PUBLIC SAFETY

Adaptive identification model for multisource element risks in electric power operations

Expand
  • 1. Jinan Power Supply Company, State Grid Shandong Electric Power Company, Jinan 250013, China;
    2. State Grid Shandong Electric Power Company, Jinan 250001, China;
    3. China National Institute of Standardization, Beijing 100191, China

Received date: 2024-01-16

  Online published: 2024-05-14

Abstract

[Objective] Electric power operators often encounter various safety risks that severely threaten their life and health. However, accurate identification of such risks is challenging because of the complex nature of work scenarios and the high randomness of risk factors. Currently, the analysis of electric power operation risks mostly involves the qualitative analysis of the entire operation process. Therefore, there is an urgent need to use appropriate technical means to quantitatively analyze the risks in electric power operations, providing safety assurance for electrical power operators.[Methods] This study develops a dynamic adaptive risk identification (ARI) model compatible with various typical spatial scenarios in the electric power industry. First, we build a macrolevel risk identification framework for typical electric power operations. Second, we establish a comprehensive risk factor classification system for multiple risk sources associated with typical operations. Finally, the ARI model is proposed, which incorporates the progressive honey badger algorithm, maximum entropy criterion, and constraint adjustment mechanism. Case studies validate the effectiveness of this model.[Results] The research outcomes based on case studies demonstrate that the ARI model exhibits high robustness, high cost effectiveness, and low bias risk. Compared with traditional risk identification methods, the ARI model effectively mitigates the effect of subjective judgments by integrating objective criteria. Its more balanced weight distribution reduces unwarranted subjective assumptions arising from inaccurate objective risk data. If the workspace undergoes alterations, there is no imperative need to solicit experts for a reassessment because the ARI model dynamically adjusts the distribution of factor weights based on typical spatial features using the progressive honey badger algorithm and constraint adjustment mechanism. This dynamic adaptation facilitates cost-effective and efficient risk identification in typical spaces.[Conclusions] The proposed model effectively quantifies the multisource element risks of typical operation scenarios in the electric power industry. Integrating the model with existing risk information systems further enhances precision and efficiency of existing risk control measures, providing crucial technical support for the safety assurance of electrical power operators.

Cite this article

XU Mingkai, ZHU Kunshuang, LI Yuanliang, YANG Xiaoshuai, QIN Tingxin, WANG Wan . Adaptive identification model for multisource element risks in electric power operations[J]. Journal of Tsinghua University(Science and Technology), 2024 , 64(6) : 1047 -1059 . DOI: 10.16511/j.cnki.qhdxxb.2024.22.018

