CN107634515A - Optimal operation model of tea area based on analysis of power consumption characteristics in tea area - Google Patents

Abstract

The present invention discloses a kind of taiwan area optimal operation model based on tea area electricity consumption signature analysis, including, data acquisition module, for gathering the nearly daily peak load data in 3 years 3 Mays of distribution transforming in tea area；ARIMA model building modules：The development and change rule that analysis tea area's distribution transforming power load amount shows over time, using prediction analysis method, establishes ARIMA models；Power consumption peak load prediction module：According to ARIMA models to then 3 May daily power consumption peak load development trend be predicted.The present invention utilizes the data such as the public change system of intelligence and power network GIS platform, excavation refinement is carried out to the power supply such as nearly 3 year spring tea area power load distributing, voltage change rule, analyze the internal relation of tea area temperature, height above sea level, local tea variety and tea area power load, form the tea making electricity consumption characteristic rule in tea picking region, the power network reinforcement of tea area and accurate services are carried out in good time according to these rules, reasonable distribution Service Source, raising supply electricity consumption satisfaction.

Description

Optimal operation model of tea area based on analysis of power consumption characteristics in tea area

technical field

The invention relates to the technical field of power analysis, in particular to the power analysis technology for tea areas.

Background technique

Xinchang County is located in Tianmu Mountain District, and is known as the "Hometown of Famous Tea" in the country. The county's tea planting area is more than 120,000 mu, and a large-scale tea production industry with the brand "Dafo Longjing" has been formed. With the comprehensive promotion of electric tea machines, Xinchang tea frying has changed from the original hand frying to the current electric frying. Coupled with the promotion and popularization of high-power three-phase tea machines, the load in tea areas has experienced a short-term explosive growth, and the situation of safe operation of the power grid has become more severe than in previous years. Xinchang has a special mountainous geographical climate, and the distribution of tea varieties in each tea-producing area is different, and the time for tea budding and picking is not exactly the same. Tea farmers are busy picking tea during the day and frying tea at night. Almost all the tea machines of the tea growers are in the starting state, and the load of tea making is huge at night, and the characteristics of electricity consumption for tea making are very obvious. The price of electricity for tea making is relatively low, and the purchase of tea machines is out of control. Power supply for spring tea is a livelihood project, and there is a certain contradiction between the tense power supply of spring tea and the increasing number of tea machines. There is an urgent need for us to explore the characteristics of electricity consumption in tea areas, optimize the operation of tea areas, guide the production of tea farmers, and improve the high-quality services of electricity consumption in tea areas.

Contents of the invention

The technical problem to be solved by the present invention is to provide an optimal operation model of the station area based on the analysis of the power consumption characteristics of the tea area, reasonably arrange the sequence of distribution transformer capacity increase, and transfer the load in time.

In order to solve the above-mentioned technical problems, the present invention adopts the following technical scheme: the optimal operation model of the station area based on the analysis of the electricity consumption characteristics of the tea area includes,

The data acquisition module is used to collect the daily maximum load data of distribution transformers in the tea area from March to May in the past three years;

ARIMA model building module: analyze the development and change law of the distribution transformer power load in the tea area over time, and use the predictive analysis method to establish the ARIMA model;

Maximum load forecasting module of electricity consumption: according to the ARIMA model, forecast the development trend of the maximum load of daily electricity consumption from March to May of the current year.

Preferably, the data acquisition module acquires data from an intelligent public transformer monitoring system.

Preferably, it also includes an abnormal distribution transformer analysis module. For a distribution transformer whose active power/common transformer rated capacity*100% is greater than 70%, there is a possibility of overload, which is defined as a heavy-duty abnormal distribution transformer. For distribution transformers of 150V and lower than 198V, there is a possibility of low voltage, which is defined as low-voltage abnormal distribution transformers. From March to May, measures for capacity expansion and shunting will be arranged for heavy-duty abnormal distribution transformers, and unified regulation will be adopted for low-voltage abnormal distribution transformers. Real-time pressure regulation in file mode.

