Optimal extraction and conditioning of historical information to
support the operational decisions in a Smart Grid context
ALFREDO ESPINOSA-REZA, MARXA LENINA TORRES-ESPINDOLA
Dirección de Tecnologías Habilitadoras
Instituto Nacional de Electricidad y Energías Limpias
Reforma 113, Col. Palmira, Cuernavaca, Morelos
MEXICO
Abstract: - This article presents a proposal for the architectural components that enable the organized and
collaborative request, transport, and effective utilization of large volumes of historical information without
compromising the performance of the information systems and the supporting technological platform. The
architecture and some variants, successfully implemented in semantic interoperability projects within the Smart
Grid context, are discussed, with a focus on the use and adoption of the Common Information Model (CIM) as
defined in the IEC 61968 and IEC 61970 standards.
Key-Words: - Optimal extraction, Common Information Model, Semantic Interoperability, Smart Grid.
Received: May 27, 2022. Revised: July 21, 2023. Accepted: September 3, 2023. Published: October 2, 2023.
1 Introduction
Traditionally, the information systems used for the
operation of an electric utility consider the handling
of large amounts of information related to the
operating status of the Electric Power System (EPS),
including substations, feeders, transformers,
switches, sectionalizers, and reclosers, among others.
This information is measured in the field by
monitoring, protection, control, and automation
devices and is collected by monitoring and control
systems, such as SCADA systems. The information
includes digital values (states, alarms, locks) and
analog values (voltage, current, real power, reactive
power, power factor, imbalance, temperature,
humidity, amount of dissolved gases, events
intensity, operation counters, among other values).
These values are generated in real-time in the EPS
and are almost always stored in large databases that
contain the memory of what happened every day. As
a whole, this database includes knowledge of the
behavior of the EPS under different operating
conditions that occurred over a considerably long
time, sometimes 10 years or more.
In this sense, recovering historical information,
processing it, and using it in an agile and effective
way for its analysis allow operators and those
responsible for the operation of the EPS to capitalize
on historical knowledge to improve current and
future operations, prevent adverse situations in the
event of failures and contingencies, improve the
response to maneuvers required for maintenance and
clearance, and, therefore, improve the productivity,
efficiency, safety, reliability, and quality indexes
associated with the operation of the EPS.
When an electric utility has enough historical
information collected directly from the devices
installed in the EPS, it has the ability to elevate the
level of support and sustenance for each operational
decision in:
Normal or steady-state operation, to meet
objectives such as productivity and
efficiency.
Emergency situations, to expedite recovery
and enhance security, reliability, and quality.
Unusual situations or cases, such as
disturbances due to natural events, failures,
unforeseen demand peaks, or specific
maintenance requirements.
2 Problem Formulation
Once the volume of data becomes considerably high,
and different operational actors use it in the electrical
utility, a typical problem arises associated with the
extraction's performance and its ease of use. It is
common for an increasing number of users to require
access to this data for their daily processes. However,
the architecture of legacy systems does not
WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2023.22.17
Alfredo Espinosa-Reza, Marxa Lenina Torres-Espindola
E-ISSN: 2224-2872
142
Volume 22, 2023
adequately consider the utility's operational
evolution.
2.1 Smart Grid Context
In this regard, the Smart Grid and its strategies for
adopting increasingly advanced analytical functions
introduce a new stress factor on technological
platforms. The evolution of the traditional grid is
particularly guided by data, communications, and the
ability to make new and better decisions with the
support of information and inherent knowledge. If
this knowledge cannot be retrieved efficiently, it
remains stagnant in information warehouses,
resulting in a wasted capacity within the utilities.
One of the most crucial functions of the Smart
Grid is effective information management to support
operational decision-making. Therefore, there is a
clear need for more effective and efficient strategies
to manage information in a unified manner. [1] [2]
2.2 Data Quality
Another serious issue that information users face is
the consistency and quality of the data, which stems
from various factors such as the acquisition processes
themselves, sensors, device configurations in the
field, communication interruptions, data channel
speed, and equipment, among others. The raw data
stored may not always be of sufficient quality to be
used correctly in high-impact analytical functions.
For example, power flow calculations for feeder
reconfiguration maneuvers, the substation’s design
and sizing, and the configuration of protection
devices, are highly sensitive to data accuracy.
