技术领域technical field
本发明属于电网防灾减灾领域，尤其涉及一种基于灾害类型和线路信息的重合闸成功率关联分析方法。The invention belongs to the field of power grid disaster prevention and mitigation, and in particular relates to a reclosing success rate correlation analysis method based on disaster types and line information.
背景技术Background technique
户外运行的输电线路受污秽、覆冰、雷击、风偏、鸟害、外力破坏等灾害的影响严重，很容易引发跳闸故障。据对2015年国家电网公司辖下330kV及以上电压等级输电线路跳闸数据的统计结果可知，引起线路故障跳闸的主要灾害类型为雷击、冰害、鸟害、风害和外力破坏。其中，雷击是造成输电线路跳闸的首要原因，约占跳闸总记录数的40.68％；冰害、鸟害、外力破坏造成线路故障跳闸的概率接近，约为16％；风害约占9.64％。Transmission lines operating outdoors are seriously affected by disasters such as pollution, icing, lightning strikes, wind deflection, bird damage, and external damage, which can easily cause tripping failures. According to the statistical results of tripping data of 330kV and above transmission lines under the State Grid Corporation of China in 2015, the main types of disasters that cause line fault tripping are lightning strikes, ice damage, bird damage, wind damage and external force damage. Among them, lightning strikes are the primary cause of transmission line trips, accounting for about 40.68% of the total number of trip records; ice damage, bird damage, and external damage have a close probability of line fault trips, about 16%; wind damage accounts for about 9.64%.
输电线路因雷击、大风、鸟类放电引发的跳闸事故常为瞬时性故障，约占电力系统所有故障的60～90％，可由重合闸装置快速恢复供电。重合闸在提高瞬时性故障时线路供电的连续性、系统运行的稳定性等方面起到了重大作用，但目前的重合闸技术还不能区分瞬时性故障和长时间故障。盲目地重合闸可能会使线路重合于长时间故障上，对电力系统将会造成巨大冲击，引发更加严重的停电事件。The tripping accidents caused by lightning strikes, strong winds, and bird discharges on transmission lines are often instantaneous faults, accounting for about 60-90% of all faults in the power system, and the power supply can be quickly restored by the reclosing device. Reclosing has played an important role in improving the continuity of line power supply and the stability of system operation during transient faults, but the current reclosing technology cannot distinguish between transient faults and long-term faults. Blind reclosing may cause the line to reclose on a long-term fault, which will cause a huge impact on the power system and cause more serious power outages.
发明内容Contents of the invention
本发明旨在提供一种基于灾害类型和线路信息的重合闸成功率关联分析方法，通过挖掘出的与重合闸成功率相关的强关联规则，优化线路重合闸策略，从而提高电网的重合闸成功率。The present invention aims to provide a reclosing success rate correlation analysis method based on disaster type and line information, and optimize the line reclosing strategy through the excavated strong correlation rules related to the reclosing success rate, thereby improving the reclosing success of the power grid Rate.
本发明解决其技术问题所采用的技术方案是：The technical solution adopted by the present invention to solve its technical problems is:
本发明提供一种基于灾害类型和线路信息的重合闸成功率关联分析方法，包括以下步骤：The present invention provides a reclosing success rate correlation analysis method based on disaster type and line information, comprising the following steps:
S1、获取线路跳闸信息，提取灾害类型以及线路参数；S1. Obtain line trip information, extract disaster type and line parameters;
S2、结合线路重合闸成功情况，完成原始关联分析数据集的输入；S2. Combining with the success of line reclosing, complete the input of the original correlation analysis data set;
S3、设置支持度阈值，采用Apriori算法搜索频繁项集；S3. Set the support threshold, and use the Apriori algorithm to search for frequent itemsets;
S4、以重合情况为规则后件，筛选出满足要求的强关联规则。S4. Taking the coincidence situation as the postcondition of the rule, screen out the strong association rules that meet the requirements.
