Cool the Generators ©imgram Publishing
System Reliability and Fault Tree Analysis of Hydrogen Cooling Systems
Gyan R. Biswal, R.P. Maheshwari, and M.L. Dewal
A
comprehensive hydrogen (H2) cooling system (HCS) is presented for the cooling of n # 250-MW generators of fossil-fuel power plants. A novel “six stage hot redundant structure” (S2HRS)-based HCS clubbed with highly reliable and efficient process control and instrumentation system is proposed for the cooling of large generators in integrated gasification combined cycle power plants (CCPPs). This article provides a comparison between the proposed and the existing systems in terms of system reliability and fault tree analysis (FTA). The effectiveness of real-time featured proposed HCS is validated by computer simulation using RS-View32 Works, a real-time automation platform. Reliability is the main impact index for any power plant’s performance quality. The designer’s prime objective is for the system/process to run at
Digital Object Identifier 10.1109/MIE.2012.2231728 Date of publication: 19 March 2013
30 IEEE industrial electronics magazine ■ march 2013
1932-4529/13/$31.00©2013IEEE
maximum efficiency, that is, with maximum availability at minimum cost. The reliability of the control and instrumentation (C&I) system of HCS is of prime importance at the plant level primarily because of the inadequacies of component reliability. The presented S2HRS-based HCS improves the system reliability of the cooling process, thereby reducing the chance of system failure. This improvement not only reduces the maintenance time but also provides higher availability. Furthermore, reduction in the maintenance time of the process means that it can be kept in active mode for longer durations. As a result, the efficiency of the entire power generation system is improved. Improved efficiency significantly reduces greenhouse gas emissions [1], [8]–[11]. Fossil-fuel power plants require a sophisticated automation system called a supervisory control and data acquisition (SCADA) system, which provides highly productive [2], [3], reliable [4], and safe [5], [6] power generation. The large generating sections of a power plant are critical components. For such components, a SCADA-operated, highly reliable process C&I system is necessary. H2 is an excellent cooling agent for large generators because of its low density, high specific heat, and high thermal conductivity [7]. Due to the need for greater system reliability and customer satisfaction, cost reduction has become a priority. A digitally automated HCS was proposed by Hargrove et al. [12]. In a follow-up article, Blatter et al. [13] proposed a three-stage cooling circuit for generators. Brosnihan et al. [14] continued the work by proposing a modular system of air, H2, and carbon dioxide (CO2) through a gas manifold for monitoring of H2-cooled generators. In the effort to reduce costs, the primary focus has been on reducing operation and maintenance expenses and minimizing investment in new plant setup, including HCS systems. Biswal et al. [15] proposed a reliable process model of HCS for a generation station of capacity up to 120–300 MW. Such complex systems
require redundant schemes that ensure reliable and uninterrupted operation [16]–[18].
Modeling of Augmentation of H2: HCS The process instrumentation of the proposed method is designed to be compatible with either programmable automation controllers (PACs) or programmable logic controllers (PLCs). The entire system architecture is supported by an L55M12-series microprocessor, 1756-series input/output (I/O) and communication modules, and an RSLogix5000 platform for programming digital controllers. Human– machine interface (HMI) is simulated using the RS-View32 Works platform [19]. The HMI platform must be interfaced with field sensors through the RSLogix5000 platform of the controllers. The schematic diagram of the proposed S2HRS-based HCS is shown in Figure 1(a) for the capacity of n # 250–300-MW unit(s). The control philosophy of the S2HRS-based HCS is very well explained in the “Proposed Algorithm of the S2HRSbased HCS” section. As depicted in Figure 1(a), the first component, the H2 separation unit (HSU), is used to produce and then supply pure H2 to the next component, typically known as “reservoir.” The matrix of storage tanks has been judiciously selected to maintain cost effectiveness. Superinsulated vacuum lines (SIVLs) supply H2 at very high pressure. The on/off and control valves interlink the HSU, reservoir, and feedback network from generating sections. Their job is to control all the sequential operations handled by the HMI and work accordingly. Different heat exchangers (HXEs) are mounted to increase/decrease the pressure rates of H2. Only major components of Figure 1(a) are considered to draw an equivalent reliability model of the proposed process model as against all the existing schemes. This simplifies the mathematical analysis of all the models, existing and proposed, in terms of system reliability. The equivalent reliability model is depicted in Figure 1(b) [11]. In the figure, “TA” represents tank
T-83A, that is, the reservoir. Similarly, “TB,” “TC,” “SI/SIVL,” and “HEx” represent T-83B, T-83C, SIVL, and HXEs, respectively.
