PROBABILISTIC CHARACTERISTICS OF WIND POWER PLANT OUTPUT FLUCTUATIONS IN THE CLIMATIC CONDITIONS OF THE AZOV-BLACK SEA REGION OF UKRAINE
Scaling up hydrogen energy is a strategic priority of the energy policies of Ukraine and the European Union. Using renewable energy sources (RES) to power autonomous technological complexes for green hydrogen production and seawater desalination as a feedstock requires finding effective technical so...
Збережено в:
| Дата: | 2026 |
|---|---|
| Автори: | , , |
| Формат: | Стаття |
| Мова: | Англійська |
| Опубліковано: |
Institute of Renewable Energy National Academy of Sciences of Ukraine
2026
|
| Теми: | |
| Онлайн доступ: | https://ve.org.ua/index.php/journal/article/view/635 |
| Теги: |
Додати тег
Немає тегів, Будьте першим, хто поставить тег для цього запису!
|
| Назва журналу: | Vidnovluvana energetika |
| Завантажити файл: |  |
Репозитарії
Vidnovluvana energetika| _version_ | 1870287581188456448 |
|---|---|
| author | Vasko , P. Mazurenko , I. Sysak , R. |
| author_facet | Vasko , P. Mazurenko , I. Sysak , R. |
| author_institution_txt_mv | [
{
"author": "P. Vasko ",
"institution": "Institute of Renewable Energy, NAS of Ukraine, Kyiv, Ukraine"
},
{
"author": "I. Mazurenko ",
"institution": "Institute of Renewable Energy, NAS of Ukraine, Kyiv, Ukraine"
},
{
"author": "R. Sysak ",
"institution": "Institute of Renewable Energy, NAS of Ukraine, Kyiv, Ukraine"
}
] |
| author_sort | Vasko , P. |
| baseUrl_str | https://ve.org.ua/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-07-09T12:14:07Z |
| description | Scaling up hydrogen energy is a strategic priority of the energy policies of Ukraine and the European Union. Using renewable energy sources (RES) to power autonomous technological complexes for green hydrogen production and seawater desalination as a feedstock requires finding effective technical solutions to align the stochastic nature of power generation with the stable energy demand of such facilities. To address this challenge, this study performs a statistical evaluation of parameters characterizing the dynamics of electricity production based on retrospective data from a wind power plant (WPP) located in the Azov-Black Sea region of Ukraine. The research primarily focuses on analyzing the duration of power deficit and surplus intervals caused by the inherent variability of WPP operation. Numerical estimates were obtained for the maximum and average duration of such intervals, their standard deviation, as well as the parameters of exponential and Weibull probability distributions used for their modelling. The results of this study are essential for designing continuous energy supply systems for large-scale autonomous RES-based green hydrogen production and seawater desalination facilities. 
Ref. 36, Tab. 2, Fig. 8 
  |
| doi_str_mv | 10.36296/1819-8058.2026.2(85).296-305 |
| first_indexed | 2026-07-10T01:00:30Z |
| format | Article |
| fulltext |
296
Відновлювана енергетика. № 1/2026 | Вітроенергетика
UDC 621.311.24.018.004.12 https://doi.org/10.36296/1819-8058.2026.1(84).296-305
PROBABILISTIC CHARACTERISTICS OF WIND POWER PLANT OUTPUT FLUCTUATIONS
IN THE CLIMATIC CONDITIONS OF THE AZOV-BLACK SEA REGION OF UKRAINE
Received May 10, 2026; accepted Jun. 26, 2026
Available online June. 30, 2026
Vasko P.1, Mazurenko I.2, Sysak R.3
Author for correspondence: Mazurenko Iryna,
e-mail: irynalmazurenko@gmail.com
Abstract. Scaling up hydrogen energy is a strategic priority of the
energy policies of Ukraine and the European Union. Using renewa-
ble energy sources (RES) to power autonomous technological com-
plexes for green hydrogen production and seawater desalination
as a feedstock requires finding effective technical solutions to align
the stochastic nature of power generation with the stable energy
demand of such facilities. To address this challenge, this study performs a statistical evaluation of parameters
characterizing the dynamics of electricity production based on retrospective data from a wind power plant (WPP)
located in the Azov-Black Sea region of Ukraine. The research primarily focuses on analyzing the duration of power
deficit and surplus intervals caused by the inherent variability of WPP operation. Numerical estimates were ob-
tained for the maximum and average duration of such intervals, their standard deviation, as well as the parame-
ters of exponential and Weibull probability distributions used for their modelling. The results of this study are
essential for designing continuous energy supply systems for large-scale autonomous RES-based green hydrogen
production and seawater desalination facilities.
Ref. 36, Tab. 2, Fig. 8
Key words: wind power plant, random processes, energy supply, green hydrogen, desalination, power, statis-
tics, fluctuations.
ІМОВІРНІСНІ ХАРАКТЕРИСТИКИ ФЛУКТУАЦІЙ ПОТУЖНОСТІ ВІТРОЕЛЕКТРИЧНОЇ СТАНЦІЇ
В КЛІМАТИЧНИХ УМОВАХ АЗОВО-ЧОРНОМОРСЬКОГО РЕГІОНУ УКРАЇНИ
Отримано 10 трав. 2026 р.; рекомендовано до публікації 26 чер. 2026 р.
Доступно онлайн 30 чер. 2026 р.
