Baxramov Umarkhodja, 1 Kaxarov Bakhodir

Solid State Technology
Volume: 65 Issue: 1 
Publication Year: 2022
66
Archives Available @ www.solidstatetechnology.us 
Fig. 1. An example of processes of consumption of the target product modeling. 
x - autocorrelation functions of the initial 
When going to node 2 of the circuit, the value t
1 
, for the first node and 28 new values t
n
,
providing the calculation of the parameters of harmonics and 24 hours of loads and for this node. The 
operation of the algorithm for a new simulation day begins after performing other calculations in 
other blocks of simulation.
When the pseudo-random number generator is operating, this algorithm uses sufficiently short 
sequences of these numbers, which guarantees the absence of any deterministic trends in these 
sequences. [5,6,9 ]. 
The number of simulated random loads in each of the circuit nodes is determined by the 
requirements for the desired accuracy of the obtained estimates of the parameters of the distribution 
functions. Assuming that the values of the coefficients of variation for all the above distribution 
functions are approximately equal to the maximum of the coefficients of variation of the loads in the 
nodes of the circuit, and the acceptable error in determining the mathematical expectations– (

) 
make up 
0.05, we get [2,7,10]required duration of simulation: 
,
2
2
m ax
2


v
t
N


(10) 
where N – number of hours of load simulation: 
v
max
-maximum load variation coefficient: 
t
ɑ
= 2 – parameter corresponding 95% the probability of hitting the mathematical expectation in 
the intervals 



2
n
where 
n
- mean value of a random variable.
Insofar as v
max
for water supply systems is 0.75 – 0.8, then according to (10) N=1280, that is, 
the simulation period is 54 days. 
The initial data required for the operation of the processes of consumption of the target product 
simulation modeling algorithm are attached below.
The initial information used for the operation of the block of the algorithm for the formula (3) 
for generating nodal selections in the utility network Figure 1, described in [4] are given in Table 3.1. 
When modeling the network (Fig.2), data for nodes 1, 2, 3 were used 

---