MICROGRID MONITORING METHOD FOR PREDICTING ENERGY ANOMALIES
The causes of excess and shortage of electrical energy in microgrid have been analyzed. The necessity of using intelligent energy management systems for smart grids with a large component of renewable energy sources is shown. An integral part of such control systems is a network monitoring method. B...
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Vidnovluvana energetika| _version_ | 1871103867025883136 |
|---|---|
| author | Gusiev , О. Magro , V. |
| author_facet | Gusiev , О. Magro , V. |
| author_institution_txt_mv | [
{
"author": "О. Gusiev ",
"institution": "Dnipro University of Technology, Dnipro, Ukraine"
},
{
"author": "V. Magro ",
"institution": "Dnipro University of Technology, Dnipro, Ukraine; Institute of Transport Systems and Technologies of the National Academy of Sciences of Ukraine, Dnipro, Ukraine"
}
] |
| author_sort | Gusiev , О. |
| baseUrl_str | https://ve.org.ua/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-07-18T06:32:21Z |
| description | The causes of excess and shortage of electrical energy in microgrid have been analyzed. The necessity of using intelligent energy management systems for smart grids with a large component of renewable energy sources is shown. An integral part of such control systems is a network monitoring method. Based on the results of the analysis of methods for monitoring energy systems, a method for monitoring microgrids has been proposed, which allows predicting changes in flows filtering procedure integrated with the three-sigma rule. The modified filtering procedure allows real-time assessment and forecasting of the observed process, and the application of the three-sigma rule allows detection of its anomalies, since, as is known, with a normal distribution, almost all process values it a probability of 0.9973 lie no further than three sigma in either direction from the mathematical expectation. The use of this method makes it possible to formulate recommendations for managing the processes of optimal redistribution of energy flows in microsystems, to predict the risk of anomalies in energy changes in the power system.   |
| doi_str_mv | 10.36296/1819-8058.2025.1(80).22-28 |
| first_indexed | 2025-07-17T11:39:49Z |
| format | Article |
| fulltext |
22
Відновлювана енергетика. №1/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
UDC 621:311 https://doi.org/10.36296/1819-8058.2025.1(80)22-28
MICROGRID MONITORING METHOD FOR PREDICTING ENERGY ANOMALIES
Received Dec. 26, 2024; accepted Mar. 14, 2025
Available online Apr. 01, 2025
Gusiev О.1, Magro V.2
Author for correspondence: Magro Valeriy,
e-mail: magrov@i.ua
Annotation: The causes of excess and shortage of electrical
energy in microgrid have been analyzed. The necessity of
using intelligent energy management systems for smart
grids with a large component of renewable energy sources
is shown. An integral part of such control systems is a network monitoring method. Based on the results of the
analysis of methods for monitoring energy systems, a method for monitoring microgrids has been proposed, which
allows predicting changes in flows filtering procedure integrated with the three-sigma rule. The modified filtering
procedure allows real-time assessment and forecasting of the observed process, and the application of the three-
sigma rule allows detection of its anomalies, since, as is known, with a normal distribution, almost all process
values it a probability of 0.9973 lie no further than three sigma in either direction from the mathematical expec-
tation. The use of this method makes it possible to formulate recommendations for managing the processes of
optimal redistribution of energy flows in microsystems, to predict the risk of anomalies in energy changes in the
power system.
Keywords: smart grid, microgrid, monitoring method, Kalman filter, forecasting, non-stationary system.
МЕТОД МОНІТОРИНГУ МІКРОМЕРЕЖ З МЕТОЮ ПРОГНОЗУВАННЯ АНОМАЛІЙ ЗМІНИ ЕНЕРГІЇ
Отримано 26 груд. 2024 р.; рекомендовано до публікації 14 бер. 2025 р.
Доступно онлайн 01 квіт. 2025 р.
