Management Optimization and Risk Assessment of 500 kV Substation Construction Projects with Multi-Professional Collaboration

Abstract

In response to the difficulties in multi-disciplinary coordination, the complexity of schedule management, and the weakness of risk control in the construction of high-voltage substations, and based on the current construction status and historical experience of high-voltage projects in Jilin Province, this paper, from the perspectives of schedule and risk management, proposes a multi-disciplinary coordination and risk control strategy that integrates the work breakdown structure (WBS), design structure matrix (DSM), critical chain project management (CCPM), and the fuzzy analytic hierarchy process (FAHP). First, the task flow is decomposed using WBS, and DSM-based coupling analysis is employed to identify interdependencies among disciplines, thereby optimizing task sequencing and parallel arrangements. Second, an optimized project schedule model is established using CCPM, with aggregated buffers that enhance the reliability and flexibility of schedule management. Finally, a risk register is developed based on field investigations, and a three-dimensional quality–schedule–safety risk assessment model is constructed using FAHP; targeted risk prevention and control measures are then proposed according to the quantitative evaluation results. A 500 kV substation project in Jilin Province is adopted as a case study for application and verification. Compared with traditional serial scheduling, the proposed schedule optimization strategy shortens the overall project duration by 29.1%. Furthermore, targeted management recommendations were proposed based on the risk assessment results of the project. The proposed optimization strategy can provide theoretical support and practical guidance for the construction of high-voltage substations and their associated projects, forming an effective technical solution that is scalable and replicable, and it is of great significance for improving the level of project construction management.

1. Introduction

With the deployment of China’s “dual carbon” strategy and the construction and development of the new power system, the installed capacity of new energy has increased rapidly, and the spatial mismatch between energy and load centers has intensified, making the demand for cross-regional power transmission channels in China’s power system increasingly prominent [1,2]. The grid connection of large-scale new energy bases places higher requirements on the safe and stable operation of the power system. Power transmission and transformation projects with a voltage level of 500 kV and above, together with new energy sending-end power grids, are key components of the backbone structure of the power grid. They are of great significance in ensuring a reliable power supply and improving new energy outward transmission capacity [3], and serve as important facilities supporting the construction of the new power system. Meanwhile, these projects are characterized by large construction scale, high technical requirements, and the involvement of multiple professions, posing challenges to project scheduling and risk management [4]. Affected by various factors, including the geographical environment, local construction conditions, and cross-professional collaboration, current engineering construction management still faces issues such as lengthy processes and insufficient information sharing, resulting in inadequate construction efficiency and resource utilization. To support the high-quality development of China’s power grid construction projects during the “15th Five-Year Plan” period, there is an urgent need to establish a scientific and efficient engineering construction management system. The construction of 500 kV substations and supporting projects involves a long cycle, multiple links, and complex professional involvement. The traditional experience-driven management model makes it difficult to form an optimal construction plan under the requirements of safety and efficiency. Constructing a whole-process management system can not only realize the systematic optimization of project processes and resource allocation but also provide a promotable construction management model and decision support plan for high-voltage substations and supporting projects [5].

Scholars at home and abroad have conducted relevant research on the multi-professional collaborative management and risk control issues of complex engineering construction projects. Traditional methods use tools such as Gantt charts for schedule planning, but they fail to fully consider uncertain factors and conflicts among multiple professions [6]. In recent years, project management research has focused on task decomposition and relationship dependency analysis, such as methods like Work Breakdown Structure (WBS) and Design Structure Matrix (DSM) [7], while the main research methods in schedule optimization include Critical Chain Method (CCM) and Building Information Modeling (BIM) [8,9]. For risk assessment, methods such as Fuzzy Analytic Hierarchy Process (FAHP) and Principal Component Analysis (PCA) have been widely applied in basic project risk assessment issues [10,11,12,13].

In terms of project management, aiming at the disjointed issues among design, procurement, and construction in the implementation of the general contracting model for power transmission and transformation projects, Wu et al. [14] proposed a whole-process consulting and management model, which realized the organic connection of work in various stages and effectively shortened the construction period by 10–15%. Addressing the difficulty in identifying task dependency relationships in complex R&D projects, Yang et al. [15] put forward a DSM-based process modeling method that achieved process optimization through quantitative analysis of inter-task coupling relationships, but failed to consider the constraints of allocable resources. Regarding schedule management, although the Critical Path Method (CPM) can identify key tasks affecting the construction period, it does not take resource constraints into account. To meet the high construction period requirements of photovoltaic power station projects, Xiong et al. [16] proposed an engineering management model based on the Critical Chain Method (CCPM). By removing part of the safety time from each task and concentrating it as a project buffer, the model shortened the overall project period and effectively improved resource utilization efficiency. For risk assessment, in response to the uncertainty in engineering risk assessment, Xu et al. [17] proposed a risk quantification method based on the Fuzzy Analytic Hierarchy Process (FAHP), which embedded fuzzy mathematics theory into the Analytic Hierarchy Process (AHP) to realize the overall risk assessment of the system. Li et al. [18] applied FAHP to risk assessment in fields such as medical decision-making projects, verifying its effectiveness in handling uncertainties of complex systems.

In summary, existing research has achieved certain progress in engineering management; however, for high-voltage supporting construction projects characterized by large regions, multiple professions, and high coupling, the following deficiencies still exist: (1) The lack of management methods oriented toward multi- professional collaboration makes it difficult to effectively address management issues arising from complex professional dependencies; (2) The absence of a multi-dimensional and systematic risk assessment system as well as the construction of a typical risk set fails to comprehensively consider multiple risk factors including quality, schedule, and safety; (3) The influence of regional practical conditions (such as cold-region characteristics, construction requirements, and existing problems) has not been incorporated. To address the above issues, targeting the characteristics of high-voltage supporting project construction and combining the historical experience of regional practical projects in Jilin Province, this study proposes a multi-professional collaborative construction management optimization and risk assessment strategy from the perspectives of management optimization and risk quantification. The main work is as follows:

(1)

We construct a multi-professional task decomposition and dependency identification method based on WBS-DSM coupling analysis, systematically sort out the connections among various professions, and arrange sequential and parallel tasks to shorten the construction period.