Data quality is often assumed to be a part of the
acquisition and storage system that contains the data,
leading end-users to believe that the data always has
the correct value and appropriate quality. However,
this assumption is not necessarily true, and the
responsibility for validation is left to the user.
2.3 Components Architecture
Traditionally, the extraction, conditioning, and use of
historical EPS information in the information
systems of an electric utility are carried out directly
by querying the databases that contain the records
(raw data) as shown in Fig.1. How the data will be
used is delegated to the system or user making the
request, without analyzing the end-use for each
extraction or analytical function.
In this traditional architecture, strategies are not
implemented to prevent the saturation of the
technological platform. Optimization strategies are
also lacking for handling multiple massive queries to
serve all concurrent users efficiently and provide
consistent responses as quickly as possible.
Based on the author's experience, there are
information systems with more than 10 years of
historical data stored for a few hundred or thousands
of devices in the field at EPS, resulting in millions of
records. These systems can be easily affected if the
architecture of their components does not account for
the situations described. For instance, with a single
direct query to the database management system, the
system can "fail" if the query is executed without
restrictions. For example:
Select * from HISTORICAL_TABLE
Under controlled conditions, it is straightforward to
carry out the necessary validations to avoid
overloading the technological platform. It is essential
to have data backup measures and the capability to
easily restore data, and physical or virtual processing
servers.
Fig. 1. Traditional components architecture for
historical information extraction.
3 Problem Solution
The proposed solution is based on an Optimal
Extractor, whose modular architecture, as shown in
Fig.2, is easily adaptable to any specific situation
because it includes several modules, and each one
addresses one or a group of situations. The main
features of the solution are described below.
3.1 Users Concurrency
When an electric utility begins to accumulate reliable
historical information from the EPS, a large number
of users and needs naturally arise for its proper
management. This management enables the
improvement of the utility's business processes,
encompassing planning, construction, operation,
WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2023.22.17
Alfredo Espinosa-Reza, Marxa Lenina Torres-Espindola
E-ISSN: 2224-2872
143
Volume 22, 2023
maintenance, optimization, reconfiguration, and
eventual replacement and disposal.
The most critical data utilization occurs in the
field of operation. Adequate analytical management
of historical information, along with timely response
times, allows EPS operators to make better
operational decisions with an approach that optimizes
both technical and commercial processes.
For an EPS operator to use the required information
correctly, it must appear on their screen as quickly as
possible, with maximum response times of 10
seconds for simple queries and 50 seconds for
complex queries. This speed is essential because,
once the operator obtains the necessary information,
they have only a few minutes to apply it effectively
in response to failures during normal and emergency
operations.
It's worth noting that, in most cases, an EPS operator
primarily requires straightforward queries to enhance
their decision-making capabilities. Typically, they
need answers to simple questions such as:
What was the maximum demand for this
circuit yesterday and last week?
At what time does peak demand typically
occur on circuits 1 and 2?
What is the typical maximum current for
circuit X?
What is the hourly profile for circuit Y during
summer holidays?
How does the voltage behave when demand
decreases during winter holidays?
Occasionally, an operator responsible for EPS
operation requires slightly more complex queries to
make operational improvements focusing on
reducing technical losses, enhancing reliability, or
improving power quality, among other goals. In these
cases, the questions may include:
Which circuits in a substation have had the
greatest current imbalance in the last week
and the last month?
What are the daily profiles for reactive power
and power factor at circuit X?
What is the maximum capacity of circuit Y to
receive an energy transfer during the
maximum daily demand in the last month?
Among circuits 1, 2, and 3, which can most
effectively receive half the power of circuit Z
on a permanent basis? [3]
On the other hand, for an EPS analyst, particularly in
planning and construction roles, information queries
tend to be more comprehensive and complex. For
instance, they may need to identify the Maximum
Demand Peak (MDP) of a circuit in a year and track
its evolution over the past 5 years. Similarly, they
may need to identify Coincident Peak Demand
(CPD) for a wide region or a set of circuits for a
specific period [4]. These types of queries, of great
interest to this user, consume significant resources on
the technological platform because the amount of
data required can range from thousands to millions,
depending on the period and the number of circuits.
Notably, the user often requires only 1 to 100
significant data points, but the data retrieval process
can be computationally complex and time-
consuming.