References

[1] 李湃,黄越辉,张金平,等.多能互补发电系统电/热/氢储能容量协调优化配置[J/OL].(2023-08-31)[2023-12-15].中国电机工程学报. https://link.cnki.net/urlid/11.2107.TM.20230830.1851.007. LI P, HUANG Y H, ZHANG J P, et al. Capacity coordinated optimization of battery, thermal and hydrogen storage system for multi-energy complementary power system[J/OL].(2023-08-31)[2023-12-15]. Proceedings of the CSEE. https://link.cnki.net/urlid/11.2107.TM.20230830.1851.007. (in Chinese)
[2] 高阳,朱坤双,黄海静,等.我国电力作业安全管理体系构建及分析研究[J].武汉理工大学学报(信息与管理工程版), 2022, 44(6):894-898. GAO Y, ZHU K S, HUANG H J, et al. Research on the construction of safety management system for electric power operation in China[J]. Journal of Wuhan University of Technology (Information&Management Engineering), 2022, 44(6):894-898.(in Chinese)
[3] 许永刚,朱坤双,王立峰,等.电力作业风险智能预警设计与实现[J].数字通信世界, 2023(6):54-56. XU Y G, ZHU K S, WANG L F, et al. Design and implementation of intelligent early warning for electric power operation risk[J]. Digital Communication World, 2023(6):54-56.(in Chinese)
[4] 陈碧云,李弘斌,李滨.基于数据挖掘和CAPSO-SNN的电力作业风险态势感知[J].电力自动化设备, 2020, 40(1):148-155. CHEN B Y, LI H B, LI B. Power operation risk situation awareness based on data mining and CAPSO-SNN[J]. Electric Power Automation Equipment, 2020, 40(1):148-155.(in Chinese)
[5] 何敏,秦亮,赵峰,等.面向电力系统现场作业的安全风险管控智能检测算法[J].高电压技术, 2023, 49(6):2442-2457. HE M, QIN L, ZHAO F, et al. Intelligent detection algorithm of security risk management and control for power system on-site operation[J]. High Voltage Engineering, 2023, 49(6):2442-2457.(in Chinese)
[6] 葛磊蛟,李元良,陈艳波,等.智能配电网态势感知关键技术及实施效果评价[J].高电压技术, 2021, 47(7):2269-2280. GE L J, LI Y L, CHEN Y B, et al. Key technologies of situation awareness and implementation effectiveness evaluation in smart distribution network[J]. High Voltage Engineering, 2021, 47(7):2269-2280.(in Chinese)
[7] 马富齐,王波,董旭柱,等.电力工业安全影像解译:基本概念与技术框架[J].中国电机工程学报, 2022, 42(2):458-474. MA F Q, WANG B, DONG X Z, et al. Safety image interpretation of power industry:Basic concepts and technical framework[J]. Proceedings of the CSEE, 2022, 42(2):458-474.(in Chinese)
[8] 葛磊蛟,李元良,汪宇倩.智能配电网态势感知实现效果综合评估模型[J].天津大学学报(自然科学与工程技术版), 2020, 53(11):1101-1111. GE L J, LI Y L, WANG Y Q. Comprehensive evaluation model for situational awareness effects of a smart distribution network[J]. Journal of Tianjin University (Science and Technology), 2020, 53(11):1101-1111.(in Chinese)
[9] WANG S X, GE L J, CAI S X, et al. Hybrid interval AHP-entropy method for electricity user evaluation in smart electricity utilization[J]. Journal of Modern Power Systems and Clean Energy, 2018, 6(4):701-711.
[10] ARMAN H, HADI-VENCHEH A, ARMAN A, et al. Revisiting the approximated weight extraction methods in fuzzy analytic hierarchy process[J]. International Journal of Intelligent Systems, 2021, 36(4):1644-1667.
[11] 程明熙.处理多目标决策问题的二项系数加权和法[J].系统工程理论与实践, 1983, 3(4):23-26. CHENG M X. Binomial coefficient weighted sum method for dealing with multi-objective decision-making problems[J]. Systems Engineering:Theory&Practice, 1983, 3(4):23-26.(in Chinese)
[12] WANG J S, DENG X C. Comprehensive economic benefit evaluation method of coastal enterprises based on AHP[J]. Journal of Coastal Research, 2020, 103(S1):24-28.
[13] LI M, WANG J L, LI Y, et al. Evaluation of sustainability information disclosure based on entropy[J]. Entropy, 2018, 20(9):689.
[14] 王依宁,解大,王西田,等.基于PCA-LSTM模型的风电机网相互作用预测[J].中国电机工程学报, 2019, 39(14):4070-4080. WANG Y N, XIE D, WANG X T, et al. Prediction of interaction between grid and wind farms based on PCA-LSTM model[J]. Proceedings of the CSEE, 2019, 39(14):4070-4080.(in Chinese)
[15] GE L J, LI Y L, LI S X, et al. Evaluation of the situational awareness effects for smart distribution networks under the novel design of indicator framework and hybrid weighting method[J]. Frontiers in Energy, 2021, 15(1):143-158.
[16] 张华一,文福拴,张璨,等.基于前景理论的电网建设项目组合多属性决策方法[J].电力系统自动化, 2016, 40(14):8-14. ZHANG H Y, WEN F S, ZHANG C, et al. Prospect theory based multiple-attribute decision-making method for determining portfolio of construction projects in power systems[J]. Automation of Electric Power Systems, 2016, 40(14):8-14.(in Chinese)
[17] 张宏博,陈伟炯,闫明.多式联运路径优化模型中的贝叶斯极大熵权重自学习方法研究[J].计算机应用与软件, 2018, 35(10):28-32, 44. ZHANG H B, CHEN W J, YAN M. The Bayesian maximun entropy weight self-learning method in the multimodal transport path optimization model[J]. Computer Applications and Software, 2018, 35(10):28-32, 44.(in Chinese)
[18] GE L J, LI Y L, ZHU X S, et al. An evaluation system for HVDC protection systems by a novel indicator framework and a self-learning combination method[J]. IEEE Access, 2020, 8:152053-152070.
[19] WANG Y Q, GE L J, ZHANG N. Hybrid evaluation method for dispatching control level of smart distribution network[J]. Journal of Electrical Engineering&Technology, 2019, 14(6):2263-2275.
[20] 阿辽沙·叶,祝恩国,成倩,等.用电设备安全评估的改进区间层次分析法[J].电力系统及其自动化学报, 2015, 27(1):32-36. ALIAOSHA Y, ZHU E G, CHENG Q, et al. Improved interval analytic hierarchy process method for electrical equipment safety assessment[J]. Proceedings of the CSU-EPSA, 2015, 27(1):32-36.(in Chinese)
[21] YADAV D. Blood coagulation algorithm:A novel bio-inspired meta-heuristic algorithm for global optimization[J]. Mathematics, 2021, 9(23):3011.
[22] 许德刚,王再庆,郭奕欣,等.鲸鱼优化算法研究综述[J].计算机应用研究, 2023, 40(2):328-336. XU D G, WANG Z Q, GUO Y X, et al. Review of whale optimization algorithm[J]. Application Research of Computers, 2023, 40(2):328-336.(in Chinese)
Outlines

/