Research on CBM of the Intelligent Substation SCADA System

Abstract

An equipment status management and maintenance platform of an intelligent substation monitoring and control system is built in the Tai-Tam substation of the Taipower company. The real-time operating status of the equipment, such as the server, supervisory control and data acquisition (SCADA) human–machine interface (HMI) software, switches, intelligent electronic devices (IEDs), merging units (MUs), as well as the entire SCADA system, are evaluated comprehensively. First, the status information of all equipment is collected, and the theory of relative deterioration degree (RDD) and fuzzy theory (FT) are applied to calculate the fuzzy evaluation matrix of the equipment influencing factors. Then, the subjective analytic hierarchy process (AHP) and the objective entropy method for weighting are combined to calculate the comprehensive weights of the equipment influencing factors. Finally, the result of the equipment status evaluation is obtained using the fuzzy comprehensive evaluation (FCE) method and is presented at the equipment status management and maintenance platform. Such equipment status evaluation results can be used by the inspection and maintenance personnel to determine the priority for equipment maintenance and repair. The result of this study may serve as a valuable reference to utility companies when making maintenance plans.

1. Introduction

Condition-based maintenance (CBM) is a preventive maintenance (PM) policy, which uses information and communication technologies to monitor equipment status and to inspect and test the degree of interior deterioration of equipment. Based on equipment reliability, and with the equipment operating status and other importance factors taken into consideration, CBM is more target-specific, rational, and scientific when making equipment inspection, maintenance, and repair decisions [1,2].

Maity et al. point out that time-based maintenance (TBM) may easily lead to over-maintenance or under-maintenance of electrical equipment. If equipment status information can be taken real-time and on-line by sensors and be provided to back-end platforms, data can be analyzed and actual equipment operating condition can be assessed. Thus, a maintenance plan based on the deterioration degree of individual equipment can be made, and proper maintenance can be achieved [3]. Unnecessary maintenance is one of the major causes of power grid failures. To avoid this from happening, Ohlen built a power system monitor and control platform for on-site measurement and on-line real-time monitoring in order to provide data needed by CBM. The goal is to achieve best balance between operation and maintenance based on field equipment status information [4]. The working environment, operation data, inspection and maintenance records, and conditions of internal parts of power transformers are included in a multistage hierarchical assessment index system. The relative deterioration degree (RDD) method is used to assess the degree of similarity between actual equipment operating status and fault condition, and the analytic hierarchy process (AHP) method and the fuzzy comprehensive evaluation (FCE) method are used to evaluate the health status of power transformers. The result may serve as the basis of preventive maintenance practices [5]. A circuit breaker (CB) maintenance research focused on reliability is proposed which integrates intelligent electronic devices (IEDs) in an intelligent maintenance model. IEDs are used to build a CB status monitoring system and to collect relevant maintenance information. The fuzzy set theory, the AHP method, and the Dempster–Shafer evidence theory are applied to analyze and evaluate CB status. In addition, important influencing factors of CBs are collected, and the Delphi method is applied to calculate the weights of the importance factors and to analyze and evaluate the importance of CBs. Finally, reliability-focused maintenance decision analysis is performed based on the result of CB status and importance evaluation [6]. The multistage FCE method is applied to evaluate the operating status of the protection system of a power grid. A multistage status index model is built based on equipment status data, actual system operating status, and the experience of operation personnel and experts. The output of the model is the comprehensive evaluation result. Finally, a maintenance strategy, including status evaluation result, maintenance grades, optimal maintenance time, and maintenance suggestions, is proposed and can be adopted by the inspection and maintenance personnel to perform CBM on power grid protection systems [7,8]. Qian et al. proposed a CBM approach for wind turbines based on long short-term memory (LSTM) algorithms to improve defect detection from supervisory control and data acquisition (SCADA). LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy for CBM [9]. Some research works presented the discrete Markov chain model as a simplified probabilistic model for damages in wind turbine blades. The classic Bayesian pre-posterior decision theory is applied for the decision-making of the CBM strategy [10,11,12]. Tian et al. presented a transformer assessing model for CBM by employing a Cauchy membership function for fuzzy grade division, and then a fuzzy evidence fusion method was represented to handle the fuzzy evidences fusion processes. This approach can recommend the condition-based maintenance of power transformer [13].

An intelligent substation is an important link in the realization of a smart grid and is responsible for electric power delivery, power dispatch, power flow, and equipment monitoring and control. As the intelligent substation evolves along with the announcement of IEC 61,850 communication standards, more equipment can be easily integrated into power monitoring and control systems, and innovative and intelligent CBM strategies can be proposed for power equipment maintenance and replacement. There are many studies on equipment maintenance and management on the substation primary side, but few on the reliability evaluation of intelligent substation SCADA systems. The goal of this study is to improve the existing maintenance methods of substation SCADA system equipment, and to propose a condition-based equipment inspection and maintenance strategy. In this study, substation equipment status information is collected following the IEC 61,850 communication protocol, equipment operating status is analyzed through status inspection and testing platform in real time, and a SCADA system maintenance and management platform applying intelligent inspection and maintenance strategy is realized. The result of this study may serve as a valuable reference to the inspection and maintenance personnel and the operation and management personnel of a power utility when making a maintenance plan, and may help to improve the stability and reliability of the utility power supply.

The objective of this paper is to propose the CBM evaluation model of a smart substation control system through a SCADA platform where the real-time health condition of substation equipment can be monitored. The result of the evaluation can be categorized into four degrees: Good Condition, Attention Required, Critical Condition, as well as Immediate Inspection and Maintenance. The maintenance staff can analyze the equipment condition and make the maintenance plan in priority order.

In this paper, an equipment status evaluation method is proposed, and a reliability evaluation model of an integrated intelligent substation SCADA system consisting of a server, SCADA human–machine interface (HMI) software, switches, IEDs, and merging units (MUs) is built in this study. First, the relative deterioration degree (RDD) method is applied to process the relevant parameters of performance-influencing factors. Then the comprehensive weighting method is applied to analyze the degree of importance of equipment. Finally, the fuzzy comprehensive evaluation (FCE) method is applied to perform comprehensive equipment status evaluation. The flowchart of the proposal is shown in Figure 1. The evaluation result serves as a useful guide for system maintenance and management personnel, equipment manufacturers, and power equipment repair crews when maintenance is made. Eventually, an intelligent substation SCADA system example platform is built in the Tai-Tam substation of the Taipower company. Two operation cases of this platform have been applied for validation.

2. Architecture of the Proposed SCADA System of an Intelligent Substation

The example configuration of the proposed intelligent substation SCADA system is shown in Figure 2. The SCADA system of the control center is at the station level, which integrates the important information of all equipment, issues controlling commands, and performs remote monitoring. The bay level is composed mainly of IEDs, which receive the power parameters and status information of the equipment, perform protection functions such as overcurrent protection and voltage differential protection, control the tripping and reclosing of CBs, and upload all status messages and event records to the SCADA system. The merging units (MUs) are the core devices at the process level, which convert analog voltage/current signals to digital signals and transmit the signals to bay level equipment through optical fibers. This study focuses on the status evaluation of the server, SCADA human–machine interface (HMI) software, switches, IEDs, and MUs.