System Reliability Evaluation: Existing Systems Versus Proposed System The network reduction technique (NRT) is one of the methods of the fault tolerance scheme that are used to evaluate the system reliability of the existing methods and the proposed scheme. In industrial applications like HCS, methods based on fault tolerance are always preferred for evaluating system reliability. Weibull’s distribution, described in (1) and (2), is considered for all the systems for resolving the design issues, evaluating the system reliability, and bringing out the comparison between the proposed method and the existing methods. All the existing models and the proposed one are simplified with redundancy concept based on (3) for carrying out comparison with a common platform. Relationships between reliability functions 0(t) and hazard rate [h (t)] are represented by (1) and (2). 0(t) and 0 s (t) are the component and system reliability, respectively, of the process model with hot redundant unit(s). 0 s (t) is the overall system reliability of HCS. b is the slope parameter and h is the scale parameter of the two-parameter Weibull distribution function. The reliability factors of components (see [11] and [15]) are assumed as per international standards and practices: IEEE 1413-2010 and IEEE 493-1997. , , h (t) = " (b/h) * (t/h) 0(t) = e (1) - (t/h) b
0 s (t) = =
t
b-1
i
/ * # h (t) dt 4 e - # h(t)dt (i!) -1 k
0
i=0
0
k=1
(h $ t) i e -h $t . i!
/
i=0
t
(2)
Henceforth, R1, R2, R3, R4, and R5 are the corresponding system reliabilities of the serial system, the system by Hargrove et al. [12], the system by Blater et al. [13], the system by Brosnihan et al. [14], and the proposed system, respectively.
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HSU RV PV 83B-1 PT PI 83B 83B PIC LI LT 83B 83B
TI 83A IC RV
T-83A LH2 Reservoir {2x8} Matrix
T-83B SIVL UV 83B-1 NRV
WT/HXE
Generator TUR TI 83
RV PV 83B-2 1-Out-of-2
FI 83
SIVL T-83C UV 83C-1 NRV
NRV
PV 83C-1 RV PI PT 83C 83C PIC LI LT 83C 83C
NRV
UV 83B-2
SIVL
1-Out-of-2
UV 83C-2 PV 83C-2 1-Out-of-2 (a) R HE1A
From HSU
R TA
R TB
R HE1B
R HE3A
R HE4A
R SI
R TC
R HE2A
R HE3B
R HE4B
To WCT and CWP
R HE2B (b) Figure 1 – (a) Schematic diagram of the proposed S2HRS-based process model. (b) Equivalent system reliability model of the proposed S2HRSbased HCS. Note: Instrumentation symbols are referred as per specifications of the International Society of Automation (ISA).
In the sections “System by Hargrove et al.,” “System by Blatter et al. [13],” “System by Brosnihan et al. [14],” and “Proposed S2HRS-Based HCS,” the reliability models are analyzed using NRT, one of the fault tolerance methods [15]–[17]. Reliability is always an implicit function of time; however, in the sections “System by Hargrove et al.,” “System by Blatter et al. [13],” “System by Brosnihan et al. [14],” and “Proposed S2HRS-based HCS,” the obtained values of system reliability are the collection of samples at different instants in time; thus, these values are discrete in nature, as shown in Figure 2. Serial System The serial system is the simplest system in terms of system reliability. The reliability model of the system is expressed as 0 1 (t) = R TA ) R TB ) R HE1 ) R HE2 = R1. (3)
System by Hargrove et al. [12] Hargrove et al. introduced a system relating to the H2-cooled generators in an electrical power station. The reliability model of the system is as follows:
for monitoring air, H2, and CO2. The reliability model of the system is as follows:
0 2 (t) = R X ) R TB ) R HE1 ) R HE2 = R2 3. a R X = R TA R SIVL
Proposed S2HRS-Based HCS The proposed method is designed and supported with a hot redundant module for an uninterrupted supply of H2. The reliability of the system improves with increase in the number of redundant components. The redundancy is carefully evaluated for designing S2HRS-based HCS. The evaluation of system reliability of the proposed method on the basis of Figure 1(b) [11] is depicted in Figure 2. The final and intermediate steps are given in (7). The respective numerically obtained values are highlighted in “System Reliability of R5.” The abscissa of Figure 2(a) and (b) represent the number of samples collected at ten different instants in time. The tenth test of RS(t) gives the difference between
(4) System by Blatter et al. [13] Blatter et al. introduced a three-stage HCS with a typical matrix of storage tanks for providing redundancy, which acts as a reservoir to feed H2. The reliability model is expressed by 0 3 (t) = R TA ) R TB ) R b ) R c = R3 a R b = R c = R HExk R HEyk, 1 # x, y # 2 4 . and 0 # k # 1 (5) System by Brosnihan et al. [14] Brosnihan et al. proposed a modular system for monitoring a H2-cooled generator. It included a skid platform
32 IEEE industrial electronics magazine ■ march 2013
0 4 (t) = R TA ) R TB ) R HE1 = R4. (6)
40 35 30 25 20 15 10 5 0
System Reliability: Proposed Versus Existing Systems
R5–R2 R5–R3 R5–R4
1
2
Relative Improvement in Percentage
Relative Improvement in Percentage
System Reliability: Proposed Versus Existing Systems
3 4 5 6 7 8 Number of Sample Tests (a)
9
10
45 40 35 30 25 20 15 10 5 0
R 5 –R 2 R 5 –R 3 R 5 –R 4
1
2
3 4 5 6 7 8 Number of Sample Tests (b)
9
10
Figure 2 – Histogram showing improvement in system reliability of R5 over R2, R3, and R4. (a) Test 1. (b) Test 2.