Гусєв О. Ю.1, Магро В. І.2
Автор для кореспонденції: Магро Валерій,
e-mail: magrov@i.ua
Анотація. Проаналізовано причини надлишку та
нестачі електричної енергії в мікромережі. Показано
необхідність використання інтелектуальних систем
енергоменеджменту для інтелектуальних мереж з
великою складовою відновлюваних джерел енергії.
Невід'ємною частиною таких систем контролю є метод моніторингу мережі. За результатами
аналізу методів моніторингу енергетичних систем запропоновано метод моніторингу мікромереж,
який дозволяє прогнозувати зміни в процедурі фільтрації потоків, інтегрованої з правилом трьох сигм.
Модифікована процедура фільтрації дає змогу в реальному часі оцінювати та прогнозувати
спостережуваний процес, а застосування правила трьох сигм дозволяє виявити його аномалії, оскільки,
як відомо, при нормальному розподілі майже всі процеси оцінюють його ймовірність 0,9973, лежать не
далі ніж три сигми в будь-якому напрямку від математичного сподівання. Використання цього методу
дає змогу сформулювати рекомендації щодо управління процесами оптимального перерозподілу
енергетичних потоків у мікросистемах, прогнозувати ризик виникнення аномалій енергетичних змін в
енергосистемі.
1 PhD, Associate Professor
http://orcid.org/ 0000-0002-0548-728X
2 PhD, Associate Professor
http://orcid.org/0000-0003-4238-6733
1, 2 Dnipro University of Technology, Dnipro, Ukraine
2 Institute of Transport Systems and Technologies of
the National Academy of Sciences of Ukraine,
Dnipro, Ukraine
1 канд. фіз.-мат. наук, доцент
http://orcid.org/ 0000-0002-0548-728X
2 канд. фіз.-мат. наук, доцент
http://orcid.org/0000-0003-4238-6733
1, 2 Національний технічний університет
«Дніпровська політехніка», Дніпро, Україна
2 Інститут транспортних систем і технологій НАН
України, Дніпро, Україна
23
Відновлювана енергетика. №1/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
Introduction
There is currently an increase in electricity consumption
worldwide. Therefore, power plants and transmission lines
must be designed according to the needs of consumers. In
this respect, the costs of distributed generation are lower
than the costs of a power plant and expansion of the distri-
bution and transmission system [1]. The overall strategy for
the development of electricity grids is aimed at connecting
the general electricity grid with microgrids [2]. This allows for
increased capacity and reliability of energy supply. Distrib-
uted systems using renewable energy sources are currently
developing rapidly [3]. According to this approach, hybrid
systems can be used to combine more energy sources [4, 5].
The evolution of conventional power systems into smart
grids allows for increased efficiency, stability and sustaina-
bility of the grid [6]. The objective factor is that the power
generated by wind power plants, solar power plants, co-
generation power plants and other alternative energy
sources is not a constant value. This value depends on nat-
ural conditions (the presence of wind, solar radiation activ-
ity) and other factors. In this case, such instability of renew-
able energy generation negatively affects the stable
operation of the power system. Therefore, the classical
principle of organizing the management of electric power
systems is not suitable for electric power systems with a
large share of renewable energy sources. In intelligent net-
works, special technologies and algorithms are used to or-
ganize work and control, including algorithms for predicting
changes in energy in the network [7], various types of en-
ergy storage devices [8-11].
The principle of distributed generation is used to balance
the load in smart grids. Within the energy and logistics clus-
ters, the following are united: consumers of electric energy;
producers of electric energy that use local solar power
plants, wind power plants, gas generators, united into a
shared network for generation; energy storage systems
[12]. In recent years, there has been significant growth in
the third component of the cluster, namely, the increase in
the capacity of energy storage systems.