2.1. Server

The server is the hardware equipment of the intelligent substation SCADA system which is capable of running various application software. The server manages the substation automation system efficiently and ensures the power quality provided by the power grid is high. The maintenance and management personnel can monitor the substation operating status, look up both real-time and historical alarms, and print out event records on-line merely through the server of the SCADA system.

2.2. SCADA HMI Software

The SCADA HMI software is a key element in the realization of intelligent substation automatic control and logical analysis. The main functions of the SCADA HMI software include equipment data collecting, system status monitoring and testing, power flow calculation, short circuit analysis, power grid energy dispatch and management, sending control commands, and security control. Statistical data show that 65% of computer system failures are caused by HMI software malfunction; HMI software abnormality may lead to system crash and even casualties and great economic loss [14].

2.3. Switch

Switches are very important in communication and equipment messages exchange. There are many real-time messages inside the substation, such as the manufacturing message specification (MMS) message from IEDs to the SCADA system, generic object-oriented substation events (GOOSE) messages among IEDs, sampling values (SVs) of power parameter messages from MUs to IEDs, and the SNTP/IRIG-B/IEEE 1588 time synchronizing messages for equipment timing accuracy adjustment. Switches can also implement virtual local area network (VLAN), reduce network traffic jam caused by a large amount of data flowing through one single path, increase performance efficiency, and improve information security [15]. A switch must communicate and transmit data in real time and with accuracy, be resistant to electromagnetic interference (EMI), avoid long delay time which may expose the system in dangerous conditions, tolerate ambient temperature variation, and not malfunction or fail due to high temperature.

2.4. IEDs

IEDs are core protection and control devices in a power system. Parameters are set and protection logics are programmed differently depending on the needs and operating conditions of different systems. IEDs receive various electrical data from potential transformers (PTs), current transformers (CTs), MUs, and other equipment. IEDs send dispatch control commands and transmit power operating data to the SCADA system at the station level, or other related equipment through network communication, thus effectively reducing power outages and harm caused by power system abnormal operation.

2.5. MUs

According to the IEC 60044-7 and IEC 60044-8 standards published by the International Electrotechnical Commission (IEC), an MU may be defined preliminarily as “an electronic transformer which replaces a conventional electromagnetic equipment, converts analog voltage/current signals to digital signals to avoid EMI to conventional analog SVs and transmit the digital signals to bay level equipment through optical fibers, thus improves the accuracies of power parameters effectively” [16].

3. Fuzzy Comprehensive Evaluation Method for Equipment Maintenance Strategy

3.1. Establishment of the Evaluation Factors of an Intelligent Substation SCADA System

When evaluating the status of an intelligent substation SCADA system, equipment influencing the normal operation of the SCADA system must be inspected and tested. Because equipment may have many attributes and characteristics, the SCADA system is inevitably affected by many uncertain factors and by many different types of equipment [7].

An intelligent substation SCADA system consists of a lot of equipment, and each performance index of individual equipment reflects the performance status of individual equipment and the system as a whole. Server (u₁), SCADA HMI software (u₂), Switch (u₃), IED (u₄), and MU (u₅) are analyzed in this study. Equipment availability (u₁₁), communication port failure rate (u₁₂), CPU usage rate (u₁₃), memory usage rate (u₁₄), and transmission load rate (u₁₅) are included in the server reliability evaluation model [17,18]. Software availability (u₂₁), data receiving rate (u₂₂), data access time (u₂₃), and failure repair time (u₂₄) are included in the SCADA HMI software reliability evaluation model [19]. Equipment availability (u₃₁), communication port failure rate (u₃₂), packet lost rate (u₃₃), transmission load rate (u₃₄), and CPU usage rate (u₃₅) are included in the switch reliability evaluation model [16]. Equipment availability (u₄₁), operation failure rate (u₄₂), communication failure rate (u₄₃), operating environment (u₄₄), and CPU usage rate (u₄₅) are included in the IED reliability evaluation model [20]. Equipment availability (u₅₁), SV packet lost rate (u₅₂), SV code error rate (u₅₃), transmission time (u₅₄), time synchronization accuracy (u₅₅), and CPU usage rate (u₅₆) are included in the MU reliability evaluation model [20].

Different comments are needed to describe different equipment status, and five ratings of influencing factor status are chosen according to the equipment operating status: Excellent, Good, Average, Poor, and Worst. The corresponding numerical values are 100, 75, 50, 25, and 0, respectively. The comment set V of intelligent substation SCADA system is shown below.

V = { Excellent, Good, Average, Poor, Worst } = { 100, 75, 50, 25, 0 }

3.2. Establishment of the Equipment Status Evaluation Matrix

The difference between current equipment status and its failure status is represented by the numerical value of the relative deterioration degree (RDD), with values ranging from 0 to 1. Two types of indices are defined in Equations (1) and (2), and either may be chosen depending on the index characteristic [21]. The larger-is-better type of index may be applied to equipment availability with values closer to 1, implying less degree of deterioration. The less-is-better type of index may be applied to equipment failure rate with values closer to 1, implying a higher degree of deterioration and more urgency for maintenance.

The larger-is-better type:

$$x_i=\frac{X_{mxu}-X_{min}}{X_{int}-X_{min}}$$

(1)

The less-is-better type:

$$x_i=\frac{X_{mxu}-X_{int}}{X_{max}-X_{int}}$$

(2)

where x_i is the RDD of the i-th evaluation factor, X_int is the initial test value of the factor, X_min is the minimum limit of the factor, X_max is the maximum limit of the factor, and X_mxu is the measured value of the factor.

The equipment influencing factors of the intelligent substation SCADA system are evaluated by trapezoidal and triangular membership functions to determine their degrees of membership in the evaluation comment set V, as shown in Figure 3. The membership functions of the linguistic variables “Excellent”, “Good”, “Average”, “Poor”, and “Worst” are defined in Equations (3)–(7), with the parameters defined and evenly distributed as α = 0.1, β = 0.3, γ = 0.5, δ = 0.7, and ε = 0.9, respectively.

$$m_1(x)=\{\begin{array}{cc}1,& x\le \alpha \\ \frac{(\beta -x)}{(\beta -\alpha )},& \alpha <x\le \beta \\ 0,& \mathrm{other}\end{array}$$

(3)

$$m_2(x)=\{\begin{array}{cc}\frac{(x-\alpha )}{(\beta -\alpha )},& \alpha <x\le \beta \\ \frac{(\gamma -x)}{(\gamma -\beta )},& \beta <x\le \gamma \\ 0,& \mathrm{other}\end{array}$$

(4)

$$m_3(x)=\{\begin{array}{cc}\frac{(x-\beta )}{(\gamma -\beta )},& \beta <x\le \gamma \\ \frac{(\delta -x)}{(\delta -\gamma )},& \gamma <x\le \delta \\ 0,& \mathrm{other}\end{array}$$

(5)

$$m_4(x)=\{\begin{array}{cc}\frac{(x-\gamma )}{(\delta -\gamma )},& \gamma <x\le \delta \\ \frac{(\epsilon -x)}{(\epsilon -\delta )},& \delta <x\le \epsilon \\ 0,& \mathrm{other}\end{array}$$

(6)

$$m_5(x)=\{\begin{array}{cc}\frac{(x-\delta )}{(\epsilon -\delta )},& \delta <x\le \epsilon \\ 1,& \epsilon <x\\ 0,& \mathrm{other}\end{array}$$

(7)

where m_k(x) is the membership function of the k-th status comment, and x is the RDD of the evaluation factor.

