Backward Forward Research Articles

Backward–forward sweep inverse power flow for distribution line parameters estimation with metering data

AbstractThe transition towards the smart grid requires better information on the electrical networks at the distribution system level. Information about electrical line parameters is a critical part of such information. An algorithm for estimating electric line parameters of medium‐voltage distribution networks is proposed. The algorithm relies upon active/reactive power and nodal voltage magnitude data from advanced metering infrastructure and combines an Ordinary Least Squares estimator with the classical Backward–Forward Sweep load‐flow method to iteratively estimate the admittance parameters of the electrical network components. The algorithm is proposed to be applied to balanced loading situations of networks where zero‐injection non‐monitored nodes may exist. Simulations using the IEEE 33‐bus system are used to demonstrate the effectiveness of the method under different loading situations and measurement noise levels.

Double inertial Forward–Backward–Forward method with adaptive step-size for variational inequalities with quasi-monotonicity

The paper introduce a new inertial forward–backward–forward method with adaptive step size constructed by double inertial extrapolation steps and relaxations to solve variational inequalities with quasi-monotonicity in real Hilbert spaces. We obtain weak and strong convergence results for our propose inertial Forward–Backward–Forward method under some mild conditions. Linear convergence results under a special case of our proposed method are given. Preliminary numerical results show that our proposed method is competitive with other related methods in the literature.

Performance Analysis of a Backward/Forward Algorithm Adjusted to a Distribution Network with Nonlinear Loads and a Photovoltaic System

Context: The backward/forward (BF) algorithm is a sweep-type technique that has recently been used as a strategy for the power flow analysis of ill-conditioned networks. The purpose of this study is to evaluate the performance of the BF algorithm compared to that of a computational tool such as Simulink, with both strategies adjusted to the operating conditions of a distribution network with nonlinear components (loads and photovoltaic system), unbalanced loads, and harmonic distortion in the voltage and current signals. Method: The study case is a low-voltage distribution network with a radial topology, unbalanced loads, and nonlinear components. The BF algorithm is adjusted to consider two approaches of the Norton model: a coupled admittance matrix and a decoupled admittance matrix. The latter is also used in the network model created in Simulink. The performance of the algorithm is evaluated by analyzing 18 operation scenarios defined according to the presence and use intensity of the loads and solar irradiance levels (low and high). Results: In general, the three strategies could successfully determine the waveform and RMS values of the voltage signals with errors of less than 0,8 and 1,3%, respectively. However, the performance of the strategies for the estimation of current signals and power parameters shows errors of 5-300% depending on the level of solar irradiance at which the photovoltaic system operates. Conclusions: The results show that the BF strategy can be used to analyze unbalanced power grids with increasing penetration of renewable generation and the integration of nonlinear devices, but the performance of this strategy depends on the load model applied to represent the behavior of nonlinear devices and generation systems.

Network Reconfiguration for Power Loss Reduction on Distribution feeder Using Particle Swarm Optimisation: Case study of 11kv feeder in Ilorin, Nigeria

The distribution system is the largest segment of the electrical power system and the final stage in delivering electricity to consumers. It experiences power losses which could be technical or non-technical due to its radial configuration and connected components. Technical losses occur as energy dissipation in the form of heat in the system components, which is unavoidable. It affects the efficiency of the system and increases operational costs. Therefore, the reduction is essential for adequate power supply and improvement in utility revenue generation. This study focuses on reducing power losses on 11 kV Taiwo distribution feeder in Ilorin using the Network reconfiguration approach. The feeder was modelled, and its power losses were evaluated based on the Backward Forward Sweep method suitable for the radial network. The reconfigured network was optimised using the Particle Swarm Optimisation technique. The study shows that the values of feeder real power losses before and after reconfiguration are 176.883 kW and 121.972 kW, respectively.Further improvement was, however, achieved when PSO was used for the reconfiguration, as the power loss value stands at 98.465 kW. This is 44.33 % reduction compared to the initial power loss value and 19.273 % after reconfiguration. Furthermore, the values of the reactive power losses before and after reconfiguration are 9.474 kVar and 6.527 kVar, respectively, amounting to a 31.11 % reduction. However, PSO reduces the value to 3.101 kVar, which is 67.27 % of the initial value. Thus, radial distribution network reconfiguration using PSO has proven to be a robust method for reducing power loss in the distribution network.

