CN113111955A - Line loss abnormal data expert system and detection method - Google Patents
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Abstract
The invention discloses a line loss abnormal data expert system.A theoretical line loss rate calculation module comprises static parameters and operation parameters, and the calculation method comprises a root-mean-square current method, an average current method and an equivalent resistance method, and calculates the loss of a typical day and further calculates the line loss of a month and a year; the actual line loss rate calculation module comprises forward electric quantity of an examination and check meter in a transformer area, on-line electric quantity of a photovoltaic user, reverse electric quantity of the examination and check meter, electric quantity used by the user and back-compensation electric quantity; the multi-label analysis and prediction module comprises an archive analysis and prediction submodule, a metering analysis and prediction submodule, an acquisition analysis and prediction submodule, an anti-electricity-stealing analysis and prediction submodule and a technical improvement analysis and prediction submodule; the abnormal reforming feedback module comprises a file reforming feedback sub-module, a metering reforming feedback sub-module, a collection reforming feedback sub-module, an anti-electricity-stealing reforming feedback sub-module and a technical improvement reforming feedback sub-module. The invention can quickly locate the abnormal reason and improve the line loss treatment efficiency.
Description
Technical Field
The invention relates to a line loss abnormal data expert system and a detection method for the field of operation and management of a power distribution network of a power system.
Background
After being produced from a power plant, the electric energy enters thousands of households through a power grid. In the process, four links of power transmission, power transformation, power distribution and power utilization are needed, and electric energy loss, referred to as line loss, is generated in each link. For the economic and technical index for measuring the comprehensiveness of power supply enterprise management, the line loss not only refers to theory, but also refers to comprehensive line loss, and mainly comprises two aspects of management line loss and technical line loss. The technical reason is that line loss fluctuation is too large due to poor power grid structure or operation state, such as unreasonable load rate, small line diameter of the line, insufficient reactive power compensation and the like. The management of the line loss refers to the line loss abnormality caused by poor line loss management work, errors of metering equipment and the like, for example, errors occur in basic files, the problems of asynchronous pushing among systems exist, the correspondence of the user variable relations is inconsistent, the polarity of the mutual inductor is reversed, wrong wiring is performed, the wiring of a metering device is unstable, the acquisition is failed, the meter reading is missed, the electricity is stolen and the like. Abnormal data of line loss is a key object of attention of power supply enterprises.
Although the power industry makes an inspection rule for the research on the analysis of the loss anomaly and has a knowledge base prototype, the method still mainly depends on an empirical method, a large amount of manpower and material resources are consumed, and the subjectivity, the efficiency and the accuracy rate are still to be improved. The traditional expert system can only seek answers in limited customized rules, has poor fault tolerance rate, lacks the capability of distinguishing conflict information, has no self-learning function, cannot obtain closed-loop feedback when encountering complex and irregular data, and has the problem of incapability of convergence. The machine learning is applied to the aspect of line loss, and the regression prediction of the line loss rate is basically centralized. However, the massive power grid data often contains error data, which has a serious influence on the calculation and prediction of the loss. Identifying abnormal data in the line data and removing are necessary steps for data preprocessing. The power grid in the running state is calculated by adopting a proper theory, and compared with an actual measured value, abnormal data deviating from a predicted value is found, related problems are timely checked, and subsequent power consumption is controlled, so that the method is an important embodiment of line loss prediction practical application. Therefore, the cause of the abnormal data is analyzed and classified on the basis of the cause, and the method has practical significance for line loss control. In an actual power system, abnormal line loss is often a complex problem, and multiple causes may exist simultaneously. The common method of machine learning two classification is not suitable for the analysis of the line loss problem.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an expert system and a detection method for line loss abnormal data, which can quickly locate the reason of the abnormality and improve the line loss treatment efficiency.
One technical scheme for achieving the above purpose is as follows: the line loss abnormal data expert system is characterized by comprising a theoretical line loss rate calculation module, an actual line loss rate calculation module, a multi-label analysis and prediction module and an abnormal correction feedback module;
the theoretical line loss rate calculation module comprises static parameters and operation parameters, the calculation method comprises a root mean square current method, an average current method and an equivalent resistance method, and the loss of a typical day is calculated so as to calculate the line loss of a month and a year;
the actual line loss rate calculation module comprises forward electric quantity of an examination and check meter in a transformer area, on-line electric quantity of a photovoltaic user, reverse electric quantity of the examination and check meter, electric quantity used by the user and back-up electric quantity;
the multi-label analysis prediction module comprises an archive analysis prediction submodule, a metering analysis prediction submodule, an acquisition analysis prediction submodule, an anti-electricity-stealing analysis prediction submodule and a technical improvement analysis prediction submodule;
the abnormal reforming feedback module comprises a file reforming feedback sub-module, a metering reforming feedback sub-module, a collection reforming feedback sub-module, an anti-electricity-stealing reforming feedback sub-module and a technical improvement reforming feedback sub-module.