The evaluation results of the evaluation factors are put together to form the fuzzy evaluation matrix R, as shown in Equation (8):

$$R=\left(\begin{array}{ccc}{r}_{11}& \dots & r_{1j}\\ ⋮& \ddots & ⋮\\ r_{i1}& \cdots & r_{ij}\end{array}\right)i=1,2,\cdot \cdot \cdot ,m;j=1,2,\cdot \cdot \cdot ,n$$

(8)

where i is the number of evaluation factors, j is the number of status comments, and r_ij is the membership degree of the j-th comment of the i-th evaluation factor.

3.3. Calculation of Equipment Weights

The importance of individual equipment in an intelligent substation SCADA system is different depending on its functionality. Therefore, it is scientifically crucial in status evaluation to assign proper weight to equipment according to its degree of importance. Subjective weighting relies heavily on human thinking pattern and is not very scientific, while objective weighting depends on parameter variation. A comprehensive weighting method is proposed in this study, which combines the subjective analytic hierarchy process (AHP) method and the objective entropy weighting method. Because the proposed comprehensive weighting method inherits the field experiences of experts and is in compliance with parameter authenticity, it can be applied to evaluate equipment status effectively.

3.3.1. Calculation of the Subjective Weights

The AHP method resolves a complex problem to a configuration of concise factor levels. Evaluation tables are formed to assess the importance degree of each factor and to serve as reference during the weighting process [22,23]. Researchers, manufacturers, and utility personnel are consulted on the importance degrees of the equipment and influencing factors of the intelligent substation SCADA system. The geometric means of their opinions are calculated to obtain the subjective weights. The steps of calculations are as follows.

Step 1: Construct a hierarchy structure model: Group the factors in the problem according to their interrelationship into three layers—ultimate target layer, evaluation item layer, and index layer. Sublayers may be formed to simplify the computation if there are too many factors in a layer.

Step 2: Construct a comparison judgement matrix: The judgement matrix A is formed by pairwise comparisons between factors of the same level to evaluate their relative degree of importance, as shown in Equation (9). The definitions and explanations of the evaluation scales are shown in

Table 1. The definition and explanation of the evaluation scales.

Evaluation Scale	Definition	Explanation
1	Equally important	The two factors are equally important.
3	Slightly more important	One factor is only slightly more important than the other.
5	Significantly more important	One factor is significantly more important than the other.
7	Highly more important	One factor is highly more important than the other.
9	Absolutely more important	One factor is absolutely more important than the other.
2,4,6,8	In-between scales	The degree of relative importance lies between the two adjacent scales.

.

$$A=\left(\begin{array}{ccc}{a}_{11}& \dots & a_{1j}\\ ⋮& \ddots & ⋮\\ a_{i1}& \cdots & a_{ij}\end{array}\right) i=1,2,\cdot \cdot \cdot ,m; j=1,2,\cdot \cdot \cdot ,n$$

(9)

Step 3: Calculate the weights: After the judgement matrix A is derived from the opinions of experts, numerical analysis is applied to calculate the maximum eigenvalue and its corresponding eigenvector, as shown in Equation (10). The relative importance or weight distribution of the factors may be sorted by analyzing the eigenvector. The relationship between the eigenvector and the weight is shown in Equation (11).

$$A\cdot E={\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{a}_{ij}}\cdot e_i}={\lambda }_{\max }\cdot E$$

(10)

$$W_a=\frac{E}{{\displaystyle \sum _{i=1}^m{e}_i}}$$

(11)

where W_a is the subjective weight, a_ij is an element of the judgment matrix A, λ_max is the maximum eigenvalue of A, and e_i is an element of eigenvector E corresponding to λ_max.

The purpose of a consistancy test is to check if the respondents’ answers to the questionnaire comply with transitivity, which is the basis to verify the validity of the questionnaire to avoid the wrong decision of evaluation. The consistanct test is the important factor to verify the accuracy of the subjective weighting.

The validity of the questionnaire is based on the Consistency Index (CI), as shown in Equation (12). The tolerance value is CI ≤ 0.1. In addition, to further test whether the hierarchical structure of the judgment matrix complies with the consistency standard, the consistency ratio (CR) is tested. In Equation (13), the tolerance value CR ≤ 0.1. If CI and CR fail the test, the steps of analytic hierarchy process must be re-executed.

$$CI=\frac{{\lambda }_{\max }-n}{n-1}$$

(12)

$$CR=\frac{CI}{RI}$$

(13)

where

• CI: Consistency Index (CI) of judgement matrix
• RI: Random Index (RI) of judgement matrix
• n: Matrix order

The random index value (RI) increases as the matrix order increases. For the 1–10 order pairing matrix, the RI values are shown in

Table 2. Order of AHP method and relative random index values.

Order (n)	1	2	3	4	5	6	7	8	9	10
RI	0.00	0.00	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49

.

If CR ≤ 0.1, the calculated weight value can be accepted. Otherwise, if the CR value is larger, the judgment matrix filled in by the respondent may be inconsistent due to the complexity of the problem and the diversity of subjective cognition.

3.3.2. Calculation of the Objective Weights

The concept of entropy is employed by the entropy weighting method in information theory to assess the degree of decision information the value of a parameter of an evaluation item can carry when a target is evaluated, and the relative weight of an index is determined based on the variation or degree of difference of a parameter. In general, an evaluation factor with higher entropy is assigned a less weight. The steps of the calculation are as follows [24].

Step 1: Standardize the parameters: Parameter values of different orders of magnitude of evaluation items are converted to relative quantities by the standardization rule shown in Equation (14). A standardization matrix Z = [ z_ij ]_m×n can be built for a system with m evaluation index data and n semantic variables.

$$z_{ij}=\frac{r_{ij}-r_{\min }}{r_{\max }-r_{\min }}$$

(14)

where r_ij is the original value of membership degree, r_max is the maximum value and r_min is the minimum value of the evaluation factor, respectively.