the system reliability of R5 and that of R2, R3, and R4 at the upper limit of the threshold value, that is, the maximum reliability of components in terms of process instrumentation. RS(t) at this threshold value is unique. However, tests 1–9 are all randomly generated values within the range of threshold values.
FTA of the Proposed Method FTA is a logical and structured process that can help to identify potential causes of system failure before it actually occurs. It is a powerful design tool that can help to ensure that the objective performances of HCS are met [9]–[11]. Figure 3 illustrates the use of a fault tree diagram (FTD) to evaluate the system failure of the proposed method F5 in comparison with the existing systems F3, and F4 [15]. The online monitoring and control of HCS is one of the key functions of SCADA. In the present model, the FTD has been employed to take a lead role in online health monitoring of the system by evaluating the performance of the proposed HCS in terms of system failure. Fewer chances of system failure reflect better efficiency and lesser maintenance requirement on the system. Mathematical Modeling of FTA of R5 The S2HRS-based HCS fails only if the power supply fails at the same time as the other four modules: the
reservoir, processing units, and HXEs at pressurization and depressurization conditions. Similarly, all the other terms that correspond to “failure” or “no failure” of HXEs, on/off valves, and control valves that are illustrated in Figure 3 can be correlated to the failure of the aforesaid four modules working at different pressures. For example, if, “p” and “q” are two components of a fault tree system, then the series and hot redundancy between two components are evaluated by (8). The intermediate steps are given in (9). Further, derivations of F+, F++, F+++, F2 , F3, and F4 are given in “Derivations of F.” System Failure of R5 Versus R2, R3, and R4 Here, the three parameters, namely, the Weibull reliable life (TR), mean time to repair (MTTR) during
maintenance time or due to sudden fault, and h(t) are considered to evaluate system failure of all the methods. The characteristics of R5 versus the existing systems in terms of TR and TRmin are given in Figure 4. The results of test 5 as depicted in Figure 4 indicate that R5 needs less time for maintenance compared with the existing systems. The results of the first four tests are system-generated values, while test 5 has always been conducted at the threshold value of the components. Figure 4(a)–(g) shows that R5 always claims the lowest maintenance time compared with R2, R3, and R4, irrespective of change/increment in the value of shape parameter b. These results are very well depicted in Figure 4(a)–(g). The symbol “ ” represents TRmin values for different values of shape parameter b, while the symbol “ ” represents small variations
a R S = R Y * R TB, R T = R Z * F TC, ` R a =R S < R T _b b R Y = R HE1A < R HE1B = R HE2A < R HE2B = R Z ` . (7) R b = R HE3A < R HE3B = R HE4A < R HE4B = R c b b a R S 2 HRS (t) = R X * R a * R b * R c = R 5 (t) = R5 a Fs (t) = 1 - 0 s (t), F (t) = 1 - R (t) 3. a Fs (t) OR = Fp + Fq - (Fp ) Fq ); Fs (t) AND = Fp ) Fq
(8)
_ FX' = FTA AND FSI, FY = FZ , a FY = FHE1A AND FHE1B b b a FS = FY OR FTB, FT = FZ OR FTC, b ` Fa = FS AND FT, Fb = FHE3A AND FHE3B = Fc ` . (9) b 2 ' ` FS HRS (t) = FX OR Fa OR Fb OR Fc = F5 (t) b b a F+ = FX' OR Fa, F++ = F+ OR Fb, ` F+++ = F++ OR Fc = FS 2 HRS (t) = F5 (t) = F5 a
march 2013 ■ IEEE industrial electronics magazine 33
System Reliability of R5 The system reliability of R5 versus R2, R3, and R4 is summarized in Table S1 below with the key points of FTPs from Figures 4 and 5. TABLE S1–SUMMARY OF TRmin, MTTR, AND h(t) WITH VARIATIONS IN b. b 1 = 1.318