进一步地，本发明的步骤S1中的灾害类型为雷击、冰害、鸟害、风害和外力破坏，线路参数为线路的管辖单位、电压类型、电压等级和线路名称。Further, the disaster types in step S1 of the present invention are lightning strike, ice damage, bird damage, wind damage and external force damage, and the line parameters are the jurisdiction unit, voltage type, voltage level and line name of the line.
进一步地，本发明的步骤S2中原始分析数据集的属性为“管辖单位-电压类型-电压等级-线路名称-灾害类型-重合情况”。Further, the attribute of the original analysis data set in step S2 of the present invention is "jurisdictional unit-voltage type-voltage level-line name-disaster type-coincidence situation".
进一步地，本发明的步骤S3中在采用Apriori算法搜索频繁项集前设置的支持度阈值为0.02。Further, in step S3 of the present invention, the support threshold set before using the Apriori algorithm to search for frequent itemsets is 0.02.
进一步地，本发明的步骤S4中用于筛选强关联规则的条件为置信度不小于0.6、提升度大于1。Further, the conditions for screening strong association rules in step S4 of the present invention are that the confidence degree is not less than 0.6 and the promotion degree is greater than 1.
本发明利用电网现有的跳闸信息，提取引发电网故障跳闸的常见灾害类型和易发生故障跳闸的线路特征参数，并采用Apriori算法挖掘二者与重合闸成功率之间的强关联规则，可为基于电网跳闸故障预判的输电线路重合闸策略优化提供依据，进一步提升电网安全稳定运行水平和持续可靠供电能力。The present invention uses the existing tripping information of the power grid to extract the common disaster types that cause the fault tripping of the power grid and the line characteristic parameters that are prone to fault tripping, and uses the Apriori algorithm to dig out the strong association rules between the two and the success rate of reclosing, which can be The optimization of transmission line reclosing strategy based on grid trip fault prediction provides a basis to further improve the safe and stable operation level of the grid and the continuous and reliable power supply capability.
附图说明Description of drawings
图1是本发明基于灾害类型和线路信息的重合闸成功率关联分析方法的流程图；Fig. 1 is the flowchart of the reclosing success rate association analysis method based on disaster type and line information of the present invention;
图2是本发明实施例的采用Apriori算法搜索频繁项集流程图。Fig. 2 is a flow chart of searching for frequent itemsets using the Apriori algorithm according to an embodiment of the present invention.
具体实施方式detailed description
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定本发明。In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
如图1所示，在本发明的一个具体实施例中，在跳闸数据中提取灾害类型和线路信息，并采用Apriori算法完成与跳闸情况的强关联规则挖掘与分析，具体包括以下步骤：As shown in Figure 1, in a specific embodiment of the present invention, in tripping data, extract disaster type and line information, and adopt Apriori algorithm to complete and the strong association rule mining and analysis of tripping situation, specifically comprise the following steps:
S1、从线路调度部门自动获取线路的EMS跳闸信息，梳理由雷击、冰害、鸟害、风害和外力破坏引发的跳闸事故，并提取跳闸记录中线路的管辖单位、电压类型、电压等级和线路名称；S1. Automatically obtain the EMS trip information of the line from the line dispatching department, sort out the trip accidents caused by lightning strike, ice damage, bird damage, wind damage and external force damage, and extract the jurisdiction unit, voltage type, voltage level and line name;
S2、结合线路重合闸成功情况(包含重合成功和重合不成功两种)，按照“管辖单位-电压类型-电压等级-线路名称-灾害类型-重合情况”的格式完成原始关联分析数据集的输入；S2. Combining the success of line reclosing (including two types of successful reclosing and unsuccessful reclosing), complete the input of the original correlation analysis data set according to the format of "jurisdictional unit-voltage type-voltage level-line name-disaster type-reclosing situation" ;