To address the concurrency situation, a commonly
used alternative is an Enterprise Service Bus (ESB).
An ESB, in addition to having a highly efficient
queue manager, allows for the implementation of
intermediary services to prioritize queries based on
the type of query and the user's request. [5]
When an ESB is used, the functions of the Optimal
Extractor are accessible to any application or client
system that requires historical data, without the
necessity of understanding the internal data structure
of the source systems (Fig.2).
Fig. 2. Proposed components architecture for
historical information extraction for syntactic
interoperability.
WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2023.22.17
Alfredo Espinosa-Reza, Marxa Lenina Torres-Espindola
E-ISSN: 2224-2872
144
Volume 22, 2023
However, if an ESB is not available, the Component
Control module must handle the sequencing and
prioritization of multiple simultaneous queries. It can
even break down queries into smaller parts to free up
machine time across the entire technological
infrastructure, as described in Section 3.5.
3.2 Data Quality Verification
The historical data stored retains the quality with
which it was acquired at the moment in real-time;
however, multiple factors can affect its quality and
precision. A viable option to ensure a response with
highly reliable data is to integrate a Validation
module. This module is responsible for analyzing the
data request in a query and applying specific
validation, verification, and completeness
algorithms. In case it detects inconsistencies, it
performs an estimate of the corresponding
replacement data and informs the requester about the
actions taken in the response calculation.
Within this Validation module, the quality of raw
data can be verified in several ways. For example:
Integrity: Counting the number of records
available for a data series in a defined period.
Consistency: Validating a data set according
to the electrical or physical laws that model it.
Accuracy: Comparing a data set with external
measurements, redundant measurements, or
manual measurements taken during the same
period or by integrating measured values at
different points in the ESP.
Behavior: Comparing the profile of a data set
with the typical profile of that measurement.
Validity: Cross-comparing measured values
with similar measurements, geographically
close measurements, or calculated values.
AI: Additionally, considering the data
complexity, it is feasible to train Artificial
Intelligence (AI) algorithms to perform much
more comprehensive validations. For
example, this can include identifying and
applying typical profiles, autonomous
autoregression, predictive models, correlation
with exogenous variables, comparison with
nearby data points (case-based reasoning),
and automatic clustering algorithms, among
others.
3.3 Handling Large Data Sets
If the database does not impose query restrictions, a
request can yield a substantial amount of data as a
response. This situation could lead to the saturation
or collapse of the technological platform, causing
delays in all other concurrently running processes.
To address this issue, the Component Control
module, working in conjunction with the Response
Builder module, can adopt a strategy to prioritize,
segment, or break down queries into smaller parts.
This approach allows for the handling of multiple
responses so that the user who requested the data
ultimately receives a complete response. In this
sense, the data is processed in manageable packages
by the technological platform. Consequently, all
other concurrent users are served, and the waiting
time is distributed among them. As a result, high-
priority users receive their answers within the
required timeframe, while users with large data
volume requests (typically not of high priority)
receive their responses only slightly later than if the
query were executed directly (in any case, the
processing time will be considerably longer than for
simple queries).
3.4 Database Operational Security
Another specific issue in traditional architecture is
that the operational stability of the technological
platform is not guaranteed. As explained in section
2.3, it is relatively easy to disrupt it through
uncontrolled use.
The solution proposed by the Optimal Extractor,
as shown in Fig.2, involves breaking down queries
into smaller parts to manage the machine time of the
technological infrastructure. In this regard, the
Component Control module is responsible for
executing the following actions:
Calculate the amount of data that will be
queried in a user request.
If the data amount exceeds an empirically
defined limit (based on the hardware
resources of the technology platform and the
granularity of stored data), the query will be
segmented or divided into sections, and the
Query Constructor and Response Builder
modules will be notified.
Multiple partial queries are generated.
A waiting period is introduced between
queries (the duration is also determined
empirically using the same criteria as the data
limit).
Partial responses are consolidated into a
single coherent response.
WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2023.22.17
Alfredo Espinosa-Reza, Marxa Lenina Torres-Espindola
E-ISSN: 2224-2872
145
Volume 22, 2023
The Validation Module algorithms are
executed.
The final result is delivered to the user who
initiated the request.