Step 2: Calculate the message entropy: By the definition of message entropy, the standardization matrix Z is used to calculate the value of entropy H_i of the evaluation factors, as shown in Equations (15) and (16):

$$H_i=-\frac{1}{\ln n}({\displaystyle \sum _{j=1}^n{f}_{ij}\cdot \ln f_{ij}})i=1,2,\cdot \cdot \cdot ,m$$

(15)

$$f_{ij}=\frac{z_{ij}+1}{{\displaystyle \sum _{j=1}^n(z_{ij}+1)}}$$

(16)

where H_i is the entropy of the i-th evaluation factor, z_ij is the standardized index of the j-th semantic variable of the i-th evaluation factor, m is the number of the evaluation factors, and n is the number of the semantic variables.

Step 3: Calculate the weights: After the values of the message entropies are obtained as described above, the degrees of difference of all the evaluation factors are known and the corresponding weights are calculated according to the entropy values, as shown in Equation (17):

$$W_e=\frac{1-H_i}{m-{\displaystyle \sum _{i=1}^m{H}_i}}$$

(17)

where W_e is the objective weight.

3.3.3. Calculation of the Comprehensive Weights

The subjective weights of the AHP method and the objective weights of the entropy weighting method are integrated in the comprehensive weighting method. The comprehensive weighting method is applied in this study to obtain the equipment weight. The mathematical model of the comprehensive weighting method is shown in Equation (18):

$$w_i=\frac{W_{ai}\cdot W_{ei}}{{\displaystyle \sum _{i=1}^m{W}_{ai}\cdot W_{ei}}}$$

(18)

where w_i, W_ai, and W_ei are the comprehensive weight, subjective weight, and objective weight of the i-th evaluation factor, respectively.

3.4. Establishment of the Equipment FCE Matrix

Te fuzzy linear transform principle and the maximum membership principle are applied in the FCE method. The relationships among the evaluation problem and the relevant factors are considered, and rational comprehensive evaluations are made [7,25]. The steps of evaluation are as follows.

Only one single value of equipment evaluation with equal importance degree can be obtained by the fuzzy evaluation matrix. To effectively improve the accuracy of the evaluation matrix, the concept of weight is introduced into the evaluation matrix, and the mathematical representation of the FCE matrix is shown in Equation (19):

$$B_i=w_i\cdot R_i=\left[b_{i1},b_{i2},b_{i3},\cdots ,b_{in}\right] i=1,2,\cdot \cdot \cdot ,m$$

(19)

where b_ij is the weighted membership degree of the j-th comment of the i-th evaluation factor.

The FCE method is applied to integrate the evaluation matrix of each factor, assign the weight of relative importance, and calculate the overall evaluation result of each factor on the final evaluation target. The mathematical representation is shown in Equation (20):

$$S=w\cdot B^T=w\cdot {[B_1,B_2,\cdot \cdot \cdot ,B_n]}^T$$

(20)

where S is the membership degree of the n evaluation factors.

3.5. Numerical Calculations of the Evaluation

The result of the FCE matrix calculation is the membership degrees of evaluation ratings of the evaluation target. Because the analyzing personnel cannot determine the real-time status of the evaluation target quickly enough, a numerical value is assigned to each evaluation rating in order to simplify the analytical evaluation result. The assessment result of the FCE matrix, Ti and T, are shown in Equations (21) and (22), respectively. The equipment status ratings and the corresponding numerical evaluation ranges are summarized in

Table 3. Equipment status ratings and the corresponding numerical evaluation ranges.

Numerical Evaluation	Equipment Status
90~100	In Good Condition
80~90	Attention Required
70~80	In Critical Condition
0~70	Immediate Inspection and Maintenance Required

.

$$T_i=B_i\cdot v^T=\left[b_{i1},b_{i2},\cdots ,b_{in}\right]{\left[100,75,50,25,0\right]}^T$$

(21)

$$T=S\cdot v^T=\left[S_1,S_2,\cdots ,S_n\right]{\left[100,75,50,25,0\right]}^T$$

(22)

4. Case Study

An intelligent substation SCADA system example platform is built in the Tai-Tam substation of the Taipower company. The hardware configuration of this platform is shown in Figure 4. Two operation cases of this platform have been explored and analyzed to determine the priority for equipment maintenance and repair. For case 1, the actual measured values of equipment influencing factors and the corresponding fuzzified RDDs are shown in

Table 4. Server status data and status evaluation.

Influencing Factor	Index Type	Measured Value	RDD/Status Membership
u11 Equipment availability	Larger-is-better	0.9664	0.8188/Excellent
u12 Communication port failure rate (%)	Less-is-better	0	0/Excellent
u13 CPU usage rate (%)	Less-is-better	32	0/Excellent
u14 Memory usage rate (%)	Less-is-better	63	0.0750/Excellent
u15 Transmission load rate (%)	Less-is-better	11	0/Excellent

,

Table 5. SCADA human–machine interface (HMI) software status data and status evaluation.

Influencing Factor	Index Type	Measured Value	RDD/Status Membership
u21 Software availability	Larger-is-better	0.9998	0.9990/Excellent
u22 Data receiving rate (%)	Larger-is-better	100	1.0000/Excellent
u23 Data access time (s)	Less-is-better	10	0.1667/Excellent
u24 Failure repair time (hr)	Less-is-better	24	0/Excellent

,

Table 6. Switches status data.

Influencing Factor	Measured Values
	A	B	C	D	E	F
u31 Equipment availability	0.9498	0.9872	0.9901	0.9901	0.9872	0.9872
u32 Communication port failure rate (%)	0	0	0	0	0	0
u33 Packet lost rate (%)	0	0	0	0	0	0
u34 CPU usage rate (%)	18	18	24	23	9	9
u35 Transmission load rate (%)	33	21	26	19	16	15
Influencing Factor	RDD/Status Membership
	A	B	C	D	E	F
u31 Equipment availability	0.5845/Excellent	0.8941/Excellent	0.8955/ Excellent	0.8955/Excellent	0.8941/Excellent	0.8941/Excellent
u32 Communication port failure rate (%)	0/Excellent	0/Excellent	0/Excellent	0/Excellent	0/ Excellent	0/Excellent
u33 Packet lost rate (%)	0/Excellent	0/Excellent	0/Excellent	0/Excellent	0/Excellent	0/Excellent
u34 CPU usage rate (%)	0.1368/ Excellent	0.1368/Excellent	0.2/ Excellent	0.1895/ Excellent	0.0421/ Excellent	0.0421/ Excellent
u35 Transmission load rate (%)	0/Excellent	0/Excellent	0/Excellent	0/Excellent	0/Excellent	0/Excellent

A: PT-G7828_A, B: PT-G7828_B, C: PT-G503_A, D: PT-G503_B, E: PT-7728_A, F: PT-7728_B.

,

Table 7. Intelligent electronic device (IED) status data and status evaluation.