b 2 = 1.718
b 3 = 2.118
Type of parameters
R5
R2
R3
R4
R5
R2
R3
R4
R5
R2
R3
R4
T = TR min, days
55
199
102
166
82
221
132
192
105
236
155
210
MTTR, days
97,007
61,001
71,717
63,312
159,043
55,809
80,429
60,699
269,003
52,686
93,073
60,049
h(t), failure/day
0.0024
0.0039
0.0033
0.0037
0.0016
0.0045
0.0031
0.0041
0.0009
0.005
0.0028
0.0044
RS(t)†
0.9052
0.5749
0.8280
0.6490
0.9052
0.5749
0.8280
0.6490
0.9052
0.575
0.8280
0.6490
Note: Tn = MTTF = MTBF = 287.8 days, Ts = Tmed = 236.3 days, and Tu = Tmod = 106.1 days at b = 1.318. Similarly, Tn = MTTF = MTBF = 278.9 days, Ts = Tmed = 252.1 days, and Tu = Tmod = 187.7 days at b = 1.718, and Tn = MTTF = MTBF = 276.3 days, Ts = Tmed = 262.4 days, and Tu = Tmod = 230.8 days at b = 2.118.
in the value of shape parameter b. If R (TR) = 0.5 , then TR = Ts = Tmed of the Weibull distribution-based reliable life of the components of the system. Test 5 represents the minimum time required for the maintenance and repair of the system for evaluating TR (TRmin) of the process model. However, tests 1–4 are random system-generated values of TR. Also, as shown in Figure 5, better MTTR of R5 results in lower maintenance duration, hence the system is available in active mode for a longer time compared with the MTTR of the
existing systems, namely, R2, R3, and R4. For example, at b = 1.1, R5 claims a higher value of MTTR than claimed by the other systems. With further increase in the values of b ranging from 1.3 to 2.4, the MTTR claimed by R5 increases consistently compared with the rise in the MTTR of other systems. This shows that the availability of R5 in active mode is accordingly higher, which, in turn, reflects much better efficiency of the proposed system (R5). At the same time, R5 achieves much lower h(t) with an increase in
b as opposed to the values of h(t) attained by R2, R3, and R4, which indicates that the process life of R5 is longer than that of the existing systems. This is illustrated in Figure 5. The trend of h(t) as a function of b for different systems is depicted in Figure 5(c). For example, R5 has gained minimal h(t), 0.003 failures/day for b = 1.1 and consistently retains same characteristics for b = 1.3 - 2.4. Again, a lower hazard rate of R5 indicates that it is available in active mode for a longer duration. The key points are
Derivations of F Derivations of F+, F++, and F+++ followed by the exact expressions of F2, F3, and F4 are as follows: F+++ = F++ OR Fc = { FX l + Fa - (FX l ) Fa)} + Fb - {FX l ) Fb + Fa ) Fb - (FX ) Fa ) Fb)} + Fc - {FX l ) Fb ) Fc + Fa ) Fb ) Fc - FX l ) Fa ) Fb ) Fc}, a F++ = F+ OR Fb = {FX l + Fa - (FX l ) Fa)} + Fb - {FX l ) Fb + Fa ) Fb - (FX l ) Fa ) Fb)}, a F+ = FX l OR Fa = FX l + Fa - (FX l ) Fa) . F2, F3, and F4 are the system failure models of the existing systems. F2 = FX l OR F TB OR FHE1A OR FHE3A = FX l + F TB - (FX l ) F TB) + FHE1A - {FX l ) FHE1A + F TB ) FHE1A - FX l ) F TB ) FHE1A} + FHE3A - [{FX l ) FHE3A + F TB ) FHE3A - FX l ) FTB ) FHE3A} + FHE1A ) FHE3A - {FX l ) FHE1A ) FHE3A + F TB ) FHE1A ) FHE3A - FX l ) F TB ) FHE1A ) FHE3A}] F3 = FX l OR F TB OR Fb OR Fc = FX l + F TB - (FX l ) F TB) + Fb - {FX l ) Fb + FTB ) Fb - FX l ) F TB ) Fb} + Fc - [{FX l ) Fc + F TB ) Fc - FX l ) F TB ) Fc} + Fb ) Fc - {FX l ) Fb ) Fc + F TB ) Fb ) Fc - FX l ) F TB ) Fb ) Fc}] F4 = F TA OR F TB OR FHE1A OR FHE3A = F TA + F TB - (F TA ) F TB) + FHE1A - {F TA ) FHE1A + F TB ) FHE1A - F TA ) F TB ) FHE1A} + FHE3A - [{F TA ) FHE3A + F TB ) FHE3A - F TA ) F TB ) FHE3A} + FHE1A ) FHE3A - {F TA ) FHE1A ) FHE3A + F TB ) FHE1A ) FHE3A - F TA ) F TB ) FHE1A ) FHE3A}] .
