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Human-centric Computing and Information Sciences
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Recent advances of the signal processing techniques in future smart grids

Human-centric Computing and Information Sciences volume 8, Article number: 2 (2018) Cite this article

Abstract

Smart grid is an emerging research field of the current decade. The distinguished features of the smart grid are monitoring capability with data integration, advanced analysis to support system control, enhanced power security and effective communication to meet the power demand. Efficient energy consumption and minimum costs are also included in the prodigious features of smart grid. The smart grid implementation requires 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 of these devices is greatly dependent on the selection and implementation of the advance digital signal processing techniques. This paper provides a comprehensive survey on the applications of signal processing techniques in smart grids, plus the challenges and shortcomings of these techniques. Furthermore, this paper also outlines some future research directions related to applications of signal processing in smart grids.

Introduction

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. The infrastructure comprises of smart meters, appliances, and resources with a combination of modern technologies like, control, power, instrumentation, and communication. In such a complex scenario, signal processing techniques are essential to understand, plan, design and operate the complex future smart electronic grids [1]. In addition to this, signal processing has wide variety of applications and is becoming an important tool for electric power system analysis. This is due to the fact that measurements retrieved from numerous locations of the grid can be used for data analysis. These measurements can also be used for a variety of issues such as voltage control, power quality and reliability, power system and equipment diagnostics, power system control and protection, etc [2,3,4,5,6].

Power quality is one of the main issue of the smart grid research where voltage, current and frequency deviations in the power system are the main concerns of the system operator [7]. 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. Moreover, the signal processing of the power patterns leads to better understanding the behavior of these equipment. Continuous monitoring is also required to capture various events and variations. To meet future demands, methods and techniques must be developed to explore the full range of signals derived from the complex interaction between suppliers, consumers and network operators [8].

A smart grid performs measurement, monitoring and processing of waveforms based on acquisition, analysis, detection and classification techniques [9]. Furthermore, these techniques can be utilized for the identification of the system events, phenomena and load characteristics [10]. 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. For instance, many methods only demand the voltage measured for an acceptable evaluation, but in some cases current, frequency or active and reactive power of the system is required. Furthermore, an understanding of electrical system behavior is needed to study digital signal processing techniques for control, protection and monitoring of the smart grids [11].

Related work

In the literature, different surveys are performed. In [12, 13], the authors discussed the applications of time frequency analysis, wavelet packet transform and the filter banks in the future smart grids. In [14], a short survey of some advance signal processing techniques used in smart grids are presented. These techniques include sparse representation, real time re-sampling, and the wavelet applications. Technological advancements of the transmission and distribution networks in smart grid are discussed in [15]. The survey presented in [16] gives an analysis of the applications of communication technologies and their requirement in smart grids. In [17], the authors reviewed the issues of electric vehicle while implementing the smart grids. The applications and characteristics of the communication networks [18] and the communication infrastructures are surveyed for smart grids in [19]. Review on the security threats in communication networks is presented in [20]. The smart grid technologies and standards are reviewed in [21]. The demand response of the smart grids is reviewed in [22]. These surveys are summarized in Fig. 1.

Fig. 1
figure1

Summary of the existing smart grid surveys

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Case studies and service scenario

The smart grid has numerous advantages as well as technological challenges concerning its practical implementation. Throughout the world researchers contributed to the smart grid challenges. Due to the availability of modern technological tools and contributions of researchers toward smart grid, practical implementation of this grid becomes possible. The signal processing techniques contributed much more toward implementation of this grid. Challenges like security, communication, and control are outstripped with various signal processing techniques. Furthermore, smart grid is a complex system that incorporates a variety of other systems like communication system, power system, stability analysis, load management system, and the interconnected systems. The analysis of these systems and detection of certain conditions is a burdensome task in such a challenging scenario. Advance signal processing techniques are required to perform this job. Some advance signal processing techniques reported in the literature and used to overwhelm the smart grid challenges are time frequency analysis, wavelet transforms, filter banks, sparse signal processing, and real time re-sampling. The time frequency analysis and the wavelet transforms are used to overcome the limitation of the Fast Fourier transform (FFT) i.e., the time frequency analysis and the wavelet transform are more efficient than the FFT. They are also applicable in case of non-stationary scenario where within the window the data are assumed stationary. Moreover, the filter banks are used to improve the efficiency of the DSP system. Sparse signal processing and real time re-sampling are also used to process the data for various tasks in the smart grid scenario. All these signal processing techniques are surveyed in [12,13,14]. In addition to this, various advancements of the transmission and distribution networks are surveyed in [15]. The applications of communication technologies and their requirements in the smart grid scenario are discussed in [16]. Furthermore, communication networks also play a vital role in the implementation of the smart grid. Various communication networks and their infrastructures are surveyed in [19] in the smart grid scenario. Security of the communication networks is also a challenging issue in the smart grid scenario. Numerous techniques are presented in the literature regarding the security of the smart grid which are summarized in [20]. Due to the complex nature of the smart grid various technologies are used in the development of the smart grid and various standards are defined which are discussed in detail in [22].