Influencing Factor	Index Type	Measured Value		RDD/Status Membership
		A	B	A	B
u41 Equipment availability	Larger-is-better	0.9950	0.9950	0.8977/Excellent	0.8977/Excellent
u42 Operation failure rate (%)	Less-is-better	0	0	0/Excellent	0/Excellent
u43 Communication failure rate (%)	Less-is-better	0	0	0/Excellent	0/Excellent
u44 Operating environment (°C)	Less-is-better	27	27	0/Excellent	0/Excellent
u45 CPU usage rate (%)	Less-is-better	21	19	0.1684/Excellent	0.1474/Excellent

and

Table 8. Merging unit (MU) status data and status evaluation.

Influencing Factor	Index Type	Measured Value	RDD/Status Membership
u51 Equipment availability	Larger-is-better	0.9967	0.8985/Excellent
u52 SV packet lost rate (%)	Less-is-better	0	0/Excellent
u53 SV code error rate (%)	Less-is-better	0	0/Excellent
u54 Transmission time (μs)	Less-is-better	1	0/Excellent
u55 Time synchronization accuracy (μs)	Less-is-better	0.1	0.1000/Excellent
u56 CPU usage rate (%)	Less-is-better	20	0.1000/Excellent

. The membership degrees of the equipment influencing factors are shown in

Table 9. Membership degrees of server influencing factors (R_u1).

Index	Membership Degree
	R1	R2	R3	R4	R5
u11	0.5938	0.4062	0	0	0
u12	1.0000	0	0	0	0
u13	1.0000	0	0	0	0
u14	1.0000	0	0	0	0
u15	1.0000	0	0	0	0

,

Table 10. Membership degrees of SCADA HMI software influencing factors (R_u2).

Index	Membership Degree
	R1	R2	R3	R4	R5
u21	1.0000	0	0	0	0
u22	1.0000	0	0	0	0
u23	0.6667	0.3333	0	0	0
u24	1.0000	0	0	0	0

,

Table 11. Membership degrees of switch influencing factors (R_u3).

Index	Membership Degree
	R1	R2	R3	R4	R5
u311	0.9706	0.0294	0	0	0
u312	1.0000	0	0	0	0
u313	1.0000	0	0	0	0
u314	0.8158	0.1842	0	0	0
u315	1.0000	0	0	0	0
u321	0.9706	0.0294	0	0	0
u322	1.0000	0	0	0	0
u323	1.0000	0	0	0	0
u324	0.8158	0.1842	0	0	0
u325	1.0000	0	0	0	0
u331	0.9774	0.0226	0	0	0
u332	1.0000	0	0	0	0
u333	1.0000	0	0	0	0
u334	0.5000	0.500	0	0	0
u335	1.0000	0	0	0	0
u341	0.9774	0.0226	0	0	0
u342	1.0000	0	0	0	0
u343	1.0000	0	0	0	0
u344	0.5526	0.4474	0	0	0
u345	1.0000	0	0	0	0
u351	0	0.9319	0.0681	0	0
u352	1.0000	0	0	0	0
u353	1.0000	0	0	0	0
u354	1.0000	0	0	0	0
u355	1.0000	0	0	0	0
u361	0.9706	0.0294	0	0	0
u362	1.0000	0	0	0	0
u363	1.0000	0	0	0	0
u364	1.0000	0	0	0	0
u365	1.0000	0	0	0	0

,

Table 12. Membership degrees of IED influencing factors (R_u4).

Index	Membership Degree
	R1	R2	R3	R4	R5
u411	0.9887	0.1113	0	0	0
u412	1.0000	0	0	0	0
u413	1.0000	0	0	0	0
u414	1.0000	0	0	0	0
u415	0.6579	0.3421	0	0	0
u421	0.9887	0.1113	0	0	0
u422	1.0000	0	0	0	0
u423	1.0000	0	0	0	0
u424	1.0000	0	0	0	0
u425	0.7632	0.2368	0	0	0

and

Table 13. Membership degrees of MU influencing factors (R_u5).

Index	Membership Degree
	R1	R2	R3	R4	R5
u51	0.9925	0.0075	0	0	0
u52	1.0000	0	0	0	0
u53	1.0000	0	0	0	0
u54	1.0000	0	0	0	0
u55	1.0000	0	0	0	0
u56	1.0000	0	0	0	0

.

The analytic hierarchy process (AHP) method is applied in the intelligent substation SCADA system analysis to calculate the equipment subjective weights, as shown in

Table 14. The subjective weights of the intelligent substation SCADA system influencing factors.

Influencing Factor	u1	u2	u3	u4	u5	Subjective Weight (w)
u1	1.0000	2.8914	2.4721	1.4612	3.1468	0.3559
u2	0.3459	1.0000	0.5109	0.4391	1.3457	0.1116
u3	0.4045	1.9573	1.0000	0.6371	2.5196	0.1858
u4	0.6844	2.2774	1.5696	1.0000	2.8143	0.2574
u5	0.3178	0.7431	0.3969	0.3553	1.0000	0.0893

. The maximum eigenvalue and the corresponding eigenvector are ${\mathsf{\lambda }}_{\max }$ = 5.0438 and E = [0.7149 0.2241 0.3732 0.5170 0.1794], respectively. The consistency index and the consistency ratio are CI = 0.0110, n = 5, RI = 1.12, and CR = 0.0098, respectively. Since CR ≦ 0.1, the weights are acceptable.

The subjective, objective, and comprehensive weights of the server status evaluation are shown in

Table 15. The weights of the server influencing factors.

Influencing Factor	u11	u12	u13	u14	u15	Subjective Weight	Index Entropy		Objective Weight	Comprehensive Weight (w1)
u11	1.0000	0.6177	1.4225	2.5189	1.2244	0.2240	h11	0.4197	0.1267	0.1435
u12	1.6189	1.0000	1.6861	4.0145	1.6329	0.3262	h12	0	0.2183	0.3600
u13	0.7030	0.5931	1.0000	2.1227	1.2139	0.1863	h13	0	0.2183	0.2056
u14	0.3970	0.2491	0.4711	1.0000	0.4764	0.0854	h14	0	0.2183	0.0943
u15	0.8167	0.6124	0.8238	2.0992	1.0000	0.1781	h15	0	0.2183	0.1966

. The AHP method is applied and the maximum eigenvalue and the corresponding eigenvector are ${\mathsf{\lambda }}_{\max }$ = 5.0164 and E = [0.2240 0.3262 0.1863 0.0854 0.1781], respectively. The consistency index and the consistency ratio are CI = 0.0041, n = 5, RI = 1.12, and CR = 0.0037, respectively. Since CR ≦ 0.1, the weights are acceptable.

The subjective, objective, and comprehensive weights of the SCADA HMI software status evaluation are shown in

Table 16. The weights of the SCADA HMI software influencing factors.