Васько П. Ф.1, Мазуренко І. Л.2, Сисак Р. М.3
Автор для кореспонденції: Мазуренко Ірина,
e-mail: irynalmazurenko@gmail.com
Анотація. Розвиток водневої енергетики є одним із страте-
гічних пріоритетних напрямів енергетичної політики Укра-
їни та Європейського Союзу. Використання відновлюваних
джерел енергії (ВДЕ) для електроживлення автономних тех-
нологічних комплексів з виробництва зеленого водню та
опріснення морської води як вихідної сировини потребує по-
шуку ефективних технічних рішень для узгодження стохас-
тичного характеру генерування потужності та стабільної потреби в енергії таких об’єктів. З метою
розв’язання цієї проблеми у межах даного дослідження на основі ретроспективних даних функціону-
вання вітроелектричної станції (ВЕС), розташованої в Азово-Чорноморському регіоні України, прове-
дено статистичне оцінювання параметрів, що характеризують динаміку виробництва електроенер-
гії. Основну увагу зосереджено на аналізі тривалості інтервалів дефіциту та надлишку потужності,
зумовлених варіабельністю роботи ВЕС. Отримано чисельні оцінки максимальної та середньої трива-
лості таких інтервалів, їх середньоквадратичного відхилення, а також параметрів експоненціального
та вейбуллівського розподілів імовірностей для їх моделювання. Результати дослідження є важливими
1 Dr. of Tech. Sciences
https://orcid.org/0000-0001-8807-7173
2 Cand. of Tech. Sciences
https://orcid.org/0000-0002-0146-7396
3 Cand. of Tech. Sciences
https://orcid.org/0000-0003-4474-4776
1, 2, 3 Institute of Renewable Energy, NAS of
Ukraine, Kyiv, Ukraine
1 д-р. техн. наук
https://orcid.org/0000-0001-8807-7173
2 канд. техн. наук
https://orcid.org/0000-0002-0146-7396
3 канд. техн. наук
https://orcid.org/0000-0003-4474-4776
1, 2, 3 Інститут відновлюваної енергетики
НАН України, м. Київ, Україна
297
Відновлювана енергетика. № 1/2026 | Вітроенергетика
для проєктування систем безперервного енергозабезпечення потужних автономних комплексів з виро-
бництва водню та опріснення морської води на основі ВДЕ. Бібл.36, табл.2, рис.8.
Ключові слова: вітроелектрична станція, випадкові процеси, енергозабезпечення, зелений водень, опрі-
снення, потужність, статистика, флуктуації.
Introduction. The territory of the Azov-Black Sea region of
Ukraine is characterized by significant wind energy re-
sources [1]. According to World Bank estimates, the achiev-
able capacity of offshore wind power plants (WPPs) stands
at 251 GW [2], while onshore plants reach 100 GW [3]. Ex-
pected annual capacity factor values, assuming full utiliza-
tion of the generated electricity, reach 35% for coastal
WPPs and up to 40% for offshore WPPs [4]. Calculation-
based studies of wind energy potential are performed using
averaged wind speed values over specific time intervals. Ex-
perimental power output curves of wind turbines, as a
function of wind speed, are also determined through aver-
aging [5]. Wind speed in the surface layer of the atmos-
phere varies chaotically in time and space, causing corre-
sponding changes in the instantaneous power of the WPP,
while the averaging of quantitative data obscures this vari-
ability, which is critical for integrating wind energy into cen-
tralized and local power systems, as well as autonomous
power supply systems. WPP power fluctuations propagate
through the electrical grid and, at certain levels, cause un-
acceptable voltage and frequency deviations from stand-
ardized ranges [6]. In such cases, to ensure stable grid op-
eration, wind power curtailment may be applied, leading to
subsequent financial consequences [7,8,9].
The variability of WPP power has an extremely important im-
pact on autonomous power supply systems for energy-inten-
sive technologies, particularly for the electrolytic production
of green hydrogen and water desalination using the reverse
osmosis method. Scaling up hydrogen energy is one of the
strategic priorities of the energy policy of Ukraine and the
European Union for the coming decades [10,11]. Ukraine is
participating in the implementation of the European pro-
gram "2×40 GW Green Hydrogen Initiative" [12], which em-
phasizes the construction of 10 GW of electrolyser capacity
for green hydrogen production using wind energy in the
Azov-Black Sea region. The program envisions an annual hy-
drogen production in Ukraine of 1.65 million tons, requiring
approximately 24 million m³/year of treated freshwater [13].
Achieving these production volumes requires the installation
of about 40 GW of renewable energy capacity [14,15]. This
capacity is too large for parallel operation with the national
power grid, which requires identifying ways to integrate the
stochastic supply of wind power into the technological
schemes of hydrogen production and seawater desalination.
Utilizing WPPs to power these complexes demands the de-
velopment of effective technical solutions to ensure their
continuous operation, taking into consideration the stochas-
tic nature of power generation and the stable energy de-
mand of electrolysers and membranes.
In the context of Ukraine, green hydrogen production is
technically feasible in southern regions close to the coast-
line, where sufficient seawater resources and the necessary
potential for wind power generation are available. Seawa-
ter requires preliminary desalination. For reverse osmosis
technology, the stability of the power supply is critical, as
frequent start-stop cycles significantly reduce the service
life of desalination membranes, and hydraulic shocks
caused by sudden voltage spikes can lead to total equip-
ment failure [16]. The presence of WPP power fluctuations
nearly halves the productivity of alkaline electrolysers [17].
The combination of these factors complicates the integra-
tion of wind power into hydrogen production and seawater
desalination technologies. One way to address this chal-
lenge is the use of energy storage systems (ESS) [18–21]
within an autonomous power supply system. ESS must have
a short response time, sufficient power and capacity, and
provide the capability for long-term (several days) energy
storage with minimal losses. Justifying the required ESS pa-
rameters requires a study of wind power generation dy-
namics, as these systems have a significant impact on the
overall technical and economic performance of the facility.
Preliminary observations. The presence of wind speed tur-
bulence in the surface layer of the atmosphere causes fluc-
tuations in the generated power of wind turbines and wind
power plants [22]. Real-time wind speed variations are char-
acterized by continuous disturbances that lead to changes in
the torque on the wind turbine shaft and, consequently, in
the parameters of the generated electricity. Fig. 1 shows a
fragment of an oscillogram of analogue signals representing
the load operating mode parameters of an experimental au-
tonomous wind turbine with a nominal power of 20 kW and
a moment of inertia of 9,000 kg·m² [23]. The oscillogram was
recorded using a multi-channel analogue oscillograph at a
constant value of electrical load resistance. The following no-
tations are used in the graph: v – the horizontal component
of the air flow velocity at the hub height; – the pitch angle
of the wind turbine blades; U, I – the voltage and current of
the valve generator; t – current time. The variable nature of
wind speed and the nonlinear aeromechanical properties of
the wind turbine are the causes of fluctuations in the param-
eters of the generated electricity.
Fig. 1. Wind speed disturbances and output electrical pa-
rameters fluctuations
of an autonomous wind turbine (oscillogram fragment)
298
Відновлювана енергетика. № 1/2026 | Вітроенергетика
Fluctuations in generated electricity are also inherent to
wind turbines during parallel operation with the electri-
cal grid. Fig. 2 shows a fragment of an oscillogram
illustrating power variations of an industrial wind tur-
bine with an induction generator operating within a
power system [24].
