Three-point Estimate Method Research Articles

Time-dependent safety and sensitivity analysis for structure involving both random and fuzzy inputs

The analyses of safety degree and sensitivity for structure are significant in engineering. For the time-dependent structure involving both random and fuzzy inputs, two indices named as the fuzzy time-dependent failure probability and the random time-dependent failure possibility are presented to measure safety degree of time-dependent structure from different aspects, i.e., the fuzzy time-dependent failure probability measures safety degree from the probabilistic view and the random time-dependent failure possibility measures safety degree from the non-probabilistic view. For efficiently estimating these two indices, Genetic Algorithms (GA) is employed. Furthermore, Sparse Grid (SG) technique combined with fourth-moment method is used to estimate the fuzzy time-dependent failure probability. Based on these two safety degree indices, two global sensitivity indices are established. By considering different aspects of the output, the importance ranking of the inputs can be obtained from these two established global sensitivity models. Some efficient procedures are proposed to estimate the global sensitivity indices by combining the three-point estimation method. Several examples containing the numerical and engineering ones are listed to illustrate the effectiveness of the proposed procedures for estimating the two safety degree indices and the two global sensitivity indices.

Placement of protective devices in distribution system considering uncertainties in loads, temporary and permanent failure rates and repair rates

Distribution system reliability is directly affected by the placement of various types of protective devices at different locations of the distribution system. To reduce the momentary interruptions, fuse-blow scheme is used which in turn increases sustained interruption. Utilities having more concern to the sustained interruptions prefer to use fuse-save scheme over fuse-blow scheme. Different models have been developed to solve the problems incorporating placement of various combinations of protective devices in a distribution network for increasing the profit of the utility. The uncertainties in temporary failure rates, permanent failure rates, repair rates and load data have been considered in the problem formulation using three-point estimate method. A generalised objective function capable of simulating different combinations of the protective devices has also been developed. The formulated problems have been solved for 58 bus and IEEE 123 bus distribution networks using mixed integer non-linear programming optimisation technique. After analysing the test results of the various scenarios for the two test systems, it is concluded that maximum profit for the utility is obtained by using the scenario corresponding to a combination of the four protective devices, namely reclosers, switches, fuse-blow fuses and fuse-save fuses.

Reliability Analysis by Combining Higher-Order Unscented Transformation and Fourth-Moment Method

In this work, the fourth-moment method and the higher-order unscented transformation is combined to calculate the failure probability. The higher-order unscented transformation is used to efficiently estimate the first four moments of the limit-state function, and then the fourth-moment method is used to estimate the failure probability. Compared with the traditional first-order and second-order reliability methods, the proposed method is not required to find the design point, and it is also not required to calculate the derivatives. Compared with the three-point estimation method, the proposed method has a good accuracy and lower computational cost. The numerical and engineering examples show the accuracy and efficiency of the proposed method.

Improved water resource management for a highly complex environment using three-dimensional groundwater modelling

A three-dimensional groundwater model was used to improve water resource management for a study area in north-west Switzerland, where drinking-water production is close to former landfills and industrial areas. To avoid drinking-water contamination, artificial groundwater recharge with surface water is used to create a hydraulic barrier between the contaminated sites and drinking-water extraction wells. The model was used for simulating existing and proposed water management strategies as a tool to ensure the utmost security for drinking water. A systematic evaluation of the flow direction between existing observation points using a developed three-point estimation method for a large number of scenarios was carried out. It is demonstrated that systematically applying the developed methodology helps to identify vulnerable locations which are sensitive to changing boundary conditions such as those arising from changes to artificial groundwater recharge rates. At these locations, additional investigations and protection are required. The presented integrated approach, using the groundwater flow direction between observation points, can be easily transferred to a variety of hydrological settings to systematically evaluate groundwater modelling scenarios.