Preferably, it also includes a correlation analysis module between the altitude of the load point of the tea area and the load of the tea area, and divides the altitude into six categories: the first category is less than 100 meters; the second category is 100-199 meters; the second category is 200-299 meters The third category; 300-399 meters for the fourth category; 400-499 meters for the fifth category; greater than or equal to 500 meters for the sixth category, analyze the average electricity consumption of each type of altitude per day from March to May in the past three years The maximum load, and make a time sequence diagram of power load changes. In areas with lower altitudes, use temporary transformers first to shunt the load of heavy-duty distribution transformers. After the picking peak in this area has passed and the power load has dropped, the temporary transformers will be moved to other high-altitude areas.

The present invention adopts the above-mentioned technical scheme, utilizes data such as the intelligent public transformation system and the power grid GIS platform, excavates and refines power supply rules such as load distribution and voltage changes in tea areas in the past three years, and analyzes the temperature, altitude, tea varieties and tea areas in tea areas. The internal relationship of electricity load forms the characteristic law of electricity consumption for tea production in tea-picking areas. According to these laws, the power grid reinforcement and precise service work in tea area should be done in a timely manner, service resources can be reasonably allocated, service costs can be saved, and power supply satisfaction can be improved. .

detailed description

Considering the obvious seasonality of electricity load in the tea area, the present invention analyzes March-May every year. The geographic location of the present invention is to select the corresponding county level as the object, therefore, the analysis level should be the county level.

The present invention provides an optimal operation model of the station area based on the analysis of the characteristics of electricity consumption in the tea area, which analyzes the load changes in the tea area in the past three years, uses the distribution in the tea area as a group, and extracts the daily load data from March to May in the past three years , displayed in time series, and analyzed the tea season load changes and future development trends in tea areas every year. Forecast the tea production load of the tea season in the tea area in the coming year, formulate a targeted tea work plan to meet the peak, provide a reference for the production, supply and marketing of tea making equipment in the tea area, and ensure the stable power supply and consumption of the tea area during the spring tea season in the coming year.

The optimal operation model of the tea area based on the analysis of the characteristics of electricity consumption in the tea area includes,

The data acquisition module is used to collect the daily maximum load data of distribution transformers in the tea area from March to May in the past three years;

ARIMA model building module: analyze the development and change law of the distribution transformer power load in the tea area over time, and use the predictive analysis method to establish the ARIMA model;

Maximum load forecasting module of electricity consumption: according to the ARIMA model, forecast the development trend of the maximum load of daily electricity consumption from March to May of the current year.

Relevant data are obtained from the database of the intelligent public transformer monitoring system. The obtained information includes: date, unit, account number, distribution transformer capacity, maximum load, minimum load, average load, etc., and the detailed list is exported to form the "Historical Load Situation of Distribution Transformers in Tea Areas" Statistical Table".

Clean the extracted data that is not within the scope of monitoring according to the following data cleaning rules. Cleaning rules: Delete and select the collected electricity load data, and eliminate individual abnormal data, such as data with a maximum load and minimum load of 0 on a certain day, otherwise the accuracy of model construction will be affected.

The ARIMA model is a well-known time series forecasting method proposed in the early 1970s. ARIMA model is called autoregressive moving average model, ARIMA (p, d, q) is called differential autoregressive moving average model, AR is autoregressive, p is autoregressive item; MA is moving average, q is the number of moving average items, d is the number of differences made when the time series becomes stationary. The essence of ARIMA(p,d,q) is to first perform d-time difference processing on the non-stationary time series to obtain a new stable data series, then fit the sequence to the ARMA(p,q) model, and finally make the original d-time difference By restoring, the forecast data of the original sequence can be obtained. Using the electricity load in the tea area in the past three years to analyze the development and change of the electricity load in the tea area over time, establish the ARIMA model, and according to the model, calculate the maximum electricity consumption from March 1st to May 31st of the year Forecast the load development trend.

Because March is greatly affected by factors such as temperature, Spring Festival, and cold wave, the maximum load of electricity consumption predicted by the ARIMA model from March 1 to May 31, 2017 is multiplied by the environmental factor 1.15 to obtain a new set of predicted value of .