3.5 Standard Data Access
A significant issue with the traditional architecture
depicted in Fig.1 is that each application or client
system requires the application of the data access
standard to the technological platform. Furthermore,
they need to have knowledge of the database's
internal structure. The problem becomes more severe
when, for specific reasons, the database undergoes
changes in technology, data access standards, or
internal structure.
To address this problem, the Optimal Extractor,
as presented in its architecture in Fig.2, incorporates
a data abstraction layer. Consequently, if an ESB is
used, all clients must adopt a single connectivity
standard determined by the ESB, which is typically
open and well-known. In cases where an ESB is not
available, the proposed architecture provides
flexibility by allowing access to the Component
Control module through one or more standard data
interfaces, such as Web Services (WS), Java Message
Service (JMS), OLE for Process Control (OPC), OLE
for Process Control - Unified Architecture (OPC-
UA), and others. If necessary, it can even
accommodate communication protocols like DNP,
Modbus, or ICCP.
An additional advantage of this architecture is that if
the database undergoes changes in technology or
internal structure, it will only require modifications
to the Data Recovery module, without any impact on
the data clients. [6] [7]
3.6 Standard Data Model
For Smart Grid applications, it is highly
recommended to implement semantic
interoperability between applications or systems. To
achieve this, a canonical data model based on
standards should be used. This model enables the
unification and formalization of the meaning of
exchanged data. It is particularly advisable to adopt
the Common Information Model (CIM) defined
primarily in the set of standards IEC 61968 and IEC
61970.
In the architecture proposed in Fig.2, a wrapper
should be added on the Component Control module
side, as well as another on the Client side. This
addition ensures that all information transported
between applications can be read, interpreted
correctly, and unified by any current or future
application client, as illustrated in Fig.3.
Furthermore, the adoption of standards allows for the
utilization of various integration patterns, including
those defined in the IEC-61968-100 standard.
Fig. 3. Proposed components architecture for
historical information extraction for semantic
interoperability.
For a comprehensive and advanced architecture, it is
essential to define a specific profile based on the
Canonical Data Model that represents the particular
data sets.
If CIM is utilized, established methodologies enable
the definition of the CIM Profile for data exchange
within a semantic interoperability strategy for the
Smart Grid. [8]
In the architecture of Fig.3, the Data Model module
is responsible for implementing the wrapper that
performs the translation between data from the
source system and the Client requiring the
information.
Fig.4 illustrates a portion of the CIM Profile
proposed for the implementation of the developed
Optimal Extractor.
This partial view encompasses the use cases and
relationships involved in integrating the CIM Profile
associated with analog measurements in the EPS. It
takes into account elements such as timestamp for
synchronization, maximum and minimum values,
WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2023.22.17
Alfredo Espinosa-Reza, Marxa Lenina Torres-Espindola
E-ISSN: 2224-2872
146
Volume 22, 2023
unit multiplier, associated equipment type, and its
unique identification within the entire context.
Fig. 4. CIM Profile (partial view) for Smart Grid
semantic interoperability strategy.
4 Optimal Extraction and Conditioning
This section describes some of the key optimal
conditioning functions for efficient data transfer
between the source system and the client in need of
the information.
4.1 Raw Data
The following data can be queried for any time range
and any variable registered in a steady-state:
Normal: This option retrieves registered data
without applying filters or validations. It is not
recommended, as it consumes the most resources
on the technology platform. The rules described
in sections 3.3 and 3.4 apply
Discrimination: This option retrieves data while
eliminating invalid values. It verifies the
consistency of measurement values based on the
rules described in section 3.2
Missing rows: In some cases, specific reasons
can prevent certain measurement equipment
from collecting data during a period, resulting in
gaps in the historical database. This function
identifies missing samples for each selected
equipment in the queried period. It includes two
options: "Only missing rows" and "Raw data
with missing rows".
The following values can be calculated for any time
range, at any of the data groupings by frequency, for
any recorded steady-state variable. The rules
described in section 3.2 apply.
Average: This represents the arithmetic mean of
the requested values.
Maximum: This corresponds to the highest value
among the requested values. It is useful for
identifying extreme values that could be outliers
or data entry errors.
Minimum: This represents the lowest value
among the requested values. It is employed to
identify extreme values that might be outliers or
data entry errors.
Sum: This is the result of adding up all the
requested values. It is useful for aggregating
values within a region, such as the real power of
substation circuits or a group of substations.
