Paris 2020
D1-101
Predictive maintenance based on continuous monitoring of OLTCs electrical
signatures
A. M. BARBOSA (1) *; C. A. M. GOMES (1); E. L. NASCIMENTO (2); A. B. BARNABÉ (2);
A. SANTA ROSA (2)
LIGHT S.E.S.A. (1);
CGTI – Centro de Gestão de Tecnologia e Inovação (2);
Brazil
SUMMARY
This article presents a new method of monitoring on-load tap-changers operational
conditions and its practical field application, as well as its accuracy in identifying failures
and ease of use. Electrical signatures associated with abnormalities in the equipment
were catalogued through tests and computer simulations on transformer phase currents
signals and OLTC motor current signals; and signal processing and artificial intelligence
techniques were applied to extract characteristics and diagnose the switches, using only
these signals. The results obtained were accurate in detecting anomalies raised by OLTC
maintenance technicians. The on-line monitoring system presented provides an
alternative for assessing the operating condition of the switches.
KEYWORDS
Predictive maintenance – Online monitoring – On load tap changer – Wavelet transform –
SMC – Electrical signature.
*aurelio.barbosa@light.com.br
1. Introduction
In recent years, predictive maintenance based on online monitoring has gained more
and more space in the market. This practice has allowed the reduction of preventive and
corrective maintenance and, consequently, minimization of financial losses. Predictive
maintenance is performed through online monitoring of data that indicates the operating
conditions of the asset, such as the wear of electrical contacts. From this, it is possible to
estimate, in advance, precisely, when the equipment will fail and the necessary conditions
for this time to be used [1], [2], [3] and [4].
However, although this technique is widespread and applied in several branches of
industry in Brazil and worldwide, it is still in an embryonic state in the electricity sector,
since most companies still perform preventive maintenance of their assets, based on time
of use or number of operations, a technique that started in the 70's [1].
The power transformer represents the equipment of greatest strategic
importance and greatest investment, being present in the different voltage levels of the
electrical system, from the generation, transmission and distribution elevating
substations to the consumer industries [3]. Among the components of a power
transformer is the On-Load Tap-Changer - OLTC. According to [1], 26% of the causes for
failures in power transformers are due to OLTCs. In addition, OLTCs preventive
maintenance involves high costs: the need to make assets unavailable for execution,
costs with large machinery and instruments, among others. From this, the OLTC requires
the adoption of a more efficient preventive maintenance policy, in order to guarantee
lower costs and better performance of the power transformers [1], [2], [3] and [4].
Despite ANEEL (Brazil’s National Electricity Agency – Agência Nacional de Energia
Elétrica) defining periods for carrying out preventive maintenance of transmission
facilities in the basic network, in [5], Article 5, allows the issuance of technical reports on
the condition of equipment, obtained from predictive techniques and online monitoring,
in order to justify not carrying out preventive maintenance within the defined periods.
Thus, when applying the concept of predictive monitoring, it is possible to reduce the
cost of preventive maintenance, as long as reputable predictive and monitoring
techniques are applied, enabling a reliable analysis of the condition of the asset.
With the advance of research, it became possible to implement algorithms capable of
determining the type of failure in an OLTC, through the monitored quantities. Here
studies are cited more commonly based on artificial neural networks, fuzzy logic and
wavelet transformed as the works of [8], [9], [10], [11], [12] and [13].
Thus, this work presents a new predictive technique for monitoring OLTCs and,
consequently, reducing the amount of maintenance, based on the continuous monitoring
and analysis of electrical signatures of abnormalities in the Transformer Phase Currents
(TPC) and the Motor Current (MC) of the OLTC (OLTC Monitoring System – Sistema de
Monitoramento de Comutadores - SMC).
From tests and computer simulations in OLTCs, electrical signatures associated with
abnormalities occurred in OLTCs were collected. This database was used in the
development of algorithms for monitoring these abnormalities, allowing them to be
precisely detected using only the TPC and MC.