From the beginning of March to the middle of May every year, it is the peak period of spring tea roasting, and it is also the peak period of electricity consumption in tea areas. The problem of heavy overload and low voltage in tea area is particularly obvious, which affects the normal tea frying of tea farmers. The abnormal distribution transformer analysis module analyzes the distribution transformer capacity, active power, three-phase voltage and other operational data based on the operation data of the past years, and analyzes the distribution transformers with a maximum load rate greater than 70% and distribution transformers with a head-end voltage lower than 198V Record it as an abnormal distribution change, dig out the distribution law, and carry out pre-control management in advance. The abnormal record identified through the original data forms an abnormal work order and dispatches it to its affiliated power supply station. The power supply station passes the on-site inspection, causes analysis, and feeds back the rectification results to the competent department to form a closed-loop rectification of problems and improve the operation and management level of power supply equipment in tea areas. At the same time, by solving the overload and low voltage problems of distribution transformers in advance, rationally layout and strengthen the power grid in tea areas, and improve the power supply capacity of tea areas.

For a distribution transformer whose active power/rated capacity of public transformer*100% is greater than 70%, there is a possibility of overload, that is, an abnormal distribution transformer. For a distribution transformer whose head-end voltage is higher than 150V and lower than 198V, there is a low voltage Possibly, it is abnormal distribution.

Obtain data from the intelligent public change monitoring system, and clean the extracted data that is not within the monitoring range according to the following data cleaning rules:

Cleaning rule 1: The public transformer load table is sorted according to the maximum load rate, and the distribution transformer with the maximum load rate <70% is removed.

Cleaning rule 2: Remove the distribution transformers whose head-end voltage is greater than 198V and less than 150V.

Monitor the load and voltage of the distribution transformer in the tea area through the public transformer monitoring system, and analyze and verify the data. If the following judgment rules are met, it can be judged as a suspected problem.

(1) Rule 1: Distribution transformers with a maximum load rate of the public transformer ≧70%.

[Description of rules] A distribution transformer with active power/rated capacity of public transformer*100%≧70%, which may be overloaded, is a suspected abnormal distribution transformer.

[Calculation formula] Active power/rated capacity of public transformer*100%≧70%.

【Rule Basis】

●A distribution transformer with a maximum load rate≧70%.

●It lasts for 2 hours, that is, the load collection density is 15 minutes, and there are 9 consecutive data collection points.

(2) Rule 2: A distribution transformer with a head-end voltage lower than 198V is suspected of being low voltage.

[Description of the rules] The voltage at the head end of the distribution transformer has been between 150 and 198, which is suspected of being a low-voltage distribution transformer.

[Calculation formula] The low voltage ratio is less than 90% for 1 hour. That is, if the voltage lasts for 5 points is between 150 and 198V, it is judged as low voltage.

[Rule basis] Distribution transformers with a head-end voltage higher than 150V and lower than 198V.

According to the above analysis results, take the following measures to solve the problem of overload and low voltage of distribution transformer.

1. Before the peak season of tea production loads, targetedly complete distribution transformer capacity expansion in tea areas, arrange construction plans in advance, arrange capacity expansion and diversion measures for distribution transformers with potential heavy loads in advance, and transform low-voltage station areas, Increase the wire diameter, shorten the power supply radius, improve the power supply capacity, and meet the increasing demand of the tea roasting load in the tea area.

2. Before the peak season of tea production load, in order to ensure that tea farmers can fry tea normally, according to the data analysis results, a unified shift method was adopted for the distribution transformer in advance to adjust the voltage of the distribution transformer in advance, which improved the tea production efficiency. District power quality.

3. Implement special personnel to monitor the operation status of tea area lines and distribution transformers at multiple times a day, especially during the time period from 17:00 to 23:00 every day. Take corresponding solutions in a timely manner for areas with fast load growth and low voltage, and conduct regular and irregular infrared temperature measurements on such distribution network equipment, keep abreast of the operation of equipment in tea areas, and conduct inspections on areas where temperatures are found to be too high In order to analyze and deal with it in a timely manner and formulate corresponding emergency plans, it is ensured that power equipment failures will not be caused by high loads during the peak tea season.

The correlation analysis module between the altitude of the load point in the tea area and the load in the tea area is based on the change trend of the distribution transformer load in the tea area, and the correlation analysis is carried out according to the altitude of the load point in the tea picking area and the load in the tea area, so as to gain an in-depth understanding of the load change and altitude in the tea area Intrinsic connection with geographical features such as tea area, targeted to carry out power supply services for tea areas.