Psychometrics of the Persian version of the COVID-19-related health literacy in the Iranian population.

Since the occurrence of the COVID-19 pandemic, information dissemination has increased rapidly. Promoting health literacy is currently crucial to prepare people to respond quickly to situations, such as the COVID-19 pandemic. Due to the importance of health literacy in this critical situation, we are looking for a questionnaire to measure COVID-19 health literacy. The COVID-19 Germany Health Literacy Questionnaire (HLS-COVID-Q22) is an excellent tool, so the study aimed to create a cultural validity of this questionnaire for the Iranian population. In this validation study, 880 samples were enrolled using a convenient sampling method. The questionnaire was translated through a backward forwarding procedure. Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) were employed for Persian version validity. McDonald's omega (Ω), Cronbach's alpha, and average inter-item correlation (AIC) coefficients were assessed for reliability. Using EFA on the random half sample (n = 440), the EFA indicated that the scale had four factors: accessing, understanding, appraising, and applying health-related information in the COVID-19 pandemic context, which explained 59.3% of the total variance. CFA was used for the sample's second part (n = 440) to evaluate the goodness of fit of the four-factor solution. CFA showed the model fit. All indices RMSEA = 0.067, CFI = 0.934, IFI = 0.934, PCFI = 0.772, PNFI = 0.747, and CMIN/DF = 2.972 confirmed the model fit. The convergent validity of the HLS-COVID-Q22 was confirmed. McDonald's omega and Cronbach's alpha were very good (α and Ω >0.80). The Persian version of the HLS-COVID-Q22 had acceptable psychometric properties and is applicable to measure COVID-19 health literacy.

Deep learning schemes for parabolic nonlocal integro-differential equations

In this paper we consider the numerical approximation of nonlocal integro differential parabolic equations via neural networks. These equations appear in many recent applications, including finance, biology and others, and have been recently studied in great generality starting from the work of Caffarelli and Silvestre by Lius and Lius (Comm PDE 32(8):1245–1260, 2007). Based on the work by Hure, Pham and Warin by Hure et al. (Math Comp 89:1547–1579, 2020), we generalize their Euler scheme and consistency result for Backward Forward Stochastic Differential Equations to the nonlocal case. We rely on Lévy processes and a new neural network approximation of the nonlocal part to overcome the lack of a suitable good approximation of the nonlocal part of the solution.

Differences of The Effect of Aerobic Sports on Cognitive Function Between Smoking and Non-Smoking Students at Daya Utama SMA, Bekasi 2017

In Indonesia, there are 20% of adolescents aged 13-15 years who smoke. The results of a survey conducted by Global Youth Tobacco in 2009 revealed that three out of ten children aged 13-15 years in Indonesia (30.4%) already smoked, and (19.4%) started smoking at the age of 10 years. Based on data from the 2017 Daya Utama High School counseling vulnerability map, it is known that of the 216 male students in Class X – XII, 35 of them have smoking cases in the school environment, and have academic achievements that are less than the KKM, for this reason, it is necessary to think about efforts to improve the cognitive function of students so that academic achievement can be improved. One of these efforts is exercising. The purpose of this study is to provide preventive and coaching efforts to adolescents to avoid smoking and increase physical/sports activity. The technique of assessing students' cognitive abilities uses the Backward Forward Digit Span method, which is to assess short-term memory for 3 days with methods test and posttest. Research results Based on the results of the T test on smokers and non-smokers for 3 measurements, pre-test and post-test, the statistical test results obtained a Pvalue = 0.000, meaning that at 5% alpha it can be concluded that there is a significant difference between pre- and post-test cognitions. on smoking students and non-smoking students in the first, second and third day measurements. From the results of this study, it is recommended that the smoking ban continue to be enforced and pursued because the impact of smoking is not only physically damaging to the body but also reduces cognitive abilities, which will also affect learning achievement.

