新型电力系统分布式家庭光伏采集场景下的信任评估模型

doi:10.11959/j.issn.1000-436x.2023137

Abstract

Abstract:

Aiming at the problem that the existing sensor network trust evaluation model could not be directly applied to the new power system distributed home photovoltaic collection scenario, which was difficult to meet the require-ments of strong computing power and high defense power of the new power system, a distributed dynamic trust evaluation model based on multi-index detection was proposed.Firstly, the communication trust evaluation based on Bayes was carried out according to the historical interaction of terminal acquisition nodes.Then, the currently col-lected data was evaluated by perceptual trust based on its historical data support degree and regional trust based on probability density.Finally, the entropy weight method was used to decentralize each trust module’s values dynamically.The node activeness and double reward and punishment mechanism were introduced to calculate the comprehensive trust value and realize the dynamic update.The experimental results show that the four levels of the trust evaluation model are suitable for the new power system environment and can be used to detect the distributed in 20 round period of household photovoltaic power generation collection given the signal in the scene, achieve dynamic and accurate trust evaluation of abnormal nodes in the case of physical environment factors, equipment quality factors, human misoperation and malicious intrusion.

Key words: sensor network, trust evaluation, support degree, probability density function

CLC Number:

TP393

Li LI, Xiaolong WANG, Zhixin ZHANG, Rongliang SHI, Xu GUO. Trust evaluation model for distributed home photovoltaic collection scenarios in new power system[J]. Journal on Communications, 2023, 44(7): 197-206.