Standard deviation: This is the square root of the
variance of the requested values. It serves as a
measure of dispersion and is particularly
characteristic.
4.3 Data Grouping by Frequency
The following grouping strategies of the calculated
values of section 4.2 allow optimizing the queries,
generating and transporting only the data that is really
useful to the end-user, depending on the function in
which it will be used.
Hourly: Returns a single data point for each
requested hour. It facilitates the creation of daily
profiles of EPS electrical behavior
Daily: Returns a single data point for each
requested day. It facilitates the generation of
weekly or monthly profiles of EPS electrical
behavior
Weekly: Returns a single data point for each
requested week. It facilitates the generation of
monthly profiles of EPS electrical behavior
Monthly: Returns a single data point for each
requested month. It facilitates the generation of
annual profiles of EPS electrical behavior.
Annual: Returns a single data point for each
requested year. It allows for comparisons of
annual EPS electrical behavior and trends
Period: Returns a single data point for the entire
requested time range. This function is used to
compare EPS electrical behavior during specific
periods of interest
4.2 Statistical Data
WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2023.22.17
Alfredo Espinosa-Reza, Marxa Lenina Torres-Espindola
E-ISSN: 2224-2872
147
Volume 22, 2023
The following values can be requested for any time
range, but metering devices must have power quality
functions.
Interruptions: These are instantaneous changes in
frequency from the steady state of current,
voltage, or both. They have unidirectional
polarity and are primarily characterized by their
rise and fall times and their maximum value
o Momentary. Obtains values with a voltage
percent less than or equal to 10% and a
duration less than or equal to 3,000 ms.
o Temporary. Obtains values with a voltage
percent less than or equal to 10% and a
duration greater than or equal to 3,000 ms but
less than or equal to 60,000 ms.
o Sustained. Obtains values with a voltage
percent of 0% and a duration greater than or
equal to 60,000 ms.
o All. Retrieves all interruptions when the
current flow stops for any reason in the
selected time range.
SAGS: These are decreases in the effective
voltage value between 0.9 and 0.1 per unit (P.U.)
with durations ranging from 16 ms up to a few
seconds
o Instant. Values with a voltage percent greater
than or equal to 10% but less than or equal to
90%, and a duration greater than or equal to
16 ms and less than or equal to 500 ms.
o Momentary. Values with a voltage percent
greater than or equal to 10% but less than or
equal to 90%, and a duration greater than 500
ms and less than or equal to 3,000 ms.
o Temporary. Values with a voltage percent
greater than or equal to 10% but less than or
equal to 90%, and a duration greater than
3,000 ms and less than or equal to 60,000 ms.
o All. Retrieves all SAGs records stored for the
selected time range.
SWELL: These are increases in the effective
voltage value between 1.1 and 1.8 P.U. with
durations ranging from 16 ms up to a few
seconds.
o Instant. Values with a voltage percent greater
than or equal to 110% but less than or equal
to 180%, and a duration greater than or equal
to 16 ms and less than or equal to 500 ms.
o Momentary. Values with a voltage percent
greater than or equal to 110% but less than or
equal to 140%, and a duration greater than
500 ms and less than or equal to 3,000 ms.
o Temporary. Values with a voltage percent
greater than or equal to 110% but less than or
equal to 120%, and a duration greater than
3,000 ms and less than or equal to 60,000 ms.
o All. Retrieves all SWELLs records stored in
the selected time range.
4.5 Calculated Data
SCADA Equivalent Value: This is used when
real-time data is unavailable, typically from a
SCADA system, or when it's necessary to
compare the actual SCADA value with an
estimated value based on historical data. It is
obtained through the following sequence.
o Calculate the equivalent previous date, such
as the day of the previous week that is similar
to the current day or the day of the previous
month that is similar to the current day
o Use the current time without minutes.
o Request the average of historical values for
the current time on the equivalent previous
date.
Last Stored Value: For any variable, this request
provides the last value that was entered in the
historical record along with the corresponding
timestamp. It allows for validation of the
operational status of the historical record and
estimation of the quality of the stored data.
5 Conclusion
The value added by the Optimal Extractor to the
processes of supporting operational decisions in a
Smart Grid context has been highly significant.
Specialist users continually discover new ways to
leverage the advantage of visualizing the EPS
behavior over time.