Influencing Factor	u21	u22	u23	u24	Subjective Weight	Index Entropy		Objective Weight	Comprehensive Weight (w2)
u21	1.0000	2.4662	3.5569	1.7100	0.4435	h21	0	0.2774	0.4664
u22	0.4055	1.0000	2.4662	1.4422	0.2453	h22	0	0.2774	0.2580
u23	0.2811	0.4055	1.0000	0.8434	0.1240	h23	0.3955	0.1677	0.0788
u24	0.5848	0.6934	1.1857	1.0000	0.1872	h24	0	0.2774	0.1969

. The AHP method is applied and the maximum eigenvalue and the corresponding eigenvector are ${\mathsf{\lambda }}_{\max }$ = 4.0803 and E = [0.8001 0.4425 0.2237 0.3376], respectively. The consistency index and the consistency ratio are CI = 0.0268, n = 4, RI = 0.9, and CR = 0.0297, respectively. Since CR ≦ 0.1, the weights are acceptable.

The subjective, objective, and comprehensive weights of the switch status evaluation are shown in

Table 17. The subjective weights of the switch influencing factors.

Influencing Factor	u31	u32	u33	u34	u35	Subjective Weight
u31	1.0000	0.6177	0.7003	2.5189	1.6166	0.2020
u32	1.6189	1.0000	1.4763	2.8257	3.9857	0.3489
u33	1.4280	0.6774	1.0000	2.1227	1.8372	0.2342
u34	0.3970	0.3539	0.4711	1.0000	0.8181	0.1006
u35	0.6186	0.2509	0.5443	1.2224	1.0000	0.1143

,

Table 18. The objective and comprehensive weights of the influencing factors of Switch A and Switch B.

Influencing Factor	Switch A				Switch B
	Index Entropy		Objective Weight	Comprehensive Weight (w31)	Index Entropy		Objective Weight	Comprehensive Weight (w32)
u31	h311	0.0825	0.1986	0.1944	h321	0.0825	0.1986	0.1944
u32	h312	0	0.2164	0.3659	h322	0	0.2164	0.3659
u33	h313	0	0.2164	0.2456	h323	0	0.2164	0.2456
u34	h314	0.2968	0.1522	0.0742	h324	0.2968	0.1522	0.0742
u35	h315	0	0.2164	0.1199	h325	0	0.2164	0.1199

,

Table 19. The objective and comprehensive weights of the influencing factors of Switch C and Switch D.

Influencing Factor	Switch C				Switch D
	Index Entropy		Objective Weight	Comprehensive Weight (w33)	Index Entropy		Objective Weight	Comprehensive Weight (w34)
u31	h331	0.0672	0.2072	0.2005	h341	0.0672	0.2070	0.1997
u32	h332	0	0.2210	0.3695	h342	0	0.2219	0.3698
u33	h333	0	0.2210	0.2480	h343	0	0.2219	0.2482
u34	h334	0.4307	0.1265	0.0610	h344	0.4272	0.1271	0.0611
u35	h335	0	0.2210	0.1210	h345	0	0.2219	0.1212

and

Table 20. The objective and comprehensive weights of the influencing factors of Switch E and Switch F.

Influencing Factor	Switch E				Switch F
	Index Entropy		Objective Weight	Comprehensive Weight (w35)	Index Entropy		Objective Weight	Comprehensive Weight (w36)
u31	h351	0.1546	0.1745	0.1763	h361	0.0825	0.1866	0.1855
u32	h352	0	0.2064	0.3601	h362	0	0.2034	0.3548
u33	h353	0	0.2064	0.2417	h363	0	0.2034	0.2382
u34	h354	0	0.2064	0.1038	h364	0	0.2034	0.1023
u35	h355	0	0.2064	0.1180	h365	0	0.2034	0.1162

. The AHP method is applied and the maximum eigenvalue and the corresponding eigenvector are ${\mathsf{\lambda }}_{\max }$ = 5.0494 and E = [0.4118 0.7113 0.4775 0.2052 0.2331], respectively. The consistency index and the consistency ratio are CI = 0.0124, n = 5, RI = 1.12, and CR = 0.0110, respectively. Since CR ≦ 0.1, the weights are acceptable.

The subjective, objective, and comprehensive weights of the IED status evaluation are shown in

Table 21. The subjective weights of IED influencing factors.

Influencing Factor	u41	u42	u43	u44	u45	Subjective Weight
u41	1.0000	0.2818	0.3555	0.8011	0.7864	0.1036
u42	3.5491	1.0000	1.0975	2.1824	2.2039	0.3236
u43	2.8129	0.9112	1.0000	2.0794	2.3647	0.2981
u44	1.2483	0.4582	0.4809	1.0000	1.1728	0.1434
u45	1.2716	0.4537	0.4229	0.8527	1.0000	0.1314

and

Table 22. The objective and comprehensive weights of the influencing factors of IED A and IED B.

Influencing Factor	IED A				IED B
	Index Entropy		Objective Weight	Comprehensive Weight (w41)	Index Entropy		Objective Weight	Comprehensive Weight (w42)
u41	h411	0.0385	0.2170	0.1083	h421	0.0385	0.2081	0.1047
u42	h412	0	0.2192	0.3419	h422	0	0.2164	0.3401
u43	h413	0	0.2192	0.3149	h423	0	0.2164	0.3133
u44	h414	0	0.2192	0.1515	h424	0	0.2164	0.1507
u45	h415	0.3992	0.1317	0.0834	h425	0.3401	0.1428	0.0911

. The AHP method is applied and the maximum eigenvalue and the corresponding eigenvector are ${\mathsf{\lambda }}_{\max }$ = 5.0096 and E = [0.2105 0.6576 0.6058 0.2915 0.2671], respectively. The consistency index and the consistency ratio are CI = 0.0024, n = 5, RI = 1.12, and CR = 0.0021, respectively. Since CR ≦ 0.1, the weights are acceptable.

The subjective, objective, and comprehensive weights of the MU status evaluation are shown in

Table 23. The weights of the MU influencing factors.

Influencing Factor	u51	u52	u53	u54	u55	u56	Subjective Weight	Index Entropy		Objective Weight	Comprehensive Weight (w5)
u51	1.0000	0.4137	0.3544	0.4311	0.3810	1.0917	0.0853	h51	0.0275	0.1628	0.0831
u52	2.4173	1.0000	1.3197	1.2097	1.0674	2.3916	0.2274	h52	0	0.1674	0.2279
u53	2.8214	0.7577	1.0000	0.9714	0.9913	2.0147	0.1974	h53	0	0.1674	0.1978
u54	2.3198	0.8267	1.0294	1.0000	0.9688	1.9358	0.1928	h54	0	0.1674	0.1932
u55	2.6249	0.9369	1.0088	1.0322	1.0000	1.9961	0.2031	h55	0	0.1674	0.2036
u56	0.9160	0.4181	0.4964	0.5166	0.5010	1.0000	0.0941	h56	0	0.1674	0.0943

. The AHP method is applied and the maximum eigenvalue and the corresponding eigenvector are ${\mathsf{\lambda }}_{\max }$ = 6.0210 and E = [0.1981 0.5283 0.4586 0.4479 0.4720 0.2187], respectively. The consistency index and the consistency ratio are CI = 0.0042, n = 6, RI = 1.24, and CR = 0.0034, respectively. Since CR ≦ 0.1, the weights are acceptable.