Differences of The Effect of Aerobic Sports on Cognitive Function Between Smoking and Non-Smoking Students at Daya Utama SMA, Bekasi 2017

In Indonesia, there are 20% of adolescents aged 13-15 years who smoke. The results of a survey conducted by Global Youth Tobacco in 2009 revealed that three out of ten children aged 13-15 years in Indonesia (30.4%) already smoked, and (19.4%) started smoking at the age of 10 years. Based on data from the 2017 Daya Utama High School counseling vulnerability map, it is known that of the 216 male students in Class X – XII, 35 of them have smoking cases in the school environment, and have academic achievements that are less than the KKM, for this reason, it is necessary to think about efforts to improve the cognitive function of students so that academic achievement can be improved. One of these efforts is exercising. The purpose of this study is to provide preventive and coaching efforts to adolescents to avoid smoking and increase physical/sports activity. The technique of assessing students' cognitive abilities uses the Backward Forward Digit Span method, which is to assess short-term memory for 3 days with methods test and posttest. Research results Based on the results of the T test on smokers and non-smokers for 3 measurements, pre-test and post-test, the statistical test results obtained a Pvalue = 0.000, meaning that at 5% alpha it can be concluded that there is a significant difference between pre- and post-test cognitions. on smoking students and non-smoking students in the first, second and third day measurements. From the results of this study, it is recommended that the smoking ban continue to be enforced and pursued because the impact of smoking is not only physically damaging to the body but also reduces cognitive abilities, which will also affect learning achievement.

An improved wild horse optimization algorithm for reliability based optimal DG planning of radial distribution networks

This paper introduces a novel technique for optimal distribution system (DS) planning with distributed generation (DG) systems. It is being done to see how active and reactive power injections affect the system’s voltage profile and energy losses. DG penetration in the power systems is one approach that has several advantages such as peak savings, loss lessening, voltage profile amelioration. It also intends to increase system reliability, stability, and security. The main goal of optimal distributed generation (ODG) is a guarantee to achieve the benefits mentioned previously to increase the overall system efficiency. For extremely vast and complicated systems, analytical approaches are not suitable and insufficient. Therefore, several meta-heuristic techniques are favored to obtain better performance from were convergence and accuracy for large systems. In this paper, an Improved Wild Horse Optimization algorithm (IWHO) is proposed as a novel metaheuristic method for solving optimization issues in electrical power systems. IWHO is devised with inspirations from the social life behavior of wild horses. The suggested method is based on the horse’s decency. To assess the efficacy of the IWHO, it is implemented on the 23 benchmark functions Reliability amelioration is the most things superb as a result of DGs incorporation. Thus, in this research, a customer-side reliability appraisal in the DS that having a DG unit was carried out by a Monte Carlo Simulation (MCS) approach to construct an artificial history for each ingredient across simulation duration. For load flow calculations, the backward Forward Sweep (BFS) technique has been employed as a simulation tool to assess the network performance considering the power handling restrictions. The proposed IWHO method has been measured on IEEE 33 69 and 119 buses to ascertain the network performing in the presence of the optimal DG and the potential benefits of the suggested technique for enhancing the tools used by operators and planners to maintain the system reliability and efficiency. The results proved that IWHO is an optimization method with lofty performance regarding the exploration–exploitation balance and convergence speed, as it successfully handles complicated problems.

Development of a Backward–Forward Stochastic Particle Tracking Model for Identification of Probable Sedimentation Sources in Open Channel Flow

As reservoirs subject to sedimentation, the dam gradually loses its ability to store water. The identification of the sources of deposited sediments is an effective and efficient means of tackling sedimentation problems. A state-of-the-art Lagrangian stochastic particle tracking model with backward–forward tracking methods is applied to identify the probable source regions of deposited sediments. An influence function is introduced into the models to represent the influence of a particular upstream area on the sediment deposition area. One can then verify if a specific area might be a probable source by cross-checking the values of influence functions calculated backward and forward, respectively. In these models, the probable sources of the deposited sediments are considered to be in a grid instead of at a point for derivation of the values of influence functions. The sediment concentrations in upstream regions must be known a priori to determine the influence functions. In addition, the accuracy of the different types of diffusivity at the water surface is discussed in the study. According to the results of the case study of source identification, the regions with higher sediment concentrations computed by only backward simulations do not necessarily imply a higher likelihood of sources. It is also shown that from the ensemble results when the ensemble mean of the concentration is higher, the ensemble standard deviation of the concentration is also increased.