2. Materials and methods
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2.1 OLTC Monitoring System – SMC
The SMC is a device whose main objective is to diagnose failures in OLTCs. Figure 1
shows the front view of the OLTC Monitoring System and Figure 2 shows the rear view of
the equipment.
The SMC has inputs for acquiring 4 currents (3 TPC and 1 MC), input for digital tap
position indicator, digital outputs that can trigger alarms for events such as: wear on
contacts, number of operations exceeded and OLTC failures. Communication with the
equipment is done using the electrical or optical ports.
Figure 1: OLTC monitoring system (front view).
Figure 2: OLTC monitoring system (rear view).
The system was designed to operate remotely and independently, allowing access to
the equipment through an ethernet network connection, both for configuration and for
real-time viewing of the monitoring. All SMC parameters can be configured using the
supervisory software, illustrated in Figure 3.
This interface allows equipment management, download and visualization of records,
temporal analysis and filtering of records, among others. In addition, in the supervisory it
is possible to view:
• TPC and MC oscillographs and RMS values, both in real-time or non-real-time;
• overcurrent alarm, active when the current in one of the phases exceeds the
limit previously established in the settings;
• tap position and number of switch operations;
• maintenance forecast and estimated wear of contacts for each phase.
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Figure 3: SMC supervisory software.
2.2 Signal acquisition
The signals are acquired through current sensors installed in A, B and C transformer
phases, and in OLTC motor current, as shown in Figure 4. In addition, the data acquisition
is external to the switch, therefore, it can be installed in any type and model of OLTC,
even in operation, without the need to interrupt its operation.
Figure 4: Acquisition of TPC (left) and MC (right) through current sensors in the
transformer.
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2.3 Network architecture
The SMC network architecture is shown in Figure 5. This architecture, of the clientserver type, provides centralized access to information, and acts in the sharing of data
from the monitoring of machines and equipment present in substations, such as OLTCs or
relays.
Figure 5 : SMC network architecture. In the case of OLTC monitoring, the data
acquired by the current sensors are sent to the SMC, which in turn is connected to the
BIT, a terminal server, through optical fiber.
The SMC is connected to the network, so that it is possible to access its information
remotely, through an analysis unit, or through a local point-to-point connection. In this
way, any alarm signaling sent by the SMC is indicated in a Local Analysis Unit, also
allowing sending to a Remote Analysis Unit for immediate signaling to the maintenance
team.
2.4 Methodology for fault diagnosis in OLTCs
Each OLTC operation produces a characteristic electrical signature in the TPC and MC,
during the instant of switching. In a healthy switch, there is no significant change in the
electrical signature of the currents. However, any degradation in an OLTC induces
changes in the electrical signature of the TPC and MC. For the case in which a degradation
always induces the same change in the pattern of the electric signature, this change
configures an electrical signature characteristic of the degradation that occurred.
In some cases, it is possible to visually observe and analyse the presence of a
distortion in the electrical signature of the TPC and MC, to deduce similarities or
differences. This process is called feature extraction and is not always performed in a
visual way, since in some cases a characteristic electrical signature is only displayed by
applying some transformation that highlights the desired information.
Based on the observations, a database of previously diagnosed healthy and unhealthy
OLTC signals is constructed. The signatures of healthy OLTCs are taken as reference
values for diagnosis, while signatures of unhealthy OLTCs allow the recognition of several
problems.
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Tests carried out in OLTCs have shown the existence of characteristic signatures in the
TPC, for example, when an interconnection cord from the main contacts breaks, and in
the MC from electrical and mechanical defects in the switching drive system. From the
analysis of the signals from the performed tests, it was possible to establish diagnostic
criteria for the tested failures, through a digital processing of the TPC and MC signals.
After this stage, the features (a numeric array) are applied in a classifier that has
the function of making the decision, classifying the OLTC as healthy or flawed. In general,
the diagnostic system works according to Figure 6.
