This presentation discuss about the possible signal processing applications for the future smart grid. Later I will discuss about the basics of digital signal processing techniques widely applied in smart grid applications.

1. Class- 27: Signal Processing Techniques in Future Smart Grids
Prof. (Dr.) Pravat Kumar Rout
Department of EEE
ITER
Siksha ‘O’ Anusandhan (Deemed to be University),
Bhubaneswar, Odisha, India
Swetalina Sarangi
(Research Scholar)
Department of EEE
ITER
Siksha ‘O’ Anusandhan (Deemed to be University),
Bhubaneswar, Odisha, India

2. Introduction

3. • The smart grid implementation need intelligent interaction
between the power generating and consuming devices that can be
achieved by installing devices capable of processing data and
communicating it to various parts of the grid.
• The efficiency and performance of these devices for a smart grid
system is greatly dependent on the selection and implementation of
the advance digital signal processing techniques.
• Smart grid is a network of electric supply that manages power
demand in reliable and economic manner by detecting and reacting
to local changes in usage.
• Adaptability of a smart grid is quite important in order to
accommodate grid connected as well as islanded mode with
proper safety, security and reliability.
Contd.(1/5)

4. • The infrastructure comprises of smart meters, appliances, and
resources with a combination of modern technologies like, control,
power, instrumentation, and communication needs advance data
handling and processing methods.
• Signal processing techniques are essential to understand, plan, design
and operate the complex future smart electronic grids .
• Signal processing has wide variety of applications and is becoming an
important tool for electric power system analysis.
• For a variety of issues such as voltage control, power quality and
reliability, power system and equipment diagnostics, power system
control and protection, etc., the measurements retrieved from
numerous locations of the grid can be used for data analysis.
Contd.(2/5)

5. • Power quality issues of the smart grid research need
to analyse voltage, current and frequency deviations
in the power system for the system operator
• The characterization of the incompatibilities caused
by these deviations requires an understanding of their
principal cause.
• Other possible aspects that need inspection are the
efficient representation of the voltage and current
variations in various electrical equipment.
Contd.(3/5)

6. • Moreover, the signal processing of the power patterns leads to better
understanding the behaviour of these equipment.
• Continuous monitoring is also required to capture various events and
variations.
• A smart grid performs measurement, monitoring and processing of waveforms
based on acquisition, analysis, detection and classification techniques.
• Furthermore, these techniques can be utilized for the identification of the
system events, phenomena and load characteristics.
• A key aspect of signal processing in power systems is signal processing
methods which provide the best characterization and analysis of the signals to
be investigated.
Contd.(4/5)

7. An understanding of electrical system
behaviour is needed to study digital
signal processing techniques for
control, protection and monitoring of
the smart grids in terms of voltage,
current, frequency or active and
reactive power evaluation.
Contd.(5/5)

8. • If the electrical information is available from the network,
signal processing can be used to estimate several
parameters of the system such as impedance, power
factors, power flow, stability, etc. where such information
can be used by the system operator for more efficient
control of the electric grid.
• For some parameters, estimation by passive monitoring is
not the most appropriate method, which can lead to
inaccuracy like harmonic impedance estimation. Thus, in
order to achieve satisfactory accuracy, they require long-
term recording and data averaging.
Active power system monitoring and processing

10. Criteria Signals

11. • An over-current is detected when the maximum load
current permissible for an item of an electrical plant
is exceeded.
• Over-current protection devices monitor the current
being conducted by the protected unit and issue a
tripping command for the circuit breaker when the
current exceeds the set threshold value (the so called
the relay pick up current).
• According to the operating speed one can distinguish
instantaneous and time delayed (definite time or
inverse time ) over-current relays.
Over-current (1/5)

12. • Over and under voltage protection react when the
voltages measured exceed the permissible limits.
• The deviation of frequency from the rated value is
an indication of power imbalance in the system.
• Difference in the current magnitudes or phase
angles at the terminals of protected plant is a clear
sign of internal fault.
Over- and under voltage, frequency, Differential
principle (2/5)

13. • Impedance is measured to detect faults on transmission systems
or under-excitation or out-of-step conditions of generators.
• Discrimination of power flow direction is used in combination
with over-current units when the over-current criterion on its
own is insufficient to preserve selectivity on tripping. Typical
applications are for making the relays selective for ring and
parallel lines.
Impedance and power direction (3/5)