S3、设置支持度阈值＝0.02，对数据集中的1项集进行扫描，保留满足支持度阈值的频繁1项集，并由频繁1项集组成新的数据集，再完成频繁2项集的搜索。如此重复，使用逐层搜索的迭代方法从频繁k项集组成的数据集中探索频繁k+1项集，直到不存在满足支持度阈值＝0.2要求的频繁项集为止，具体的流程如附图2所示；S3. Set the support threshold = 0.02, scan the 1-itemset in the data set, keep the frequent 1-itemset that meets the support threshold, and form a new data set from the frequent 1-itemset, and then complete the search for the frequent 2-itemset . Repeat this, use the iterative method of layer-by-layer search to explore frequent k+1 itemsets from the data set composed of frequent k itemsets, until there is no frequent itemsets that meet the requirement of support threshold = 0.2, the specific process is shown in Figure 2 shown;
其中，某一k项集的支持度表示该k项集在原始关联分析数据集中出现的频繁程度，即该k项集出现的次数与原始关联分析数据集总记录数的比值。S4、以重合情况为规则后件，筛选出置信度不小于0.6、提升度大于1的强关联规则；Among them, the support degree of a k-itemset indicates the frequency of the k-itemset appearing in the original association analysis data set, that is, the ratio of the number of occurrences of the k-itemset to the total number of records in the original association analysis data set. S4. Taking the coincidence situation as the postcondition of the rule, screen out the strong association rules with a confidence degree not less than 0.6 and a promotion degree greater than 1;
其中，对于关联规则X＝>Y，X为原始关联分析数据集中属性为{管辖单位，电压类型，电压等级，线路名称，灾害类型}中一种或多种的具体取值组合，如{500kV，雷击}或{陕西，交流，500kV，风害}等，Y为原始关联分析数据集中与具体X取值对应的重合情况，存在{重合成功}和{重合不成功}两种情况，则置信度和提升度的定义及计算公式如下：Among them, for the association rule X=>Y, X is the original association analysis data set attribute is {jurisdictional unit, voltage type, voltage level, line name, disaster type} in one or more specific combination of values, such as {500kV , lightning strike} or {Shaanxi, AC, 500kV, wind damage}, etc., Y is the coincidence situation corresponding to the specific value of X in the original correlation analysis data set, if there are two situations of {coincidence success} and {coincidence failure}, then confidence The definitions and calculation formulas of degree and lift degree are as follows:
置信度表示原始关联分析数据集中在出现X的前提下再出现Y的可能性，即包含X的项集中再包含Y的概率。此参数是对关联规则可信程度的衡量，数值越高，X出现时Y出现的可能性越大，即由X推出Y越可靠，在这种情况下可提前为Y的可能出现采取有效的应对措施。记作confidence(X＝>Y)，则有Confidence indicates the possibility of Y appearing under the premise of X appearing in the original association analysis data set, that is, the probability that Y is included in the item set containing X. This parameter is a measure of the credibility of association rules. The higher the value, the greater the possibility of Y appearing when X appears, that is, the more reliable it is to deduce Y from X. In this case, effective measures can be taken in advance for the possible appearance of Y. Responses. Recorded as confidence(X=>Y), then we have
confidence(X＝>Y)＝P(Y/X)＝P(XY)/P(X)confidence(X=>Y)=P(Y/X)=P(XY)/P(X)
提升度表示在原始关联分析数据集中X的出现对Y出现可能性的提升程度，即为在出现X的前提条件下同时出现Y的概率与单独出现Y的概率之比。此参数用于判断关联规则的有效性，弥补了支持度、置信度参数无法保证X与Y不是相互独立的缺陷。记作lift(X＝>Y)，则有The promotion degree indicates the promotion degree of the occurrence of X to the possibility of Y in the original association analysis data set, that is, the ratio of the probability of Y appearing at the same time under the precondition of X appearing to the probability of Y appearing alone. This parameter is used to judge the validity of the association rules, and makes up for the defect that the support and confidence parameters cannot guarantee that X and Y are not independent of each other. Recorded as lift(X=>Y), then we have
lift(X＝>Y)＝P(Y/X)/P(Y)＝P(XY)/[P(X)·P(Y)]lift(X=>Y)=P(Y/X)/P(Y)=P(XY)/[P(X)·P(Y)]