Motivation and contribution

In power systems, signal processing provides the best characterization and analysis of the signals to be inspected. Signal processing also determines the correct parameter to be measured and its level of accuracy. Also, the time invariant analysis of the smart grid requires signal processing techniques. These techniques comprises of digital filters, moving average, and trapezoidal integration. Special digital systems like estimation of the differentiator, time-domain harmonic distortions and the notch filters are also included. Moreover, spectral analysis is an important application of digital signal processing that determines the frequency of current or voltage signal. The applications of signal processing in power systems can also be found in power quality analysis, protection and control. Furthermore, signals in electrical power systems are time and frequency dependent where frequency domain analysis is used to extract features and information for possible transient conditions associated with the presence of high frequency harmonics and other disturbances. Finally, the complexity of the future smart grid will require not only advanced signal processing that can identify specific parameters, but also intelligent methods for identifying particular patterns of behavior.

Several reviews published in recent years addressed limited signal processing algorithms [12,13,14]. Therefore a thorough and detailed review of the applications of signal processing techniques in smart grids will be beneficial for the research community. In this paper, we concentrated on different areas of the smart grid where various signal processing techniques are used. These areas mainly include the smart metering, vehicular transportation, power quality, fault diagnosis, and modern instrumentation and control. Main contributions of this paper are listed below:

  • This paper highlights the importance of signal processing techniques in smart grids due to their large number of applications.

  • The smart grid technologies and implementation issues are discussed while implementing signal processing techniques.

  • The applications and limitations of the important signal processing tools in power system analysis are reviewed.

  • Future research directions regarding the signal processing applications in smart grid are proposed.

Remaining paper is organized into five sections. "The smart grids" section gives an overview of the smart grid concepts. Review of the signal processing applications in smart grid is given in "Signal processing applications in smart grids" section. The challenges and limitations of the signal processing techniques in smart grid are analyzed in "Role of signal processing in overcoming the challenges and limitations of smart grids" section. Future research directions are discussed in "Discussion" section and, finally a conclusion is given in "Conclusion" section. Moreover, the list of abbreviations used in this article is illustrated in the end of the article.

The smart grids

The main characteristics of the existing electric grid are one way energy flow to consumers, mostly centralized energy production, few communication nodes, limited automation and utilities usually, only have monthly contact with customers. The smart grid is quite a new concept introduced in the late 1990 with the first basic practical system introduced in the early 2000. The smart grid is an electric power grid that employs information technology and signal processing techniques to constantly optimize electrical power generation, delivery and consumption [23]. The smart grid is a power grid equipped with numerous sensors that are connected through advance communication and data acquisition systems. The functionalities of theses sensors become possible with the latest information technologies and signal processing techniques [24] as shown in Fig. 2.

Fig. 2
figure2

Smart grid architecture highlighting communication, control and signal processing

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Smart grid saves fuel, optimises electricity consumption and transmission cost. Smart grid aslo improves reliability and enhances customer service and satisfaction. It is climate friendly as reduces emissions from power generation and transmission lines and has enabled operators of industrial, commercial, municipal buildings as well as homeowners to take part in greening the grid. All these factors together positively affects the economy [25]. Although in the existing grid, power is generated and distributed by the utility companies with very less interaction with the consumers. However, the modern grid is still largely based on the existing grid [26, 27]. Some of the other benefits of the smart grid are summarized in Fig. 3.

Fig. 3
figure3

Benefits of the smart grids

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A smart grid is not a single upgrade to the electric transmission and distribution but a complete overhaul with twenty-first century infrastructure, metering and communication technologies. Each part of the smart grid brings its own system and societal benefits with the goal of improving electricity delivery and utility [28].

Signal processing applications in smart grids

In power systems signal processing provides the best characterization and analysis of the signals to be investigated. Secondly, it determines which parameters should be measured and to what level of accuracy. In addition to this, 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. Although the smart grid context will introduce many time varying variables in the behavior of the electric power network, the utilization of classical linear and time invariant systems will continue to be the main tool to analyze and design signal processing algorithms for the future smart grid. Current smart grids demand more signal processing techniques for electrical parameters to keep the network under control and operating at the desired quality. Furthermore, 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 [29].