14. • In many applications the symmetrical components are more
suitable for protection purposes.
• The basic types faults detected by monitoring of symmetrical
components:
 phase-to-phase and ground faults in solid and low resistance
grounded systems ,
 ground faults in medium voltage systems (under grounded and
inductively grounded networks),
 asymmetric system configuration,
 asymmetric load and open circuit phase conductor.
Symmetrical components (4/5)

15. • Temperature (typical application: transformer oil temperature)
• Rate of oil flow, accumulation of gas (for detecting internal transformer faults)
• harmonicas in the neutral current and voltage (for detecting ground faults in inductively grounded
systems)
• harmonics in the generator current (for detecting internal generator faults)
• transient voltage and current signals, travelling waves ( e.g. in transmission line and fault location)
• environmental condition (like humidity, wind direction, nature of weather)
• distance (for impedance calculation, for transmission efficiency)
Other criteria (5/5)

16. Signal processing
applications in smart grids

17. • In power systems signal processing provides the best
characterization and analysis of the signals to be
investigated.
• It determines which parameters should be measured and to
what level of accuracy.
• The time invariant analysis of the smart grid requires signal
processing techniques comprises of digital filters, moving
average, trapezoidal integration and special digital systems
such as the estimation of the differentiator, time-domain
harmonic distortions and the notch filters.
Contd.(1/6)

18. • The smart grid context will introduce many time varying
variables in the behaviour of the electric power network.
The utilization of classical linear and time invariant systems
is much needed to be the main tool to analyze and design
signal processing algorithm
• Current smart grids demand more signal processing
techniques for electrical parameters to keep the network
under control and operating at the desired quality for the
future smart grid.
Contd.(2/6)

19. • Analytical tools are required for the state estimation of system parameters due to the uncertainty
and non-feasibility of monitoring system parameters at various locations. This makes the estimation
and further processing of electrical power system parameters an essential feature of the power
system analysis.
• Power frequency is an important parameter in a power system that is determined using spectrum
estimation or spectral analysis. The applications of spectral analysis in power systems can be found
in power quality analysis, protection and control.
Contd.(3/6)

20. • The spectral analysis is used to estimate the harmonic component of a stationary signal. However,
spectrum analysis of non-stationary signals with a time-varying frequency and inter-harmonics is
the current focus of researchers.
• Signals in electrical power system are time and frequency dependent. Frequency domain analysis is
used to extract features and information for possible transient conditions. These transient conditions
are associated with the presence of high frequency harmonics and other disturbances.
• As the electric smart grid of the future becomes more complex in terms of the variability of loads
and generation, growth in response to market incentives and utilization of power electronics for
energy processing is required.
Contd.(4/6)

21. • Therefore, electrical signals will require a broader set of tools
and methods for signal processing.
• The basic bridge between time and frequency domains is the
Fourier transform (FT). The FT is not the best tool to analyze
power system signals because power system signals are non-
stationary signals but FT assumes that the signals under
analysis are stationary.
• In order to overcome this limitation, alternative methods have
been proposed such as the short-time Fourier transform
(STFT), wavelets and filter banks. These techniques are
commonly known as joint time-frequency analysis .
Contd.(5/6)

22. • The complexity of the future smart grid requires not only advanced signal processing that can identify
specific parameters, but also intelligent methods for identifying particular patterns of behaviour.
• Pattern recognition applications received a boost in the last four decades due to the increasing demand for
automation, both commercially and domestically. This demand has been met by the evolution of
computers, digital signal processing and processors.
• Examples of the applications of pattern recognition in power systems include fault identification, power
quality, consumer profile identification and protection. The pattern recognition will be very useful in
future power systems due to the variability of electrical signals from diverse generators and loads, to aid
the system operator to properly identify problems and to control the grid’s power delivery process.
Contd.(6/6)

23. Recent advances of the
signal processing techniques
in smart grids

24. • Fault localization: The fault localization problem is analyzed in the power networks by using the
electromagnetic time reversal technique. In addition to this, a sensor network based algorithm is
proposed for fault localization in smart grids. This technique is based on the minimum measurement
error criteria. Moreover, ensemble empirical mode decomposition (EMD) and Hilbert Huang
transform are used for noise reduction and fault identification in the smart grid scenario .
• Fault detection technique: This is developed utilizing the change in bus susceptance parameters of
the smart grids. This technique is based on least square and generalized likelihood ratio. This
technique is based on least square and generalized likelihood ratio.
Contd.(1/6)