(2)

We adopt the CCPM method to establish a schedule optimization model considering resource constraints, and improve schedule robustness through reasonable buffer setting.

(3)

We use FAHP to construct a three-dimensional risk assessment system of “quality-schedule-safety”, realize quantitative risk assessment, and propose targeted control suggestions, to provide theoretical support and practical guidance for the construction management of high-voltage projects.

To address the challenges of multi-disciplinary coordination and risk control in 500 kV substation projects, this study aims to establish a scientific framework for process optimization and quantitative assessment. Distinct from traditional experience-driven models, the novelty of this work lies in the proposed integrated strategy combining WBS, DSM, CCPM, and FAHP, which quantifies professional coupling and constructs a three-dimensional “Quality-Schedule-Safety” risk system. The application results demonstrate that this approach optimized task parallelism and buffer management, effectively shortening the construction period and identifying safety as the predominant risk factor guiding project management.

2. Analysis of the Characteristics and Management Issues of 500 kV Substation Projects

2.1. Characteristics of 500 kV Substation Construction Projects

The construction of 500 kV substation projects is generally divided into six main stages: planning, design, construction, commissioning, operation, and filing & final accounts. The specific task content and construction period of each stage are shown in Figure 1 Taking Jilin Province as an example, as an important carrier of the “Songliao New Energy Base” and the “Onshore Wind-Solar Three Gorges”, it is one of the main regions for new energy development in Northeast China and new energy outward transmission such as “Jilin Power Transmission to Beijing”. The construction of its 500 kV power grid at the sending end has good typicality and representativeness [19,20]. In terms of the overall project characteristics, the construction of 500 kV substation projects in the province has the following features: ① Large construction scale and long cycle. The main transformer capacity of a single station is usually 2 × 1000 MVA, with a construction period of 18–24 months, which is much longer than that of 220 kV and below projects. ② High coupling among multiple professions. It involves more than 10 professions, including civil engineering, primary electrical engineering, secondary electrical engineering, power lines, fire protection, and intelligent auxiliary control, with complex dependency relationships among professions. ③ High technical requirements. A single main transformer weighs 200–300 tons, and the height of iron towers ranges from 50 to 70 m, imposing extremely high technical requirements on key processes such as hoisting and erection. ④ Significant impact of cold-region environment. The winter construction period is as long as 5 months, with temperatures as low as −30 °C, which imposes special requirements on material selection, construction technology, and equipment performance. ⑤ Great difficulty in external coordination. It involves the approval of land occupation for forests and grasslands, and cumbersome procedures for crossing highways, railways, and power lines, resulting in a long coordination cycle [21].

2.2. Analysis of Existing Problems in Engineering Management

Combined with the characteristics of 500 kV substation construction projects listed in Section 2.1, and based on the investigation and analysis of the construction processes of completed 500 kV projects in Jilin Province in recent years, such as Qian’an Substation, Busu Substation, and Shuangyang Substation, the main problems existing in construction engineering management can be summarized into the following categories:

2.2.1. Multi-Professional Collaboration Issues

(1) Insufficient collaboration during the design phase: There is a lack of effective coordination among various professions in design units, leading to mismatched drawing interfaces. For example, in the Qian’an Project, problems such as inconsistent dimensions between foundations and cabinets, and inconsistent positions of lighting poles and cameras emerged, requiring rework in the later stage.

(2) Chaotic management of construction interfaces: Professional manufacturers for fire protection, intelligent auxiliary control, and other fields intervene late, and lack synchronous planning with civil engineering and electrical professions, resulting in chaotic arrangement of buried pipes and pre-embedded cabinets in buildings.

(3) Conflicts in cross-professional operations: Multiple professions, including civil engineering, electrical engineering, and secondary electrical engineering, conduct a large number of cross-professional operations at the construction site. The absence of a unified operation plan coordination leads to resource conflicts and work idleness.

2.2.2. Schedule Management Issues

(1) Schedule compression leading to rushed construction: To meet the demand for new energy outward transmission, the construction period of projects has generally been compressed by 10–20%, forcing construction units to rush construction and resulting in inadequate implementation of safety and quality measures.

(2) Delayed material supply: Materials such as secondary optical cables and pigtail cables supplied by manufacturers on demand are delayed due to lagging design confirmation, which affects the progress of construction.

(3) Lag in the handling of preliminary procedures: The processing cycle for land occupation procedures for forests and grasslands is long. For some projects, line route adjustments are required due to the issue of “holdouts”, which impacts the overall construction period.

2.2.3. Quality and Safety Risk Issues

(1) Inadequate control over material quality: The quality of raw materials supplied by secondary contractors (such as steel and cement) varies, and construction units select low-quality materials due to price factors. Lax supervision and inspection have led to the entry of inferior materials into the construction site.

(2) Insufficient control over high-risk operations: Special plans for high-risk operations such as main transformer hoisting (200–300 tons), iron tower erection (50–70 m), and live-line crossing are inadequately formulated, with inadequate on-site control.

(3) Cold-region construction risks: Concrete pouring, welding operations, and equipment installation under low-temperature environments face special risks, lacking targeted technical measures and emergency plans.