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. Previously, spectral analysis was 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 [12].

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. 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 [13].

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 behavior. Pattern recognition applications received a boost in the last four decades due to the increasing demand for automation, both commericially 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. All of these are creating the complex smart grid of the future where pattern recognition is an important enabling tool for operation and control [14].

Recent advances of the signal processing techniques in smart grids

A smart grid is the combination of various advanced sensing nodes, control devices and modern communication systems that make the smart grid a very complex system. Due to the increased complexity fault localization is necessary. In [30], a fault detection technique is developed utilizing the change in bus susceptance parameters of the smart grids. This technique is based on least square and generalized likelihood ratio. In [31], 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 [32]. 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 [33]. Applications of the signal processing techniques in smart grids are illustrated in Fig. 4.

Fig. 4
figure4

Applications of signal processing techniques in smart grids

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Smart metering is one of the important component of the future smart grid. In [34], the authors discussed the smart meter privacy issues by suing mutual information rate and the Bahl Cocke Jelinek Raviv algorithm. In [35], 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 in [36]. In addition, the smart grid safety and security issues are discussed in [37] and [38] by using various signal processing techniques. In [37], image processing techniques are introduced for the safety of dams and smart grids. The cyber security issues of the bad data injection are discussed in [38], where the authors proposed the independent component analysis technique to handle the situation. Furthermore, the state estimation of smart grid is discussed in [39]. The authors used Kalman filter based approach to resolve the synchronization problem in phase measurement units while using large scale deployment. The authors of [40] proposed a system that can generate any arbitrary pricing signal. The proposed system is able to detect the correct pricing signal and protect any attack against pricing. In [41], a method based on short term state forecasting is proposed that is able to detect false data injection in smart grids. A new routing protocol is presented in [42] for smart grid applications. In [43], instruction detection system is developed for smart grids. The proposed system fulfills real time communication requirements with the available limited resources in the smart grid scenario. Moreover, the authors in [44] suggested big data computing architecture for the smart grid. The proposed technique consists of communication architecture for enabling big data aware communication for smart grid. Furthermore, in [45] some security issues are discussed related to distributed demand management protocols and proposed a protocol that is able to share information among users providing privacy and confidentiality. In [45], the authors also proposed a protocol that can identify untruthful users in the network. Singular value decomposition (SVD) based method is developed in [46] for lossy data compression in smart distribution systems. The developed method reduces computational burden over communication networks. In [47], 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 in [41]. Short term state forecasting in combination with temporal correlation is used to detect such attacks.

The authors introduced auto regressive moving average technique for controlled charging of electrical vehicles [48]. Moreover, [49] utilizes wavelet transform for islanding detection and improving islanding delay. The islanding detection problem is also addressed in [50], where authors used fuzzy neural networks for islanding detection. Optimization of mobile networks in smart grids is discussed in [51]. The proposed system generate green energy in individual base stations and the base stations can share these energies to reduce the power consumption from the grid. A technique is developed in [52] for efficient energy storage systems in the smart grid scenario. The developed technique is probabilistic that is able to determine the optimal operation at each load state. A load side frequency control mechanism is developed in [53] 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. In [54], the developed technique can self repair the smart grid. This technique builds coordination for smart transformer that runs in three healing modes and performs collective decision making of the phase angles in the lines of a transmission system to improve reliability under disruptive events.

Due to the severe complexity of smart grid, the quality of electrical power is an important concern. The authors in [55] presented a signal processing based approach for power quality detection and classification in smart grids. Power quality detection and classification is performed employing the wavelet transform in combination with neural networks for the smart grids. In [56], the authors highlighted the importance of signal processing for power quality improvements in smart grids. This article addressed the demand response management and load forecasting for better power quality of smart grid. Moreover, the downlink throughput maximization of the smart meters in smart grid is discussed in [57] by using the stochastic sub-gradient approach for quality improvement of the smart grid. In [58], 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. Figure 5 contains information regarding the signal processing in smart grid technologies. A transformerless active filter based technique is developed in [59] to improve the power quality of a single phase household. In [60], the effects of some advance technologies on power quality are discussed in the smart grid scenario. The technologies considered are microgrids, voltage controllers, feeder configurations, and demand side management. Study regarding investment in renewable energy by a household is performed in [61]. The possibility of providing electric power to grid is analyzed that can be performed by net metering. Secondly, the authors discussed the issues regarding the smart meters installation.