Fig. 2. Power output fluctuations of wind turbine operating
within a power system (oscillogram fragment)
It is worth noting that the presented oscillograms were rec-
orded over short time intervals; therefore, these results
should not be taken as a benchmark, much less as a stand-
ard. Since the occurrence of wind gusts is probabilistic in na-
ture, the oscillograms merely demonstrate their presence
and the corresponding response of the wind turbine. To ob-
tain statistically robust results regarding wind power fluctu-
ations, long-term experimental studies are required [25].
Problem statement and research methods. The study of
the random process of WPP power generation over long
time intervals involves measuring and recording ordinate
values at specific moments in time with a predefined sam-
pling frequency [26,27]. For the analysis of wind energy
processes, an acceptable sampling step is one hour, which
aligns with the commonly used value for power supply sys-
tems [28]. In this case, the observation results represent a
random function of time, where the argument takes only
specific numerical values. Random functions of time ob-
tained in this manner are referred to by the term "random
sequence." As an example, Fig. 3 shows a random sequence
of the power generation process for a modern WPP (with a
wind turbine hub height of approximately 100 meters
above the ground surface) over a monthly interval with a 1-
hour sampling step in the climatic conditions of the Azov-
Black Sea region of Ukraine. The generation process is char-
acterized by significant power variability, the values of
which are given in per-unit (p.u.) relative to the installed
capacity of the wind turbines within the plant [4].
Fig. 3. WPP power fluctuations over a monthly time interval
Typically, the analysis of random sequence fluctuations in-
volves the statistical estimation of the number of extreme
trajectory values, the height of local maxima, the number
of predefined level crossings, the duration of trajectory ex-
cursions above a given level, the relative time the trajectory
spends within a specified range, and other indicators.
299
Відновлювана енергетика. № 1/2026 | Вітроенергетика
The aim of this study is to perform a statistical evaluation
of the probability distributions of the duration of wind
power trajectory excursions above specific characteristic
levels, as well as the probability distribution of the duration
of “zero-generation” periods for a WPP in the climatic con-
ditions of the Azov-Black Sea region of Ukraine, based on
the theoretical foundations of random processes and se-
quences [29,30].
Methodology for the determination of probabilistic char-
acteristics. The input array of retrospective data consists of
power generation measurements from a coastal WPP in the
Azov-Black Sea region of Ukraine over a 1-year period, rec-
orded with a 1-hour step. A fragment of this random se-
quence of the WPP power generation process ( )tp over a
monthly interval is shown in Fig. 3. Power values are ex-
pressed in per-unit (p.u.) to standardise the results.
Consider a generalised representation of a possible trajec-
tory of the power generation random process ( )tp (Fig.4).
Let us define two power levels: minimum mnpL = and max-
imum mxpH = . We assume that when ( ) mnptp , there is
a power deficit; therefore, previously stored energy is used
to power the equipment. When ( ) mxptp , there is a
power surplus, meaning the generated energy is sufficient
not only to provide power to the equipment but also to
charge the ESS. Fig. 4 illustrates the periods of power deficit
and surplus, each corresponding to a specific start time it
and a specific duration i . In this case, periods 1, 2, and 4
represent a deficit, while period 3 represents a surplus.
Since the process ( )tp is random, both it and i are ran-
dom variables. It is evident that over a sufficiently long ob-
servation interval T , a large number of i values can be
obtained. The objective of the study is to evaluate the prob-
abilistic parameters of the ,,, 421mn = and
,3mx = random sequences.
Fig. 4. Periods of power deficit and surplus
In several studies, for example [31], similar issues are ex-
amined in the most general formulation, where a continu-
ous differentiable random process is considered, and the
dynamics of its trajectory is investigated. An event in which
the process trajectory crosses a fixed level H from bottom
to top is called a positive excursion (up-crossing), whereas
if the trajectory crosses a level L from top to bottom, it is
called a negative excursion (down-crossing). It has been
shown that obtaining an analytical expression for the prob-
ability distribution of the random process trajectory excur-
sion duration is mathematically complex and remains not
fully resolved in the general case. Therefore, in this study,
the determination of this distribution is implemented using
statistical methods.
According to the results in [31], for large values of , it can
be assumed that the probability decreases exponentially as
increases. The exponential distribution is widely applied
in the theory of queuing, as well as in reliability theory, to
describe the probability of failure-free operating time dur-
ing sudden failures [32,33]. The probability density function
of a random variable exp with an exponential distribution
depends on a single parameter and is described by the for-
mula:
( ) be
b
bf
−
=
1
; , 0 , 0b , (1)
and the cumulative distribution function has the form:
( ) bebF
−
−=1; , 0 , 0b . (2)
Since the mathematical expectation of an exponentially dis-
tributed random variable exp
is estimated as:
b=expΜ ,
then to find an estimate of the parameter b of the distri-
bution in form (1), it is sufficient to determine the average
value for the available sample, which significantly simplifies
the application of this type of distribution. In addition, the
advantage of this distribution is the simplicity of its expres-
sion. Despite this, the exponential distribution, as a model
for the duration of excursions of the WPP generation pro-
cess ( )tp , has the following significant disadvantages: 1) at
small values , the exponential distribution can signifi-
cantly deviate from the actual distribution of the duration
of excursions of the process ( )tp ; 2) the exponential distri-
bution exists on an infinite interval ) ,0 , whereas the
actual durations are limited; 3) the use of the exponential
distribution has been theoretically confirmed for describing
the intervals between independent rare events [32,33],
while the values of the studied process ( )tp at close mo-
ments in time can be correlated, which reduces the accu-
racy of such a distribution model.
Another distribution often used in reliability theory to de-
scribe the time to failure is the two-parameter Weibull dis-
tribution [32,33], the density of which is described by the
expression [33,34]:
( )
−
−
= ef
1
,; , 0 , 0, , (3)
where – scale factor, – shape parameter.
The cumulative distribution function has the form:
300
Відновлювана енергетика. № 1/2026 | Вітроенергетика
( )
−
−= eF 1,; , 0 , 0, . (4)
Owing to the fact that the Weibull distribution is character-
ised by two parameters, it can describe the empirical distri-
bution more accurately. For 1= , it simplifies to an expo-
nential distribution with the parameter .
