Substation Projects Research Articles

Research on Application of Full Attribute 3D Design Model in Engineering

In order to take full advantages of the integration of 3D technology graphics and data, this paper proposes the concept of a full-attribute 3D model that satisfies the full-process data application of substation engineering construction. All stages of engineering construction extract the required data from the full-attribute 3D model for calculation, Analysis, statistics, management and other functions, to achieve all-round coverage of three-dimensional data information from design to construction and construction management, laying the foundation for data circulation and management of the entire life cycle of substation projects. A method of applying a full-attribute three- dimensional model for construction, operation and maintenance management is proposed, which lays a foundation for the realization of refined, digital and intelligent project management.

The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects

Accurate and stable cost forecasting of substation projects is of great significance to ensure the economic construction and sustainable operation of power engineering projects. In this paper, a forecasting model based on the improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm (WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation projects. Firstly, the optimal features are selected through the data inconsistency rate (DIR), which helps reduce redundant input vectors. Secondly, the wolf pack algorithm is used to optimize the parameters of the improved least square support vector machine. Lastly, the cost forecasting method of WPA-DIR-ILSSVM is established. In this paper, 88 substation projects in different regions from 2015 to 2017 are chosen to conduct the training tests to verify the validity of the model. The results indicate that the new hybrid WPA-DIR-ILSSVM model presents better accuracy, robustness, and generality in cost forecasting of substation projects.

A 2015 inventory of embodied carbon emissions for Chinese power transmission infrastructure projects

The spatial mismatch of energy resources and electricity demand in China drives the large-scale construction of power transmission infrastructure, which consumes a large amount of carbon-intensive products. However, a systematic accounting framework for the carbon emissions of power transmission infrastructure has not yet been established. This study for the first time compiles an embodied carbon emissions inventory covering 191 typical power transmission infrastructure projects in China in 2015, including 145 types of alternating current (AC) transmission line projects, 37 typical AC substation projects, 8 typical direct current (DC) transmission line projects and 1 typical DC converter station project. The inventory also shows the detailed inputs of all the projects. These data not only enable a quantitative assessment of the embodied carbon emissions of power transmission infrastructure in China but also provide essential information for climate mitigation policy design in the power sector.

Research on Investment Forecast of Substation Project Based on Neural Network Model

Based on historical datas of substation projects, this report builds an artificial neural network intelligent prediction algorithm by identifying key influence factors. After predicting the cost of 110kV substation projects in a certain province, the results show that the neural network prediction model can quickly predict the construction cost of transmission and transformation projects by inputting key influence factors of the project, and it can achieve the accuracy of project investment in the investment plan filling.

Intelligent Prediction of the Construction Cost of Substation Projects Using Support Vector Machine Optimized by Particle Swarm Optimization

To establish and consummate the electric power network, the construction and investment scale of power substation projects is expanding every year. As a capital-technology-intensive project, it has high requirements for power substation project management. Accurate cost forecasting can help to reduce the project cost, improve the investment efficiency, and optimize project management. However, affected by many factors, the construction cost of a power substation project usually presents strong nonlinearity and uncertainty, which make it difficult to accurately forecast the cost. This paper presents a new hybrid substation project cost forecasting method called PCA-PSO-SVM model, which is a support vector machine (SVM) model optimized by a particle swarm optimization (PSO) algorithm with principal component analysis (PCA). In this intelligent prediction model, the PCA method is introduced to reduce the data dimension. Furthermore, the PSO algorithm is used to optimize the model parameters. In the example, 65 sets of substation construction data are input into PCA-PSO-SVM model for construction cost prediction, and the prediction results are compared with other prediction methods to verify the forecasting accuracy. The results show that the MAPE and RMSE of the PCA-PSO-SVM model is 6.21% and 3.62, respectively. And, the prediction accuracy of this model is better than that of other models, which can provide a reliable basis for investment decision-making of substation projects.

Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

Precise and steady substation project cost forecasting is of great significance to guarantee the economic construction and valid administration of electric power engineering. This paper develops a novel hybrid approach for cost forecasting based on a data inconsistency rate (DIR), a modified fruit fly optimization algorithm (MFOA) and a deep convolutional neural network (DCNN). Firstly, the DIR integrated with the MFOA is adopted for input feature selection. Simultaneously, the MFOA is utilized to realize parameter optimization in the DCNN. The effectiveness of the MFOA–DIR–DCNN has been validated by a case study that selects 128 substation projects in different regions for training and testing. The modeling results demonstrate that this established approach is better than the contrast methods with regard to forecasting accuracy and robustness. Thus, the developed technique is feasible for the cost prediction of substation projects in various voltage levels.

Research on Investment Decision of Substation Project Based on Life Cycle Cost

In this paper, the life cycle cost theory is applied to the study of investment decision-making of substation engineering, and the scientific, reasonable and objective comprehensive evaluation of substation engineering is realized. Based on the analysis of the calculation method of life cycle cost, a theory-based investment decision model for substation engineering is established. Finally, taking a substation project as an example, it proves that the model can effectively analyse the investment cost of different schemes, objectively reflects the economic differences between schemes, and provides a favorable judgment basis for investment decision-making of Power Grid Substation projects, which has a certain practical value.