按照步骤S1提取2015年国家电网公司辖下330kV及以上电压等级输电线路跳闸数据为样本，按照步骤S2中规定的数据格式进行跳闸数据的整理，按照步骤S3中给出的Apriori算法找出频繁项集(如图2)，并根据支持度的定义计算出每一个频繁项集的支持度，筛选出满足支持度阈值要求的频繁项集，最后在满足支持度阈值要求的频繁项集中以重合情况为规则后件，以属性为{管辖单位，电压类型，电压等级，线路名称，灾害类型}中一种或多种的具体取值组合为规则前件，构成形如X＝>Y的不同关联规则，根据关联规则置信度和提升度的计算公式算出每一种关联规则的置信度和提升度，按照步骤S4中的要求筛选出强关联规则。按照上述步骤挖掘出的强关联规则形式为：According to step S1, extract the trip data of 330kV and above transmission lines under the State Grid Corporation of China in 2015 as a sample, organize the trip data according to the data format specified in step S2, and find out the frequent items according to the Apriori algorithm given in step S3 Set (as shown in Figure 2), and calculate the support degree of each frequent itemset according to the definition of support, filter out the frequent itemsets that meet the support threshold requirements, and finally use the coincidence condition in the frequent itemsets that meet the support threshold requirements It is the postcondition of the rule, and the combination of one or more specific values of the attributes {jurisdictional unit, voltage type, voltage level, line name, disaster type} is the antecedent of the rule, forming different associations in the form of X=>Y Rules, calculate the confidence and promotion of each association rule according to the calculation formula of the confidence and promotion of the association rules, and filter out the strong association rules according to the requirements in step S4. The form of strong association rules mined according to the above steps is:
按照上述获得的强关联规则，对重合闸策略进行如下调整：对于事先可判断为雷暴或鸟类引起的跳闸事件，一般为瞬时性故障，按照原有的保护设置直接重合闸；对于判断为强风或冰雪引起的跳闸事件，常为长时间故障，可产生闭锁信号不启动自动重合闸装置；对于无法判断故障类型的跳闸事件，沿用原有的保护设置进行重合闸。According to the strong association rules obtained above, adjust the reclosing strategy as follows: For tripping events that can be judged as thunderstorms or birds in advance, generally transient faults, reclose directly according to the original protection settings; for tripping events judged to be strong wind Or the tripping event caused by ice and snow, which is often a long-term fault, can generate a blocking signal and not start the automatic reclosing device; for the tripping event that cannot determine the type of fault, the original protection setting is used for reclosing.
经过调整后，2015年国网公司超、特高压输电线路灾害故障的重合闸成功率可达81.63％，相比之前提高了18.16％。而且由雷击、风害和冰害引起线路故障跳闸的比例较大，2015年这一比值为67.48％，表明提出的重合闸策略的有效性较为明显。After adjustment, in 2015, the reclosing success rate of State Grid Corporation's super- and UHV transmission line disaster faults reached 81.63%, which was 18.16% higher than before. Moreover, the proportion of line fault trips caused by lightning strikes, wind damage, and ice damage is relatively large, and this ratio was 67.48% in 2015, indicating that the proposed reclosing strategy is more effective.
上述重合闸策略的调整无需额外的增加相关的电网监测设备，也不受电网运行状态的影响，具有很强的实用性和可操作性。The adjustment of the above-mentioned reclosing strategy does not need to add additional related grid monitoring equipment, and is not affected by the operating status of the grid, so it has strong practicability and operability.
| Application Number | Priority Date | Filing Date | Title |
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| CN201710413224.9ACN107092993A (en) | 2017-06-05 | 2017-06-05 | Reclosing success rate association analysis method based on Disasters Type and line information |
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| CN201710413224.9ACN107092993A (en) | 2017-06-05 | 2017-06-05 | Reclosing success rate association analysis method based on Disasters Type and line information |
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| CN201710413224.9APendingCN107092993A (en) | 2017-06-05 | 2017-06-05 | Reclosing success rate association analysis method based on Disasters Type and line information |
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