As a demonstration, the procedures and the numerical calculations of the FCE matrix for case 1 in this study are described in detail as follows.

(1)

Server FCE matrix B₁ and its numerical value T₁:

$$\begin{array}{l}{B}_1=w_1\cdot R_{u_1}\\ \hspace{1em}=[0.1435,0.3600,0.2056,0.0943,0.1966]\left[\begin{array}{cccc}\begin{array}{c}0.5938\\ 1.0000\\ 1.0000\\ \begin{array}{c}1.0000\\ 1.0000\end{array}\end{array}& \begin{array}{c}0.4062\\ 0\\ 0\\ \begin{array}{c}0\\ 0\end{array}\end{array}& \begin{array}{c}0\\ 0\\ 0\\ \begin{array}{c}0\\ 0\end{array}\end{array}& \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\end{array}\end{array}\end{array}\right]\\ \hspace{1em}=[0.9417,0.0583,0,0,0]\end{array}$$

$$\begin{array}{l}{T}_1=B_1\cdot V^{\mathrm{T}}\\ \hspace{1em}=[0.9417,0.0583,0,0,0]\left[\begin{array}{c}100\\ 75\\ 50\\ \begin{array}{c}25\\ 0\end{array}\end{array}\right]\\ \hspace{1em}=98.54\end{array}$$

(2)

SCADA HMI software fuzzy evaluation matrix B₂ and its numerical value T₂:

$$\begin{array}{l}{B}_2=w_2\cdot R_{u_2}\\ \hspace{1em}=[0.4664,0.2580,0.0788,0.1969]\left[\begin{array}{cccc}\begin{array}{c}1.0000\\ 1.0000\\ 0.6667\\ 1.0000\end{array}& \begin{array}{c}0\\ 0\\ 0.3333\\ 0\end{array}& \begin{array}{c}0\\ 0\\ 0\\ 0\end{array}& \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\end{array}\end{array}\right]\\ \hspace{1em}=[0.9738,0.0262,0,0,0]\end{array}$$

$$T_2=B_2\cdot V^{\mathrm{T}}=99.35$$

(3)

Switches fuzzy evaluation matrix B₃:

The PT-G7828_A fuzzy evaluation matrix B₃₁ and its numerical value T₃₁:

$$\begin{array}{l}{B}_{31}=w_{31}\cdot R_u{}_{{}_{31}}\\ \hspace{1em} =[0.1944,0.3659,0.2456,0.0742,0.1199]\left[\begin{array}{cccc}\begin{array}{c}0.9706\\ 1.0000\\ 1.0000\\ \begin{array}{c}0.8158\\ 1.0000\end{array}\end{array}& \begin{array}{c}0.0294\\ 0\\ 0\\ \begin{array}{c}0.1842\\ 0\end{array}\end{array}& \begin{array}{c}0\\ 0\\ 0\\ \begin{array}{c}0\\ 0\end{array}\end{array}& \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\end{array}\end{array}\end{array}\right]\\ \hspace{1em} =[0.9806,0.0194,0,0,0]\end{array}$$

$$T_{31}=B_{31}\cdot V^{\mathrm{T}}=99.52$$

The PT-G503_A fuzzy evaluation matrix B₃₃ and its numerical value T₃₃:

$$\begin{array}{l}{B}_{33}=w_{33}\cdot R_u{}_{{}_{33}}\\ \hspace{1em} =[0.2005,0.3695,0.2480,0.0610,0.1210]\left[\begin{array}{cccc}\begin{array}{c}0.9774\\ 1.0000\\ 1.0000\\ \begin{array}{c}0.5000\\ 1.0000\end{array}\end{array}& \begin{array}{c}0.0226\\ 0\\ 0\\ \begin{array}{c}0.500\\ 0\end{array}\end{array}& \begin{array}{c}0\\ 0\\ 0\\ \begin{array}{c}0\\ 0\end{array}\end{array}& \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\end{array}\end{array}\end{array}\right]\\ \hspace{1em} =[0.9651,0.0349,0,0,0]\end{array}$$

$$T_{33}=B_{33}\cdot V^{\mathrm{T}}=99.13$$

In order to improve the data transmission reliability, parallel redundancy protocol (PRP) and high-availability seamless redundancy (HSR) are introduced in the network communication configuration, where PRP is followed using switches PT-G7828_A and PT-G7828_B, while HSR is achieved using PT-G503_A, PT-G503_B, PT-7728_A, and PT-7728_B. The six switches are represented by one equivalent switch to simplify the calculation with fuzzy evaluation matrix B₃ and its numerical value T₃:

$$\begin{array}{l}{B}_3=0.5[0.5(B_{31}+B_{32})+0.25(B_{33}+B_{34}+B_{35}+B_{36})]\\ \hspace{1em} =[0.9806,0.0194,0,0,0]\end{array}$$

$$T_3=B_3\cdot V^{\mathrm{T}}=99.52$$

(4)

IEDs fuzzy evaluation matrix R₄:

The IED_A fuzzy evaluation matrix B₄₁ and its numerical value T₄₁:

$$\begin{array}{l}{B}_{41}=w_{41}\cdot R_{u_{41}}\\ \hspace{1em} =[0.1083,0.3419,0.3149,0.1515,0.0834]\left[\begin{array}{cccc}\begin{array}{c}0.9887\\ 1.0000\\ 1.0000\\ \begin{array}{c}1.0000\\ 0.6579\end{array}\end{array}& \begin{array}{c}0.0113\\ 0\\ 0\\ \begin{array}{c}0\\ 0.3421\end{array}\end{array}& \begin{array}{c}0\\ 0\\ 0\\ \begin{array}{c}0\\ 0\end{array}\end{array}& \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\end{array}\end{array}\end{array}\right]\\ \hspace{1em} =[0.9702,0.0298,0,0,0]\end{array}$$

$$T_{41}=B_{41}\cdot V^{\mathrm{T}}=99.25$$

$$\begin{array}{l}{B}_{42}=w_{42}\cdot R_u{}_{{}_{42}}\\ \hspace{1em} =[0.1047,0.3401,0.3133,0.1507,0.0911]\left[\begin{array}{cccc}\begin{array}{c}0.9887\\ 1.0000\\ 1.0000\\ \begin{array}{c}1.0000\\ 0.7632\end{array}\end{array}& \begin{array}{c}0.0113\\ 0\\ 0\\ \begin{array}{c}0\\ 0.2368\end{array}\end{array}& \begin{array}{c}0\\ 0\\ 0\\ \begin{array}{c}0\\ 0\end{array}\end{array}& \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\end{array}\end{array}\end{array}\right]\\ \hspace{1em} =[0.9772,0.0228,0,0,0]\end{array}$$

$$T_{42}=B_{42}\cdot V^{\mathrm{T}}=99.43$$

The equivalent IED fuzzy evaluation matrix B₄ and its numerical value T₄:

