中国电机工程学报
中國電機工程學報
중국전궤공정학보
ZHONGGUO DIANJI GONGCHENG XUEBAO
2015年
10期
2471-2479
,共9页
智能配用电%电力无线通信%频谱感知%能量感知%循环平稳特征感知
智能配用電%電力無線通信%頻譜感知%能量感知%循環平穩特徵感知
지능배용전%전력무선통신%빈보감지%능량감지%순배평은특정감지
smart distribution and utilization%electric power wireless communication%spectrum sensing%energy sensing%cyclostationary feature sensing
新型电力无线宽带系统是为满足智能配用电系统的通信需求,在电力专用230 MHz频谱资源上研究开发的电力无线专网,该系统在应用过程中可能存在与230数传电台共用同一段频谱的情况。为保证2个系统同时稳定、高效的运行,提出一种结合能量感知和循环平稳特征感知2种检测算法的两级双门限本地频谱感知算法。这种算法首先通过能量感知法进行粗检测,然后根据噪声不确定性情况选择使用循环平稳特征感知算法进行细检测。若噪声的不确定性大,就自动增加循环平稳特征感知的比例,若噪声的不确定性小,则自动减小甚至不进行循环平稳特征感知。这样,既保证了检测的正确性,又能动态调整计算量。仿真结果和性能分析表明,该算法在保证系统检测性能的同时,降低计算的复杂度,并有效提升检测性能。
新型電力無線寬帶繫統是為滿足智能配用電繫統的通信需求,在電力專用230 MHz頻譜資源上研究開髮的電力無線專網,該繫統在應用過程中可能存在與230數傳電檯共用同一段頻譜的情況。為保證2箇繫統同時穩定、高效的運行,提齣一種結閤能量感知和循環平穩特徵感知2種檢測算法的兩級雙門限本地頻譜感知算法。這種算法首先通過能量感知法進行粗檢測,然後根據譟聲不確定性情況選擇使用循環平穩特徵感知算法進行細檢測。若譟聲的不確定性大,就自動增加循環平穩特徵感知的比例,若譟聲的不確定性小,則自動減小甚至不進行循環平穩特徵感知。這樣,既保證瞭檢測的正確性,又能動態調整計算量。倣真結果和性能分析錶明,該算法在保證繫統檢測性能的同時,降低計算的複雜度,併有效提升檢測性能。
신형전력무선관대계통시위만족지능배용전계통적통신수구,재전력전용230 MHz빈보자원상연구개발적전력무선전망,해계통재응용과정중가능존재여230수전전태공용동일단빈보적정황。위보증2개계통동시은정、고효적운행,제출일충결합능량감지화순배평은특정감지2충검측산법적량급쌍문한본지빈보감지산법。저충산법수선통과능량감지법진행조검측,연후근거조성불학정성정황선택사용순배평은특정감지산법진행세검측。약조성적불학정성대,취자동증가순배평은특정감지적비례,약조성적불학정성소,칙자동감소심지불진행순배평은특정감지。저양,기보증료검측적정학성,우능동태조정계산량。방진결과화성능분석표명,해산법재보증계통검측성능적동시,강저계산적복잡도,병유효제승검측성능。
To meet the communication needs of smart power distribution and utilization system, a novel power private communication network is developed. The system faces a problem that it may operate coexisted with a 230 data transceiver system. In order to keep the two systems operating stably and efficiently at the same time, this paper presented a two-stage double-threshold spectrum sensing algorithm. The algorithm integrates the energy sensing and cyclostationary feature sensing. The algorithm is used for coarse detection firstly by energy sensing, then used for fine detected by cyclostationary sensing. The threshold will be adjusted adaptively according to the noise uncertainty. If the noise uncertainty increases, it automatically increases the proportion of cyclostationary feature sensing; if the noise uncertainty decreases, it automatically reduces or even no cyclostationary feature sensing. Thus, it can ensure the detection performance and adjust the amount of computation dynamically as well. The analysis of the simulation results show that the algorithm improves the detection performance obviously and reduces the computing complexity.