To find the estimates ̂ and ̂ for the Weibull distribution
parameters and respectively, based on the sampled
data i , ni ,1= , the relations obtained by the maximum
likelihood method were used [34,35]:
ˆ
1
1
ˆ1
ˆ
=
=
n
i
i
n
, (5)
1
0
2
1
3
2
1
41
2
3
0
2
1
3 1ˆ
−
−
−
−
+−=
n
s
s
s
s
sss
n
s
s
s
, (6)
where
075.1
0
−= v ;
=
=
n
i
i
n 1
1
;
s
v = ;
( )
2
1
1
21
−=
=
n
i
i
n
s ;
=
=
n
i
is
1
1
0 ;
=
=
n
i
is
1
2 ln ; (7)
=
=
n
i
iis
1
3 ln0 ; ( )
=
=
n
i
iis
1
2
4 ln0 .
Research Results. Statistics for the duration of WPP power
generation trajectory ( )tp excursions throughout the year,
with 1-hour sampling step, are presented on a monthly ba-
sis in Table 1. It should be noted that the selection of spe-
cific values for the levels mnp and mxp depends on the ra-
tio between the nominal power consumption of the
equipment and the installed capacity of the WPP, that have
not been defined at this stage. Therefore, within the frame-
work of this study, the full range of power variation is rep-
resented by several level values: 0.1 p.u., 0.35 p.u., 0.7 p.u.,
and 0.9 p.u.
Table 1. Statistics of the WPP output power excursions above and below the fixed levels
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Power
level Indicator Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Entire
year
p≥0.90
Mean 4.2 2.56 2.44 1.5 1 - 2.5 1 - 4 6.5 2.36 3.39
Std. dev. 3.56 1.74 1.67 1.0 - - 3.0 0.0 - 2.38 8.12 2.50 4.237
Number 5 9 9 4 1 0 4 2 0 7 12 11 64
Max 10 6 6 3 1 - 7 1 - 7 29 9 29
p>0.70
Mean 4.6 9.2 6.5 6.4 3.5 4.0 5.9 3.4 4.1 6.7 10.4 6.4 6.0
Std. dev. 5.56 15.14 7.52 8.99 3.32 5.73 7.86 2.59 3.31 8.67 13.84 7.83 8.51
Number 40 23 25 21 17 25 26 18 32 34 29 35 325
Max 26 55 27 40 12 26 36 10 14 43 57 32 57
p>0.35
Mean 10.0 15.5 10.7 8.4 5.2 6.7 7.6 5.5 7.0 9.8 11.9 14.0 9.1
Std. dev. 10.14 22.30 10.86 14.59 6.25 9.64 9.48 5.07 6.59 11.21 15.61 18.89 12.28
Number 47 23 27 33 38 33 40 42 39 39 36 30 423
Max 36 83 42 78 36 52 44 20 23 46 63 91 91
p>0.10
Mean 36.0 22.7 12.4 15.5 9.9 9.4 12.0 10.6 13.2 22.9 32.1 26.7 16.0
Std. dev. 30.52 37.78 16.66 24.82 19.80 14.28 13.91 14.15 24.49 22.08 24.85 29.34 23.15
Number 17 22 38 32 44 51 46 44 35 25 18 21 387
Max 124 139 64 94 84 81 56 63 133 69 79 108 146
p≤0.10
Mean 8.3 8.9 7.2 7.0 7.0 4.8 4.2 6.2 7.1 7.1 8.4 9.2 6.7
Std. dev. 6.06 11.71 7.87 7.23 7.91 4.57 4.79 6.73 9.36 6.68 6.28 9.27 7.55
Number 16 22 38 32 44 50 46 45 36 24 17 20 386
Max 22 42 37 26 39 17 23 35 38 24 26 32 42
The minimum level value p=0.1 p.u. characterises the initial
power required for the effective integration of wind energy
into consumer technological schemes, while the value
p=0.9 p.u. represents the maximum achievable power of a
multi-unit wind farm, accounting for all types of losses. The
level p=0.35 p.u. aligns with the capacity factor of wind
power plants in the southern regions of Ukraine [36], and
the level p=0.7 p.u. corresponds to one of the possible load
301
Відновлювана енергетика. № 1/2026 | Вітроенергетика
operating modes. For each of the investigated levels, the
respective rows of Table 1 provide quantitative estimates
of the mean value, standard deviation, as well as the num-
ber of elements in the corresponding sequence and its max-
imum element. This information is presented for each
month of the year separately (columns 3 to 14) and for the
entire year as a whole (column 15).
Based on the data in Table 1, preliminary assumptions re-
garding the required ESS parameters can be made. In par-
ticular, the mean annual duration of continuous generation
at nominal power can be expected to be 3.39 hours, with
the maximum duration reaching 29 consecutive hours.
These values characterise the power and the maximum
quantity of energy that the ESS can accumulate during a sin-
gle charging cycle. The longest “zero generation” period
was observed for 42 consecutive hours, which can serve as
baseline information for determining the minimum re-
quired ESS capacity for a given load power. The results of
the heuristic analysis of monthly random sequences re-
garding the duration of “zero generation” at the level of the
WPP capacity factor indicated the necessity for continuous
ESS operation for 10–12 consecutive days.
To mathematically model the probability distributions of
power generation trajectory excursions beyond the investi-
gated threshold levels, statistical estimates of their param-
eters were obtained according to (1,3) and (5,6), as pre-
sented in Table 2.
Table 2. Parameters of exponential and Weibull probabil-
ity distributions
for modelling WPP power excursion durations
Power level
Distribution parameters
Exponential
Weibull
(two-parameter)
b̂ ̂ ̂
p≥0.9 3.39 3.3251 0.9585
p>0.7 6.00 5.3178 0.8103
p>0.35 9.10 8.0127 0.8016
p>0.1 16.00 12.5908 0.7073
p≤0.1 6.70 6.5202 0.9360
Frequency polygons and their analytical approximation by
continuous distributions for each power level are shown in
Fig. 5. The results demonstrate that for large values of the
variable both approximations align sufficiently well with
the empirical distributions for each investigated level. How-
ever, for small values of the approximation error is sig-
nificant, particularly for the levels 7.0p ; 35.0p ;
1.0p . The value of the Weibull distribution density at
0= is infinitely large, as 1ˆ . For the exponential dis-
tribution, the density value at 0= equals b̂/1 and is fi-
nite. However, in all considered cases, it is substantially
lower than the height of the first bin of the empirical den-
sity, indicating insufficient approximation accuracy for the
investigated distributions at small values of .