Multi-period integrated natural gas and electric power system probabilistic optimal power flow incorporating power-to-gas units

The increasing adoption of gas-fired power plants directly strengthens the coupling between electric power and natural gas systems. Current industrial practice in optimal power flow for electric power systems has not taken the security constraints of gas systems into consideration, resulting in an overly-optimistic solution. Meanwhile, the operation of electric power and natural gas systems is coupled over multiple periods because of the ramp rate limits of power generators and the slow dynamical characteristics of gas systems. Based on these motivations, we propose a multi-period integrated natural gas and electric power system probabilistic optimal power flow (M-GEPOPF) model, which includes dynamic gas flow models. To address the uncertainties originating from wind power and load forecasting, a probabilistic optimal power flow (POPF) calculation based on a three-point estimate method (3PEM) is adopted. Moreover, power-to-gas (PtG) units are employed to avoid wind power curtailment and enable flexible bi-directional energy flows between the coupled energy systems. An integrated IEEE RTS 24-bus electric power system and the Belgian 20-node natural gas system are employed as a test case to verify the applicability of the proposed M-GEPOPF model, and to demonstrate the potential economic benefits of PtG units.

Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS) is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN) and hybrid multi-objective particle swarm optimization (HMOPSO) to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V) sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE) 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.

A Two-stage Stochastic Programming Model for Optimal Reactive Power Dispatch with High Penetration Level of Wind Generation

The increasing of wind power penetration level presents challenges in classical optimal reactive power dispatch (ORPD) which is usually formulated as a deterministic optimization problem. This paper proposes a two-stage stochastic programming model for ORPD by considering the uncertainties of wind speed and load in a specified time interval. To avoid the excessive operation, the schedule of compensators will be determined in the first-stage while accounting for the costs of adjusting the compensators (CACs). Under uncertainty effects, on-load tap changer (OLTC) and generator in the second-stage will compensate the mismatch caused by the first-stage decision. The objective of the proposed model is to minimize the sum of CACs and the expected energy loss. The stochastic behavior is formulated by three-point estimate method (TPEM) to convert the stochastic programming into equivalent deterministic problem. A hybrid Genetic Algorithm-Interior Point Method is utilized to solve this large-scale mixed-integer nonlinear stochastic problem. Two case studies on IEEE 14-bus and IEEE 118-bus system are provided to illustrate the effectiveness of the proposed method.

Multi-Objective Optimization Model for Non-Suspending Runway Reconstruction Project Scheduling in Busy Airports

As a result of the urgency of runway reconstruction and the inevitability of taking non-suspending reconstruction, time, cost and quality are taken as three basic objectives of the optimization with the complex environment of the runway, and the special related constraints are taken into consideration as well. Combining the other two objectives with the time objective and quantifying three objectives in the same way help to present the multi-objective model that is based on the multi-attribute utility function theory. Establishing the project network of asphalt repaving project and using the critical path method contribute to dealing with the uncertainty and randomness based on the distribution of process time by the three-point estimation method. Particle swarm optimization (PSO) algorithm helps solve the model and offers a scheduling plan of the critical path. In the end, one non-suspending the construction project of the runway is taken as an example and it proves the validity of the model compared with the related researches and the actual applied schedule.