3. Construction Engineering Management Optimization Model for Multi-Professional Collaboration

3.1. Overall Framework of the Model

To address the main issues in the construction management of 500 kV substation projects, this study constructs a management optimization and risk assessment method for 500 kV substation construction projects, considering multi-professional collaboration. Firstly, a multi-professional collaborative optimization model integrating WBS and DSM is established, which realizes the sequencing and parallel optimization of engineering tasks by quantifying the coupling intensity among various professions. Furthermore, considering both task dependencies and resource constraints, the controllability of engineering tasks and allocable resources is ensured by defining buffers, so as to achieve the process optimization of substation construction projects. Finally, targeting each stage of the project, a fuzzy hierarchical assessment of substation construction project risks is conducted from three dimensions (quality, schedule, and safety), and effective and targeted risk prevention and control suggestions are put forward. The main technical framework of the proposed management optimization and risk assessment method is shown in Figure 2.

3.2. WBS-DSM-Based Collaborative Task Process Optimization Model

3.2.1. Work Breakdown Structure (WBS)

The Work Breakdown Structure (WBS) is a method in engineering project management that decomposes complex tasks into controllable management units. In this section, the overall tasks of the 500 kV substation project are decomposed into manageable work packages, and a three-level WBS is established: the first-level WBS corresponds to the six stages shown in Figure 1, namely planning, design, construction, commissioning, operation, and filing & final accounts; the second-level WBS consists of the main work content of each stage; the third-level WBS further refines the main work content into specific task packages.

3.2.2. Task Process Optimization Based on DSM Analysis

The Design Structure Matrix (DSM) can be used to represent the dependency relationships among internal factors of complex projects. Targeting the management issues of engineering projects, this study adopts DSM to analyze the coupling connections between different professions, providing a quantitative basis for optimizing task sequences and identifying critical paths [22].

The DSM is a b × b square matrix, with the rows and columns of the matrix representing various elements in the system (such as disciplines, tasks, or activities). A non-zero element indicates that the row element depends on the column element, and the magnitude of the element reflects the strength of the dependency. The dependency strength $d\left(i,j\right)$ of discipline i on discipline j is defined as:

$$d\left(i,j\right)={\displaystyle \frac{1}{2}}\left[w\left(i,j\right)+w\left(j,i\right)\right]$$

(1)

where $w\left(i,j\right)$ represents the information flow weight from discipline i to discipline j; $w\left(j,i\right)$ represents the reverse information flow weight. Furthermore, the coupling strength $c\left(i,j\right)$ is defined to quantify the bidirectional dependency, namely:

$$c\left(i,j\right)={\displaystyle \frac{d\left(i,j\right)\cdot d\left(j,i\right)}{d\left(i,j\right)+d\left(j,i\right)}}$$

(2)

This article defines that when $c\left(i,j\right)\ge 0.7$ occurs, profession i and profession j are considered to have a strong coupling relationship and require focused collaborative management. Finally, based on the identification results of the coupling intensity between different professions according to DSM, the overall engineering project process is optimized as follows:

(1)

Optimize the involvement time of key pre-requisite specialties.

(2)

Clarify the ‘interface-responsibility-delivery’ collaborative management mechanism: define the work boundaries and handover points of tightly coupled specialties, designate the responsible parties (design, construction, supervision) for each collaboration node, and specify the technical standards, time nodes, and acceptance criteria for each deliverable.

(3)

Optimize task sequence and parallel strategies: For strongly coupled tasks ($c\ge 0.7$), advance through synchronous design and joint review; for weakly dependent tasks ($c<0.7$), schedule parallel construction under conditions of spatial and resource independence.

3.3. Engineering Schedule Management Optimization Model Based on CCPM

Traditional project management methods suffer from the phenomenon of excessive safety time reservation in construction period estimation. By adding a certain time margin to each task, risks can be avoided to a certain extent. However, this decentralized time arrangement is difficult to address engineering uncertainties, leading to incomplete controllability of the project construction period [23]. Based on historical project experience and behavioral perspectives, the main reasons are as follows: when tasks are allocated sufficient construction periods, executors tend to slow down their pace or pursue excessive perfection, resulting in the ineffective consumption of safety time; task executors are accustomed to procrastinating until the deadline approaches before starting work, and even with sufficient reserved safety time, it cannot be effectively utilized [24]; according to a systematic study of global construction engineering projects in recent years, only about 31% of projects can meet schedule, budget, and scope objectives simultaneously, approximately 50% of projects face varying degrees of delay or cost overrun challenges, and 19% of projects fail completely [25]. The delay rate of construction engineering projects is generally high, especially for large-scale infrastructure projects, with more than 90% of projects experiencing cost overruns [26]. This current situation highlights the fundamental flaw of traditional methods in addressing project uncertainties.

The Critical Chain Project Management (CCPM) is a project schedule management method developed on the basis of the Critical Path Method (CPM). Its core idea is to abandon part of the construction period time budget, reduce the safety time margin of each task, and centrally set it as a buffer, thereby more effectively managing project uncertainties and resource conflicts and improving the reliability of project completion on schedule. Based on statistical principles, with sufficient samples, approximately 50% of tasks can be completed ahead of schedule, and 50% of tasks will be delivered late, with the overall deviation tending to zero [27]. Unlike the traditional safety time which is concealed within individual tasks to manage local uncertainties, the CCPM buffer is an explicit, centralized time reserve. It aggregates the safety margins stripped from tasks to protect the overall project schedule rather than specific activities.

