Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network
Abstract
:1. Introduction
- By combining the Markov model and the risk transfer network together, an effective risk assessment method is proposed for the smart substation’s RPS;
- The operation states and the risk losses of all the devices in the protection system are fully discussed by using the Markov model;
- The risk losses of the bus protection system, the main transformer protection system, and the line protection system are analyzed. After that, the risk of the whole protection system is evaluated by risk with the transfer network;
- The proposed state evaluation and risk evaluation approach can increase the provided theoretical support to the state maintenance of the smart substation.
2. State Evaluation of Relay Protection Device and Markov Model
3. State Rating and Risk Assessment of Protection System
- (a)
- Network node
- (b)
- Agent
- (c)
- Agent set
- (d)
- Directed edges
- (e)
- Degree
4. Numerical Example
5. Discussions and Future Work
- Compared to Monte Carlo simulation [12,13,14], neural network [26,27,28,29,30,31,32,33,34,35,36,37], and fault tree [35,36,37], there is a strong correlation between the state transition mechanism of the three-state progressive model based on the Markov model and the change mechanism of the RPS’s operation state, which can more accurately reflect the operation state change of the protection system compared with other models.
- In the proposed assessment method, not only is the operation risk of the secondary equipment itself considered, but the function abnormality of the protection system caused by the abnormality of the secondary equipment, which may lead to the operation risk of the unprotected operation of the primary equipment, is taken into account as well. In addition, the risk loss of the primary equipment is quantitatively analyzed; compared with other methods, i.e., [21,22,23,24,26,27,28,34,35,36], it is more intuitive and accurate to reflect the operational risk of the protection system.
- Most of the existing operation state evaluation methods focus on the risk assessment of the secondary system or a protection function. When the system or a protection function operates abnormally, it is difficult to find the abnormal operation equipment in time, which may affect the normal operation of the primary equipment and cause power failure loss. In the developed results in this paper, more attention is paid to the operation state and the operation risk of a certain secondary equipment, thus much more objective and accurate risk assessment results could be obtained.
- In the future, with the development of information and communication techniques, the overall perception of the secondary system’s operation state would be realized. Therefore, with the perception results, how to improve the accuracy of the risk assessment results will be an interesting topic.
- A complete monitoring statistical database is important, which can closely reflect the operation state of the secondary system. Hence, in the future, how to construct this database and effectively use this data would be worthy of being investigated.
- Based on the developed Markov model, how to improve and establish the model for hidden faults in the protection system is also a future research topic.
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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State Transition Probability 1 | Y1 | Y2 | Y3 | Y4 | Y5 | Y6 |
---|---|---|---|---|---|---|
Line protection | 10.19 | 3.70 | 8.69 | 1.13 | 0.41 | 0.96 |
Busbar protection | 8.60 | 3.12 | 7.2 | 0.96 | 0.35 | 0.75 |
Main transformer protection | 6.11 | 2.1 | 5.12 | 0.67 | 0.22 | 0.58 |
Bus protection | 5.25 | 1.81 | 4.2 | 0.55 | 0.24 | 0.44 |
Merge unit | 11.82 | 4.35 | 10.08 | 1.25 | 0.45 | 1.10 |
Intelligent terminal | 5.66 | 2.1 | 4.84 | 0.63 | 0.20 | 0.58 |
Network switch | 9.16 | 3.12 | 7.69 | 1.02 | 0.37 | 0.86 |
Failure Probability of Protection Device | W4 | W5 | W6 | W7 |
---|---|---|---|---|
Line protection | 1.42 | 1.02 | 0.98 | 1.64 |
Busbar protection | 1.12 | 0.84 | 0.8 | 1.2 |
Main transformer protection | 0.88 | 0.58 | 0.59 | 0.96 |
Bus protection | 0.81 | 0.51 | 0.42 | 0.82 |
Merge unit | 1.62 | 1.18 | 1.08 | 1.85 |
Intelligent terminal | 0.78 | 0.52 | 0.53 | 0.89 |
Network switch | 1.19 | 0.90 | 0.95 | 1.39 |
F1 | F2 | F3 | F7 |
---|---|---|---|
833.33 | 1250 | 416.67 | 208.33 |
Steady-State Probability | PES | PAS | PIS |
---|---|---|---|
Line protection | 0.7553 | 0.2387 | 0.006 |
Busbar protection | 0.8145 | 0.1809 | 0.0046 |
Main transformer protection | 0.7623 | 0.2342 | 0.0035 |
Bus protection | 0.7155 | 0.2816 | 0.0029 |
Merge unit | 0.7555 | 0.2377 | 0.0068 |
Intelligent terminal | 0.7390 | 0.2578 | 0.0032 |
Network switch | 0.7965 | 0.1982 | 0.0053 |
Number | Subject Set | Primary Equipment Served |
---|---|---|
1 | Ma1Mb1Mi1Mj1 | Line interval |
2 | Mc1Md1Mi2Mk1 | Busbar interval |
3 | Me1Mf1Mg1Mh1Mi3Ml1 | Main transformer interval |
4 | Ma2Mb2Mc2Md2Me2Mf2 Mg2Mh2Mi4Mm1 | Lines, busbars, and main transformer interval |
Price | Overhaul Price (/Ten Thousand) | Plug-In or Equipment Price (/Ten Thousand) |
---|---|---|
Line protection | 1.46 | 5 |
Busbar protection | 0.56 | 5 |
Main transformer protection | 1.59 | 5 |
Bus protection | 0.56 | 5 |
Merge unit | 0.28 | 5 |
Intelligent terminal | 0.28 | 5 |
Network switch | 0.23 | 5 |
Breaker | 2.07 | 80 |
Transformer | 8.47 | 300 |
Isolating switch and other primary equipment | 0.81 | 60 |
Protection System | Line | Busbar | Main Transformer | Bus |
---|---|---|---|---|
System failure rate | 0.0213 | 0.0199 | 0.0288 | 0.0482 |
Risk Loss | Secondary Loss (/Yuan) | Primary Loss (/Yuan) | Total Loss (/Yuan) |
---|---|---|---|
Line protection system | 1192.79 | 16.78 | 1209.57 |
Busbar protection system | 1060.95 | 3.23 | 1064.18 |
Main transformer protection system | 1563.84 | 22.34 | 1586.18 |
Bus protection system | 2550.43 | 42.35 | 2592.78 |
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Nan, D.; Wang, W.; Mahfoud, R.J.; Haes Alhelou, H.; Siano, P.; Parente, M.; Zhang, L. Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network. Energies 2020, 13, 1777. https://doi.org/10.3390/en13071777
Nan D, Wang W, Mahfoud RJ, Haes Alhelou H, Siano P, Parente M, Zhang L. Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network. Energies. 2020; 13(7):1777. https://doi.org/10.3390/en13071777
Chicago/Turabian StyleNan, Dongliang, Weiqing Wang, Rabea Jamil Mahfoud, Hassan Haes Alhelou, Pierluigi Siano, Mimmo Parente, and Lu Zhang. 2020. "Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network" Energies 13, no. 7: 1777. https://doi.org/10.3390/en13071777
APA StyleNan, D., Wang, W., Mahfoud, R. J., Haes Alhelou, H., Siano, P., Parente, M., & Zhang, L. (2020). Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network. Energies, 13(7), 1777. https://doi.org/10.3390/en13071777