Further, the file analysis and prediction submodule is used for inputting the multiplying power and the user details of the marketing business application system, the power utilization information acquisition system and the integrated electric quantity and line loss management system, comparing the data of the marketing business application system, the power utilization information acquisition system and the integrated electric quantity and line loss management system, and increasing the corresponding label weight value if the data of the marketing business application system, the power utilization information acquisition system and the integrated electric quantity and line loss management system;
the metering analysis prediction submodule is used for inputting voltage, current and power factors of a transformer area general meter, a single-phase electric energy meter and a three-phase electric energy meter, combining historical data such as a same-proportion and a ring-proportion, and increasing corresponding label weight values if the historical data is abnormal;
the acquisition analysis prediction submodule is used for inputting frozen electric quantity, and if the frozen electric quantity is missing or falls away, a corresponding label weight value is increased;
the anti-electricity-stealing analysis and prediction submodule is used for inputting an uncovering record, a power failure event, a voltage loss and current loss event and a load curve deviation, and increasing a corresponding label weight value to be used as a suspected user for key investigation;
the technical improvement analysis and prediction submodule is used for inputting the station area radius and the power supply setting position of the geographic information system, zero line current, load, rated capacity and station area power factor of the electricity utilization information acquisition system, and if the zero line current, the load, the rated capacity and the station area power factor are abnormal, the corresponding label weight value is increased.
The line loss abnormal data detection method adopting the line loss abnormal data expert system comprises the following steps:
step 1: calculating the theoretical line loss rate by adopting a theoretical line loss rate calculation module, and setting a normal range;
step 2: calculating the actual line loss rate by adopting an actual line loss rate calculation module, calculating the difference value between the actual line loss rate and the theoretical line loss rate, and judging whether the line loss rate is within a normal range;
and step 3: outputting to a multi-label analysis and prediction module for detection;
and 4, step 4: performing label judgment by adopting an archive analysis and prediction submodule, and outputting a label judgment result;
and 5: performing label judgment by adopting a metering analysis prediction submodule, and outputting a label judgment result;
step 6: performing label judgment by adopting an acquisition analysis prediction submodule, and outputting a label judgment result;
and 7: performing label judgment by adopting an anti-electricity-stealing analysis and prediction submodule, and outputting a label judgment result;
and 8: performing label judgment by adopting a technical improvement analysis and prediction submodule, and outputting a label judgment result;
step 9, if no label judgment result exists in the step 4-8, returning to the step 2 to repeat the step 2-8, if one or more label judgment results exist, synthesizing the label judgment results and outputting the label judgment results to the rectification feedback module, initiating a process, and adding and deleting labels according to actual conditions;
step 10: and (3) repeating the step (1) to the step (2) after the process is finished, and if the difference value is within a reasonable range, training the multi-label classifier according to the label judgment result. If the steps 1-9 are not repeated within a reasonable range.
The invention discloses a line loss abnormal data expert system and a detection method, and provides a line loss abnormal data expert system and an analysis method thereof, wherein the line loss abnormal data expert system and the analysis method are based on a multi-label learning algorithm and respectively analyze factors such as archives, metering, collecting, electricity stealing prevention, technology and the like by applying multiple modules. By using the system, the abnormal reasons can be rapidly positioned, and the line loss treatment efficiency is improved.
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FIG. 1 is a system architecture diagram of a line loss anomaly data expert system according to the present invention;
fig. 2 is a schematic flow chart of a line loss abnormal data detection method according to the present invention.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is made by specific examples:
referring to fig. 1, the expert system for line loss abnormal data of the present invention includes a theoretical line loss rate calculating module 1, an actual line loss rate calculating module 2, a multi-tag analyzing and predicting module 3, and an abnormal modifying and feeding back module 4.