Figures/Tables 14

References 23

[1]	杨梓, 姚美娇 . 新型电力系统下的数字能源生态[N]. 中国能源报, 2022-09-05(9).
	YANG Z , YAO M J . Digital energy ecology under new power system[N]. China Energy News, 2022-09-05(9).
[2]	徐岩, 何宸, 付超 ,等. 多种调度模式下的光储电站经济性最优储能容量配置分析[J]. 太阳能学报, 2019,40(6): 1632-1640.
	XU Y , HE C , FU C ,et al. Analysis of economical optimal energy storage capacity allocation of optical storage power station under multiple scheduling modes[J]. Acta Energiae Solaris Sinica, 2019,40(6): 1632-1640.
[3]	FENG R , HAN X , LIU Q ,et al. A credible Bayesian-based trust management scheme for wireless sensor networks[J]. International Journal of Distributed Sensor Networks, 2015,11(11): 678926.
[4]	周治平, 邵楠楠 . 基于贝叶斯的改进WSNs信任评估模型[J]. 传感技术学报, 2016,29(6): 927-933.
	ZHOU Z P , SHAO N N . An Improved Trust Evaluation Model Based on Bayesian for WSNs[J]. Chinese Journal of Sensors and Actuators, 2016,29(6): 927-933.
[5]	ZHANG B , HUANG Z , XIANG Y . A novel multiple-level trust management framework for wireless sensor networks[J]. Computer Networks, 2014,72: 45-61.
[6]	秦丹阳, 贾爽, 杨松祥 ,等. 基于信任感知的无线传感器网络安全路由机制研究[J]. 通信学报, 2017,38(10): 60-70.
	QIN D Y , JIA S , YANG S X ,et al. Research on trust sensing based secure routing mechanism for wireless sensor network[J]. Journal on Communications, 2017,38(10): 60-70.
[7]	韩优佳, 胡黄水, 姚美琴 . 基于信任感知的无线传感器网络安全路由协议[J]. 计算机工程, 2021,47(9): 145-152.
	HAN Y J , HU H S , YAO M Q . Trust-aware secure routing protocol for wireless sensor networks[J]. Computer Engineering, 2021,47(9): 145-152.
[8]	朱子豪, 刘光杰 . 一种面向物联网节点的动态信任评估模型[J]. 重庆理工大学学报(自然科学), 2022,36(7): 188-196.
	ZHU Z H , LIU G J . Dynamic trust assessment model for Internet of things node[J]. Journal of Chongqing University of Technology(Natural Science) , 2022,36(7): 188-196.
[9]	YE Z W , WEN T , LIU Z Y ,et al. An efficient dynamic trust evaluation model for wireless sensor networks[J]. Journal of Sensors, 2017,2017: 1-16.
[10]	周远林, 陶洋, 李正阳 ,等. 基于双簇头的无线传感器网络反馈信任模型[J]. 计算机工程, 2021,47(3): 174-182.
	ZHOU Y L , TAO Y , LI Z Y ,et al. Double cluster head-based feedback trust model for wireless sensor network[J]. Computer Engineering, 2021,47(3): 174-182.
[11]	赵立瑾, 王新胜, 夏纯中 . 基于信任模型的PBFT共识机制研究[J]. 计算机应用与软件, 2022,39(7): 304-309.
	ZHAO L J , WANG X S , XIA C Z . PBFT consensus mechanism based on trust model[J]. Computer Applications and Software, 2022,39(7): 304-309.
[12]	许蕴韬, 朱俊武, 孙彬文 ,等. 选举供应链:基于区块链的供应链自治框架[J]. 计算机应用, 2022,42(6): 1770-1775.
	XU Y T , ZHU J W , SUN B W ,et al. Election-based supply chain:a supply chain autonomy framework based on blockchain[J]. Journal of Computer Applications, 2022,42(6): 1770-1775.
[13]	ZHU Y X , TIAN D Z , YAN F . Effectiveness of entropy weight method in decision-making[J]. Mathematical Problems in Engineering, 2020,2020: 1-5.
[14]	朱明星, 张毅恒, 张华赢 ,等. 考虑指标特性的区域电网电能质量评估方法[J]. 电力系统及其自动化学报, 2022,34(8): 150-158.
	ZHU M X , ZHANG Y H , ZHANG H Y ,et al. Regional power grid quality evaluation method considering index characteristics[J]. Proceedings of the CSU-EPSA, 2022,34(8): 150-158.
[15]	于秩彬, 王春芳, 周钊正 ,等. 青岛地区四季天气对光伏发电量的影响[J]. 广东电力, 2019,32(6): 48-54.
	YU Z B , WANG C F , ZHOU Z Z ,et al. Influence of weathers in four seasons on photovoltaic power generation in Qingdao[J]. Guangdong Electric Power, 2019,32(6): 48-54.
[16]	马岩岩 . 高海拔荒漠地区光伏系统发电量受光照强度的影响分析[J]. 机电信息, 2020,629(23): 44-45.
	MA Y Y . Analysis of the influence of light intensity on the power generation of photovoltaic system in high altitude desert area[J]. Mechanical and Electrical Information, 2020,629(23): 44-45.
[17]	王卫卫, 徐光辉 . 分布式光伏发电系统发电量预测方法[J]. 电源技术, 2020,44(11): 1630-1633.
	WANG W W , XU G H . Prediction method of generating capacity of distributed photovoltaic power generation system[J]. Chinese Journal of Power Sources, 2020,44(11): 1630-1633.
[18]	闫强强, 张敏, 荀亚玲 . 采用有效邻近点和适应密度的密度聚类算法[J]. 计算机技术与发展, 2022,32(9): 14-22.
	YAN Q Q , ZHANG M , XUN Y L . A density clustering algorithm based on effective neighboring points and adaptive density distribution[J]. Computer Technology and Development, 2022,32(9): 14-22.
[19]	YAGER R R . The power average operator[J]. IEEE Transactions on Systems,Man,and Cybernetics - Part A:Systems and Humans, 2001,31(6): 724-731.
[20]	王德文, 杨凯华 . 基于生成式对抗网络的窃电检测数据生成方法[J]. 电网技术, 2020,44(2): 775-782.
	WANG D W , YANG K H . A data generation method for electricity theft detection using generative adversarial network[J]. Power System Technology, 2020,44(2): 775-782.
[21]	KNORR E M , NG R T . Algorithms for mining distance-based outliers in large datasets[C]// Proceedings of the 24rd International Conference on Very Large Data Bases. New York:ACM Press, 1998: 392-403.
[22]	陈辉, 张春雨 . 基于信任模型的分簇 WSNs 可靠数据采集方法[J]. 传感技术学报, 2021,34(11): 1530-1536.
	CHEN H , ZHANG C Y . A reliable data gathering algorithm for clustering WSNs based on trust model[J]. Chinese Journal of Sensors and Actuators, 2021,34(11): 1530-1536.
[23]	赵国生, 王甜甜, 王健 . 一种边缘设备动态信任度的评估模型[J]. 计算机工程与科学, 2021,43(9): 1574-1583.
	ZHAO G S , WANG T T , WANG J . A dynamic trust evaluation model for edge devices[J]. Computer Engineering and Science, 2021,43(9): 1574-1583.

