrying a bag. This paper proposed a new two-stream deep learning framework for human gait recognition. The first step proposed a contrast enhancement technique based on the local and global filters information fusion. The high-boost operation is finally applied to highlight the human region in a video frame. Data augmentation is performed in the second step to increase the dimension of the preprocessed dataset (CASIA-B). In the third step, two pre-trained deep learning models—MobilenetV2 and ShuffleNet—are fine-tuned and trained on the augmented dataset using deep transfer learning. Features are extracted from the global average pooling layer instead of the fully connected layer. In the fourth step, extracted features of both streams are fused using a serial-based approach and further refined in the fifth step by using an improved equilibrium state optimization-controlled Newton–Raphson (ESOcNR) selection method. The selected features are finally classified using machine learning algorithms for the final classification accuracy. The experimental process was conducted on 8 angles of the CASIA-B dataset and obtained an accuracy of 97.3, 98.6, 97.7, 96.5, 92.9, 93.7, 94.7, and 91.2%, respectively. Comparisons were conducted with state-of-the-art (SOTA) techniques, and showed improved accuracy and reduced computational time. Full article

In finance, implied volatility is an important indicator that reflects the market situation immediately. Many practitioners estimate volatility by using iteration methods, such as the Newton–Raphson (NR) method. However, if numerous implied volatilities must be computed frequently, the iteration methods easily reach the processing speed limit. Therefore, we emulate the NR method as a network by using PyTorch, a well-known deep learning package, and optimize the network further by using TensorRT, a package for optimizing deep learning models. Comparing the optimized emulation method with the benchmarks, implemented in two popular Python packages, we demonstrate that the emulation network is up to 1000 times faster than the benchmark functions. Full article

It is important to accurately calculate flattening points when reconstructing ship hull models, which require fast and high-precision computation. However, some search algorithms, such as the bisection method, iterate near the optimal value too many times before converging in high-precision computation. The paper proposes a fast high-precision bisection feedback search (FHP-BFS) algorithm to solve the problem. In the FHP-BFS algorithm, the Newton–Raphson (NR) method is adopted to accelerate the convergence speed by considering the iteration characteristics of subintervals. Furthermore, a new feedback mechanism is proposed to control the feedback directions. In addition, an acceleration algorithm, called the interval reformation method, is used to guide the FHP-BFS algorithm for fast convergence. Finally, the flattening algorithm is improved by the FHP-BFS algorithm. In the comparative experiments, the practical efficacy of the FHP-BFS algorithm is first demonstrated, and then the optimal range of the threshold precision is determined. Next the FHP-BFS algorithm is compared to the best existing algorithms. Finally, the performance of the improved flattening algorithm is verified. The experiments demonstrate that the FHP-BFS algorithm has optimal performance among the compared algorithms, and it has an improved computation efficiency while maintaining robustness. The improved flattening algorithm reduces the computation time, ensures smoothness and meets practical engineering requirements. Full article

One of the most difficult steps in estimating the cruise performance characteristics of high-subsonic transport-category turbofan-powered airplanes is the estimation of the transonic wave drag. Modern jet airplanes cruise most efficiently in the vicinity of the drag-divergence or drag-rise Mach numbers. In the initial design phase and later when the preliminary wind-tunnel and/or CFD computations and drag polars are known with increased accuracy, a method of estimating cruise performance is needed. In this study, a new semi-empirical transonic wave drag model using modified Lock’s equation was developed. For maximum range cruise estimations, an optimization criterion based on maximizing specific air range was used. The resulting nonlinear equations are of 12th- and 13th-order. Numerical Newton–Raphson nonlinear solvers were used to find real positive roots of such polynomials. The NR method was first tested for accuracy and convergence using known analytical solutions. A methodology for an initial guess was developed starting with the maximum-range cruise Mach without the wave-drag included. This guess resulted in fast quadratic convergence in all computations. Other novel features of this article include a new semi-empirical fuel-flow law, which was also extensively tested. Additionally, a semi-empirical turbofan thrust model usable for a wide range of bypass ratios and the entire flight envelope was developed. Such physics-based semi-empirical model can be used for a wide range of turbofans. The algorithm can be utilized to identify most beneficial input parameter values and combinations for the cruise flight phase. The model represents a powerful tool to estimate important cruise performance airspeeds located in the transonic regime. An intended application is in the conceptual development stages for early design optimizations of future airplanes. It is possible with additional effort to extend existing model capabilities to deal with supersonic transports optimal cruise parameters. Full article

At the core of every system for the efficient control of the network steady-state operation is the AC-power-flow problem solver. For local distribution networks to continue to operate effectively, it is necessary to use the most powerful and numerically stable AC-power-flow problem solvers within the software that controls the power flows in these networks. This communication presents the results of analyses of the computational performance and stability of three methods for solving the AC-power-flow problem. Specifically, this communication compares the robustness and speed of execution of the Gauss–Seidel (G–S), Newton–Raphson (N–R), and Newton–Raphson method with Iwamoto multipliers (N–R–I), which were tested in open-source pandapower software using a meshed electrical network model of various topologies. The test results show that the pandapower implementations of the N–R method and the N–R–I method are significantly more robust and faster than the G–S method, regardless of the network topology. In addition, a generalized Python interface between the pandapower and the SciPy package was implemented and tested, and results show that the hybrid Powell, Levenberg–Marquardt, and Krylov methods, a quasilinearization algorithm, and the continuous Newton method can sometimes achieve better results than the classical N–R method. Full article