Fig. 5
figure5

Signal processing in smart grid technologies

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Modern smart grid requires intelligent instrumentation techniques to overcome its various challenges. Smart grid also need efficient and smart algorithms for communication and information sharing. In [62], a new signal processing technique is proposed for intelligent monitoring of smart grid. A compression technique which is an essential part of all types of data storage and communications is developed for the smart grid waveforms [63]. Furthermore, game theory based approach is presented for home power demand management in [64]. In [65], a signal processing based energy management in coordinated multipoint system is proposed for the smart grids. A newly developed signal processing based method of load disaggregation is proposed in [66]. Moreover, a recursive discrete Fourier transform (RDFT) algorithm is developed to estimate instantaneous frequencies in smart grids. In references [69, 70], the authors presented the concept of a modern smart home and the inclusion of renewable energy with the smart grid scenario to reduce the electric bills accordingly. A global overview of the applications of signal processing techniques in smart grids is given in Table 1.

Table 1 A global overview of the signal processing techniques in smart grids
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Role of signal processing in overcoming the challenges and limitations of smart grids

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:

  • 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.

  • 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.

  • 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.

  • 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.

Discussion

In the literature various surveys are published regarding signal processing techniques in smart grids. Limited applications of the signal processing techniques in smart grid are addressed in [12,13,14]. That is why we concentrated on the detailed review of the signal processing techniques in smart grids. In this paper, we concentrated on different areas of the smart grid where various signal processing techniques are used. These areas mainly include the smart metering, vehicular transportation, power quality, fault diagnosis, and modern instrumentation and control. This paper mainly highlights the importance of signal processing techniques in smart grids due to their large number of applications. Secondly, the smart grid technologies and implementation issues are discussed while implementing signal processing techniques. Thirdly, the applications and limitations of the important signal processing tools in power system analysis are reviewed. Finally, future research directions regarding the signal processing applications in smart grid are proposed which are given below:

  • Independent component analysis (ICA) is used in smart grid [48, 55] 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, [55] 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.

  • 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.

Conclusion

Smart grid is one of the important technological advancement for the efficient utilization of electrical energy. This efficient utilization not only conserves electrical energy but also reduces the tariff enabling smart grid friendly towards the utility companies as well as consumers. In this research work a thorough review of signal processing techniques in smart grids is presented. Recent advances of the smart grids are also reviewed followed by suggestions for further improvement and future research direction. It is hoped that this paper would provide a solid base for research in the field of applications of signal processing techniques in smart grids.

Abbreviations

FFT:

fast Fourier transform

EMD:

empirical mode decomposition

SNR:

signal-to-noise ratios

FT:

Fourier transform

STFT:

short-time Fourier transform

CRN:

cognitive radio network

ICA:

independent component analysis

GERI:

Gachon Energy Research Institute

DR:

demand response

OFDM:

orthogonal frequency division multiplexing

TQOS:

trustworthiness-based quality of service

CPT:

conservative power theory

PEVs:

plug-in electric vehicles

SFCL:

super-conducting fault current limiters

TCI:

thyristor controlled impedance

CNSPG:

cooperative network of smart power grids

PQ:

power quality

CPES:

cyber physical energy systems

SCADA:

supervisory control and data acquisition

WSN:

wireless sensor network

RTDS:

real time digital simulator

AGC:

automatic generation control

DMS:

distribution management system

OPF:

optimal power flow

IEDs:

intelligent electronic devices

ICT:

information and telecommunication technologies

DSM:

demand side management

PEA:

provincial electricity authority

FCC:

fault current controller

DOE:

Department of Energy

US:

United States

CRSN:

cognitive radio sensor networks

References

  1. 1.

    Jiang Z, Li F, Qiao W, Sun H, Wan H, Wang J, Zhang P (2009) A vision of smart transmission grids. In: IEEE power & energy society general meeting, 2009, PES’09. IEEE, New Jersey pp 1–10

  2. 2.

    Masoum MA, Moses PS, Deilami S (2010) Load management in smart grids considering harmonic distortion and transformer derating. In: IEEE innovative smart grid technologies (ISGT), 2010, pp 1–7

  3. 3.

    Dong X, Lin H, Tan R, Iyer RK, Kalbarczyk Z (2015) Software-defined networking for smart grid resilience: opportunities and challenges. In: Proceedings of the 1st ACM workshop on cyber-physical system security. ACM, New York, pp 61–68

  4. 4.

    Lugmaier A, Fechner H, Pruggler W (2008) National technology platform-smart grids austria

  5. 5.