34 IEEE industrial electronics magazine ■ march 2013
HCS Fails
F5 4 Bar 32 Bar
16 Bar
8 Bar
4 Bar
No Power Supply
RTA and SIVL Blocked
Process Units Failed
HXEs Failed to Pressurize
HXEs Failed to Depressurize
F0
F X’
Fa
Fb
Fc
160 Bar
64 Bar
F TA
F HE3A
F SI Active Mode Failed
Hot Mode Failed
FS
FT
16 Bar On/Off Valves Failed
Control Valves Failed
FY
FZ
F HE1A
F HE4A
F HE4B
16 Bar
Control Valves Failed
F TB
F HE3B
F HE1B
F HE2A
On/Off Valves Failed
F TC
F HE2B
Figure 3 – FTD of R5 for system design and safety analysis [15].
summarized in “System Reliability of R5,” and the corresponding discussions are given in the “System Reliability Performance of the Proposed Method” and “Experimental Analysis of FTP” sections.
Proposed Algorithm of the S2HRS-Based HCS The control strategy of the proposed S2HRS-based HCS, or R5, is a group of four events for continuous feeding of H 2 gas to the generating sections. These four events are “filling,” “pressurization,” “feeding,” and “depressurization” [15]. The filling event of both the cold converters
is represented by Fl_T83B and Fl_T83C. The pressurization event of both the converters is represented by P_T83B and P_T83C; the feeding event of both the cold converters is represented by Fd_T83B and Fd_T83C; and the depressurization event of both the converters is represented by Dp_T83B and Dp_T83C. To execute these decision steps, both the analog and the digital (binary pulses) signals are generated by the HMI. As mentioned in Table 1, all the closed actions are symbolized by “C,” open actions by “O,” and controlled actions by “CNT.” The on/off valves UV83B-1, UV83B-2, UV83C-1, and UV83C-2
represent attempted “O” or “C” actions. However, the control valves PV83B-1, PV83B-2, PV83C-1, and PV83C-2 have one of the three possible actions: O, C, or CNT. The HMI generates analog values (4–20 mA) to operate these control valves through remote terminal units (RTUs). However, if needed, execution of any event operation is controlled through HMI with the obvious priority over RTUs. Such events include pressing the standby module simultaneously into the active mode, if needed. As given in Table 1, the present algorithm shows the execution of the feeding event of T-83B and depressurization events
march 2013 ■ IEEE industrial electronics magazine 35
TR w.r.t. Shape Parameter b
1,200
TR →
800
TR →
T R_R5 T R_R2 T R_R3 T R_R4
1,000
600 400 200 0
1
2
3
4
TR w.r.t. Shape Parameter b
900 800 700 600 500 400 300 200 100 0
T R_R5 T R_R2 T R_R3 T R_R4
5
1
2 3 4 Number of Tests at b = 1.318 (b)
800 700 600 500 400 300 200 100 0
TR w.r.t. Shape Parameter b
T R_R5 T R_R2 T R_R3 T R_R4
1,000 800 600 400 200
1
2 3 4 Number of Tests at b = 1.7
0
5
1
2 3 4 Number of Tests at b = 1.718 (d)
TR w.r.t. Shape Parameter b T R_R5 T R_R2 T R_R3 T R_R4
1
2 3 4 Number of Tests at b = 2.1 (e)
300
TRmin →
250 200
TR →
TR →
(c)
800 700 600 500 400 300 200 100 0
5
TR w.r.t. Shape Parameter b
1,200
T R_R5 T R_R2 T R_R3 T R_R4
TR →
TR →
Number of Tests at b = 1.3 (a)
5
TR w.r.t. Shape Parameter b
800 700 600 500 400 300 200 100 0
T R_R5 T R_R2 T R_R3 T R_R4
5
1
2 3 4 Number of Tests at b = 2.118 (f)
5
TRmin w.r.t. Change in Shape Parameter b TRmin_R5 TRmin_R2 TRmin_R3 TRmin_R4
150 100 50 0
1
1.1
1.3
1.318
1.5
1.7
1.718
1.9
2.1
2.118
2.2
2.4
Shape Parameter b → (g) Figure 4 – Histograms showing comparison between R5, R2, R3, and R4 models in terms of impact of change in b and small variation in b on the Weibull reliable life (TR, in days), and T = TR min (in days) of these models. Symbol “ ” shows TRmin for different values of shape parameter b, whereas “ ” represents small variations in shape parameter b. 36 IEEE industrial electronics magazine ■ march 2013
Results and Discussion This section is divided into three subsections, namely, “System Reliability Performance of the Proposed Method,” “Experimental Analysis of Fault Tree Parameters (FTPs),” and “Fault Diagnosis Cum Display Module of the Proposed Method.”