Fig. 5. Empirical and analytical probability distributions of
WPP power generation trajectory excursion durations
over an annual interval for various levels:
a) p≥0.9; b) p>0.7; c) p>0.35; d) p>0.1; e) p≤0.1
302
Відновлювана енергетика. № 1/2026 | Вітроенергетика
In subsequent analysis, the Weibull distribution will be
used, as its analytical description is more flexible for appli-
cation due to the presence of two parameters. Fig. 6 shows
the plots of the Weibull probability density function and cu-
mulative distribution function for the parameters given in
Table 2. For visual clarity, only the left part of the plots is
shown, where they have sufficiently large values. The right
part of the density plots rapidly approaches zero, while the
distribution functions approach unity, making them less in-
formative. As can be seen, the slope of the distribution
functions depends on the level value and varies synchro-
nously with it. An interesting fact is that the Weibull density
and distribution curves for levels 35.0p and 7.0p are
located fairly to each other, which characterises the pres-
ence of comparable random sequences throughout the
year within this range of generated power.
Fig. 6. Plots of Weibull probability density functions (a) and cu-
mulative distribution functions (b) for WPP power generation
excursion durations throughout the year for various levels
There is also practical interest in determining the statistical
parameters of WPP power generation fluctuations for fixed
hours of the day throughout a calendar month. Knowledge
of these statistics is important when creating hybrid power
systems based on WPPs and photovoltaic plants. In partic-
ular, Fig. 7 illustrates the maximum number of consecutive
days when WPP generation was absent for a specific hour
of the day. In the first half of the day, ranging from 00:00 to
12:00, the maximum duration of zero WPP generation is
observed in March, lasting from five to ten consecutive
days, and in May, from five to eight days. In the time inter-
val from 12:00 to 24:00, the longest absence of WPP power
generation is observed in September, ranging from four to
eight consecutive days. A similar analysis can be performed
for any interval of the day.
The cumulative characteristic of the total duration of low
WPP generation across calendar months is shown in Fig. 8.
The longest “zero generation” periods are expected in
March, May, August, and September. Accordingly, the total
duration of WPP generation with power exceeding 0.1 p.u.
in March, May, August, and September is expected to be at
a level of 450–500 hours per month. For the remaining
months, the generation duration will range between 500
and 600 hours per month.
Conclusions
1. This publication initiates research aimed at creating au-
tonomous power supply systems based on WPPs for large-
scale green hydrogen production technologies and sea-
water desalination as a feedstock in the climatic conditions
of the Azov-Black Sea region of Ukraine. The study validates
the application of the principles of random process theory
and random sequences to analyse the variability of power
generation in modern WPPs with wind turbine hub heights
of 100 metres and above. The durations of power genera-
tion trajectory excursions beyond specified threshold levels
were investigated within the range of 0.1 ≤ Р ≤ 0.9 per unit
relative to the total installed capacity of the wind turbines
within the plant.
2. The duration of WPP operation at nominal power over
an annual interval is estimated at approximately 217 hours,
while on monthly intervals it ranges from 0 to 78 hours. The
lowest duration values are expected in May, June, August,
and September, while the highest are expected in Novem-
ber. For the remaining months, the total duration of gener-
ation at nominal power ranges between 20 and 28 hours.
The mean annual duration of continuous generation at
nominal power can be expected to be 3.39 hours, with the
maximum duration reaching 29 consecutive hours in No-
vember.
3. The total duration of WPP generation with power ex-
ceeding 0.1 p.u. in March, May, August, and September is
expected to be at a level of 450–500 hours per month. For
the remaining months, the generation duration will range
between 500 and 600 hours per month.
4. The longest “zero generation” period over an annual
time interval is expected to last for 42 consecutive hours,
which can serve as baseline information for determining
the minimum required capacity of the energy storage sys-
tem for a given load. The results of the analysis of monthly
303
Відновлювана енергетика. № 1/2026 | Вітроенергетика
Fig. 7. Maximum number of consecutive days with low WPP generation for a specific hour of the day
Fig. 8. Total monthly number of hours totalT
of WPP “zero
generation”
random sequences regarding the duration of “zero genera-
tion” at the level of the WPP capacity factor indicated the
necessity for the continuous operation of the energy stor-
age system for 10–12 consecutive days.
5. The use of the two-parameter Weibull probability distri-
bution for the mathematical modelling of WPP power gen-
eration fluctuation durations has been justified.
Funding. This study was performed under the research pro-
ject of the National Academy of Sciences of Ukraine: “Sci-
entific and technological principles of using renewable en-
ergy sources for seawater desalination” (State registration
No. 0123U100871).
REFERENCES
1. Benton B.N., Buster G., Pinchuk P. et al. Super
Resolution for Renewable Energy Resource Data with
Wind from Reanalysis Data (Sup3rWind) and
Application to Ukraine.
https://docs.nrel.gov/docs/fy24osti/89241.pdf
[Accessed: 10 April 2026].
2. Going Global: Expanding Offshore Wind to Emerging
Markets: Technical Potential for Offshore Wind in
Ukraine. Washington, D.C.: World Bank Group.
https://documents1.worldbank.org/curated/en/70939
1586844502062/pdf/Technical-Potential-for-Offshore-
Wind-in-Ukraine-Map.pdf [Accessed: 12 April 2026].
3. Atlas of the Energy Potential of Renewable Energy
Sources of Ukraine. Kyiv: Institute of Renewable
Energy of the NAS of Ukraine. 2024. 56 p.
https://doi.org/10.36296/atlas-2024 [in Ukrainian]
4. Vasko P.F., Mazurenko I.L. Express analysis of
probabilistic characteristics of wind power stations as a
source of energy for seawater desalination in the Azov-
Black Sea region of Ukraine. Energy Technologies &
Resource Saving. 2023. No. 4. Pp. 42–56.
https://doi.org/10.33070/etars.4.2023.04 [in
Ukrainian]
5. IEC 61400-12-1 Edition 2.0 2017-03. Wind Energy
Generation Systems - Part 12-1: Power Performance
Measurements Of Electricity Producing Wind Turbines.