A Riemannian optimization approach to the radial distribution network load flow problem

The Load Flow (LF) equations in power networks are the foundation of several applications on active and reactive power flow control, distributed and real-time control and optimization. In this paper, we formulate the LF problem in radial electricity distribution networks as an unconstrained Riemannian optimization problem, consisting of two manifolds, and we consider alternative retractions and initialization options. We introduce a Riemannian approximate Newton method tailored to the LF problem, as an exact solution method guaranteeing monotonic descent and local superlinear convergence rate. Extensive numerical comparisons on several test networks illustrate that the proposed method outperforms standard Riemannian optimization methods (Gradient Descent, Newton’s), and achieves comparable performance with the traditional Newton–Raphson method (in Euclidean coordinates), albeit besting it by a guarantee to convergence. We also consider an approximate LF solution obtained by the first iteration of the proposed method, and we show that it significantly outperforms other approximants in the LF literature. Lastly, we derive an interesting analogy with the well-known Backward–Forward Sweep (BFS) method showing that BFS iterations move on the power flow manifold, and highlighting the advantage of our method in converging under high constant impedance loading conditions, whereas BFS may diverge.

A Robust Electric Spring Model and Modified Backward Forward Solution Method for Microgrids with Distributed Generation

The electric spring (ES) is a contemporary device that has emerged as a viable alternative for solving problems associated with voltage and power stability in distributed generation-based smart grids (SG). In order to study the integration of ESs into the electrical network, the steady-state simulation models have been developed as an essential tool. Typically, these models require an equivalent electrical circuit of the in-test networks, which implies adding restrictions for its implementation in simulation software. These restrictions generate simplified models, limiting their application to specific scenarios, which, in some cases, do not fully apply to the needs of modern power systems. Therefore, a robust steady-state model for the ES is proposed in this work to adequately represent the power exchange of multiples ESs in radial micro-grids (µGs) and renewable energy sources regardless of their physical location and without the need of additional restrictions. For solving and controlling the model simulation, a modified backward–forward sweep method (MBFSM) is implemented. In contrast, the voltage control determines the operating conditions of the ESs from the steady-state solution and the reference voltages established for each ES. The model and algorithms of the solution and the control are validated with dynamic simulations. For the quasi-stationary case with distributed renewable generation, the results show an improvement higher than 95% when the ESs are installed. On the other hand, the MBFSM reduces the number of iterations by 34% on average compared to the BFSM.

Backward Sled Pulling Load–Velocity Relationship in Youth: A Backward–Forward Comparison

To establish the load–velocity relationships during backward sled pulling and compare this to forward sled pulling and determine the reliability of running speeds and associated load–velocity regression slopes in youth athletes. Twenty-one boys (age, 13.6 ± 0.28 years old; height, 1.72 ± 0.09 m; mass, 66.1 ± 8.2 kg; maturity, 0.57 ± 0.72 years from peak height velocity) performed backward and forward resisted running on three occasions. Load–velocity relationships were established using an un-resisted sprint and resisted sled pull with loads of 25%–81% body mass (BM) in both directions, and the reliability of sprint times at each load and the load–velocity regression slopes were examined. The load–velocity data was reliable across multiple testing occasions for backward (CV ≤ 7.2%; ICC 0.67–0.91) and forward sled pulling (CV ≤ 7.2%; ICC 0.66–0.91). From the regression lines (R2 ≥ 0.99; P ≤ 0.01), it was observed that averaged loads across the sample of 31%, 46% and 61% BM resulted in 27%, 38% and 48% decrease in velocity during backward sled pulling and 23%, 33% and 43% during forward sled pulling. Increasing backward and forward loads by ∼ 13% and ~ 15% BM respectively, reliably resulted in ∼ 10% decreases in velocity. If contractile overload of the lower limbs is a focus of training then practitioners should preferentially use backward sled pulling. Both backward and forward sled pulling can be used in a similar manner regarding loading, though backward velocity is more sensitive to load and therefore overloading backward motion should be prescribed with this in mind.