CCPM considers both task dependencies and resource constraints, defining three types of buffers: project buffer, feeder buffer, and resource buffer. They are used to ensure the controllability of critical tasks (the longest task chain considering resource conflicts), non-critical tasks, and critical resources, respectively, as detailed below:

(1) The project buffer (PB) is set based on the uncertainty of all tasks in the critical chain, and its buffer time $T^{\mathrm{PB}}$ is calculated using the root mean square method, that is:

$$T^{\mathrm{PB}}=\alpha \sqrt{{\displaystyle \sum _{i\in N_{\mathrm{PB}}}{\left(t_i^{\mathrm{s}}\right)}^2}}$$

(3)

where $t_i^{\mathrm{s}}$, $t_i^{\mathrm{c}}$, and $t_i^{\mathrm{z}}$ are the safety time, traditional duration, and baseline duration of the i-th task on the critical chain, respectively; $t_i^{\mathrm{s}}=t_i^{\mathrm{c}}-t_i^{\mathrm{z}}$ and $N_{\mathrm{PB}}$ are the total number of tasks on the critical chain; α is the buffer coefficient (usually 0.5–0.6), and in this paper α = 0.6.

(2) The feeding buffer (FB) is set at the point where a non-critical chain merges into a critical chain, protecting the critical chain from delays of the non-critical chain. It is usually taken as 30% to 50% of the duration of the corresponding non-critical chain. The calculation for the feeding buffer time $T_k^{\mathrm{FB}}$ is:

$$T_k^{\mathrm{FB}}=\beta \sqrt{{\displaystyle \sum _{i\in N_{\mathrm{FB}}}{\left(t_{k,i}^{\mathrm{f}}\right)}^2}}$$

(4)

where $t_{k,i}^{\mathrm{f}}$ is the safety time of the i-th task on the non-critical chain k; $N_{\mathrm{FB}}$ is the total number of tasks on the k-th non-critical chain; β is the buffer coefficient, which is taken as 0.4 in this article.

(3) The resource buffer (RB) is implemented through backup key personnel, redundant equipment configurations, etc. Generally, key resources (personnel, equipment) are notified 3 to 5 days in advance to be ready; that is, the resource buffer time $T^{\mathrm{RB}}$ is taken as 3 to 5 days [28].

In summary, CCPM uses the buffer consumption rate $\eta$ as an early warning indicator for project progress, namely:

$$\eta ={\displaystyle \frac{T^{\mathrm{cs}}/T^{\mathrm{total}}}{P^{\mathrm{cp}}/P^{\mathrm{total}}}}\times 100\%$$

(5)

where $T^{\mathrm{cs}}$ and $T^{\mathrm{total}}$ represent the consumed buffer time and the total buffer time, respectively, while $T^{\mathrm{total}}=T^{\mathrm{PB}}+T^{\mathrm{FB}}+T^{\mathrm{RB}}$, $P^{\mathrm{cp}}$ and $P^{\mathrm{total}}$ represent the duration of completed critical chain tasks and the total duration.

Based on the buffer consumption rate, a project progress monitoring mechanism can be established, dividing the project progress status into three zones: the green zone ($\eta <33\%$) indicates normal project execution progress; the yellow zone ($33\%\le \eta <67\%$) indicates a need for attention; the red zone ($\eta \ge 67\%$) indicates immediate corrective actions are necessary. The project team should update the buffer consumption statistics weekly, and if the buffer is exhausted in advance, an emergency plan should be promptly activated.

4. Construction Engineering Risk Assessment Method Based on FAHP

4.1. Establishment of Risk Database for Substation Construction Projects

Focusing on 500 kV substation projects completed in Jilin Province over the past five years (e.g., Lesheng, Qianan, Busu, and Shuangyang), this study systematically identifies typical project risks based on historical cases, on-site investigations, and the standard Construction Safety Risk Management Regulations for Transmission and Transformation Projects (Q/GDW 12152-2025). Furthermore, by integrating the practical expertise of management personnel from the Construction Branch of the State Grid, a two-dimensional ‘stage-attribute’ risk framework was established. Dimension 1 represents the project stages, including: planning and design stage, construction implementation stage, and commissioning and acceptance stage; Dimension 2 represents risk labels, including 7 categories: design, civil engineering construction, electrical installation, line construction, material supply, coordination and management, and environmental impact. A total of 70 typical risks are counted. The statistical information of risk database labels is shown in

Table 1. Risk database information labels.

Statistic Item	Quantity/Item	Statistic Item	Quantity/Item
Design Risk	12	Material Supply Risk	6
Civil construction risks	15	Coordinated Risk Management	10
Electrical Installation Risk	12	Environmental impact risk	5
Line Construction Risk	10	Total	70

, and the specific items are shown in Appendix A 

Table A1. 70 typical risks included in the risk database.

Serial Number	Risk Category	Typical Risk
1	Design Risks (12 items)	Insufficient design depth, mismatched drawing interfaces, lack of coordination among design disciplines, unreasonable selection of building materials, improper layout of sockets and electric heaters, frequent design changes, inadequate data survey, untimely submission of owner-supplied materials, inaccurate submission of owner-supplied materials, unrecognized secondary risk operations, missing general design, slow submission of equipment parameters
2	Civil Construction Risks (15 Items)	Civil construction is affected by the drawings of primary equipment, traditional wall construction cycles are long, pipelines and embedded boxes are disorganized, building material quality is poor, the construction cycle of cable trenches across roads is long, temporary roads in the substation are blocked, building decoration planning is insufficient, special-shaped covers for cable trenches have not been customized, there are quality issues with asphalt shingle roofs, cable trench drainage covers are suspended, rebar types are chosen improperly, soil replacement under building floors is inadequate, cable trench covers for boundary walls are delayed in delivery, finished product protection is inadequate, topsoil stripping is handled improperly
3	Electrical Installation Risks (12 Items)	Main transformer hoisting operations, GIS installation quality control, cable laying quality issues, discrepancies between substation fittings bidding and on-site conditions, damage to the main control room and small room doors, unclear responsibilities for fire alarm and firefighting systems, firewall framework guide lines not built in one go, high difficulty of construction at the substation exit delays in the supply of secondary optical cable tails, insufficient quantity of equipment and material inspections, chaotic management of manufacturer service personnel on site, omissions in the substation fittings bidding.
4	Line Construction Risks (10 Items)	Insufficient distance control for wires crossing trees, inconsistent forms of line foot pins, serious staff reduction in later stages of the project, inconsistent principles among multiple design units, high difficulty in safety management during winter construction, many issues during the first tender for materials supplied by the subcontractor, lack of experience in the application of prefabricated foundations, issues with fixed-length wires on long-distance special alignment segments, multiple difficulties in the construction of spiral anchor foundations, uneven stress on double-string insulators
5	Material Supply Risks (6 items)	Delayed reporting of owner-supplied materials, untimely supply of materials, chaotic management of owner-supplied materials, loss or damage of small materials, mismatch between material arrival and project progress, insufficient reserves for testing material consumption
6	Coordinated Risk Management (10 Items)	The on-site project team is unable to work on the internal network, construction organization is unbalanced, the bill of quantities is inaccurate, awareness of finished product protection is weak, management of manufacturer service personnel on the work site is chaotic, environmental protection measures are not properly implemented, consideration is insufficient when introducing new processes and equipment, there are many repetitive and habitual violations, initial issues in line projects are prominent, it is difficult to coordinate 66 kV and 220 kV power outage plans
7	Environmental Impact Risks (5 items)	Low temperatures in cold regions affect concrete pouring, quality control of foundation construction on frozen soil in cold regions, high safety risks during winter construction, extreme weather affecting construction progress, complex geological conditions leading to adjustments in foundation plans