281 p. https://standards.iteh.ai/catalog/
standards/iec/b6b43db0-b0ba-41a0-baaa-
2a027c60cc9b/iec-61400-12-1-2017 [Accessed: 12
April 2026].
6. Haehne H., Schmietendorf K., Tamrakar S., Peinke J.,
Kettemann S. Propagation of wind-power-induced
fluctuations in power grids. 2019. Physical Review E
99(5). https://doi.org/10.1103/PhysRevE.99.050301
7. Jon Ungoed-Thomas. Millions paid out to keep
Shetland windfarm idle - even in a gale. The Observer.
Sunday 19 April 2026.
https://observer.co.uk/tags/energy-bills [Accessed: 22
April 2026].
https://docs.nrel.gov/docs/fy24osti/89241.pdf
https://standards.iteh.ai/catalog/
https://doi.org/10.1103/PhysRevE.99.050301
https://observer.co.uk/tags/energy-bills
304
Відновлювана енергетика. № 1/2026 | Вітроенергетика
8. Freya Barnes. Shetland windfarm owner is paid
£10million-a-year to turn off turbines during gale force
winds - while people living in its shadow fork out for
some of Britain's highest energy bills. Daily Mail. 20
April 2026. https://www.dailymail.com/news/article-
15748055/Shetland-windfarm-owner-paid-10million-
year-turn-turbines-gale-force-winds.html [Accessed:
22 April 2026].
9. Irina Slav. Brits Pay Billions To Waste Wind Energy. 03
December 2024. https://oilprice.com/Alternative-
Energy/Wind-Power/Brits-Pay-Billions-To-Waste-Wind-
Energy.html [Accessed: 22 April 2026].
10. Hydrogen Energy.
https://mev.gov.ua/en/storinka/hydrogen-energy
[Accessed: 03 April 2026].
11. Memorandum of Understanding between the
European Union and Ukraine on a Strategic
Partnership on Biomethane, Hydrogen and other
Synthetic Gases. 02.02.2023. 8 p.
https://energy.ec.europa.eu/memorandum-
understanding-between-european-union-and-ukraine-
strategic-partnership-biomethane_en [Accessed: 04
January 2026].
12. Ad van Wijk, Jorgo Chatzimarkakis. Green Hydrogen for
a European Green Deal: A 2×40 GW Initiative. Brussels:
Hydrogen Europe. 40 p.
https://hydrogeneurope.eu/wp-
content/uploads/2021/11/Hydrogen-Europe_2x40-
GW-Green-H2-Initative-Paper.pdf [Accessed: 22 April
2026].
13. Philip Woods, Heriberto Bustamante, Kondo-Francois
Aguey-Zinsou. The hydrogen economy –Where is the
water? Energy Nexus. Vol. 7. September 2022. 100123.
https://doi.org/10.1016/j.nexus.2022.100123
14. Pidkomorna Yu. The Ministry of Energy has completed
the discussion of the Hydrogen Strategy. 27 September
2022. https://mev.gov.ua/novyna/minenerho-
zavershylo-obhovorennya-vodnevoyi-stratehiyi-yuliya-
pidkomorna. [Accessed: 24 April 2026]. [in Ukrainian]
15. Hydrogen Strategy of Ukraine for the period until
2050. Project.
https://www.mev.gov.ua/sites/default/files/field/file/
vodneva-strategiya17.05.2024.pdf [Accessed: 07 April
2026]. [in Ukrainian]
16. Ali A., Tufa R. A., Macedonio F., Curcio E., Drioli E.
Membrane technology in renewable-energy-driven
desalination. Renewable and Sustainable Energy
Reviews. 2018. Vol. 81. Pp. 1–21.
https://doi.org/10.1016/j.rser.2017.07.047
17. Kate R. Ward, Olusola Bamisile, Chukwuebuka J. Ejiyi,
Iain Staffell. Time-averaged wind power data hides
variability critical to renewables integration. Energy
Strategy Reviews. 2023. 50(8):101235.
https://doi.org/10.1016/j.esr.2023.101235
18. Energy Storage Systems Market Size & Share 2025 –
2034. https://www.gminsights.com/industry-
analysis/energy-storage-systems-market [Accessed:30
March 2026].
19. Kebede A.A., Kalogiannis T., Van Mierlo J., Berecibar M.
A comprehensive review of stationary energy storage
devices for large scale renewable energy sources grid
integration. Renewable and Sustainable Energy
Reviews. 2022. Vol. 159. 112213.
https://doi.org/10.1016/j.rser.2022.112213
20. Vasko P., Verbovij A., Moroz A., Pazych S., Ibragimova
M., Sahno L. Concept of accumulation of energy from
photovoltaic and wind power plants by means of
seawater pumped hydroelectric energy storage. 2019
IEEE 6th International Conference on Energy Smart
Systems (2019 IEEE ESS). April 17-19. 2019. Kyiv.
Ukraine. Pp. 188–191.
https://doi.org/10.1109/ESS.2019.8764167
21. VaskoP.F., Verbovyi A.P., Ibrahimova M.R., Pazych S.T.
Hydro-storage power plants are the technological basis
for integrating powerful wind and solar power plants
into the electricity system of Ukraine. Hidroenerhetyka
Ukrainy. 2017. No. 1–2. Pp. 20–25. [in Ukrainian]
22. P. Milan, M. Wächter, J. Peinke. Turbulent character of
wind energy. Physical Review Letters. 2013.
110(13):138701.
https://doi.org/10.1103/PhysRevLett.110.138701
23. Vasko P.F., Vasko V.P. Dynamics of load modes of
operation of a wind power plant, due to gusts of wind
speed. Bulletin of Donbas national academy of civil
engineering and architecture. Construction structures,
buildings and structures. 2001. 1(4). Pp. 140–144. [in
Russian]
24. Vasko P.F., Vasko V.P., Danylenko O.I., Doliuk V.V.
Experimental studies of modes of generation and
consumption of reactive power by a serial wind power
plant with an asynchronous generator. Vidnovluvana
energetika. 2008. No 1. Pp. 34–38. [in Ukrainian]
25. Prusov V.A., Snizhko S.I. Methods of applied system
analysis in hydrometeorology: a textbook. Kyiv: Print
Servis. 2017. 701 p. ISBN 978-617-7069-43-9. [in
Ukrainian]
26. Julius S. Bendat, Allan G. Piersol. Random Data:
Analysis and Measurement Procedures. 4th Edition.
