CN112487665B - Method for calculating actual demand coefficient of ocean engineering electrical equipment based on probability statistics - Google Patents
Method for calculating actual demand coefficient of ocean engineering electrical equipment based on probability statistics Download PDFInfo
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Abstract
The invention discloses a method for calculating an actual demand coefficient of ocean engineering electrical equipment based on probability statistics. The data used by the invention are acquired from the field instead of simple table lookup, so that the reliability is higher. In addition, the invention considers the model of the equipment and the running state of the equipment, and has good adaptability to different working conditions.
Description
Technical Field
The invention relates to an actual demand coefficient estimation method for ocean engineering electrical equipment, in particular to an actual demand coefficient prediction method for large ocean engineering electrical equipment.
Background
The demand coefficient is the ratio of the power actually required by the electric equipment group to the power required at rated load, and is formulated as
K c ＝P sb /P sn
In the formula:
P sb -the power actually required by the consumer.
P sn -the power rating of the consumer.
The demand factor of large packages of electrical equipment in power distribution and supply designs has a significant impact on the computational load of distribution transformers, which directly determines the capacity of the transformer selected.
At present, the margin reserved by the capacity of a transformer of a workshop substation is generally large. The coefficient of demand of the electric equipment in actual design comes from a reliable design manual or authoritative design data, but the design manual or authoritative data are not updated since the electric equipment is published decades ago.
Therefore, a novel method for predicting the actual demand coefficient of the large ocean engineering electrical equipment needs to be provided, and factors such as daily operation conditions of the equipment, types of the equipment and the like are taken into consideration.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for calculating the actual demand coefficient of the ocean engineering electrical equipment based on probability statistics, which can predict a more reasonable demand coefficient and ensure that the predicted demand coefficient can meet the requirements of different working conditions.
The method for calculating the actual demand coefficient of the ocean engineering electrical equipment based on probability statistics is characterized by comprising the following steps of:
step one, recording the models of all electric equipment and the rated power P of the equipment corresponding to the models of all electric equipment ρ ；
Recording the actual power of each type of electric equipment according to the set sampling frequency in the set sampling period;
step three, carrying out normal distribution curve fitting on the actual power of the same electric equipment at different dates and the same measuring time, and solving the average value mu of the actual power normal distribution curve of each electric equipment at each set sampling time in the set sampling period ρj Standard deviation σ ρj ；
Step four, calculating the actual power of each electric device at each sampling moment, and the specific process is as follows:
in the first step, the actual power of the same electric device at different measurement time is taken as P' ρj ＝μ ρj +3σ ρj (ii) a Rho is the equipment model, and j is the sampling time;
secondly, MATLAB software is used for fitting a curve of the actual power of each device changing along with time in one day to form an actual power curve, MATLAB software is used for solving a function expression corresponding to the actual power curve, and the function expression is usedThe number expression is defined as the actual power time function, and the actual power time function of each type of equipment is recorded as f ρ (t), ρ represents a device model number;
step five, counting the number of equipment of each model;
step six, solving the total rated power P of all types of equipment through the following formula sn ；
P sn ＝∑x ρ P ρ
In the formula:
x ρ representing the number of devices with model number rho; p ρ Representing the rated power of the device with model p;
step seven, solving the total actual power time function f of all types of equipment through the following formula Fruit of Chinese wolfberry (t)；
f Fruit of Chinese wolfberry (t)＝∑x ρ f ρ (t)
x ρ Representing the number of devices of type p; f. of ρ (t) represents the actual power time function of the device of type p;
step eight, calculating a required coefficient K c ；
K c ＝(1+S)K cMAX
S is the safety factor required by the project, K cMAX The maximum value of the required coefficient;
said K cMAX The calculation process of (2) is as follows: will require a coefficient time function K c (t) performing derivation, wherein the calculated maximum value is K cMAX Said K is c (t)＝f Fruit of Chinese wolfberry (t)/P sn In the formula f Fruit of Chinese wolfberry (t) is the total actual power time function for all models of equipment.