.

4.2. Engineering Risk Assessment Based on FAHP

4.2.1. Basic Principles of FAHP

FAHP is a multi-criteria decision-making method that combines fuzzy mathematics theory with the Analytic Hierarchy Process (AHP). Traditional AHP relies on deterministic values for judgment and does not consider the fuzziness and uncertainty of expert judgment conclusions in practical engineering risk assessment [29]. FAHP, on the other hand, introduces triangular fuzzy numbers to describe this fuzzy judgment, allowing for a more realistic reflection of the evaluation results [30].

A triangular fuzzy number $\left(l,m,u\right)$ is defined by three parameters: the lower limit l, the most likely value m, and the upper limit u. Suppose Y experts make relative importance judgments of elements i and j as $\left(l_1,m_1,u_1\right)$, $\left(l_2,m_2,u_2\right)$, …, $\left(l_n,m_n,u_n\right)$, respectively, the integrated fuzzy judgment can be expressed as:

$${\tilde{l}}_{ij} ={\left({\displaystyle \prod _{y\in Y}{l}_y}\right)}^{1/Y},{\tilde{m}}_{ij}={\left({\displaystyle \prod _{y\in Y}{m}_y}\right)}^{1/Y},{\tilde{u}}_{ij}={\left({\displaystyle \prod _{y\in Y}{u}_y}\right)}^{1/Y}$$

(6)

where y represents the y-th expert. Subsequently, the centroid method is used to defuzzied the triangular fuzzy numbers:

$$r_{ij}={\displaystyle \frac{1}{6}}\left(l_{ij}+4m_{ij}+u_{ij}\right)$$

(7)

where $r_{ij}$ is the defuzzied judgment value, $r_{ij}\in R$ and R are the judgment matrices; ${\tilde{l}}_{ij}$, ${\tilde{m}}_{ij}$, and ${\tilde{u}}_{ij}$ are the lower limit, most likely value, and upper limit of the triangular fuzzy numbers, respectively. Based on the clear judgment matrix R obtained in Equation (7), the geometric mean method is used to calculate its weight vector:

$$w_i={{\left({\displaystyle \prod _{j\in J}{r}_{ij}}\right)}^{1/Y}/{\displaystyle \sum _{y\in Y}\left({\displaystyle \prod _{j\in J}{r}_{yj}}\right)}}^{1/Y}$$

(8)

where J is the total number of elements to be evaluated. To ensure the rationality of expert judgments, the consistency check of FAHP is evaluated using the consistency ratio CR [31], namely:

$$CR=CI/RI$$

(9)

where CI is the consistency index, and RI is the random consistency index. When $CR<0.1$, it is considered that the judgment matrix meets the consistency requirement.

4.2.2. Three-Level Risk Assessment Method Based on FAHP

In order to achieve quantitative assessment and ranking of project risks, a three-level FAHP risk evaluation system, including the goal layer, criteria layer, and scheme layer, is established from the perspective of the impact of risk consequences on construction quality, schedule, and safety.

(1)

Goal layer: Comprehensive risk of 500 kV substation engineering construction.

(2)

Criteria layer: Class A-Quality risk (affecting project quality, such as causing quality defects, rework, and rectifications); Class B-Schedule risk (affecting project progress, such as causing delays or postponed commissioning); Class C-Safety risk (affecting construction safety, such as potentially causing personal injury or equipment damage).

(3)

Scheme layer: Composed of key risk factors. From the constructed risk database, based on the probability of occurrence and degree of impact, four of the most representative risk items are selected for each category, as shown in

Table 2. Risk preset items at the scheme layer.

Label	Risk Name	Risk Causes and Impacts
A1	Insufficient design depth	Construction requires numerous changes, affecting project quality
A2	Confusion between buried pipes and the box	The pipe layout is chaotic, affecting both appearance and quality.
A3	Raw material quality issues	Poor quality construction materials, inadequate supervision and inspections, and substandard materials entering the site
A4	Finished product protection is inadequate	Edges and top surfaces are unprotected, susceptible to damage from subsequent construction impacts.
B1	Shortened the construction schedule to speed up work	-
B2	Delay in the supply of materials	The delayed supply of materials caused construction delays.
B3	Delay in the processing of preliminary procedures	The approval process for forestry and grassland projects is lengthy and highly uncertain.
B4	Cross-disciplinary job conflict	Multiple professional overlaps, resource conflicts, and idle work
C1	Main Transformer Hoisting Operations	Heavy individual unit, high lifting risk
C2	Live-line work	-
C3	Cold-region permafrost construction	Risks of concrete pouring and welding at low temperatures
C4	Risk of falling from height	Tower assembly and GIS installation, among other high-altitude tasks, carry high risks

.