Life Cycle Cost Analysis and Optimization of Modular Substation

Due to the lack of high standardization and specialization, the production scale of modular substation is difficult to meet the established requirements. Compared with traditional substation construction, there are still practical problems such as high production costs. Therefore, it is still difficult to promote modular substation. Considering the life cycle of modular substation project, Cost Breakdown Structure (CBS) and Engineering Breakdown Structure (EBS) of the modular substation were defined through literature reading and data analysis. Then use the life cycle cost mapping model to further determine the mapping relationship between CBS and EBS. Finally, the connotation and goal of the life cycle cost optimization of modular substation were proposed, which can effectively promote the cost management of modular substation projects and optimization.

Cost Forecasting of Substation Projects Based on Cuckoo Search Algorithm and Support Vector Machines

Accurate prediction of substation project cost is helpful to improve the investment management and sustainability. It is also directly related to the economy of substation project. Ensemble Empirical Mode Decomposition (EEMD) can decompose variables with non-stationary sequence signals into significant regularity and periodicity, which is helpful in improving the accuracy of prediction model. Adding the Gauss perturbation to the traditional Cuckoo Search (CS) algorithm can improve the searching vigor and precision of CS algorithm. Thus, the parameters and kernel functions of Support Vector Machines (SVM) model are optimized. By comparing the prediction results with other models, this model has higher prediction accuracy.

Assessing the Impact of Advanced Technologies on Utilization Improvement of Substations

The smart substation is the heart of a transmission system, which is particularly emphasized as the most significant composition of smart grids in China. In order to assess the functionality performance of substation technologies, this paper presents methods used to identify the most promising solutions for smart substation design and to evaluate the technical levels of available technologies. The multi-index optimization model is presented to address the issue of smart substation planning. A mathematical model of the planning decision problem is established with multiple objectives consisting of economic, reliability, and green key indices, and many kinds of concerns including physical and environmentally friendly operations are formulated as a set of constraints. With respect to the assessment of the technical level regarding integration of advanced technologies into a substation, a modified grey whitenization weight function is adopted to structure a novel grey clustering method. The proposed grey clustering approach is used to overcome the difficulty of insufficient quantitative assessment capacity for traditional methods. The evaluation of technical effects provides the classification definition for the development phase and the maturity level of the smart substation. The effectiveness of the proposed approaches in planning decision-making and evaluation of construction efforts is demonstrated with case studies involving the actual smart substation projects of Wenchongkou substation in China Southern Power Grid (CSG) and Mengzi substation in State Grid Corporation of China (SGCC).

Fuzzy model for predicting project performance based on procurement experiences

Abstract This paper presents a data-clustering-based fuzzy model for predicting the performance of construction projects delivered through different procurement methods. To illustrate the model, a sample of 96 substation projects of an electric power company, delivered through the design–bid–build (DBB), design–build (DB), and turnkey (TK) methods, were used as training and testing data for model development and verification. Through a factor analysis, an initial set of 48 variables was reduced to nine inputs to the model for predicting eight performance metrics as outputs of the model. To establish the input–output relationships for each metric, Sugeno-type fuzzy inference systems were built, wherein the fuzzy rules were derived from selected projects as the estimated cluster centers in the training data. For the limited sample, zeroth-order systems built with hybrid training were found to outperform both first-order systems and stepwise regression models in prediction accuracy, while sensitivity analyses confirmed their robustness. As the data represents the company's procurement experiences of substation projects, the model may be regarded as a way of implementing organizational learning to improve procurement decision.

Factors Causing Design Schedule Delays in Turnkey Projects in Taiwan: An Empirical Study of Power Distribution Substation Projects

Because of its benefits, the turnkey project delivery method has received much global attention; however, turnkey contractors often have difficulty completing projects within the contract schedule. Through case studies, expert interviews, questionnaire surveys, and statistical data analysis, this study revealed that, of 27 delay factors that affect design schedules in power distribution substation projects, the most common delay factors are “public contending or political involvement” and “tedious review processes of government agencies.” Furthermore, the study proposes assorted strategies from the perspectives of the owner, turnkey contractor, and designer that can be used to help prevent delays in similar turnkey projects.

The Unified Management Platform of IEC61850 Configuration Files Based Plug-In

With the promotion of the standard IEC61850, the configurations of substation automation system and intelligent electronic device (IED) in the substation project play an important role. In practical engineering applications also exist some problems: In Substation Project configuration, many manufacturer device configuration tools, Project configuration file distributed, is Management difficulties. So the implementation of the project substation on-site and post-maintenance are inconvenienced.This paper presents a unified management platform of configuration file in substation project, to implement private configuration of manufacturers with the plug-in of manufacturers, to realize a one-stop configuration of substation projects.

APPLYING A GREY THEORY ON REGIONAL POWER DEMAND FORECASTING IN TAIWAN AREA

This paper proposes a grey theory to evaluate regional power demand in Taiwan. This will result in a useful demand forecast as regional special properties can be extracted. These results will drive recommendations helpful for the planning of transmission line or substation projects. The results of this paper will be compared with the real power record which can be referenced from the power authority.

An exploratory study into recurring construction problems

The pattern of recurring construction problems encountered in electrical transmission substation projects is a very pertinent phenomenon. The purpose of this study is to explore the possible reasons for such recurrences and to uncover unusual factors. To achieve this, unstructured interviews were conducted. A list of possible reasons were collected. The reasons were found to be interrelated and have mutual influence. The study revealed four unusual factors: `management did not care', `inherited problems from earlier phases', `perceptions of project success were not the same', and `projects were completed anyway'. The study also uncovered some underlying conceptual ambiguities in which further investigations were necessary. These ambiguities included the perspectives of project, the perception of project success and the criteria of project success.