For example, the Optimal Extractor enables the
graphical representation or export to Excel of hourly
average measurements by phase for each substation
circuit in a year. This query generates approximately
8,760 values per electrical parameter (365*24),
regardless of the equipment's sampling frequency. In
contrast, a standard extraction of raw data with a 10-
minute sampling frequency involves transferring
approximately 52,560 values for each parameter
(365*24*6), and double that if the sampling
frequency is 5 minutes (365*24*12). Additionally, it
consumes time and hardware resources on the client-
side to process all the obtained data.
The integration of technology, optimal data
extraction strategies, and the adoption of standards
have enabled the execution of high-level functions
with exceptional performance, reducing user
response waiting times by up to 95%. For instance:
4.4 Power Quality Events
WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2023.22.17
Alfredo Espinosa-Reza, Marxa Lenina Torres-Espindola
E-ISSN: 2224-2872
148
Volume 22, 2023
An EPS operator can generate the graph of the
hourly real and reactive power profiles for the
last 24 hours in approximately 5 seconds.
The graph or table displaying the hourly
maximum values for voltage or real power
measurements of a circuit over a year can be
generated in approximately 50 seconds.
If the above query is requested daily, the
response time is less than 20 seconds.
A very representative and valuable query of the
Optimal Extractor in support of operational
decisions for an EPS operator during a failure and
reestablishment event is the ability to calculate, on
the fly (OLAP), the maximum hourly demand profile
for the circuits involved, including the circuit that
experienced the fault and those that can support the
restoration. This integrated function allows for
displaying the graph on screen in less than 3 seconds
for each circuit.
Another integrated function that provides significant
value for users responsible for EPS operational
analysis is the computation of the Coincident Peak
Demand (CPD) for all circuits in a geographical
region over a year [4]. Manually, this analysis for a
geographical region with at least 500 circuits can take
from 2 to 3 months. The Optimal Extractor
calculates the value in approximately 30 seconds, and
2 minutes if data quality algorithms are applied,
along with generating the necessary calculation
memory to support the results and operational
decisions. This specific function enables impressive
time savings and eliminates human errors that may
occur when manually managing large amounts of
data.
Table 1 displays the results obtained by comparing
the performance of three architectures.
ARQ1: Traditional components architecture.
ARQ2: Proposed components architecture for
syntactic interoperability.
ARQ3: Proposed components architecture for
semantic interoperability.
In all the Test Cases, ARQ2 provides the best
response time for the end user, with notable
improvements compared to ARQ1. Regarding
ARQ3, when the data volume is relatively low, the
response time for the end user may be greater than
ARQ1 due to the metadata required by implementing
the CIM Instances. However, in general, this effect
does not occur as the data volume increases, and the
end user's perception is not affected since the total
added time is less than 2 seconds.
The architecture and strategies proposed for the
Optimal Extractor facilitate the implementation of
functions for the Smart Grid within the context of
EPS operations. These functions, along with the
architecture for semantic interoperability, were
successfully implemented in multiple information
systems in Mexico, supporting EPS operations.
Future work for advanced Smart Grid applications
includes the application of Artificial Intelligence to
enhance response times, improve data quality,
implement new validations, and enhance the user
experience. For example, this could involve
incorporating prognostics for demand, voltage drops,
or climate-related impacts; all without affecting
hardware performance or increasing end-user waiting
times.
WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2023.22.17
Alfredo Espinosa-Reza, Marxa Lenina Torres-Espindola
E-ISSN: 2224-2872
149
Volume 22, 2023
References:
[1] A. Espinosa-Reza, A. Quintero-Reyes, R.
Garcia-Mendoza, J.F. Borjas-Diaz, T.M.
Calleros-Torres, B. Sierra-Rodriguez and R.
Torres-Abrego, On-Line Simulator for Decision
Support in Distribution Control Centers in a
Smart Grid Context, WSEAS Transactions on
Systems and Control, Vol. 5, No. 10, 2010, pp.
814-816.
[2] A. Espinosa-Reza, H.R. Aguilar-Valenzuela, M.
Molina-Marin, M.L. Torres-Espindola, T.M.
Calleros-Torres, E. Granados-Gomez, R.