Metrics

Recommended 0

No Suggested Reading articles found!

节点信用等级	定义	操作
可信节点	0.8≤ATe＜1	正常通信，停止奖励因子
一般节点	0.2≤ATe＜0.8	正常通信
异常节点	0≤ATe＜0.2	停止通信

参数	数值
实验区域	100 m×100 m
时间滑动窗口大小r	30
支持度函数调节变量A	1
支持度衰弱因子τ	0.5
动态惩罚系数初值δ	0.5
奖励因子λ	0.3
惩罚因子θ	0.7
感知信任初值PT	0.8
综合信任初值ATe	0.5

Trust evaluation model for distributed home photovoltaic collection scenarios in new power system

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 23

Related Articles 15

Metrics

Recommended 0

[1]	Xin SUN, Guifu ZHANG, Hongyan XING, Wang Zenghui. Research on intrusion detection for maritime meteorological sensor network based on balancing generative adversarial network [J]. Journal on Communications, 2023, 44(4): 124-136.
[2]	Yue ZHAO, Zan LI, Bing LI, Benjian HAO. TDoA localization-refused area analysis and node placement strategy research [J]. Journal on Communications, 2023, 44(1): 1-13.
[3]	Cuiran LI, Xuejie WANG, Jianli XIE, Anqi LYU. Routing algorithm for railway monitoring linear WSN based on improved PSO [J]. Journal on Communications, 2022, 43(5): 155-165.
[4]	Xiujuan DU, Lijuan WANG, Jingping LIU, Long JIN. Ranging localization method for nodes in underwater wireless sensor network based on zeroing neural dynamics [J]. Journal on Communications, 2022, 43(10): 177-185.
[5]	Aijing SUN, Shichang LI, Yicai ZHANG. WSN clustering routing algorithm based on PSO optimized fuzzy C-means [J]. Journal on Communications, 2021, 42(3): 91-99.
[6]	Yanjing SUN,Junqiu YANG,Yan CHEN,Yanfen WANG,Xiaojing CHEN,Fengzhen LIU,Zhi SUN. RAW online regrouping method in IEEE 802.11ah protocol for industrial Internet of things [J]. Journal on Communications, 2020, 41(4): 92-101.
[7]	Junbin LIANG,Xiang ZHOU,Fangqiang MA,Chan JIANG,Zongjian HE. Low-latency neighbor discovery algorithm based on multi-beacon message in mobile low-duty-cycle sensor network [J]. Journal on Communications, 2019, 40(8): 178-188.
[8]	Tianjing WANG,Xiuqin LI,Guangwei BAI,Hang SHEN. Multi-target localization algorithm based on adaptive grid in wireless sensor network [J]. Journal on Communications, 2019, 40(7): 197-207.
[9]	Linlan LIU,Shengrong GAO,Jian SHU. Link quality prediction based on random forest [J]. Journal on Communications, 2019, 40(4): 202-211.
[10]	Xiaochen HAO,Ning YAO,Lixia XIE,Jiaojiao WANG,Liyuan WANG. Research on WSN lifetime optimization game algorithm combined power and channel [J]. Journal on Communications, 2019, 40(4): 62-70.
[11]	Xiaonian WU,Chuyun ZHANG,Runlian ZHANG,Yaping SUN. Clustering routing protocol based on improved PSO algorithm in WSN [J]. Journal on Communications, 2019, 40(12): 114-123.
[12]	Haoran LIU,Heyao ZHAO,Yujing DENG,Xingqi WANG,Rongrong YIN. Coverage control algorithm for wireless sensor networks based on non-cooperative game [J]. Journal on Communications, 2019, 40(1): 71-78.
[13]	Jian SHU,Manlan LIU,Yaqing SHANG,Yubin CHEN,Linlan LIU. Link quality prediction model based on Gaussian process regression [J]. Journal on Communications, 2018, 39(7): 148-156.
[14]	Tian WANG,Guangxue ZHANG,Shaobin CAI,Weijia JIA,Guojun WANG. Survey on trust evaluation mechanism in sensor-cloud [J]. Journal on Communications, 2018, 39(6): 37-51.
[15]	Xiaochen HAO,Liyuan WANG,Jinshuo LIU,Lixia XIE,Wenhuan ZHANG. Resource allocation optimization algorithm based on double populations differential evolution in WSN [J]. Journal on Communications, 2018, 39(4): 68-75.