Wiley & Sons, Inc. 2010. 640 p. ISBN: 978-0-470-24877-
5.
27. Volodarskyi Ye.T., Kosheva L.O. Theory and practice of
experimental research. Kyiv: Igor Sikorsky Kyiv
Polytechnic Institute. 2023. 299 p.
https://ela.kpi.ua/items/07b61b15-2eb6-4ff7-a10f-
e5ca7b121ad6 [in Ukrainian]
28. Claire Petersen, Mar Reguant, Lola Segura. Measuring
the Impact of Wind Power and Intermittency. Institute
for Policy Research. 2022. 41 p.
https://www.dailymail.com/news/article-15748055/Shetland-windfarm-owner-paid-10million-year-turn-turbines-gale-force-winds.html
https://www.dailymail.com/news/article-15748055/Shetland-windfarm-owner-paid-10million-year-turn-turbines-gale-force-winds.html
https://www.dailymail.com/news/article-15748055/Shetland-windfarm-owner-paid-10million-year-turn-turbines-gale-force-winds.html
https://oilprice.com/contributors/Irina-Slav/
https://oilprice.com/Alternative-Energy/Wind-Power/Brits-Pay-Billions-To-Waste-Wind-Energy.html
https://oilprice.com/Alternative-Energy/Wind-Power/Brits-Pay-Billions-To-Waste-Wind-Energy.html
https://oilprice.com/Alternative-Energy/Wind-Power/Brits-Pay-Billions-To-Waste-Wind-Energy.html
https://hydrogeneurope.eu/wp-content/uploads/2021/11/Hydrogen-Europe_2x40-GW-Green-H2-Initative-Paper.pdf
https://hydrogeneurope.eu/wp-content/uploads/2021/11/Hydrogen-Europe_2x40-GW-Green-H2-Initative-Paper.pdf
https://hydrogeneurope.eu/wp-content/uploads/2021/11/Hydrogen-Europe_2x40-GW-Green-H2-Initative-Paper.pdf
https://doi.org/10.1016/j.nexus.2022.100123
https://www.mev.gov.ua/sites/default/files/field/file/vodneva-strategiya17.05.2024.pdf
https://www.mev.gov.ua/sites/default/files/field/file/vodneva-strategiya17.05.2024.pdf
https://ieeexplore.ieee.org/abstract/document/8764167/
https://ieeexplore.ieee.org/abstract/document/8764167/
https://ieeexplore.ieee.org/abstract/document/8764167/
https://www.wiley.com/en-be/search?filters%5Bauthor%5D=Julius+S.+Bendat&pq=++
https://www.wiley.com/en-be/search?filters%5Bauthor%5D=Allan+G.+Piersol&pq=++
305
Відновлювана енергетика. № 1/2026 | Вітроенергетика
29. Ramon van Handel. Probability and Random Processes.
ORF309/MAT380. Lecture Notes Princeton University
Thisversion: February 22. 2016. 195 p.
30. M. R. Leadbetter, G. Lindgren, H. Rootzen. Extremes
and Related Properties of Random Sequences and
Processes. Springer-Series in Statistics. Springer-Verlag.
New York-Heidelberg-Berlin. 2011. 336 p.
31. Tikhonov V.I., Khimenko V.I. Emissions of trajectories
of random processes. Мoscow: Nauka. 1987. 304 p. [in
Russian]
32. Larin O., Vodka О., Potopalska К. Computer and
mathematical modeling in reliability prediction
problems with gradual failures. Kharkiv: NTU «KhPI»,
Yurait. 2020. 232 p. https://doi.org/10.20998/978-617-
7450-20-6 [in Ukrainian]
33. Kartashov M.V. Probability, processes, statistics:
course of lectures. Kyiv: Tekhnika. 2006. 872 p.
http://www.krivosoft.com/zksm/TViMS/_TViMS-
books/tv2.pdf [Accessed: 30 March 2026].
34. Kobzar A.I. Applied mathematical statistics. For
engineers and scientists. Moscow: Fizmatlit. 2012. 816
p. IBSN 5-9221-0707-0. [in Russian]
35. Kotsiubynskyi V.O. Applied statistics: course of
lectures. Ivano-Frankivsk: Vasyl Stefanyk Precarpathian
National University. 2019. 269 p. [in Ukrainian]
36. Vasko P., Mazurenko I., Sysak R. The influence of wind
speed probability distribution parameters on the
energy efficiency of coastal and offshore wind farms in
enclosed seas: a case study of the Azov-Black sea
region of Ukraine. Vidnovluvana Energetika. 2025.