In summary, the comprehensive risk score F for the construction of a 500 kV substation project can be calculated using Equation (10), that is:

$$F=w_{\mathrm{A}}{S}_{\mathrm{A}}+w_{\mathrm{B}}{S}_{\mathrm{B}}+w_{\mathrm{C}}{S}_{\mathrm{C}}$$

(10)

where $w_{\mathrm{A}}$, $w_{\mathrm{B}}$, and $w_{\mathrm{C}}$ are the weights of the three dimensions of quality, schedule, and safety in the criterion layer obtained by FAHP, and $S_{\mathrm{A}}$, $S_{\mathrm{B}}$, and $S_{\mathrm{C}}$ are the scores of each dimension. The calculation formula for the scores of each dimension is:

$$S_x={\displaystyle \sum \left(w_{X,i}\cdot P_{X,i}\cdot I_{X,i}\right)}$$

(11)

where $w_{X,i}$ is the weight of the i-th risk factor within dimension X (X = A, B, C), determined by the FAHP method in Section 3.2.1; $P_{X,i}$ is the probability of occurrence of the risk; $I_{X,i}$ is the impact level of the risk. $P_{X,i}$ and $I_{X,i}$ are also determined by expert scoring and calculated using the following Equation (12).

$$\left\{\begin{array}{l}{P}_{X,i}={\displaystyle \frac{1}{Y}}{\displaystyle \sum _{y\in Y}{P}_{y,X,i}}\\ I_{X,i}={\displaystyle \frac{1}{Y}}{\displaystyle \sum _{y\in Y}{I}_{y,X,i}}\end{array}\right.$$

(12)

5. Case Study

Taking a 500 kV substation construction project in Jilin Province as an example. The project includes the construction of 2 new main transformers with a capacity of 2 × 1000 MVA, 4 × 500 kV outgoing lines (two double-circuit lines), and 6 × 220 kV outgoing lines. The WBS method is adopted to decompose the 500 kV substation construction project process shown in Figure 1 into 147 three-level tasks. On this basis, the correctness and effectiveness of the proposed strategies are verified and analyzed from two aspects: engineering management optimization and engineering risk assessment. All computer modeling and simulations in this study were performed using MATLAB 2021b.

5.1. Analysis of Engineering Management Optimization

5.1.1. Analysis of Engineering Task Process Optimization Based on WBS-DSM

As can be seen from Figure 1, the main duration of the project is concentrated in the construction phase. Therefore, this section first takes the multi-disciplinary coordination issues in this phase as an example for analysis. The construction phase involves eight disciplines: civil engineering, primary electrical, secondary electrical, wiring, fire protection, intelligent auxiliary control, supervision, and design. Through surveys and investigations of the project team, design, and construction units, the frequency of information exchange between each discipline was statistically analyzed, and an 8 × 8 DSM for the construction phase was created, as shown in Figure 3. The elements in the matrix represent the strength of dependence between disciplines. Based on Figure 3, three strongly coupled discipline pairs can be identified: civil–primary electrical, civil–fire protection, and primary–secondary electrical.

(1) Civil–Primary Electrical ($c=0.85$)

These two disciplines have a strong coupling relationship during the foundation stage and need to closely coordinate during typical tasks such as grounding network installation and foundation construction.

(2) Civil–Fire Protection ($c=0.78$), Civil–Intelligent Auxiliary Control ($c=0.75$), and Fire–Intelligent Control ($c=0.68$)

These three disciplines show strong coupling relationships during the building construction phase. The installation paths of fire protection pipelines and auxiliary control cables need to be considered simultaneously during tasks such as pipe laying and pre-embedded boxes. Although the coupling strength between fire protection and intelligent control does not reach 0.7, it is still significantly higher than that of other disciplines.

(3) Primary–Secondary Electrical ($c=0.72$)

These two disciplines are highly coupled during the installation and commissioning of specialized electrical equipment. Frequent interaction and confirmation are required for the configuration of main transformers, protection devices, communication equipment, wiring, and joint commissioning plans.

Furthermore, based on the professional coupling intensity shown in Figure 3 and the optimization logic proposed in Section 3.2.2, the task process during the construction phase can be optimized according to the coupling intensity as follows:

(1)

Advance the intervention time of the fire protection and intelligent auxiliary control professions. Manufacturers of fire protection and intelligent auxiliary control professions are required to participate in the preliminary design stage, and jointly carry out the planning and design of pipe embedding paths and embedded parts in parallel with the civil engineering profession. Clarify the technical requirements and interface conditions of the fire protection and intelligent auxiliary control systems at the preliminary design review meeting; complete the pipe embedding scheme and embedded box layout drawing of the fire protection and intelligent auxiliary control systems before the design of civil engineering construction drawings.

(2)

Collaborative management of “interface-responsibility-delivery”. Establish a clear interface management mechanism for the identified strongly coupled professions. Taking civil engineering–electrical primary as an example, define interface nodes: grounding grid design confirmation (responsible party: design unit; delivery standard: grounding grid plan and grounding resistance calculation report), foundation dimension review (responsible party: construction unit; delivery standard: measured deviation < ±5 mm), and embedded part acceptance (responsible party: supervision unit; delivery standard: compliance with design drawings and construction specifications). By clarifying the responsible party, delivery standards, and acceptance conditions, quality problems and schedule delays caused by ambiguous interfaces are avoided.