Through the intelligent public transformer monitoring system, the distribution transformer information of previous years within the monitoring range is obtained. The extracted information includes: date, unit, account number, distribution transformer capacity, maximum load, minimum load, average load, etc., and the tea can be queried through the external network For the altitude data of the load points in the district distribution substation area, the import program is written according to the altitude data format and imported into the system database. The extracted information includes: regional location, altitude, etc., and the detailed list forms the "Meteorological Information Data List".

Delete and select the data collected on individual abnormal dates of electricity load, and eliminate individual abnormal data, such as the data of the highest load, the lowest load, and the power consumption of a certain day are eliminated, otherwise the accuracy of model construction will be affected.

The altitude and the maximum power consumption load are corresponded one by one through the account number. The altitude is divided into six categories: less than 100 meters is the first category; 100-199 meters is the second category; 200-299 meters is the third category; 300-399 meters is the fourth category; 400-499 meters is the fifth category category; the sixth category is greater than or equal to 500 meters. Summarize the maximum load of average power consumption for each type of altitude in 2014, 2015 and March-May 2016, and import the data into the software Eviews for analysis.

The altitude of the load point has a certain correlation effect on the load of the tea area. The altitude affects the sooner or later the tea matures, and also affects the sooner or later the high load of the tea area arrives. The temperature at low altitudes is higher than that at high altitudes, the tea leaves germinate earlier, and the load of roasted tea appears earlier. From the early stage of tea mining, the load of tea areas in low-altitude areas is at a high load, and the development is stable. As time goes by, large areas of high-altitude tea are picked, and the load of high-altitude tea areas gradually reaches a high load level. Analyze the maximum load of average power consumption for each type of altitude every day from March to May in the past three years, and make a time series diagram of power load changes.

According to the load characteristics of tea areas in different altitude areas, effectively guide power supply service personnel to carry out targeted spring tea service work. In areas with lower altitudes, use temporary transformers first to shunt the load of heavy-duty distribution transformers. After the picking peak in this area has passed and the power load has dropped, the temporary transformers will be moved to other high-altitude areas.

Claims (6)

1. The optimization operation model of the station area based on the analysis of the characteristics of electricity consumption in the tea area, which is characterized in that, including, The data acquisition module is used to collect the daily maximum load data of distribution transformers in the tea area from March to May in the past three years; ARIMA model building module: analyze the development and change law of the distribution transformer power load in the tea area over time, and use the predictive analysis method to establish the ARIMA model; Maximum load forecasting module of electricity consumption: according to the ARIMA model, forecast the development trend of the maximum load of daily electricity consumption from March to May of the current year. 2. The optimal operation model of the station area based on the analysis of the characteristics of electricity consumption in the tea area according to claim 1, wherein the data collection module obtains data from an intelligent public transformer monitoring system. 3. The platform area optimization operation model based on the analysis of power consumption characteristics in tea areas according to claim 1, characterized in that it also includes an abnormal distribution transformer analysis module, for active power/common transformer rated capacity*100% greater than 70% The distribution transformer has the possibility of overloading, which is defined as a heavy-duty abnormal distribution transformer. For a distribution transformer whose head-end voltage is higher than 150V and lower than 198V, there is a possibility of low voltage, which is defined as a low-voltage abnormal distribution transformer. 4. According to claim 3, the optimal operation model of the tea area based on the analysis of the electricity consumption characteristics of the tea area is characterized in that, in March-May, measures for increasing capacity and shunting are arranged for abnormal heavy-duty distribution transformers, and for abnormal low-voltage distribution transformers. The variable adopts a unified gear adjustment method for real-time pressure regulation. 5. The platform optimal operation model based on the analysis of electricity consumption characteristics in tea areas according to claim 1, characterized in that it also includes a correlation analysis module between the altitude of the load point in the tea area and the load in the tea area, and divides the altitude into six categories : Less than 100 meters is the first category; 100-199 meters is the second category; 200-299 meters is the third category; 300-399 meters is the fourth category; 400-499 meters is the fifth category; greater than or equal to 500 meters The sixth category; analyze the maximum load of average power consumption of each type of altitude every day from March to May in the past three years, and make a time series diagram of power load changes. 6. The optimal operation model of the station area based on the analysis of the characteristics of electricity consumption in the tea area according to claim 5, characterized in that, in areas with lower altitudes, temporary transformers are preferentially used to shunt the load of heavy load distribution transformers, and to be harvested in this area After the peak is over and the power load drops, the temporary transformer will be moved to other high-altitude areas.