MTTR w.r.t. Shape Parameter b
MTTR "
of T-83C. In the proposed method, the process C&I is performed by two cold converters, T-83B (active mode) and T-83C (hot mode), as shown in Figure 6.
450,000 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0
MTTR_R5 MTTR_R2
1
MTTR_R3 MTTR_R4
1.1 1.3 1.318 1.5 1.7 1.718 1.9 2.1 2.118 2.2 2.4 Shape Parameter b " (a) h(t) w.r.t. Shape Parameter b
0.006 0.005
h(t)_R5
h(t)_R3
h(t)_R2
h(t)_R4
h(t ) "
0.004 0.003 0.002 0.001 0
1
1.1
1.3 1.318 1.5
1.7 1.718 1.9
2.1 2.118 2.2
2.4
Shape Parameter b " (b) Trend of Variation in h(t ) with Shape Parameter b 0.006 h(t )_R5 h(t )_R2
0.005
h(t )_R3 h(t )_R4
0.004 h(t ) "
System Reliability Performance of the Proposed Method Figure 2(a) and (b) depicts the two different sets of tests performed to evaluate system reliability performance of the proposed method vis-àvis the other methods. The abscissa represents the samples collected at different within-range threshold values of the component reliability. For example, the tenth sample is at the highest limit of the threshold value of the reliability of various components. The ordinate represents the system reliability performance of R5 over R2, R3, and R4. The most important observation is at the tenth test sample, where R s (t) is unique at the upper limits of the threshold values of various components. This unique observation can be seen in Table S1. The relative difference between the reliability of R5 versus that of R2, R3, and R4 shows improvement in R5 in terms of the overall system reliability. This clearly establishes that the system reliability of R5 is always better than that of R2, R3, and R4 as seen in the histograms in Figure 2(a) and (b). At the upper limits of the threshold values, the R5 showed 32.85% (= (0.9045 - 0.5760) # 100) improvement in system reliability compared to that of R2. Similarly, R5 obtained 7.51 and 25.65% improvement compared to that of R3 and R4, respectively. However, maximum improvement in system reliability of R5 when compared with that of R2 varied from 35.17 to 40.51% as depicted in Figure 2. Similarly, these values
0.003 0.002 0.001 0
1
1.1 1.3 1.318 1.5 1.7 1.718 1.9 2.1 2.118 2.2
2.4
Shape Parameter b " (c) Figure 5 – Histograms (a) and (b) showing comparison between R5, R2, R3, and R4 models in terms of impact of change in b and small variation in b on the MTTR (in days) and h(t) (in failure/day) of these models. (c) The trend of variation in h(t) with b. The symbol “ ” shows the measuring effect over FTPs due to small variation in shape parameter b.
corresponding to improvement in reliability of R5 with respect to R3 and R4 varied from 6.69 to 8.49% and 23.43 to 25.14%, respectively. Furthermore, it is observed that R5 has a much smaller chance of failure compared with the existing systems due to more if–else conditions as illustrated in Figure 3. Thus, it can be summarized that the proposed S2HRS-based HCS model exhibits superiority over all other existing models in terms of system reliability.