When building a cluster, they strive to establish a balance be-
tween producers of electric energy and systems for storage
of electric energy on the one hand, and consumers of electric
energy on the other. However, under conditions of increased
solar and wind activity, excess electrical energy generation
may be observed within the cluster. And there is a need to
direct excess generated electrical energy to energy storage
systems where electrical energy can be stored. Due to the
operation of solar (wind) generation only during a certain pe-
riod in smart grids, intelligent energy management systems
are becoming a major research issue [13, 14]. In highly un-
stable generation and demand conditions, it is very im-
portant to assess the instantaneous state of the power grid
energy balance to automatically maintain the energy balance
and avoid generation deficit or overload situations.
On the other hand, in adverse weather conditions, such as
reduced solar activity in winter or minimal wind speed,
there may be a shortage of generation within the cluster,
and there is a need to use the energy accumulated within
the cluster. If the energy accumulated within the cluster is
insufficient, then it is necessary to take energy from other
sources, such as cogeneration power plants and nuclear
power plants. Thus, workload management and resource
management are two key aspects in grid computing that
aim to ensure that the best services are provided to the us-
ers of the grid environment.
The above-described problem is true only for microgrids
that are part of the smart grid but is also relevant for smart
homes [15, 16]. Intelligent energy management systems
are also used in isolated microgrids [17, 18].
Thus, there is a need for forecasting flows (effective dis-
patching) of the smart grid, which operates in automatic
mode and allows redirecting flows of electric energy consid-
ering the needs of the consumer, considering the possibility
of generation, and considering the volume of accumulated
energy. There is a need for load flow optimization for the in-
tegration of smart grid components, including hybrid photo-
voltaic and wind power systems combined with a battery
management system and unified power flow controller.
Problem statement
The aim of the work is to provide energy management and
energy optimization in microgrid systems. The method of
monitoring microgrids is provided by the developed
method of predicting changes in flows in the electrical net-
work based on using the Kalman filter. Based on this meth-
odology, it is possible to formulate recommendations for
managing processes of optimal redistribution of energy
flows.
The main problem in solving the problem is to evaluate and
predict the state of the energy source in two states:
When the amount of energy generated exceeds the limits
determined by the objective need for supplied energy.
When the boundaries of optimal generation are unknown,
it is necessary to determine the system's exit from the op-
erating mode based on a given criterion.
In the first case, it is necessary to evaluate and predict the
excess of a given (known) threshold of excess of the energy
source “from above” or “from below”. In the second case,
it is necessary to evaluate and predict the deviation of the
state of the energy supply source from the operating mode.
Materials and research results
The task is to develop a method for assessing and predict-
ing the state of non-stationary processes in the case where
the analytical model of the observed process is unknown.
The following solution is proposed in the paper. For the
case of discrete signal measurements, we present the sig-
nal source model in the form of an additive sum
( ) ( ) ( )S t X t Q tn n n= + , (1)
24
Відновлювана енергетика. №1/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
where ( )X tn – useful signal; ( )Q tn – additive noise
with mathematical expectation [ ( )] 0M Q tn = ; R –
noise variance, which is defined as
( )
==
=−
−
=
n
i
i
n
i
i x
n
xxx
n
R
11
2 1
;
1
1
.
It is known that the Kalman filter equation can be writ-
ten as:
ˆ ˆ
1 1
1
1
.
X F X K Y H F Xn n n n n nn n
T TK A H H A H Rn n n n n n
TA F P Fn n nn
P A K H An n n n n
= + −
− −
−
= +
=
−
= −
(2)
Here X̂n – process state evaluation vector; Fn – the
transition matrix of state n – 1 to n; Kn – matrix Kalman
filter gain; Hn – measurement conditions matrix; Yn –
measured process value; Pn – error matrix. The index "T"
means the transposition procedure.
The system of equations (2) can be applied directly if the
matrix Fn is known, that is, if there is complete infor-
mation about the process under study. As a rule, we do not
have complete information about the process under study.
In this case, the Kalman filtering procedure, presented in a
suboptimal form, can be an assessment and forecasting
tool.
We propose the following approach. We perform ma-
trix approximation Fn at each reference point of the nth-
order Taylor series. Restricting ourselves to terms of the se-
ries not higher than the nth order for each element of the
matrix Fn we can write
( )
!