    Lu W, Zhang D (2009) Research of enterprise application integration base on service oriented architecture. In: International conference on Computational intelligence and software engineering. CiSE 2009. IEEE, New Jersey, pp 1–9

  6. 6.

    Johnson AP (2010) The history of the smart grid evolution at southern california edison. In: Innovative smart grid technologies (ISGT), 2010. IEEE, New Jersey, pp 1–3

  7. 7.

    Lee J, Jung D-K, Kim Y, Lee Y-W, Kim Y-M (2010) Smart grid solutions, services, and business models focused on telco. In: Network operations and management symposium workshops (NOMS Wksps), 2010 IEEE/IFIP. IEEE, New Jersey, pp 323–326

  8. 8.

    Li F, Qiao W, Sun H, Wan H, Wang J, Xia Y, Xu Z, Zhang P (2010) Smart transmission grid: vision and framework. IEEE Tans Smart Grid 1(2):168–177

    Article  Google Scholar 

  9. 9.

    Ahmad A, Hassan NU (2016) Smart grid as a solution for renewable and efficient energy. Advances in environmental engineering and green technologies (AEEGT) book series. IGI Global, Hershey. https://doi.org/10.4018/978-1-5225-0072-8. https://www.igi-global.com/book/smart-grid-solution-renewable-efficient/142123

  10. 10.

    Rietveld G, Braun J, Wright P, Grottker U (2010) Metrology for smart electrical grids. In: Conference on precision electromagnetic measurements (CPEM), 2010. IEEE, New Jersey, pp 529–530

  11. 11.

    Hauttekeete L, Stragier J, Haerick W, De Marez L (2010) Smart, smarter, smartest the consumer meets the smart electrical grid. In: 9th conference on telecommunications internet and media techno economics (CTTE), 2010. IEEE, New Jersey, pp 1–6

  12. 12.

    Carvalho T, Duque C, Silveira P, Mendes M, Ribeiro P (2012) Review of signal processing techniques for time-varying harmonic decomposition. In: Power and energy society general meeting, 2012. IEEE, New Jersey, pp 1–6

  13. 13.

    Carvalho T, Duque C, Silveira P, Ribeiro P (2013) Considerations on signal processing for power systems in the context of smart grids. In: Power and energy society general meeting (PES), 2013. IEEE, New Jersey, pp 1–5

  14. 14.

    Silva LRM, Duque CA, Ribeiro PF (2015) Recent developments on signal processing for smart grids. In: Power & energy society general meeting, 2015. IEEE, nwe Jersey, pp 1–5

  15. 15.

    Hamidi V, Smith KS, Wilson RC (2010) Smart grid technology review within the transmission and distribution sector. In: Innovative smart grid technologies conference Europe (ISGT Europe), 2010, IEEE PES. IEEE, New Jersey, pp 1–8

  16. 16.

    Gungor VC, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke GP (2013) A survey on smart grid potential applications and communication requirements. IEEE Trans Ind Inform 9(1):28–42

    Article  Google Scholar 

  17. 17.

    Richardson DB (2013) Electric vehicles and the electric grid: a review of modeling approaches, impacts, and renewable energy integration. Renew Sustain Energy Rev 19:247–254

    Article  Google Scholar 

  18. 18.

    Khan RH, Khan JY (2013) A comprehensive review of the application characteristics and traffic requirements of a smart grid communications network. Compu Netw 57(3):825–845

    Article  Google Scholar 

  19. 19.

    Yan Y, Qian Y, Sharif H, Tipper D (2013) A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun Surv Tutor 15(1):5–20

    Article  Google Scholar 

  20. 20.

    Lu Z, Lu X, Wang W, Wang C (2010) Review and evaluation of security threats on the communication networks in the smart grid. In: Military communications conference, 2010-MILCOM. IEEE, New Jersey, pp 1830–1835

  21. 21.

    Gungor VC, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke GP (2011) Smart grid technologies: communication technologies and standards. IEEE Trans Ind inform 7(4):529–539

    Article  Google Scholar 

  22. 22.

    Balijepalli VM, Pradhan V, Khaparde S, Shereef R (2011) Review of demand response under smart grid paradigm. In: Innovative smart grid technologies-India (ISGT India), 2011 IEEE PES. IEEE, New Jersey, pp 236–243

  23. 23.

    Bouffard F (2010) The challenge with building a business case for smart grids. In: Power and energy society general meeting, 2010. IEEE, New Jersey, pp 1–3

  24. 24.