25. • Smart metering: The independent component analysis
technique in combination with principle component analysis
technique is used for data recovery from various smart
meters in the presence of wide band noise. Using the concept
of enhanced event driven metering, the collection of
information in low voltage systems for the smart metering is
addressed.
• Smart grid safety and security issues: The cyber security
issues of the bad data injection are discussed, where the
authors proposed the independent component analysis
technique to handle the situation.
Contd.(2/6)

26. • State estimation: The state estimation of smart grid is calculated by using Kalman filter based
approach to resolve the synchronization problem in phase measurement units while using large
scale deployment.
• Pricing signal: It is proposed a system that can generate any arbitrary pricing signal and is able to
detect the correct pricing signal and protect any attack against pricing.
• Short term state forecasting: It is proposed that short term state forecasting is able to detect false data
injection in smart grids.
• Routing protocol development: A new routing protocol is presented for smart grid applications.
Instruction detection system is developed for smart grids. The proposed system fulfils real time
communication requirements with the available limited resources in the smart grid scenario.
Contd.(3/6)

27. • Data compression: Singular value decomposition (SVD)
based method is developed for lossy data compression in
smart distribution systems. The developed method
reduces computational burden over communication
networks. A Bayesian network is introduced for obtaining
quantitative loss event frequency results of high
granularity using traceable and repeatable process. This
proposed technique differentiates the most effective part
of a certain threat that is useful for plan countermeasures
in a better way. Moreover, the false data injection issues
are discussed.
Contd.(4/6)

28. • Controlled charging of EV: The auto regressive moving average technique is applied for controlled
charging of electrical vehicles.
• Islanding detection: Wavelet transform and other DSP based transform for islanding detection and
improving islanding delay are applied.
• Control mechanism: A load side frequency control mechanism is developed which is able to keep the
grid within operational limits. The proposed technique re-adjust the supply and demand after
disturbances and also restore the frequency to its desired value. The developed technique can self
repair the smart grid.
Contd.(5/6)

29. • Power quality detection and classification: In smart grids, a signal processing based approach for power
quality detection and classification is presented in smart grids. Power quality detection and classification is
performed employing the wavelet transform in combination with neural networks and other classifiers for the
smart grids.
• Demand response management and load forecasting: It is addressed the demand response management and
load forecasting for better power quality of smart grid. Maximization of the smart meters in smart grid is
discussed by using the stochastic sub-gradient approach for quality improvement of the smart grid. The
independent component analysis (ICA) technique is utilized to overcome the coherency problem in different
power systems connected together to improve the power quality of the smart grid.
• Active filter: A transformer less active filter based technique is developed to improve the power quality of a
single phase household
Contd.(6/6)

30. Related Applications

31. Contd.(1/2)
Power system
signals
Power Flow
Harmonics voltage
and current
Unbalance voltage
Voltage
Fluctuations
Stability
Transients
Power profile
Voltage profile
Steady state
Time-varying
Steady state
Time-varying
Flickers
Sag/Swell
Voltage
Frequency
Switching
Lightning
Short-circuits

32. Time
frequency
analysis
Power
quality
Fault
detection
Protection
Control
Fault
location
Filter
Contd.(2/2)

33. Role of signal processing in
overcoming the challenges
and limitations
of smart grids

34. A smart grid is not a single technology but an integration of important
technologies like instrumentation, control, signal processing, and wireless
communication, etc. Advance signal processing techniques are required for
secure and efficient communication in future smart grid. In this regard, the
challenges and limitations of the signal processing techniques are
summarized as follows:
1: Efficient processing: Efficient signal processing is a major issue in the
development of the future grid due to the interconnection of various
technologies and diverse nature of the smart grid.
2: Secure communication: Security is a major challenge in the next generation
power grid. Advance signal processing techniques should be developed to
ensure security of information.
Contd.(1/3)

35. • Large number of sensor nodes: Sensor networks are suggested to be used in future smart grids. Due
to the presence of large number of sensor nodes in smart grid, the existing signal processing
techniques are unable to produce quality results.
• Fast and accurate processing: Diverse nature of the future power grid limits the speed and accuracy
of the existing signal processing techniques that is why more accurate and fast signal processing
techniques should be developed.
• Time varying scenario: One of the most challenging aspects of the future grid is its varying nature
due to varying loads and the wireless channel condition.
Contd.(2/3)

36. • In case of fault alternative techniques: In case of failure some alternate signal processing techniques
should be developed to overcome the situation in case of occurrence of failure of the existing
algorithm.
• Signal processing in noisy area: Due to the presence of large amplitude noise, it is difficult for
existing signal processing techniques to process the noisy data in smart power grid with acceptable
signal quality.
Contd.(3/3)