0 ,
!( )!
0
j
j i i n i j n
i j iF
ij
j i
− −
−=
As a result, for example, for a second-order transition ma-
trix (n=2) we obtain
. (3)
Since the signal itself is measured, and not its derivatives
100Hn = , substituting (3) into (2) gives an algorithm
for estimating Xn for the adopted approximation in the
form
ˆ ˆ ˆ ˆ
1 1 1
ˆ ˆ ˆ2
1 1
ˆ ˆ
1
ˆ ˆ ˆ ,
1 1 1
X X Y Z Cn n nn n n
Y Y Z Cn n nn n
Z Z Cn nnn
C S X Y Zn n n n n
= + + +
− − −
= + +
− −
= +
−
= − − −
− − −
(4)
here
11 1 21 1 31 1, ,P R P R P Rn n n n nn
− − −= = = –
filter gain matrix elements, defined through the elements
P
ij
matrices Pn , Ŷn and Ẑn have the meaning of es-
timates according to the first and second derivatives.
To implement the forecast procedure, it is sufficient to (2)
choose Hn = [000] and represent the index n as n=n+k,
where k is the number of predefined forecast steps.
Let us consider the efficiency of the proposed forecast-
ing procedure on model data.
Fig. 1 shows a graph that shows a one-step forward forecast
considering previous values.
Fig. 2 shows a histogram of the distribution of the average
value of the relative forecast error, calculated by the for-
mula
1
( ) ( )1
( )
N
pr
i
S
x i x i
N x i=
=
−
, (5)
here ( )x i – actual value, ( ) prx i – predictive value.
From the histogram (Fig. 2), we obtain the average relative
forecast error for a one-step forecast of 4.05%, according
to (5).
The calculation results show that for the value of the aver-
age relative forecast errors, with an increase in the number
of steps, the average relative forecast error decreases. This
circumstance may be due to several reasons:
− the Kalman filter updates its estimates based on new
data, and as the number of forecast steps increases, a
larger number of observations are taken into account,
which increases the accuracy of the forecast;
− as the number of prediction steps increases, the filter
uses previous state estimates and refines the prediction;
− like many other forecasting methods, the Kalman filter,
can smooth out or detect general trends in data behavior
as the number of forecast steps increases. This allows the
model to understand general patterns of data behavior
better and reduce prediction errors [19-20].
Let us consider the application of the proposed estimation
and forecasting method to detect changes in energy flows
of electrical networks.
25
Відновлювана енергетика. №1/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
Fig. 1. One-step-ahead forecast histogram considering previous values
Fig. 2. Histogram of the distribution of the average value of the relative forecast error
As already mentioned, two cases are possible.
In the first case, the current value of the energy in terms of
modulus exceeds the previously set permissible value. In
this case, it is necessary to predict and detect this event to
redirect excess energy for its conservation or, conversely,
to fill the energy shortage from the existing source. In the
second case, it is necessary to detect an anomalous devia-
tion of the energy source from its average value.
For the first case, it is proposed to use the procedure for
calculating statistics of this type:
MlBbB
M
l
lM ,...,2,1,0, 0
1
===
=
(6)
−−
−+
=−=
0,1
0ˆ,1
)ˆsgn(
ll
ll
lll
XS
XS
XSb
(7)
on the interval (n-M, n). The values (ВМ - minВМ) and (max
ВМ - ВM) determined in this interval are compared with the
given threshold h modulo. Thus, we set the threshold value
h as the number of deviations with the same plus or minus
26
Відновлювана енергетика. №1/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
sign. Then, the result is compared with the specified num-
ber of deviations (threshold). When the threshold value h
is exceeded by one of these values, a decision is made
about the source’s excess or shortage of energy in the elec-
trical network, and appropriate actions are taken to redi-
rect energy.
In the second case, it is necessary to determine the anom-
alous deviation of the energy source value from the aver-
age value, i.e. from the operating mode.