Construction Duration and Cost Simulation via Network-Program-Diagram

This paper outlines the creation of a simulation model used to extrapolate duration and resource requirements needed for the construction of bridges based on a sampling of data obtained during a field research conducted on the construction process of a single bridge. A sampling of statistical data was taken during a field investigation to measure schedule and resource requirements (labor, raw materials and machinery) at various stages of a bridge construction project. This data was used to identify the probability distribution and the associated parameters for the project examined, and a simulation model was built to extrapolate the necessary schedule and resource requirements needed for various stages of similar bridge projects based on ThreePoint Estimation Method of Program Evaluation and Review Technique (PERT) and Monte Carlo Method. This simulation model’s resultant data for every process in an applicable construction project can be aggregated to form overall project duration and resource requirement statistical distribution using the Critical Path Method (CPM). The whole construction process will be visualized by a 4-dimensional (4D) model of the project which is created by appending time and resource requirements and to the 3-dimensional (3D) model that is built using the Building Information Modeling (BIM) and Alternativa 3D technology. Based on the simulation results of every procedure’s duration and resource requirements, the Network Program Diagram and Gantt Chart can be drawn Manuscript received February 20, 2014; accepted April 30, 2014 Wei-Dong Wang( ) Department of Civil Engineering, Central South University, Changsha 410075, China Email: 147745@163.com Jie Wu National Engineering Laboratory for High Speed Rail Construction, Changsha 410075, China Kai-Jun Wang The Third Railway Survey and Design Institute Group Corporation, Tianjin 300142, China Xin Wen Costing Department of Economic Planning Institute of the Ministry of Railways, Beijing 100038, China with Flex and ActionScript language. Furthermore, with the Network Program Diagram at the core, the 4D model can incorporate simulation’s resultant data with respect to total time and cost of the project to show the condition of resource requirements and the project’s progressing at any time.

Effects of the application of activity calendars on the distribution of project duration in PERT networks

The Program Evaluation and Review Technique (PERT) has been criticized for the proposed (beta) distribution of the activities, the three-point estimation method, the assumption of task independence and the assumed distribution of the project duration since its birth. Developments and generalizations of the succeeding years and decades were mostly aimed at resolving these issues. However, there are still pending questions that have not been examined in detail, if at all. One of these issues is the effect of activity calendars on the distribution of the project duration. Using a simple artificially created project we prove that the application of activity calendars has a greater influence on the distribution of the project duration than any of the above-mentioned criticisms. In the absence of analytic solutions, a robust tool performing Monte Carlo simulations has been used for calculations. • Gives a comprehensive survey about the most important critiques of PERT • Gives a comprehensive survey about the most important developments of PERT • Shows the effects of activity calendars on distribution of the project duration • Opens new gateways to the research of PERT

Probabilistic Algorithms for Power Load Flow and Short-Circuit Analysis in Distribution Networks with Dispersed Generation

In this paper, an approach for probabilistic analysis of unbalanced three-phase weakly meshed distribution systems with dispersed generation is presented. The approach considers uncertainty in both load demand and the injected power of dispersed generators. In order to achieve high computational efficiency, the approach makes use of both an efficient method for probabilistic analysis and a radial power flow. The probabilistic strategy used is the well-known Three-Point Estimate Method. In the other hand, classical models based on the sequence impedance are implemented for modeling synchronous machines, asynchronous machines, and electronically interfaced sources. These models aim to consider the impact on the power flows from the installation of dispersed generators. A small-size test system, as well as the modified IEEE 123 bus system, is used to show the operation and effectiveness of the proposed approach. A Monte Carlo Simulations method is used to validate the results.

Experimental Estimating Deflection of a Simple Beam Bridge Model Using Grating Eddy Current Sensors

A novel three-point method using a grating eddy current absolute position sensor (GECS) for bridge deflection estimation is proposed in this paper. Real spatial positions of the measuring points along the span axis are directly used as relative reference points of each other rather than using any other auxiliary static reference points for measuring devices in a conventional method. Every three adjacent measuring points are defined as a measuring unit and a straight connecting bar with a GECS fixed on the center section of it links the two endpoints. In each measuring unit, the displacement of the mid-measuring point relative to the connecting bar measured by the GECS is defined as the relative deflection. Absolute deflections of each measuring point can be calculated from the relative deflections of all the measuring units directly without any correcting approaches. Principles of the three-point method and displacement measurement of the GECS are introduced in detail. Both static and dynamic experiments have been carried out on a simple beam bridge model, which demonstrate that the three-point deflection estimation method using the GECS is effective and offers a reliable way for bridge deflection estimation, especially for long-term monitoring.