The method has the advantages that the reliability of the prediction result of the required coefficient is high, and the method is flexibly suitable for different working conditions. The data used by the method are acquired from the field instead of simply by table lookup, so that the method has high reliability. Different field conditions can be guaranteed.
Drawings
FIG. 1 is a flow chart adopted by the method for calculating the actual demand coefficient of the ocean engineering electrical equipment based on probability statistics.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
As shown in fig. 1, the method for calculating the actual demand coefficient of the ocean engineering electrical equipment based on probability statistics of the invention comprises the following steps:
step one, recording the models of all electric equipment and the rated power P of the equipment corresponding to the models of all electric equipment ρ 。
Recording the actual power of each type of electric equipment according to the set sampling frequency in the set sampling period;
and thirdly, performing normal distribution curve fitting on the actual power of the same electric equipment at different dates and the same measuring time (the method can be referred to probability theory and mathematical statistics lecture-high education press-8.8.1 st-177 p.) to obtain the average value mu of the actual power normal distribution curve of each electric equipment at each set sampling time in the set sampling period ρj Standard deviation σ ρj 。
The process of fitting a normal distribution curve is described as an example.
And assuming that the model of the electric equipment is rho, recording the actual power of the rho once every 5 minutes in one day, continuously recording the actual power of the equipment in one day for 30 days, and if the equipment does not work in a certain time period, taking the actual power as zero.
And fitting a normal distribution curve of the actual power of the electric equipment rho at the jth measurement moment of each day. Setting the mean value of normal distribution curve of actual power at each set sampling moment in set sampling period as mu ρj Standard deviation of σ ρj 。
The variance of the actual power is obtained by the following formula
In the formula, P ρj,i Representing the actual power of the device p measured at the jth measurement instant on day i, i e [1,30 ]]；
n is the number of sampling cycle days, where n is 30.
At this time, the mean value mu is aligned ρj And (6) estimating.
Due to the standard deviation σ ρj Unknown, sigma can be used ρj Unbiased estimation of
Instead of sigma ρj I.e. by
Then the random variable T:
the above formula T can be used as pivot quantity (the pivot quantity can be defined in the definition of "probability theory and mathematical statistics lecture" (high education press-8.2012, 1 st edition-174), T n-1 The representation of the pivot quantity T follows a T-distribution with a degree of freedom n-1(n being 30).
Since the probability density function of the t-distribution is unimodal symmetric, for a given confidence a, looking up the t-distribution table yields the upper quantile of the t-distribution with a degree of freedom n-1
(this value can be found in the Provisions for probability theory and mathematical statistics, high education Press-8 months of 2012, 1 st edition-appendix)
Then
I.e. mu ρj With a confidence interval of 1-alpha
Mu therefore ρj Taking values from the obtained data.
Step four, calculating the actual power of each electric device at each sampling moment, and the specific process is as follows:
firstly, the actual power normal distribution curve of each electric device at each set sampling time in a set sampling period is obtained, and each normal distribution standard value sigma is ρj Sum mean μ ρj Has also been solved. For normal distribution curves, the statistic values are greater than μ ρj +3σ ρj Has a probability of only 0.15%, and thus the maximum value of the actual power in the measurement time can be considered to be μ ρj +3σ ρj Therefore, the actual power at different measurement times of the same power consumer is taken as P' ρj ＝μ ρj +3σ ρj . ρ is the device model and j is the sampling time.
And secondly, fitting a curve of the actual power of each device changing along with time in one day by using MATLAB software to form an actual power curve. And using MATLAB software to obtain a function expression corresponding to the actual power curve, defining the function expression as an actual power time function, and recording the actual power time function of each type of equipment as f ρ (t), ρ represents the device model, and if can be represented by A, B, C, then f A (t)，f B (t)，f C (t) … … represents A and B, respectivelyActual power time function for model C … … devices.