Experimental Analysis of FTP Here, three b-dependent parameters, TRmin, MTTR, and h(t), are considered for evaluating system availability. Their values are summarized in “System Reliability of R5” due to the small variation in shape parameter b. It is observed that TRmin and MTTR of R5 increase, and h(t) decreases with increase in b. Figure 4(g) shows that TRmin of R5 attains the lowest values for all bs between 1 and 2.4. TRmin is examined extensively, and it is found
march 2013 ■ IEEE industrial electronics magazine 37
Furthermore, Figure 5(a) explicitly TABLE 1–ALGORITHMS FORMULATED FOR THE “FEEDING” shows that MTTR of AND “DEPRESSURIZATION” ACTIONS OF R5. R5 attains the high1 IF T-83B reaches maximum threshold level then est values for all the 2 UV83B-2 ! O ! Fd_T83B; values of b between 3 Until reaches minimum threshold level ! CNT ! UV83B-2; 1 and 2.4. MTTR is 4 PV83B-2 ! C ! once having reached minimum threshold level; examined widely, and it is found that, 5 ELSE for system R5, it is 6 PV83B-1 ! zero level ! CNT ! Dp_T83B; greater than the 7 UV83B-1 ! maximum threshold level ! O ! Fl_T83B; MTTR of R2, R3, and 8 Maximum threshold level ! P_T83B; R4. For example, at 9 ELSE IF b = 2.4, the values 10 T-83C is at minimum threshold level then of MTTR are 395,309, 51,331, 104,672, and 11 PV83C-1 ! CNT ! Dp_T83C; 60,468 days for sys12 Zero level ! Dp_T83C; tems R5, R2, R3, 13 ELSE and R4, respective14 UV83C-2 ! maximum threshold level ! O ! Fl_T83C ly. For b = 2.4, the 15 END_IF MTTR required for 16 END_IF R5 is also greater, 395,309 days, than 17 END_IF that required for R2, 18 Go to step 1 for next iteration. R3, and R4. Figure 5 also demonstrates that h(t) of R5 attains the lowest values for all bs bethat, for system R5, the time required tween 1 and 2.4. The h(t) is examined for repair is minimal compared with extensively, and it is found that the the time required for repair by the chances of failure/rate of failure of other systems. This is clearly shown system R5 is minimal compared with in Figure 4 for all the values of b. that of R2, R3, and R4. This is clearly However, TRmin by itself varies with shown in Figure 5(b) and (c) for all different values of b as shown in the values of b. Figure 5(c) depicts Figure 4(g). For example, for b = 1, the trend of h(t) as a function of b the values of TRmin are 31, 172, 71, and for R5, R2, R3, and R4. The h(t) of R3 135 days for systems R5, R2, R3, and and R5 increases with increase in b, R4, respectively. For b = 1, TRmin rewhile that of R2 and R4 decreases as quired for R5 is also minimal 31 days, b increases. For example, at b = 2.4, compared with that required by R2, R3, and R4. the values of h(t) are 0.0007, 0.0052,
0.0026, and 0.0044 failures per day for systems R5, R2, R3, and R4, respectively. For b = 2.4, h(t) required for R5 is also minimal, 0.0007 failures per day, compared with that required for R2, R3, and R4. Thus, the proposed S2HRS-based HCS model reveals an advantage over all the existing models in terms of FTPs. As explained in the “Experimental Analysis of FTP” section, data related to three parameters, TRmin, MTTR, and h(t), are compiled in Table 2 for reference. Fault Diagnosis Cum Display Module of the Proposed Method Figure 7 shows a section of HMI employed as a fault diagnosis cum display module for online event monitoring of the proposed method. The module is used to identify faults and determine the location of the fault occurrence in the process model of H2. The identification and location of faults are the primary and most timeconsuming aspects of the troubleshooting process. Figure 7 depicts the two different sets of four events each of the monitoring section for two cold converters, T-83B and T-83C. The horizontal line represents the set of four control switches for the on/off cum control valves of T-83B and T-83C. The vertical line has a string of four control switches per cold converter to display the particular active mode of the respective cold converter. Each vertical string belongs to one particular event of the respective cold converter. The present diagram in Figure 7 shows
Figure 6 – The Piping and Instrumentation (P&I) diagram: active and hot redundant units of the proposed S2HRS-based HCS. 38 IEEE industrial electronics magazine ■ march 2013
that the cold converter T-83B is in filling mode, whereas T-83C is in feeding mode. Activation of more than one vertical ladder of switches corresponds to a malfunction at the corresponding location of the process model. The proposed display module of HMI possesses the implicit capability of identifying faults and determining fault location. The identification/ display of fault appreciably offsets the maintenance time of the faulted section. The fault diagnosis module of the proposed method is equipped with both manual and automatic fault diagnosis capability.
Conclusions An S2HRS-based HCS model was designed for cooling large generators.