B₄ = 0.5(B₄₁ + B₄₂) = [0.9737, 0.0263, 0, 0, 0]

T₄ = B₄·V ^T = 99.34

(5)

The MU fuzzy evaluation matrix R₅ and its numerical value T₅:

$$\begin{array}{l}{B}_5=w_5\cdot R_{u_5}\\ \hspace{1em}=[0.0831,0.2279,0.1978,0.1932,0.2036,0.0943]\left[\begin{array}{cccc}\begin{array}{c}0.9925\\ 1.0000\\ 1.0000\\ \begin{array}{c}1.0000\\ 1.0000\end{array}\end{array}& \begin{array}{c}0.0075\\ 0\\ 0\\ \begin{array}{c}0\\ 0\end{array}\end{array}& \begin{array}{c}0\\ 0\\ 0\\ \begin{array}{c}0\\ 0\end{array}\end{array}& \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\end{array}\end{array}\end{array}\right]\\ \hspace{1em}=[0.9994,0.0006,0,0,0]\end{array}$$

$$T_5=B_5\cdot V^{\mathrm{T}}=99.99$$

(6)

The fuzzy evaluation matrix S and its numerical value T:

$$\begin{array}{l}S=w\cdot B^T\\ \hspace{1em}=[0.3559,0.1116,0.1858,0.2574,0.0893]\left[\begin{array}{cccc}\begin{array}{c}0.9417\\ 0.9738\\ 0.9806\\ \begin{array}{c}0.9737\\ 0.9994\end{array}\end{array}& \begin{array}{c}0.0583\\ 0.0262\\ 0.0194\\ \begin{array}{c}0.0263\\ 0.0006\end{array}\end{array}& \begin{array}{c}0\\ 0\\ 0\\ \begin{array}{c}0\\ 0\end{array}\end{array}& \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\\ \begin{array}{c}\begin{array}{cc}0& 0\end{array}\\ \begin{array}{cc}0& 0\end{array}\end{array}\end{array}\end{array}\right]\\ \hspace{1em}=[0.9659,0.0341,0,0,0]\end{array}$$

$$T=S\cdot v^T=99.15$$

The equipment FCE result of case 1 is shown in

Table 24. The equipment FCE result of case 1 and 2.

Equipment	Evaluation Result
	Case 1	Case 2
Server	98.54	79.46
SCADA HMI Software	99.35	55.74
Switch_A	99.52	22.64
Switch_B	99.52	22.64
Switch_C	99.13	7.74
Switch_D	99.20	22.68
Switch_E	99.86	22.64
Switch_F	99.86	22.64
IED_A	99.25	88.89
IED_B	99.43	88.89
MU	99.99	87.72
SCADA System	99.15	69.07

. All of the equipment is newly purchased in case 1. The status evaluation of the intelligent substation SCADA system of case 1 is 99.15 or “in Good Condition” and no inspection or maintenance schedule is required. Taipower company Tai-Tam substation CBM platform of the intelligent substation SCADA system is shown in Figure 5.

The system in case 2 is old and has run for years, with all of the switches functioning in abnormal status, as shown in

Table 25. Case 2 measured value of influencing factor of equipment.

Server		SCADA HMI		Switch							IED			MU
IF *1	MV	IF	MV	IF	MV *2						IF	MV		IF	MV
					A	B	C	D	E	F		A	B
u11	0.9025	u21	0.9991	u31	0.9498	0.9498	0.9610	0.9610	0.9498	0.9498	u41	0.9851	0.9851	u51	0.9900
u12	0	u22	80	u32	3	3	4	3	3	3	u42	1	1	u52	0
u13	32	u23	20	u33	2	2	3	2	2	2	u43	0	0	u53	0
u14	80	u24	120	u34	90	90	100	90	90	90	u44	50	50	u54	5
u15	11			u35	100	100	100	100	100	100	u45	25	25	u55	0.6
														u56	20

^*1 IF: Influencing factor, ^*2 MV: Measured value.

. The status evaluation of the SCADA system of case 2 is 69.07 or “Immediate Inspection and Maintenance Required”, as shown in Table 24. Although the SCADA system has a high reliability network configuration, its reliability is reduced drastically due to the serious deterioration of the switch, and immediate maintenance of all switches is required. The recommended maintenance order for the switches is Switch_C → Switch_A, Switch_B, Switch_E, and Switch_F → Switch_D. If the system is still in critical condition after the communication problem is resolved, the SCADA HMI software should be maintained too.

5. Conclusion

Smart substations play a vital role in power systems. However, the installation of a large number of secondary equipment makes the traditional correct maintenance and time base maintenance unable to meet the system requirements. It is imperative to develop a new equipment maintenance management strategy. In recent years, equipment maintenance strategies have been further developed and optimized by monitoring equipment abnormalities and considering the importance of equipment. Implementing a CBM system for a smart substation can effectively avoid over-maintenance or lack of maintenance of equipment, reducing unnecessary power outage tests, as well as the maintenance workload and cost. It can significantly improve the system operational reliability and economic efficiency.

In contrast to a conventional maintenance strategy, a condition-based maintenance and management strategy is proposed in this study, where equipment status information is gathered through the IEC 61,850 communication protocol. The relative deterioration degree theory (RDD) and fuzzy theory (FT) are used to evaluate the condition of the equipment, combining the subjective analytic hierarchy process (AHP) method with the objective entropy weighting method to analyze the important factor of equipment. The fuzzy comprehensive evaluation (FCE) method is applied to evaluate the equipment of an intelligent substation SCADA system based on the equipment condition and their importance. The evaluated equipment includes the server, SCADA HMI software, switch, IED, and MU.

Each equipment in the substation plays a different role according to its function. Assigning them reasonable weights by the scientific approach is an important process for state evaluation. The comprehensive weighting method proposed in this paper that combines the subjective analytic hierarchy process (AHP) method and the objective entropy weighting method can inherit the advantages of two kinds of weighting methods. This approach not only presents the subjective professional experience of decision makers, but also considers the truth of the objective facts. It can accurately evaluate equipment status in the smart substation control system.

The result shows that the equipment status maintenance and management platform developed in this study can diagnose the equipment operating status in real time. The inspection and maintenance personnel can analyze the overall equipment condition by knowing which one of the four status ratings, i.e., "in Good Condition", "Attention Required", "in Critical Condition", and "Immediate Inspection and Maintenance Required", the equipment status has, and can determine the priority for equipment maintenance. The evaluation result can serve as a valuable reference to utility companies when making maintenance plans.