Power loss minimization in radial distribution systems with obstructed solar astronomical model and temperature effect using grey wolf optimization technique

ABSTRACT This article presents implementation of a solar energy-based DG (Distribution Generation) system, which is optimally designed by taking combined effect of solar irradiation and temperature into consideration to minimize the power loss in the Radial Distribution Systems (RDS). A power gap of 35 W/panel is reduced when the solar panel is considered under ambient temperature effect. Novelty of this work lies in the implementation of the obstructed solar astronomical model to improve the accuracy in the prediction of radiation level. In this work, the considered radiation level is determined to be 14.86% of the global radiation data. Consideration of self-shadowing losses for maximum efficiency from PV panels is another significant aspect of this work. Two different test systems are designed by integrating the proposed DG system to IEEE-15 and IEEE-28 RDS. Load Impedance Matrix (LIM) method is adopted to perform load flow in above mentioned test systems due to its single step analysis with faster results unlike traditional Backward Forward Sweep (BFS) method. Grey Wolf Optimization (GWO) technique is used to obtain optimal location of DG for both the test systems for its simple evaluation, good flexibility, avoidance of local optimal point and derivation free tool, which makes this technique popular among the other heuristic methods. Following DG allocation in IEEE-15 bus RDS, it is observed that active power loss is reduced by 39.69%. Similarly, for IEEE-28 bus RDS, reduction in active power loss is found to be 41.20% after integration of a DG unit. There is also a remarkable improvement in voltage profile in both systems after integration of DG units.

An efficient load flow solution for distribution system with addition of distributed generation using improved harmony search algorithms

The distribution system analysis is very important to know the system condition at any given point of time. This distribution power flow plays an important role. Many power flow techniques have been proposed in the past for distribution power flow (DPF). The implicit Z-bus Gauss–Seidel (GS) method is good when dispersed generations are modelled as PQ nodes in the distribution system but, when dispersed generations are modelled as PV nodes, it leads to divergence problems. In a Newton-like method, a slight change in the execution of the bus-type switching logic leads to an unlike convergence characteristic. Though backward/forward (BW/FW) method is effective and efficient, it slows down under heavy-load condition. Now, with the addition of distributed generation (wind/solar, etc.) at the distribution level, the classical configuration of the distribution system is also changed. This poses a need for more robust and efficient technique. This paper investigates the application of a new algorithm known as improved harmony search (IHS) for DPF. The IHS can determine multiple power flow solutions which fix the limitations of other methods while determining a solution for the highly stressed condition. The results for a standard test system are taken with IHS, Newton–Raphson (NR) and BW/FW method without and with the impact of the wind energy system, to show the potential of the proposed algorithm.

Modeling of distributed generators and converters control for power flow analysis of networked islanded hybrid microgrids

This paper proposes the incorporation of distributed and decentralized control models of generators and converters into the power flow analysis of islanded hybrid microgrids (IHMG). The absence of a slack bus in an islanded microgrid presents challenges to its frequency and voltage regulation as well as power flow calculation. While droop mechanism as the primary control has been well incorporated into load flow analysis, distributed secondary control has only recently been developed for islanded microgrids. This paper proposes a distributed secondary control for a) generators in an AC subgrid of an IHMG and b) interlinking converters in networked IHMGs. Both the distributed and droop control are incorporated into a three-phase power flow calculation based on a modified Backward Forward Sweep method. The algorithm has been tested on several case studies, and the power flow results have been verified against a time-domain simulation. The proposed method proves to be accurate and useful for the planning and design of three-phase networked IHMGs.

Optimal Integration of Distributed Generation in a Radial Distribution System Using BW/FW Sweep and PSO Algorithm

Installation of distributed generations (DGs) could be an effective solution to the problem of shortage of the electric energy especially in populated areas. Installation of DG in non-suitable places can result in more energy losses and voltage instability which leads to higher operating cost. DGs should be placed optimally in the network to get maximum efficiency of the system. This paper presents a new method to solve the optimal sizing and placement of DGs with the aim of minimizing real power loss and improving voltage profile in a distribution system. A power flow technique based on Backward/Forward (BW/FW) sweep is used to calculate the system losses through different branches. Particle Swarm Optimization algorithm is used to find out the optimal size and to identify the DG units placement in a radial distribution system simultaneously. Different scenarios of DG capacity are considered. The constraints of voltage and current through branches are investigated. The method is tested on 33-bus and 69-bus radial distribution systems to demonstrate the performance and the effectiveness of the proposed method. The results obtained are discussed and analyzed where they proved the usefulness of the applied algorithm.