The theoretical line loss rate calculation module comprises static parameters and operation parameters, the calculation method comprises a root mean square current method, an average current method and an equivalent resistance method, and the loss of a typical day is calculated so as to calculate the line loss of a month and a year;
the actual line loss rate calculation module comprises forward electric quantity of an examination and check meter in a transformer area, on-line electric quantity of a photovoltaic user, reverse electric quantity of the examination and check meter, electric quantity used by the user and back-up electric quantity;
the multi-label analysis prediction module comprises an archive analysis prediction submodule, a metering analysis prediction submodule, an acquisition analysis prediction submodule, an anti-electricity-stealing analysis prediction submodule and a technical improvement analysis prediction submodule;
the abnormal reforming feedback module comprises a file reforming feedback sub-module, a metering reforming feedback sub-module, a collection reforming feedback sub-module, an anti-electricity-stealing reforming feedback sub-module and a technical improvement reforming feedback sub-module.
The file analysis and prediction submodule is used for inputting the multiplying power and the user details of the marketing business application system, the electricity consumption information acquisition system and the integrated electricity and line loss management system, comparing the data of the marketing business application system, the electricity consumption information acquisition system and the integrated electricity and line loss management system, and increasing the corresponding label weight value if the data of the marketing business application system, the electricity consumption information acquisition system and the integrated electricity and line loss management system are;
the metering analysis prediction submodule is used for inputting voltage, current and power factors of a transformer area general meter, a single-phase electric energy meter and a three-phase electric energy meter, combining historical data such as a same-proportion and a ring-proportion, and increasing corresponding label weight values if the historical data is abnormal;
the acquisition analysis prediction submodule is used for inputting frozen electric quantity, and if the frozen electric quantity is missing or falls away, a corresponding label weight value is increased;
the anti-electricity-stealing analysis and prediction submodule is used for inputting an uncovering record, a power failure event, a voltage loss and current loss event and a load curve deviation, and increasing a corresponding label weight value to be used as a suspected user for key investigation;
the technical improvement analysis and prediction submodule is used for inputting the station area radius and the power supply setting position of the geographic information system, zero line current, load, rated capacity and station area power factor of the electricity utilization information acquisition system, and if the zero line current, the load, the rated capacity and the station area power factor are abnormal, the corresponding label weight value is increased.
The basic framework of the invention is that the whole online analysis module adopts an application program packaging mode. The windows operating system is used in accordance with a given computing environment configuration. And logging in a web browser through the selenium to obtain the cookie, and performing data interaction with the webpage data by using the xpath to obtain the original data. The stage partition processing in the line loss modeling part is realized by python and SQL, and the multi-label classification regression analysis is realized by python and a random forest improvement algorithm. gui the interface is implemented in part by tkiner. The configuration function comprises all functions of the low-voltage transformer area online analysis module, including table configuration, prediction, classification, visual display and the like.
Please refer to fig. 2 for a process of the line loss abnormal data detection method applying the line loss abnormal data expert system, which specifically includes the following steps:
step 1: calculating the theoretical line loss rate by adopting a theoretical line loss rate calculation module, and setting a normal range;
step 2: calculating the actual line loss rate by adopting an actual line loss rate calculation module, calculating the difference value between the actual line loss rate and the theoretical line loss rate, and judging whether the line loss rate is within a normal range;
and step 3: outputting to a multi-label analysis and prediction module for detection;
and 4, step 4: performing label judgment by adopting an archive analysis and prediction submodule, and outputting a label judgment result;
and 5: performing label judgment by adopting a metering analysis prediction submodule, and outputting a label judgment result;
step 6: performing label judgment by adopting an acquisition analysis prediction submodule, and outputting a label judgment result;
and 7: performing label judgment by adopting an anti-electricity-stealing analysis and prediction submodule, and outputting a label judgment result;
and 8: performing label judgment by adopting a technical improvement analysis and prediction submodule, and outputting a label judgment result;
step 9, if no label judgment result exists in the step 4-8, returning to the step 2 to repeat the step 2-8, if one or more label judgment results exist, synthesizing the label judgment results and outputting the label judgment results to the rectification feedback module, initiating a process, and adding and deleting labels according to actual conditions;
step 10: and (3) repeating the step (1) to the step (2) after the process is finished, and if the difference value is within a reasonable range, training the multi-label classifier according to the label judgment result. If the steps 1-9 are not repeated within a reasonable range.