    Meenual T (2010) Roadmapping the pea smart grids. In: Proceedings of the international conference on Energy and sustainable development: issues and strategies (ESD), 2010. IEEE, New Jersey, pp 1–6

  25. 25.

    Tenti P, Paredes HKM, Marafão FP, Mattavelli P (2010) Accountability and revenue metering in smart micro-grids. In: IEEE International workshop on applied measurements for power systems (AMPS), 2010. IEEE, New Jersey, pp 74–79

  26. 26.

    Deconinck G, Labeeuw W, Vandael S, Beitollahi H, De Craemer K, Duan R, Qui Z, Ramaswamy PC, Meerssche BV, Vervenne I, et al (2010) Communication overlays and agents for dependable smart power grids. In: 5th international conference on critical infrastructure (CRIS), 2010. IEEE, New Jersey, pp 1–7

  27. 27.

    Stragier J, Hauttekeete L, De Marez L (2010) Introducing smart grids in residential contexts: Consumers’ perception of smart household appliances. In: IEEE conference on innovative technologies for an efficient and reliable electricity supply (CITRES), 2010. IEEE, New Jersey, pp 135–142

  28. 28.

    Budka K, Deshpande J, Hobby J, Kim Y-J, Kolesnikov V, Lee W, Reddington T, Thottan M, White CA, Choi J-I, et al (2010) Geri-bell labs smart grid research focus: economic modeling, networking, and security & privacy. In: 1st IEEE international conference on smart grid communications (SmartGridComm), 2010. IEEE, New Jersey, pp 208–213

  29. 29.

    Ribeiro PF, Ribeiro PM, Cerqueira AS, Ribeiro PF, Duque CA, Ribeiro PM, Cerqueira AS (2013) Power systems signal processing for smart grids. Wiley, New York

    Book  MATH  Google Scholar 

  30. 30.

    Wei C, Wiesel A, Blum RS (2012) Change detection in smart grids using errors in variables models. In: 7th sensor array and multichannel signal processing workshop (SAM), 2012. IEEE, New Jersey, pp 17–20

  31. 31.

    Manesh H, Lugrin G, Razzaghi R, Romero C, Paolone M, Rachidi F (2012) A new method to locate faults in power networks based on electromagnetic time reversal. In: 13th international workshop on signal processing advances in wireless communications (SPAWC), 2012. IEEE, New Jersey, pp 469–474

  32. 32.

    Korkali M, Abur A (2012) Optimal sensor deployment for fault-tolerant smart grids. In: 13th international workshop on signal processing advances in wireless communications (SPAWC), 2012. IEEE, New Jersey, pp 520–524

  33. 33.

    Yalcin T, Ozdemir M (2016) Noise cancellation and feature generation of voltage disturbance for identification smart grid faults. In: 16th international conference on environment and electrical engineering (EEEIC), 2016. IEEE, New Jersey, pp 1–6

  34. 34.

    Varodayan D, Khisti A (2011) Smart meter privacy using a rechargeable battery: minimizing the rate of information leakage. In: International conference on acoustics, speech and signal processing (ICASSP), 2011. IEEE, New Jersey, pp 1932–1935

  35. 35.

    Qiu RC, Hu Z, Chen Z, Guo N, Ranganathan R, Hou S, Zheng G (2011) Cognitive radio network for the smart grid: experimental system architecture, control algorithms, security, and microgrid testbed. IEEE Trans Smart Grid 2(4):724–740

    Article  Google Scholar 

  36. 36.

    Simonov M, Li H, Chicco G (2015) Gathering process data in low-voltage systems by enhanced event-driven metering. IEEE Syst J 11:1755–1766

    Article  Google Scholar 

  37. 37.

    de Oliveira AL, Magrini LC, Kim HY, Carneiro E, Pínfari J (2015) Image processing as an integration tool between a dam safety system and smart grids. In: Innovative smart grid technologies Latin America (ISGT LATAM), 2015 IEEE PES. IEEE, New jersey, pp 650–654

  38. 38.

    Huang Y, Esmalifalak M, Nguyen H, Zheng R, Han Z, Li H, Song L (2013) Bad data injection in smart grid: attack and defense mechanisms. IEEE Commun Mag 51(1):27–33

    Article  Google Scholar 

  39. 39.

    Yang P, Tan Z, Wiesel A, Nehorai A (2013) State estimation with consideration of PMU phase mismatch for smart grids. In: Innovative smart grid technologies (ISGT), 2013 IEEE PES. IEEE, New Jersey, pp 1–6

  40. 40.