And step five, counting the number of equipment of each model.
Step six, solving the total rated power P of all types of equipment through the following formula sn 。
P sn ＝∑x ρ P ρ
In the formula:
x ρ representing the number of devices with model number rho;
P ρ representing the rated power of the device model p.
Step seven, solving the total actual power time function f of all types of equipment through the following formula Fruit of Chinese wolfberry (t)。
The total actual power time function of all models of equipment is recorded as f Fruit of Chinese wolfberry (t), then f Fruit of Chinese wolfberry (t) is the superposition of the actual power time functions of all the devices, namely:
f fruit of Chinese wolfberry (t)＝∑x ρ f ρ (t)
x ρ Representing the number of devices with model number rho; f. of ρ (t) represents the actual power time function for a device of type p.
Step eight, calculating a required coefficient K c 。
K c ＝(1+S)K cMAX
And S is a safety factor required by the engineering, is determined according to the actual requirements of a demand side, and can also be obtained by referring to a related engineering manual.
K cMAX The maximum value of the required coefficient obtained by calculation.
The derivation process is as follows:
let K be the function of the change of the required coefficient with time c (t), according to the definition of the required coefficient:
K c (t)＝f fruit of Chinese wolfberry (t)/P sn
In the formula:
f fruit of Chinese wolfberry (t) is the total actual power time function for all models of equipment.
Will require a coefficient time function K c (t) Calculating the maximum value by derivation, and recording the maximum value as K cMAX If the safety factor required by the engineering is S, the coefficient K is required c The method comprises the following steps:
K c ＝(1+S)K cMAX 。
Claims (1)
1. the method for calculating the actual demand coefficient of the ocean engineering electrical equipment based on probability statistics is characterized by comprising the following steps of:
step one, recording the models of all electric equipment and the rated power P of the equipment corresponding to the models of all electric equipment ρ ；
Recording the actual power of each type of electric equipment according to the set sampling frequency in the set sampling period;
step three, carrying out normal distribution curve fitting on the actual power of the same electric equipment at different dates and the same measuring time, and solving the average value mu of the actual power normal distribution curve of each electric equipment at each set sampling time in the set sampling period ρj Standard deviation σ ρj ；
Step four, calculating the actual power of each electric device at each sampling moment, and the specific process is as follows:
in the first step, the actual power of the same electric device at different measurement time is taken as P' ρj ＝μ ρj +3σ ρj (ii) a Rho is the equipment model, and j is the sampling time;
secondly, using MATLAB software to fit a curve of the actual power of each equipment changing along with time in one day to form an actual power curve, using the MATLAB software to obtain a function expression corresponding to the actual power curve, defining the function expression as an actual power time function, and recording the actual power time function of each type of equipment as f ρ (t), ρ represents a device model;
step five, counting the number of equipment of each model;
step six, solving the total rated power P of all types of equipment through the following formula sn ；
P sn ＝∑x ρ P ρ
In the formula:
x ρ representing the number of devices with model number rho; p is ρ Representing the rated power of the device with model p;
step seven, solving the total actual power time function f of all types of equipment through the following formula Fruit of Chinese wolfberry (t)；
f Fruit of Chinese wolfberry (t)＝∑x ρ f ρ (t)
x ρ Representing the number of devices with model number rho; f. of ρ (t) represents the actual power time function of the device of type p;
step eight, calculating a required coefficient K c ；
K c ＝(1+S)K cMAX
S is the safety factor required by the project, K cMAX The maximum value of the required coefficient;
said K cMAX The calculation process of (2) is as follows: will require a coefficient time function K c (t) deriving to obtain a maximum value K cMAX Said K c (t)＝f Fruit of Chinese wolfberry (t)/P sn In the formula f Fruit of Chinese wolfberry (t) is the total actual power time function for all models of equipment.
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