TABLE 2–RESPONSE OF R5 VERSUS THAT OF R2, R3, AND R4 IN TERMS OF PARAMETERS TRmin, MTTR, and h(t). Systems
TRmin (days) for b = 1
MTTR (days) for b = 2.4
h(t) (failure/day) for b = 2.4
R2
172
51,331
0.0052
R3
71
104,672
0.0026
R4
135
60,468
0.0044
R5
31
395,309
0.0007
It has been shown to be reliable and cost effective for the capacity of n # 250–300 -MW units for the total plant capacity of 1000 MW or below. At the upper limits of threshold values, the proposed model (R5) showed significant, 32.85, 7.51, and 25.65%, improvement in system reliability compared with the existing
systems, R2, R3, and R4, respectively. It has been shown that the proposed model has a much lower chance of failures than the existing systems due to more if–else conditions. FTPs are examined extensively, and it is found that, for system R5, the time required for repair (TRmin) and the h(t) are minimal compared with the
Diagnosis_HMI FILLING T-83B UV 83B-1
UV 83B-2
PV 83B-1
PV 83B-2 PRESSURIZATION T-83B
UV 83B-1
UV 83B-2
PV 83B-1
PV 83B-2 FEEDING T-83B
UV 83B-1
UV 83B-2
PV 83B-1
PV 83B-2 DEPRESSURIZATION T-83B
UV 83B-1
UV 83B-2
PV 83B-1
PV 83B-2 FILLING T-83C
UV 83C-1
UV 83C-2
PV 83C-1
PV 83C-2 PRESSURIZATION T-83C
UV 83C-1
UV 83C-2
PV 83C-1
PV 83C-2 FEEDING T-83C
UV 83C-1
UV 83C-2
PV 83C-1
PV 83C-2 DEPRESSURIZATION T-83C
UV 83C-1 Cooling Section
UV 83C-2
PV 83C-1
PV 83C-2
H2-Cooling System
Trends
Figure 7 – Fault diagnosis module of the S2HRS-based HCS indicating present operating status of tank T-83B and T-83C.
march 2013 ■ IEEE industrial electronics magazine 39
References
The online monitoring and control of HCS is one of the key functions of SCADA. The module is used to identify faults and determine the location of the fault occurrence in the process model of H2. other systems, whereas the MTTR of R5 is greater than that of the existing systems, R2, R3, and R4. Hence, the proposed S2HRS-based HCS model exhibits superiority over all other existing models in terms of system reliability. In this way, the R5 enhances the gainful application of the heat recovery steam generator sections of CCPPs and has a direct impact on plant performance. Furthermore, the proposed display module of HMI possesses the implicit capability of identifying faults and determining fault location. The identification/ display of fault appreciably offsets the maintenance time of the faulted section. Plant health is continuously monitored and controlled by an FTViewSE platform designed for an online C&I system. The SCADA-based HCS intelligently processes the H 2 and provides an uninterrupted supply to the generators, taking corrective measures in case a fault occurs in the cooling process.
Biographies Gyan R. Biswal (gyan.biswal@snu. edu.in/
[email protected]) received his B.E. in electronics engineering from the Pandit Ravishankar Shukla University, Raipur, Chhattisgarh, India, in 1999, his M.Tech. (Honors) in instrumentation and control engineering from the Chhattisgarh Swami Vivekanand Technical University, Bhilai, India, in 2009, and his Ph.D. in electrical engineering (specializing in power system instrumentation) from the Indian Institute of Technology Roorkee, Uttarakhand, India, in 2012. He has around 25 publications to his credit and holds one patent as well. He has about eight years experience in academic institutions and more than a year industry
exposure in real-time instrumentation design and development work. His current research interests are focused on the area of C&I engineering and energy sciences. He is a Member of the IEEE. R.P. Maheshwari received his B.E. and M.Sc. (Engg.) degrees in electrical engineering from Aligarh Muslim University, India, in 1982 and 1985, respectively, and Ph.D. degree from University of Roorkee, India in 1996. He is currently a professor with the Department of Electrical Engineering, Indian Institute of Technology Roorkee, Uttarakhand, India, and a consultant in the area of small hydropower plants. He has published more than 140 research papers in various journals and conferences and has authored one book. His research interests include power system instrumentation and protection and digital signal and image processing. He is a Member of the IEEE. M.L. Dewal received his B.Tech. degree in electrical engineering in 1972 from the Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Udham Singh Nagar, Uttarakhand, India, and his M.E. and Ph.D. degrees in electrical engineering in 1974 and 1982, respectively, from the Indian Institute of Technology Roorkee, Uttarakhand, India. He has worked at the University of Technology, Baghdad, Iraq. He was a technical manager of two major power and desalination plants in the Sultanate of Oman from 1988–1991 and 1996–1997, respectively. He has published 80 research papers in international and national journals and conferences. His areas of interest are power system protection and digital image processing.
40 IEEE industrial electronics magazine ■ march 2013
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