    Giraldo J, Cárdenas A, Quijano N (2017) Integrity attacks on real-time pricing in smart grids: impact and countermeasures. IEEE Trans Smart Grid 8(5):2249–2257. https://doi.org/10.1109/TSG.2017.2665654

    Article  Google Scholar 

  41. 41.

    Zhao J, Zhang G, La Scala M, Dong ZY, Chen C, Wang J (2017) Short-term state forecasting-aided method for detection of smart grid general false data injection attacks. IEEE Trans Smart Grid 8(4):1580–1590

    Article  Google Scholar 

  42. 42.

    Yang Z, Ping S, Sun H, Aghvami A-H (2017) CRB-RPL: a receiver-based routing protocol for communications in cognitive radio enabled smart grid. IEEE Trans Veh Technol 66(7):5985–5994

    Article  Google Scholar 

  43. 43.

    Genge B, Haller P, Dumitru C-D, Enachescu C (2017) Designing optimal and resilient intrusion detection architectures for smart grids. IEEE Trans Smart Grid 8(5):2440–2451. https://doi.org/10.1109/TSG.2017.2665654

    Article  Google Scholar 

  44. 44.

    Wang K, Wang Y, Hu X, Sun Y, Deng D-J, Vinel A, Zhang Y (2017) Wireless big data computing in smart grid. IEEE Wirel Commun 24(2):58–64

    Article  Google Scholar 

  45. 45.

    Rahman MA, Manshaei MH, Al-Shaer E, Shehab M (2017) Secure and private data aggregation for energy consumption scheduling in smart grids. IEEE Trans Dependable SecureComput 14(2):221–234

    Article  Google Scholar 

  46. 46.

    de Souza JCS, Assis TML, Pal BC (2017) Data compression in smart distribution systems via singular value decomposition. IEEE Trans Smart Grid 8(1):275–284

    Article  Google Scholar 

  47. 47.

    Le A, Chen Y, Chai KK, Vasenev A, Montoya L (2017) Assessing loss event frequencies of smart grid cyber threats: encoding flexibility into fair using Bayesian network approach. In: Smart grid inspired future technologies: 1st international conference, SmartGIFT 2016, Liverpool, UK, May 19–20, 2016, Revised Selected Papers. Springer, Berlin, pp 43–51

  48. 48.

    Kwasinski A, Kwasinski A (2012) Signal processing in the electrification of vehicular transportation: techniques for electric and plug-in hybrid electric vehicles on the smart grid. IEEE Signal Process Mag 29(5):14–23

    Article  Google Scholar 

  49. 49.

    Vatani M, Sanjari M, Gharehpetian G, Noroozian M (2012) A new fast and reliable method for islanding detection based on transient signal. In: 2nd Iranian conference on smart grids (ICSG), 2012. IEEE, New Jersey, pp 1–4

  50. 50.

    Kermany SD, Joorabian M, Deilami S, Masoum MA (2016) Hybrid islanding detection in microgrid with multiple connection points to smart grids using fuzzy-neural network. IEEE Trans Power Syst 32:2640–2651

    Article  Google Scholar 

  51. 51.

    Huang X, Han T, Ansari N (2017) Smart grid enabled mobile networks: Jointly optimizing BS operation and power distribution. In: IEEE/ACM transactions on networking. IEEE, New Jersey

  52. 52.

    Awad AS, El-Fouly TH, Salama MM (2017) Optimal ESS allocation for benefit maximization in distribution networks. IEEE Trans Smart Grid 8(4):1668–1678

    Article  Google Scholar 

  53. 53.

    Mallada E, Zhao C, Low S (2017) Optimal load-side control for frequency regulation in smart grids. IEEE Trans Autom Control 62:6294–6309

    Article  Google Scholar 

  54. 54.

    Pournaras E, Espejo-Uribe J (2017) Self-repairable smart grids via online coordination of smart transformers. IEEE Trans Ind Inform 13(4):1783–1793

    Article  Google Scholar 

  55. 55.

    Alshahrani S, Abbod M, Alamri B (2015) Detection and classification of power quality events based on wavelet transform and artificial neural networks for smart grids. In: Smart grid (SASG), 2015 Saudi Arabia. IEEE, New Jersey, pp 1–6

  56. 56.

    Chan S-C, Tsui KM, Wu H, Hou Y, Wu Y-C, Wu FF (2012) Load/price forecasting and managing demand response for smart grids: methodologies and challenges. IEEE Signal Process Mag 29(5):68–85

    Article  Google Scholar 

  57. 57.