(3)

Optimize task sequence and implement parallel strategies. Replan the construction operation sequence; for strongly coupled tasks, adopt synchronous design and joint review to promote them collaboratively. For tasks of weakly dependent professions ($c<0.3$), arrange parallel construction under the conditions of independent space and resources to shorten the length of the critical path. For example, the grounding grid laying and main transformer foundation construction tasks are spatially independent and weakly dependent, so parallel operation can be arranged; while the main transformer hoisting and equipment commissioning have strong dependence, and wiring testing shall be carried out after hoisting and positioning, which needs to be executed sequentially.

On this basis, according to the above-proposed engineering task process optimization logic, some projects are selected from 147 three-level WBS tasks to verify the effectiveness of the proposed WBS-DSM-based engineering task process optimization method. The original serial process and the optimized parallel process are shown in Figure 4a,b, respectively, and the construction period parameters of the selected tasks are set as shown in Appendix A 

Table A2. Task duration and safety time setting.

Task i	Task Name	Baseline Schedule	Traditional Construction Period ${\mathit{t}}_{\mathit{i}}^{\mathit{c}}$	Task i	Task Name	Baseline Schedule ${\mathit{t}}_{\mathit{i}}^{\mathit{z}}$	Traditional Construction Period ${\mathit{t}}_{\mathit{i}}^{\mathit{c}}$
1	Preliminary preparation	20	26	10	GIS Installation	45	55
2	Site leveling	30	38	11	Busbar Installation	29	38
3	Grounding grid installation	30	38	12	Mid-term Evaluation	15	19
4	Main Transformer Foundation	45	55	13	Electrical commissioning	40	50
5	Main Control Building	60	75	14	System Integration Testing	30	38
6	Introduction to GIS	35	45	15	Pre-acceptance Rectification	12	16
7	Cable trench	30	38	16	Completion Acceptance	10	13
8	Winter impact	60	69	17	Official Acceptance	8	10
9	Main Transformer Hoisting	45	38

. It can be seen from the figures that the overall project arranges tasks in the order of “civil engineering-electrical engineering-commissioning”, but the original serial process has obvious limitations, such as a long construction period and low resource utilization. In Figure 4b, it can be seen that the main control building construction and GIS foundation construction are parallel (coupling intensity $c=0.32$; parallel execution can be achieved by clarifying interfaces in advance); GIS installation and busbar installation are parallel (the two equipment are spatially independent, with coupling intensity). The optimized parallel process shortens the overall construction period from 677 days to 522 days, a reduction of 22.9%. In fact, since each task still retains safety time, the potential for construction period compression has not been fully released, and further engineering schedule management optimization based on CCPM is required. The optimized task process is shown in Figure 4c.

5.1.2. Analysis of Engineering Schedule Management Optimization Based on CCPM

On the basis of DSM collaborative optimization, the CCPM method is further adopted to optimize the construction schedule. According to the critical chain theory, part of the safety time of tasks is separated and concentrated to set up a project buffer, so as to improve the reliability and flexibility of the plan. It can be seen from Figure 4c that by removing part of the safety time from each task (50% is adopted in this paper), the task duration is compressed from the expected time to the most likely time, and the critical chain duration is shortened from 522 days to 471 days. The calculated project buffer is about 9 days, which is centrally set at the end of the critical chain (marked as the gray area in the figure) to respond to uncertainties during the construction process. The total duration of CCPM is 480 days, which is a further reduction of 8% compared with the DSM scheme. Through the combination of DSM and CCPM methods, systematic management and control from task dependency optimization to overall buffer management is effectively achieved.

Based on the proposed buffer consumption rate monitoring mechanism, the schematic diagram of full-cycle project schedule management and monitoring is shown in Figure 5. This diagram illustrates the entire progress process of the project from start to finish: when the actual consumption curve is below the ideal consumption line, the current progress is normal; when the consumption curve enters the yellow area, continuous attention is required; and when the consumption rate reaches the red area, emergency response measures should be taken to accelerate progress, such as increasing resource input or adjusting construction organization. It should be noted that the buffer consumption data shown in Figure 5 is generated through historical project experience and uncertainty simulation methods, and normal distribution noise is superimposed on the scheduled construction period to quantitatively characterize the uncertainties during the construction process. In practical engineering applications, data should be updated weekly based on the actual completion status to achieve dynamic schedule management and control.

5.2. Engineering Risk Assessment and Control Recommendations

5.2.1. Analysis of Engineering Risk Assessment Results Based on FAHP

For the 500 kV substation construction project, in this case, five experts from related industries (2 from construction management, 1 from a design unit, 1 from a construction unit, and 1 from an operation and maintenance unit) were invited to perform pairwise comparisons at the criteria level, using triangular fuzzy numbers to represent judgments. The expert evaluation scale is as follows: 0.5 indicates equal importance, 0.5–0.7 indicates slightly more important, 0.6–0.8 indicates obviously more important, 0.7–0.9 indicates significantly more important, and 0.8–1.0 indicates extremely important. This allows for the calculation of weights for both the criteria and alternative levels, as shown in Figure 6.

The consistency of the criteria-level fuzzy judgment matrix in Figure 6a was verified using de-fuzzified values. The calculated indices were CI = 0.009 and CR = 0.017. As the CR value is below the threshold of 0.1, the consistency of the experts’ judgments is considered satisfactory.

Using the geometric mean method to calculate weights and normalizing them, the weights are obtained as follows: quality risk (Class A) is 0.35, schedule risk (Class B) is 0.25, and safety risk (Class C) is 0.40. The same calculation method is applied to the alternative level, resulting in the comprehensive weight ranking of individual risk factors shown in Table 2 and illustrated in Figure 6b.

Based on the above weight results, the risk scores for each dimension and the overall score are calculated using Equations (10) and (11), as shown in Figure 7. From the figure, it can be seen that safety risk accounts for a significantly larger proportion compared to the other two types of risk, while quality risk and schedule risk are relatively lower. Therefore, targeted risk management measures should be implemented during the project execution process.