To solve the problem, it is proposed to use the so-called
three-sigma rule. The three-sigma rule is used to identify
anomalies in stochastic time series data. In this context, this
method allows you to detect values that deviate signifi-
cantly from the expected range and are based on the char-
acteristics of the data distribution.
In this work, we use this method, integrating it with a mod-
ified Kalman filtering and forecasting procedure. Thus, the
final overall algorithm developed is as follows:
Evaluating the current value ˆ
lX based on the system of
equations (2) considering expression (3) and the standard
deviation (standard method).
The statistics BM are calculated from expression (6) consid-
ering (7).
The control limit values are compared with the threshold
3h = .
If the value h of one of the control limit values is exceeded,
a decision is made to detect an anomaly and take appropri-
ate control action.
Figure 3 shows a graph illustrating the operation of the pro-
posed modified forecasting algorithm based on the Kalman
filter with a built-in anomaly detection function using the
three-sigma rule. In Fig. 3, real measurements are shown in
green, predicted values in red, and detected anomalies in
energy changes in pink. As seen from Fig. 3, integrating the
proposed Kalman estimation and forecasting algorithm
with the three-sigma rule allows for the effective identifi-
cation of anomalous values of the time series. Thus, we ob-
tain a reliable tool that allows us to use it to detect anoma-
lous deviations in energy flows to optimally manage these
flows. Redirect excess energy either into battery energy
storage systems [19] or into hydro energy storage [20], or
into gravity storage [21-25]. The latter type of energy stor-
age is a promising mechanical energy storage technology
and offers significant advantages, especially in areas with
limited water resources, where there is no available space
for hydro energy storage. Gravity-based energy storage sys-
tems can be economically feasible when locating a mi-
crogrid near abandoned mine workings.
Fig. 3. Detection of energy change anomalies
Conclusions
1. The developed method for assessing and forecasting
energy flows in electrical networks is invariant to the
nature of the studied stochastic non-stationary pro-
cesses .
2. The proposed method enables the efficient detection of
anomalies in the studied processes in real time and can
be used in energy flow control systems in electrical net-
works.
3. Integrating the proposed method into automation sys-
tems of energy enterprises in the form of structural and
algorithmic components will ensure their hardware, de-
sign and software compatibility.
4. This approach to load forecasting in the electrical net-
work allows for the automatic timely directing of energy
27
Відновлювана енергетика. №1/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
flows to consumers, thereby effectively using renewa-
ble energy sources while considering the availability of
such stable generation sources as nuclear power plants.
Moreover, the developed flow forecasting methodol-
ogy allows for the use of not only battery energy stor-
age systems but also gravity-based energy storage.
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| id | veorgua-article-502 |
| institution | Vidnovluvana energetika |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-19T01:15:01Z |
| publishDate | 2025 |
| publisher | Institute of Renewable Energy National Academy of Sciences of Ukraine |
| record_format | ojs |
| resource_txt_mv | veorgua/6a/43425da199bd3b86db545c8b2450c66a.pdf |