Garcia-Mendoza and C.F. Villatoro-Hernandez,
Semantic Interoperability for Operational
Planning for the Electric Power Distribution
System, WSEAS Transactions on Power
Systems, Vol. 11, 2016, pp. 289-298.
[3] M. Molina-Marin, E. Granados-Gomez, A.
Espinosa-Reza, and H. R. Aguilar-Valenzuela,
CIM-Based System for Implementing a
Dynamic Dashboard and Analysis Tool for
Losses Reduction in the Distribution Power
Systems in México, WSEAS Transactions on
Computers, Vol. 15, 2016, pp. 24-33.
[4] A. Espinosa, S. Gonzalez, and H.R. Aguilar,
Results of applying a semantic interoperability
strategy in Smart Grid applications for DSO in
Mexico, CIGRE Session 2016, General
Meeting, Paris, France, 2016.
[5] A. Espinosa-Reza, H. R. Aguilar-Valenzuela,
M. Molina-Marin, M. L. Torres-Espindola, T.
M. Calleros-Torres, E. Granados-Gomez, R.
Garcia-Mendoza and C.F. Villatoro-Hernandez,
Implementation of a CIM-Based Semantic
Interoperability Strategy for Smart Grid in
Mexico, IARAS International Journal of Power
Systems, Vol 1, 2016, pp. 33-40.
[6] A. Espinosa-Reza, M.L. Torres-Espindola, M.
Molina-Marin, E. Granados-Gomez, and H.R.
Aguilar-Valenzuela, Semantic Interoperability
for Historical and Real-Time Data Using CIM
and OPC-UA for the Smart Grid in Mexico,
WSEAS Transactions on Computers, Vol. 15,
2016, pp. 1-11.
[7] Jose Alfredo Sánchez-López, Eduardo Islas-
Pérez, Alfredo Espinosa-Reza, and Agustín
Quintero-Reyes, Deploying SCADA Data to
Web Services for Interoperability Purposes,
IEEE 2015 Global Information Infrastructure
and Networking Symposium GIIS 2015, Mexico,
2015.
[8] R. Rhodes, Common Information Model Primer,
Fourth Edition, EPRI Technical Report, 2018.
Contribution of individual authors
Alfredo Espinosa-Reza was responsible for the
design of the architecture proposed, CIM Profile
validation, and testing of the final products.
Marxa Torres-Espindola carried out the development
of the components in C#.NET and implementation in
many information systems.
Case
Grouped by
frequency
Period
Float values*
transferred
Total time for
user [s]
Float values*
transferred
1 Hour 420 0.15 35 0.09 60.0% 35 0.28 186.7%
1 Day 10,080 0.47 840 0.16 34.0% 840 1.52 323.4%
1 Week 70,560 0.80 5,880 0.14 17.5% 5,880 2.42 302.5%
1 Month 302,400 6.73 25,200 0.86 12.8% 25,200 5.57 82.8%
1 Year 3,679,200 71.36 306,600 6.92 9.7% 306,600 15.89 22.3%
1 Day 10,080 0.37 35 0.13 35.1% 35 0.32 86.5%
1 Week 70,560 0.70 245 0.29 41.4% 245 1.33 190.0%
1 Month 302,400 5.93 1,050 1.36 22.9% 1,050 2.73 46.0%
1 Year 3,679,200 70.86 12,775 2.62 3.7% 12,775 6.88 9.7%
1 Month 302,400 6.23 35 0.27 4.3% 35 0.43 6.9%
1 Year 3,679,200 70.06 426 1.92 2.7% 426 3.06 4.4%
Anualy 1 Year 3,679,200 69.26 35 1.74 2.5% 35 1.89 2.7%
CPD Anualy 1 Year 18,396,000 ^ 86729.55 4 3.90 0.004% 4 4.09 0.005%
* 32 Electrical measurements + Timestamp (date - time) + Circuit ID
^ Includes 86,400 [s] for CPD processing in the client side
Test Case
ARQ1
ARQ2
AQR3
Statistical
(AVG)
Hourly
Daily
Monthly
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US
Table 1. Comparison results using the three architectures described.
WSEAS TRANSACTIONS on COMPUTERS
DOI: 10.37394/23205.2023.22.17
Alfredo Espinosa-Reza, Marxa Lenina Torres-Espindola
E-ISSN: 2224-2872
150
Volume 22, 2023