The invention provides an improved multi-label learning algorithm based on random forests, a line loss abnormal data expert system is set up, the interaction is friendly, and samples are allowed to belong to multiple categories simultaneously. The method changes from single man-made addition to the knowledge database into automatic acquisition and classification, and has self-learning capability. The inference mechanism is converted from traditional inductive inference into competition for weight values, and has better fault tolerance. By using the system, the abnormal reasons can be rapidly positioned, and the line loss treatment efficiency is improved.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.
Claims (3)
1. The line loss abnormal data expert system is characterized by comprising a theoretical line loss rate calculation module, an actual line loss rate calculation module, a multi-label analysis and prediction module and an abnormal correction feedback module;
the theoretical line loss rate calculation module comprises static parameters and operation parameters, the calculation method comprises a root mean square current method, an average current method and an equivalent resistance method, and the loss of a typical day is calculated so as to calculate the line loss of a month and a year;
the actual line loss rate calculation module comprises forward electric quantity of an examination and check meter in a transformer area, on-line electric quantity of a photovoltaic user, reverse electric quantity of the examination and check meter, electric quantity used by the user and back-up electric quantity;
the multi-label analysis prediction module comprises an archive analysis prediction submodule, a metering analysis prediction submodule, an acquisition analysis prediction submodule, an anti-electricity-stealing analysis prediction submodule and a technical improvement analysis prediction submodule;
the abnormal reforming feedback module comprises a file reforming feedback sub-module, a metering reforming feedback sub-module, a collection reforming feedback sub-module, an anti-electricity-stealing reforming feedback sub-module and a technical improvement reforming feedback sub-module.
2. A line loss anomaly data expert system as defined in claim 1, wherein:
the file analysis and prediction submodule is used for inputting the multiplying power and the user details of the marketing business application system, the electricity consumption information acquisition system and the integrated electricity and line loss management system, comparing the data of the marketing business application system, the electricity consumption information acquisition system and the integrated electricity and line loss management system, and increasing the corresponding label weight value if the data of the marketing business application system, the electricity consumption information acquisition system and the integrated electricity and line loss management system are;
the metering analysis prediction submodule is used for inputting voltage, current and power factors of a transformer area general meter, a single-phase electric energy meter and a three-phase electric energy meter, combining historical data such as a same-proportion and a ring-proportion, and increasing corresponding label weight values if the historical data is abnormal;
the acquisition analysis prediction submodule is used for inputting frozen electric quantity, and if the frozen electric quantity is missing or falls away, a corresponding label weight value is increased;
the anti-electricity-stealing analysis and prediction submodule is used for inputting an uncovering record, a power failure event, a voltage loss and current loss event and a load curve deviation, and increasing a corresponding label weight value to be used as a suspected user for key investigation;
the technical improvement analysis and prediction submodule is used for inputting the station area radius and the power supply setting position of the geographic information system, zero line current, load, rated capacity and station area power factor of the electricity utilization information acquisition system, and if the zero line current, the load, the rated capacity and the station area power factor are abnormal, the corresponding label weight value is increased.
3. The line loss abnormal data detection method adopting the line loss abnormal data expert system is characterized by comprising the following steps:
step 1: calculating the theoretical line loss rate by adopting a theoretical line loss rate calculation module, and setting a normal range;
step 2: calculating the actual line loss rate by adopting an actual line loss rate calculation module, calculating the difference value between the actual line loss rate and the theoretical line loss rate, and judging whether the line loss rate is within a normal range;
and step 3: outputting to a multi-label analysis and prediction module for detection;
and 4, step 4: performing label judgment by adopting an archive analysis and prediction submodule, and outputting a label judgment result;
and 5: performing label judgment by adopting a metering analysis prediction submodule, and outputting a label judgment result;
step 6: performing label judgment by adopting an acquisition analysis prediction submodule, and outputting a label judgment result;
and 7: performing label judgment by adopting an anti-electricity-stealing analysis and prediction submodule, and outputting a label judgment result;
and 8: performing label judgment by adopting a technical improvement analysis and prediction submodule, and outputting a label judgment result;
step 9, if no label judgment result exists in the step 4-8, returning to the step 2 to repeat the step 2-8, if one or more label judgment results exist, synthesizing the label judgment results and outputting the label judgment results to the rectification feedback module, initiating a process, and adding and deleting labels according to actual conditions;
step 10: and (3) repeating the step (1) to the step (2) after the process is finished, and if the difference value is within a reasonable range, training the multi-label classifier according to the label judgment result. If the steps 1-9 are not repeated within a reasonable range.
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