Figure 6: Main steps during the recognition process.
2.5 Failures addressed and methodology for detection
2.5.1
Switching cord breakage
The OLTC operates by changing the taps of a transformer, which can be increased or
decreased, even with the presence of rated load current. The main contacts are
responsible for changing the taps and are connected to the switch by means of a chord.
During the tap change, the switch performs rotation, causing this chord to twist. Thus,
over time chords may break, impairing the switching, and potentially causing internal
arcs, formation of oil sludge and heating in the switch.
Using two commercial equipment with a sampling rate of 10 kHz, tests were carried
out to obtain the switching chord breakage electrical signature, in a phase of a bank of
transformers. Photographs taken during the tests are shown in Figure 7.
Figure 7: Test for obtaining switching chord breakage electrical signature at the OLTC.
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Figure 8: OLTC switching chord breakage electrical signature in the transformer phase A
current.
Using the electrical signature of Figure 8, a computational algorithm based on
digital signal processing techniques for detecting OLTC broken chords was implemented.
The feature extraction process has the purpose of verifying the presence of switching
chord breakage electrical signature characteristic in the TPC. This process is based on the
application discrete wavelet transform. As a result of signal processing, dissipated
energies by the presence of each OLTC fault are calculated. Thus, if the OLTC is healthy,
the energies dissipated by all faults, calculated by the algorithm, are zero. Figure 9 shows
the result of applying the wavelet transform algorithm for diagnosing broken chords in
the signal shown in Figure 8.
Figure 9: Instant energy dissipated by the failure and calculated by the wavelet transform
algorithm. The total energy dissipated by the failure is is the area below the curve.
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In Figure 9, demonstrates that there is a change in the transformed signal only at
the instant of the fault signature shown in Figure 8. To calculate the total energy
dissipated by the fault, the area below the instantaneous energy is calculated.
Figure 10 presents a scatter plot for the energies dissipated by the failure, returned
by the algorithm. In this graph, each point represents a phase A power transform signal.
Figure 10: Energies dissipated by the switching of the analysed signal bank.
It can be seen from Figure 10 that switching with broken strings dissipates more
energy than healthy switching. Since the data is linearly separable, a neural network is
used to calculate the value of the limit energy and classify the switching. The result of the
validation of the classifications returned to an assertiveness rate of 100%.
Using a boxplot, the possible values of dissipated energies were expected. The
result is illustrated in Figure 11.
Figure 11: Switching dissipated energies boxplot due to switching chord breakage.
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It can be seen from the diagram in Figure 11 that the upper limit of normal
operation is less than the lower limit of defective operation. This is an important result,
since it statistically shows the impossibility of a false positive or negative due to the fact
that there is no probability of transition.
2.5.2
Maintenance prediction
To enable maintenance prediction, an algorithm for signal extrapolation was
developed in the embedded software. This algorithm returns the date on which the
switching number and the wear signals of the contacts of each phase current will reach a
target value, as configured in the software. To illustrate, we can take as a base Figure 12,
which simulates the wear behaviour of a contact over time.
Figure 12: Wear of a phase A contact of a transformer monitored by SMC.
3. Conclusion
An OLTC online monitoring system capable of detecting equipment failures and
wear on contacts, based only on the electrical signatures of TPC and MC, through the use
of digital signal processing techniques was presented.
The combination of these tools for data extraction and classification enables the
SMC to diagnose, in real time, the degree of deterioration of OLTC components. From
this, it is possible to estimate precisely when the OLTC will fail, in advance, reducing the
amount of preventive and corrective maintenance. The results obtained were accurate in
detecting anomalies raised by OLTC maintenance technicians
In short, the system presented is a new predictive maintenance technique for
OLTCs, which provides a non-invasive alternative to known maintenance techniques. With
its use, companies can assertively predict the appropriate time for preventive
maintenance of OLTCs.
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