| spelling | veorgua-article-5022026-07-18T06:32:21Z MICROGRID MONITORING METHOD FOR PREDICTING ENERGY ANOMALIES МЕТОД МОНІТОРИНГУ МІКРОМЕРЕЖ З МЕТОЮ ПРОГНОЗУВАННЯ АНОМАЛІЙ ЗМІНИ ЕНЕРГІЇ Gusiev , О. Magro , V. smart grid, microgrid, monitoring method, Kalman filter, forecasting, non-stationary system. розумна мережа, мікромережа, метод моніторингу, фільтр Калмана, прогнозування, нестаціонарна система. The causes of excess and shortage of electrical energy in microgrid have been analyzed. The necessity of using intelligent energy management systems for smart grids with a large component of renewable energy sources is shown. An integral part of such control systems is a network monitoring method. Based on the results of the analysis of methods for monitoring energy systems, a method for monitoring microgrids has been proposed, which allows predicting changes in flows filtering procedure integrated with the three-sigma rule. The modified filtering procedure allows real-time assessment and forecasting of the observed process, and the application of the three-sigma rule allows detection of its anomalies, since, as is known, with a normal distribution, almost all process values it a probability of 0.9973 lie no further than three sigma in either direction from the mathematical expectation. The use of this method makes it possible to formulate recommendations for managing the processes of optimal redistribution of energy flows in microsystems, to predict the risk of anomalies in energy changes in the power system.   Проаналізовано причини надлишку та нестачі електричної енергії в мікромережі. Показано необхідність використання інтелектуальних систем енергоменеджменту для інтелектуальних мереж з великою складовою відновлюваних джерел енергії. Невід'ємною частиною таких систем контролю є метод моніторингу мережі. За результатами аналізу методів моніторингу енергетичних систем запропоновано метод моніторингу мікромереж, який дозволяє прогнозувати зміни в процедурі фільтрації потоків, інтегрованої з правилом трьох сигм. Модифікована процедура фільтрації дає змогу в реальному часі оцінювати та прогнозувати спостережуваний процес, а застосування правила трьох сигм дозволяє виявити його аномалії, оскільки, як відомо, при нормальному розподілі майже всі процеси оцінюють його ймовірність 0,9973, лежать не далі ніж три сигми в будь-якому напрямку від математичного сподівання. Використання цього методу дає змогу сформулювати рекомендації щодо управління процесами оптимального перерозподілу енергетичних потоків у мікросистемах, прогнозувати ризик виникнення аномалій енергетичних змін в енергосистемі.    Institute of Renewable Energy National Academy of Sciences of Ukraine 2025-03-31 Article Article application/pdf https://ve.org.ua/index.php/journal/article/view/502 10.36296/1819-8058.2025.1(80).22-28 Vidnovluvana energetika ; No. 1(80) (2025): Scientific and applied Journal renewable energy ; 22-28 Возобновляемая энергетика; ##issue.no## 1(80) (2025): Scientific and applied Journal renewable energy ; 22-28 Відновлювана енергетика; № 1(80) (2025): Науково-прикладний журнал Відновлювана енергетика; 22-28 2664-8172 1819-8058 10.36296/1819-8058.2025.1(80) en https://ve.org.ua/index.php/journal/article/view/502/411 Copyright (c) 2025 О. Gusiev , V. Magro https://creativecommons.org/licenses/by-nc-nd/4.0 |
| spellingShingle | smart grid microgrid monitoring method Kalman filter forecasting non-stationary system. Gusiev , О. Magro , V. MICROGRID MONITORING METHOD FOR PREDICTING ENERGY ANOMALIES |
| title | MICROGRID MONITORING METHOD FOR PREDICTING ENERGY ANOMALIES |
| title_alt | МЕТОД МОНІТОРИНГУ МІКРОМЕРЕЖ З МЕТОЮ ПРОГНОЗУВАННЯ АНОМАЛІЙ ЗМІНИ ЕНЕРГІЇ |
| title_full | MICROGRID MONITORING METHOD FOR PREDICTING ENERGY ANOMALIES |
| title_fullStr | MICROGRID MONITORING METHOD FOR PREDICTING ENERGY ANOMALIES |
| title_full_unstemmed | MICROGRID MONITORING METHOD FOR PREDICTING ENERGY ANOMALIES |
| title_short | MICROGRID MONITORING METHOD FOR PREDICTING ENERGY ANOMALIES |
| title_sort | microgrid monitoring method for predicting energy anomalies |
| topic | smart grid microgrid monitoring method Kalman filter forecasting non-stationary system. |
| topic_facet | smart grid microgrid monitoring method Kalman filter forecasting non-stationary system. розумна мережа мікромережа метод моніторингу фільтр Калмана прогнозування нестаціонарна система. |
| url | https://ve.org.ua/index.php/journal/article/view/502 |
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