Dual technique of reconfiguration and capacitor placement for distribution system

Radial Distribution System (RDS) suffer from high real power losses and lower bus voltages. Distribution System Reconfiguration (DSR) and Optimal Capacitor Placement (OCP) techniques are ones of the most economic and efficient approaches for loss reduction and voltage profile improvement while satisfy RDS constraints. The advantages of these two approaches can be concentrated using of both techniques together. In this study two techniques are used in different ways. First, the DSR technique is applied individually. Second, the dual technique has been adopted of DSR followed by OCP in order to identify the technique that provides the most effective performance. Three optimization algorithms have been used to obtain the optimal design in individual and dual technique. Two IEEE case studies (33bus, and 69 bus) used to check the effectiveness of proposed approaches. A Direct Backward Forward Sweep Method (DBFSM) has been used in order to calculate the total losses and voltage of each bus. Results show the capability of the proposed dual technique using Modified Biogeography Based Optimization (MBBO) algorithm to find the optimal solution for significant loss reduction and voltage profile enhancement. In addition, comparisons with literature works done to show the superiority of proposed algorithms in both techniques.

The psychometric properties of the Barkin index of maternal functioning (BIMF) for the Iranian population

BackgroundMothers’ capability for childcare and compatibility with the maternal role represent important challenges in postpartum care. Given the significance of evaluating maternal functioning, and the lack of adequate standard instruments in Iran for this purpose, the present study was aimed at translating and conducting a psychometric assessment of the Barkin Index of Maternal Functioning (BIMF) for Iranian women.MethodsThe instrument was translated into Persian using the Backward Forward method. The study included 530 women in the postpartum period admitted to healthcare centers in Tabriz, Iran; they were selected through the cluster sampling method. Face, content, and construct (through exploratory and confirmatory analyses) validity were presently examined. Reliability of the questionnaire was determined using the internal consistency and test-retest reliability methods.ResultsTwo factors (mom’s needs and competency), emerged based on exploratory factor analysis. The x2/df ratio was less than 5, and the values of the Root Mean Square Error of Approximation (RMSEA) and the Root Mean Square Residual (RMR) were less than 0.08 and 0.1, respectively, verifying the model validity. Cronbach’s alpha coefficient and Intra-class Correlation Coefficient (ICC) were calculated as 0.88 and 0.85, respectively, indicating reliability.ConclusionThe Persian version of the BIMF is a valid and reliable instrument for measuring the postpartum functioning of Iranian mothers.

E‐VAR: enhanced void avoidance routing algorithm for underwater acoustic sensor networks

Underwater acoustic sensor networks (UASNs) have gained attention among researchers due to its various aquatic applications. On the other hand, UASNs encounter many research challenges due to its inherent characteristics such as high propagation delay, limited bandwidth, high bit-error-rate, limited energy, and communication void during routing. These limitations severely affect the performance of delay-sensitive and reliable applications of UASNs. The primary objective of this study is to address the communication void during routing. Various methods, such as backward–forwarding, passive participation, flooding, heuristic, and transmission power adjustments, are proposed to address the communication void during routing. The major drawbacks of these methods are void as a part of routing, loops, unreachable data to the sink, and more number of transmission of duplicate packets. This study proposes a void avoidance routing algorithm referred to as enhanced-void avoidance routing (E-VAR) using an idea of void awareness among the nodes. The E-VAR inhibits the participation of void in routing, thereby resulting in better performance in comparison with the state-of-the-art. Through MATLAB simulations, E-VAR is compared with interference-aware routing and state-of-the-art backward–forwarding, in terms of the number of nodes reachable and unreachable due to looping to the sink, average hop-count, and distance.