3(82). Pp. 125–136. https://doi.org/10.36296/1819-
8058.2025.3(82)
https://www.google.com.ua/search?hl=uk&tbo=p&tbm=bks&q=inauthor:%22M.+R.+Leadbetter%22
https://www.google.com.ua/search?hl=uk&tbo=p&tbm=bks&q=inauthor:%22G.+Lindgren%22
https://www.google.com.ua/search?hl=uk&tbo=p&tbm=bks&q=inauthor:%22H.+Rootzen%22
https://doi.org/10.20998/978-617-7450-20-6
https://doi.org/10.20998/978-617-7450-20-6
|
| id | veorgua-article-635 |
| institution | Vidnovluvana energetika |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-10T01:00:30Z |
| publishDate | 2026 |
| publisher | Institute of Renewable Energy National Academy of Sciences of Ukraine |
| record_format | ojs |
| resource_txt_mv | veorgua/d2/9aea7597546d12251cd84a4ea71e47d2.pdf |
| spelling | veorgua-article-6352026-07-09T12:14:07Z PROBABILISTIC CHARACTERISTICS OF WIND POWER PLANT OUTPUT FLUCTUATIONS IN THE CLIMATIC CONDITIONS OF THE AZOV-BLACK SEA REGION OF UKRAINE ІМОВІРНІСНІ ХАРАКТЕРИСТИКИ ФЛУКТУАЦІЙ ПОТУЖНОСТІ ВІТРОЕЛЕКТРИЧНОЇ СТАНЦІЇ В КЛІМАТИЧНИХ УМОВАХ АЗОВО-ЧОРНОМОРСЬКОГО РЕГІОНУ УКРАЇНИ Vasko , P. Mazurenko , I. Sysak , R. wind power plant, random processes, energy supply, green hydrogen, desalination, power, statistics, fluctuations. вітроелектрична станція, випадкові процеси, енергозабезпечення, зелений водень, опріснення, потужність, статистика, флуктуації. Scaling up hydrogen energy is a strategic priority of the energy policies of Ukraine and the European Union. Using renewable energy sources (RES) to power autonomous technological complexes for green hydrogen production and seawater desalination as a feedstock requires finding effective technical solutions to align the stochastic nature of power generation with the stable energy demand of such facilities. To address this challenge, this study performs a statistical evaluation of parameters characterizing the dynamics of electricity production based on retrospective data from a wind power plant (WPP) located in the Azov-Black Sea region of Ukraine. The research primarily focuses on analyzing the duration of power deficit and surplus intervals caused by the inherent variability of WPP operation. Numerical estimates were obtained for the maximum and average duration of such intervals, their standard deviation, as well as the parameters of exponential and Weibull probability distributions used for their modelling. The results of this study are essential for designing continuous energy supply systems for large-scale autonomous RES-based green hydrogen production and seawater desalination facilities.  Ref. 36, Tab. 2, Fig. 8    Розвиток водневої енергетики є одним із стратегічних пріоритетних напрямів енергетичної політики України та Європейського Союзу. Використання відновлюваних джерел енергії (ВДЕ) для електроживлення автономних технологічних комплексів з виробництва зеленого водню та опріснення морської води як вихідної сировини потребує пошуку ефективних технічних рішень для узгодження стохастичного характеру генерування потужності та стабільної потреби в енергії таких об’єктів. З метою розв’язання цієї проблеми у межах даного дослідження на основі ретроспективних даних функціонування вітроелектричної станції (ВЕС), розташованої в Азово-Чорноморському регіоні України, проведено статистичне оцінювання параметрів, що характеризують динаміку виробництва електроенергії. Основну увагу зосереджено на аналізі тривалості інтервалів дефіциту та надлишку потужності, зумовлених варіабельністю роботи ВЕС. Отримано чисельні оцінки максимальної та середньої тривалості таких інтервалів, їх середньоквадратичного відхилення, а також параметрів експоненціального та вейбуллівського розподілів імовірностей для їх моделювання. Результати дослідження є важливими для проєктування систем безперервного енергозабезпечення потужних автономних комплексів з виробництва водню та опріснення морської води на основі ВДЕ. Бібл.36, табл.2, рис.8.  Institute of Renewable Energy National Academy of Sciences of Ukraine 2026-06-30 Article Article application/pdf https://ve.org.ua/index.php/journal/article/view/635 10.36296/1819-8058.2026.2(85).296-305 Vidnovluvana energetika ; No. 2(85) (2026): Scientific and applied Journal renewable energy ; 296-305 Возобновляемая энергетика; № 2(85) (2026): Scientific and applied Journal renewable energy ; 296-305 Відновлювана енергетика; № 2(85) (2026): Науково-прикладний журнал Відновлювана енергетика; 296-305 2664-8172 1819-8058 10.36296/1819-8058.2026.2(85) en https://ve.org.ua/index.php/journal/article/view/635/546 Copyright (c) 2026 Vidnovluvana energetika |
| spellingShingle | wind power plant random processes energy supply green hydrogen desalination power statistics fluctuations. Vasko , P. Mazurenko , I. Sysak , R. PROBABILISTIC CHARACTERISTICS OF WIND POWER PLANT OUTPUT FLUCTUATIONS IN THE CLIMATIC CONDITIONS OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title | PROBABILISTIC CHARACTERISTICS OF WIND POWER PLANT OUTPUT FLUCTUATIONS IN THE CLIMATIC CONDITIONS OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title_alt | ІМОВІРНІСНІ ХАРАКТЕРИСТИКИ ФЛУКТУАЦІЙ ПОТУЖНОСТІ ВІТРОЕЛЕКТРИЧНОЇ СТАНЦІЇ В КЛІМАТИЧНИХ УМОВАХ АЗОВО-ЧОРНОМОРСЬКОГО РЕГІОНУ УКРАЇНИ |
| title_full | PROBABILISTIC CHARACTERISTICS OF WIND POWER PLANT OUTPUT FLUCTUATIONS IN THE CLIMATIC CONDITIONS OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title_fullStr | PROBABILISTIC CHARACTERISTICS OF WIND POWER PLANT OUTPUT FLUCTUATIONS IN THE CLIMATIC CONDITIONS OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title_full_unstemmed | PROBABILISTIC CHARACTERISTICS OF WIND POWER PLANT OUTPUT FLUCTUATIONS IN THE CLIMATIC CONDITIONS OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title_short | PROBABILISTIC CHARACTERISTICS OF WIND POWER PLANT OUTPUT FLUCTUATIONS IN THE CLIMATIC CONDITIONS OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title_sort | probabilistic characteristics of wind power plant output fluctuations in the climatic conditions of the azov-black sea region of ukraine |
| topic | wind power plant random processes energy supply green hydrogen desalination power statistics fluctuations. |
| topic_facet | wind power plant random processes energy supply green hydrogen desalination power statistics fluctuations. вітроелектрична станція випадкові процеси енергозабезпечення зелений водень опріснення потужність статистика флуктуації. |
| url | https://ve.org.ua/index.php/journal/article/view/635 |
| work_keys_str_mv | AT vaskop probabilisticcharacteristicsofwindpowerplantoutputfluctuationsintheclimaticconditionsoftheazovblacksearegionofukraine AT mazurenkoi probabilisticcharacteristicsofwindpowerplantoutputfluctuationsintheclimaticconditionsoftheazovblacksearegionofukraine AT sysakr probabilisticcharacteristicsofwindpowerplantoutputfluctuationsintheclimaticconditionsoftheazovblacksearegionofukraine AT vaskop ímovírnísníharakteristikifluktuacíjpotužnostívítroelektričnoístancíívklímatičnihumovahazovočornomorsʹkogoregíonuukraíni AT mazurenkoi ímovírnísníharakteristikifluktuacíjpotužnostívítroelektričnoístancíívklímatičnihumovahazovočornomorsʹkogoregíonuukraíni AT sysakr ímovírnísníharakteristikifluktuacíjpotužnostívítroelektričnoístancíívklímatičnihumovahazovočornomorsʹkogoregíonuukraíni |