37. Future research directions
regarding the signal
processing applications in
smart grid

38. • Independent component analysis (ICA) is used in smart grid but, the
performance of the existing ICA algorithms is not reliable in case of
highly time varying scenarios. One can develop algorithms to efficiently
handle large variations in the wireless channel. Secondly, most of the
current employed ICA algorithms assumed a noise free environment
while processing the mixed signals for un-mixing. Due to the presence of
large amplitude noise in smart grid, the existing ICA algorithms should
be modified to perform well in noisy scenarios.
• For efficient communication in smart grid, proposed wireless sensor
networks and cognitive radio networks. One can combine the two
techniques in a single framework called the cognitive radio sensor
networks (CRSN) to improve the performance of smart grid.
Contd.(1/2)

39. • Large amount of sensor nodes are required in smart grid while utilizing the wireless sensor networks.
New algorithms are demanded to handle the resultant large amount of information in smart grid.
• Due to the existence of large amplitude noise in the power grid, the existing algorithms are unable to
produce better results. Sophisticated signal processing algorithms must be developed to handle the
noise intense environment of smart grid.
Contd.(2/2)

40. DSP
Transform
Techniques
Wavelet
Transform
Discrete
Fourier
Transform
S-
Transform
Kalman
Filter
Empirical
mode
decomposi
tion
Variational
mode
decomposi
tion
Hilbert
transform
Digital signal processing Techniques

41. Other
techniques
Autoregressive
Moving
Average
Game
Theory
Gradient
Based
Techniques
ICA and
PCA
Neural
Network
and Fuzzy
Logic
Mutual
Information
and
likelihood
ratio
Other techniques

42. Signal processing in smart
grid technologies

43. Smart Meters and PLC (1/6)
Application:
Smart
Meters and
PLC
Data
privacy in
smart
meters
Load
disaggregation
Measurement
in wide band
noise
Kalman filter
based PLC

44. Power Quality
Load
Management
Wavelet based
power quality
improvement
Throughout
maximization
Coherency
management
Power Quality (2/6)

45. Islanding Detection:
1-2
1-Fuzzy
based
network
2-Wavelet for
delay
improvement
Islanding Detection (3/6)

46. Fault
detection
Apply
electromagnetic
time reversal
Using sensor
network
Noise
reduction in
fault
identification
Using bus
susceptance
parameter
Fault Detection (4/6)

47. Instrumentation
Power
demand
management
DSP based
energy
management
Low
voltage
smart
metering
DFT based
frequency
estimation
Compression
based wave
form
Intelligent
monitoring
Instrumentation (5/6)

48. Safety and Security:
Cyber Security
Vehicular Technology:
Controlled Charging of Battery ,
optimizing utilization of power
Safety and Security, Vehicular Technology (6/6)

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processing (Vol. 91). John Wiley & Sons.
3. Rai, A., & Upadhyay, S. H. (2016). A review on signal processing techniques utilized in the fault
diagnosis of rolling element bearings. Tribology International, 96, 289-306.
4. Barros, J., Apraiz, M., & Diego, R. I. (2013, May). Review of signal processing techniques for
detection of transient disturbances in voltage supply systems. In 2013 IEEE International
Instrumentation and Measurement Technology Conference (I2MTC) (pp. 450-455). IEEE.
5. Aftab, M. A., Hussain, S. S., Ali, I., & Ustun, T. S. (2020). Dynamic protection of power systems with
high penetration of renewables: A review of the traveling wave based fault location
techniques. International Journal of Electrical Power & Energy Systems, 114, 105410.
Reference

50. 1. What is the need of Signal processing techniques for microgrid architecture?
2. What are the basic parameters to monitor for smooth, efficient and reliable operation of microgrid
based DG system?
3. How the deviation of signal effects the whole system?
4. What are the additional parameters to be considered for designing of protection algorithm for
transformers?
5. How spectral analysis helps for better controllability?
6. What are the drawbacks of FT and how these are overcome by STFT?
7. How efficient is short term state forecasting?
8. Which DSP method is more efficient for islanding detection?
9. What is the effect of noise in DSP based algorithms and processes to mitigate the abnormality?
10. What are the basic parameters considered for developing a communication protocol for DSP based
system?
11. What is the future scope of PLC and how it can be enhanced by signal processing techniques?
12. Define the basic check points for Cyber security?
13. How optimization techniques can be integrated with signal processing methods?
Questions

51. 51