    Chen X, Chen T, Wang X, Giannakis GB (2016) Stochastic online control for smart-grid powered MIMO downlink transmissions. In: International conference on acoustics, speech and signal processing (ICASSP), 2016. IEEE, New Jersey, pp 3451–3455

  58. 58.

    Ariff M, Pal BC (2013) Coherency identification in interconnected power systeman independent component analysis approach. IEEE Trans Power Syst 28(2):1747–1755

    Article  Google Scholar 

  59. 59.

    Javadi A, Hamadi A, Ndtoungou A, Al-Haddad K (2017) Power quality enhancement of smart households using a multilevel-thseaf with a PR controller. IEEE Trans Smart Grid 8(1):465–474

    Article  Google Scholar 

  60. 60.

    Bollen MH, Das R, Djokic S, Ciufo P, Meyer J, Rönnberg SK, Zavodam F (2017) Power quality concerns in implementing smart distribution-grid applications. IEEE Trans Smart Grid 8(1):391–399

    Article  Google Scholar 

  61. 61.

    Dato P, Durmaz T, Pommeret A (2017) Smart grids and renewable electricity generation by households. Technical report, FAERE Working Paper

  62. 62.

    Gu IY, Bollen MH, Le CD (2011) Signal processing and classification tools for intelligent distributed monitoring and analysis of the smart grid. In: 2nd IEEE PES international conference and exhibition on innovative smart grid technologies (ISGT Europe), 2011. IEEE, New Jersey, pp 1–7

  63. 63.

    Tcheou MP, Lovisolo L, Ribeiro MV, da Silva EA, Rodrigues MA, Romano JM, Diniz PS (2014) The compression of electric signal waveforms for smart grids: state of the art and future trends. IEEE Trans Smart Grid 5(1):291–302

    Article  Google Scholar 

  64. 64.

    Zhu Z, Lambotharan S, Chin WH, Fan Z (2015) A game theoretic optimization framework for home demand management incorporating local energy resources. IEEE Trans Ind Inform 11(2):353–362

    Google Scholar 

  65. 65.

    Xu J, Zhang R (2016) Cooperative energy trading in comp systems powered by smart grids. IEEE Trans Veh Technol 65(4):2142–2153

    Article  Google Scholar 

  66. 66.

    Koutitas GC, Tassiulas L (2016) Low cost disaggregation of smart meter sensor data. IEEE Sens J 16(6):1665–1673

    Article  Google Scholar 

  67. 67.

    Noreen U, Baig S (2013) Modified incremental bit allocation algorithm for powerline communication in smart grids. In: 1st international conference on communications, signal processing, and their applications (ICCSPA), 2013. IEEE, New Jersey, pp 1–6

  68. 68.

    Ykhlef F, Ykhlef H, Ykhlef F (2015) Frequency estimation of power systems in context of smart grids. In: 3rd international renewable and sustainable energy conference (IRSEC), 2015. IEEE, New Jersey, pp 1–5

  69. 69.

    Vanus J, Belesova J, Martinek R, Nedoma J, Fajkus M, Bilik P, Zidek J (2017) Monitoring of the daily living activities in smart home care. Human-centric computing and information sciences, vol 7. Springer, Berlin, p 30

    Google Scholar 

  70. 70.

    Boumkheld N, Ghogho M, El Koutbi M (2015) Energy consumption scheduling in a smart grid including renewable energy. J Inform Process Syst 11:116–124

    Google Scholar 

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Authors’ contributions

ZU collected, reviewed and classified main literature for the paper. AA identified the challenges of signal processing techniques in smart grid. AQ drafted the smart grid related part of the manuscript. MA identified future research directions. All authors read and approved the final manuscript.

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    Affiliations

    1. Department of Electrical Engineering, COMSATS Institute of Information Technology, Wah Cantt, 47040, Pakistan

      Zahoor Uddin, Ayaz Ahmad, Aamir Qamar & Muhammad Altaf

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    1. Zahoor Uddin, Ayaz Ahmad, Aamir Qamar and Muhammad Altaf contributed equally to this work

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      1. Zahoor Uddin
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      2. Ayaz Ahmad
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      3. Aamir Qamar
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      Correspondence to Ayaz Ahmad.

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      Uddin, Z., Ahmad, A., Qamar, A. et al. Recent advances of the signal processing techniques in future smart grids. Hum. Cent. Comput. Inf. Sci. 8, 2 (2018). https://doi.org/10.1186/s13673-018-0126-9

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      Keywords

      • Smart grid
      • Signal processing techniques
      • Wireless communication
      • Control
      • Security