5.2.2. Engineering Risk Management Recommendations

This section combines risk scores with the comprehensive weight of different risks and proposes targeted risk control recommendations as follows:

(1)

The principle of reducing first, controlling later. The proposed risk control strategies include: designing for source avoidance, optimizing overall site layout, reserving space, selecting modular equipment, reducing individual unit weight, adopting standardized designs, and minimizing on-site changes; improving process technology through the use of new construction methods and advanced equipment (such as dual-crane lifting and intelligent monitoring systems); optimizing engineering measures by setting up protective facilities and warning zones, and equipping emergency devices and materials; optimizing management measures by developing special plans, enhancing staff training, implementing full-process monitoring, and enforcing strict acceptance standards; accepting risk through purchasing insurance and formulating emergency response plans.

(2)

Key risk control measures. Develop targeted measures for high-weight risks: For the risk control of main transformer hoisting, reserve hoisting space during the design phase, design permanent hoisting foundations, and select dual cranes of over 200 tons for construction preparation; monitor weather forecasts 72 h in advance, and halt operations if wind force exceeds level 3 during work; conduct full video monitoring with dedicated personnel in command; prepare emergency response teams, standby cranes, and purchase equipment insurance for emergencies.

(3)

Risk control measures for live-line crossings. Optimize the route during the design phase to avoid crossings as much as possible, select high-crossing schemes, and increase safety distances; during construction preparation, apply for a power outage in advance, and if a power outage is not possible, develop a special crossing plan; during work, set up crossing frames, install protective nets, assign dedicated personnel for monitoring, and measure safety distances; prepare emergency communication equipment and formulate an electric shock emergency plan.

6. Discussion

The integrated optimization strategy proposed in this study provides a systematic management framework for high-voltage substation construction, which is of great significance for promoting the digital transformation of power engineering construction management. Through multi-disciplinary coordination optimization and quantitative risk assessment, this approach effectively addresses the management challenges posed by expanding project scales and increasing technical requirements in the construction of new power systems, thereby providing theoretical support and practical guidance for the efficient construction of large-scale renewable energy grid integration projects under the “dual carbon” goals. The research findings are not only applicable to 500 kV substation projects but can also be extended to more complex power infrastructure construction scenarios, such as ultra-high voltage and smart substations, contributing to the overall improvement in engineering management capabilities in the power industry.

To further clarify the methodological advantages, a brief comparison with established techniques is presented. Unlike the Precedence Diagramming Method (PDM), which excels in linear sequential logic but struggles with iterative feedback loops, the WBS-DSM approach explicitly quantifies task coupling intensity to manage complex interdependencies. Furthermore, compared to the Multi-Domain Matrix (MDM), which offers comprehensive multi-view modeling but requires extensive data input, the proposed method provides a more streamlined and practical solution for efficiently identifying critical interface conflicts in construction management.

Compared to international practices that often emphasize contractual integration (e.g., Integrated Project Delivery) and full-lifecycle information sharing via Building Information Modeling (BIM), the construction of high-voltage substations in China faces unique challenges driven by rapid grid expansion and strict administrative timelines. While international approaches focus on collaborative contracts, the Chinese context requires a stronger emphasis on resolving complex inter-departmental coupling and resource conflicts under rigid schedules. Consequently, this study proposes a strategy integrating WBS-DSM for dependency decoupling and CCPM for schedule robustness, offering a solution specifically optimized for the high-intensity, multi-disciplinary coordination environment of China’s power infrastructure.

To adapt the proposed framework to other power engineering projects, three key adjustments are required: Reconstruct the WBS to align with the specific workflows of the target project; Recalibrate parameters, specifically adjusting CCPM buffer coefficients and FAHP weights based on project complexity; Update the risk database to include scenario-specific risk factors.

Although this study utilized historical statistical data from actual engineering projects for the case analysis, certain limitations remain due to the inherent complexity of large-scale power transmission and transformation projects. These projects are characterized by multi-departmental involvement and extended construction cycles. Consequently, while a risk list was established, it may not exhaustively cover every potential uncertainty encountered in practice. Furthermore, the quantitative analysis of the complex coupling relationships among these risk factors requires further investigation. Future research should explicitly incorporate external environmental factors—such as precipitation, equipment failure rates, and labor shortages—and aim to validate the proposed framework across broader contexts to enhance its generalizability.

In addition, future work will focus on integrating intelligent algorithms with data-driven technologies to construct a unified project database. The primary advantage of this approach lies in breaking down information silos between multi-disciplinary departments and transforming static historical records into dynamic data assets. This will enable real-time interaction between on-site monitoring and schedule optimization models, thereby significantly improving the precision and efficiency of engineering decision-making.

7. Conclusions

This study proposed a systematic optimization framework integrating WBS, DSM, CCPM, and FAHP to address the challenges of multi-disciplinary coordination and risk control in 500 kV substation construction. Based on the case study analysis, the following conclusions are drawn:

(1)

Schedule Optimization: The combined WBS-DSM and CCPM strategy effectively resolved the coupling conflicts among multi-disciplinary tasks. By converting subjective safety times into centralized buffers, the proposed method significantly shortened the construction cycle compared to traditional serial scheduling and enhanced the robustness of the schedule against uncertainties

(2)

Risk Assessment: The constructed “Quality-Schedule-Safety” three-dimensional assessment system realized the transition from qualitative estimation to quantitative evaluation. The results identified safety risks as the most critical dimension, providing a scientific basis for prioritizing management resources towards high-risk operations.

(3)

Practical Implication: The proposed data-driven framework successfully transforms experience-based management into objective decision support. It provides a replicable digital solution for high-voltage infrastructure projects, facilitating the efficient construction of the new power system.