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This Special Issue focuses on the state-of-the-art results from the definition and design of filters for low- and high-frequency applications and systems. Different technologies and solutions are commonly adopted for filter definition, from electrical to electromechanical and mechanical solutions, from passive to active devices, and from hybrid to integrated designs. Aspects related to both theoretical and experimental research in filter design, CAD modeling and novel technologies and applications, as well as filter fabrication, characterization and testing, are covered. The proposed research articles deal with different topics as follows: Modeling, design and simulation of filters; Processes and fabrication technologies for filters; Automated characterization and test of filters; Voltage and current mode filters; Integrated and discrete filters; Passive and active filters; Variable filters, characterization and tunability.

History of engineering & technology --- analogue-to-digital conversion --- ATHOS soft X-ray beamline --- broadband noise --- Hall probe --- offset fluctuation and drift --- three-axis teslameter --- undulator --- power line communication (PLC) --- conducted disturbances --- anti-interference filter --- smart home --- low-pass filter (LPF) --- stepped impedance resonator (SIR) --- hairpin resonator --- internal coupling --- defected ground structure (DGS) --- current mode --- universal filter --- VCII --- voltage conveyor --- SIMO filter --- microwave dielectric ceramics --- filter --- additive manufacturing --- digital light processing --- post annealing --- dielectric properties --- wideband --- bandpass filter --- quarter wavelength --- stepped-impedance resonator (SIR) --- ultra-wideband --- stub-loaded --- stepped impedance resonator --- active filters --- anti-aliasing filters --- HBT --- inductorless --- low-pass filters --- SiGe --- switched-capacitor filters --- low-voltage --- finFET --- operational amplifier --- voltage-controlled oscillator --- unity-gain bandwidth --- varactor --- total harmonic distortion --- phase noise --- active inductor --- MMIC --- tunable filters

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This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries.

Technology: general issues --- History of engineering & technology --- Energy industries & utilities --- virtual power plant (VPP) --- power quality (PQ) --- global index --- distributed energy resources (DER) --- energy storage systems (ESS) --- power systems --- long-term assessment --- battery energy storage systems (BESS) --- smart grids --- conducted disturbances --- power quality --- supraharmonics --- 2-150 kHz --- Power Line Communications (PLC) --- intentional emission --- non-intentional emission --- mains signalling --- virtual power plant --- data mining --- clustering --- distributed energy resources --- energy storage systems --- short term conditions --- cluster analysis (CA) --- nonlinear loads --- harmonics, cancellation, and attenuation of harmonics --- waveform distortion --- THDi --- low-voltage networks --- optimization techniques --- different batteries --- off-grid microgrid --- integrated renewable energy system --- cluster analysis --- K-means --- agglomerative --- ANFIS --- fuzzy logic --- induction generator --- MPPT --- neural network --- renewable energy --- variable speed WECS --- wind energy conversion system --- wind energy --- frequency estimation --- spectrum interpolation --- power network disturbances --- COVID-19 --- time-varying reproduction number --- social distancing --- load profile --- demographic characteristic --- household energy consumption --- demand-side management --- energy management --- time series --- Hidden Markov Model --- short-term forecast --- sparse signal decomposition --- supervised dictionary learning --- dictionary impulsion --- singular value decomposition --- discrete cosine transform --- discrete Haar transform --- discrete wavelet transform --- transient stability assessment --- home energy management --- binary-coded genetic algorithms --- optimal power scheduling --- demand response --- Data Injection Attack --- machine learning --- critical infrastructure --- smart grid --- water treatment plant --- power system --- virtual power plant (VPP) --- power quality (PQ) --- global index --- distributed energy resources (DER) --- energy storage systems (ESS) --- power systems --- long-term assessment --- battery energy storage systems (BESS) --- smart grids --- conducted disturbances --- power quality --- supraharmonics --- 2-150 kHz --- Power Line Communications (PLC) --- intentional emission --- non-intentional emission --- mains signalling --- virtual power plant --- data mining --- clustering --- distributed energy resources --- energy storage systems --- short term conditions --- cluster analysis (CA) --- nonlinear loads --- harmonics, cancellation, and attenuation of harmonics --- waveform distortion --- THDi --- low-voltage networks --- optimization techniques --- different batteries --- off-grid microgrid --- integrated renewable energy system --- cluster analysis --- K-means --- agglomerative --- ANFIS --- fuzzy logic --- induction generator --- MPPT --- neural network --- renewable energy --- variable speed WECS --- wind energy conversion system --- wind energy --- frequency estimation --- spectrum interpolation --- power network disturbances --- COVID-19 --- time-varying reproduction number --- social distancing --- load profile --- demographic characteristic --- household energy consumption --- demand-side management --- energy management --- time series --- Hidden Markov Model --- short-term forecast --- sparse signal decomposition --- supervised dictionary learning --- dictionary impulsion --- singular value decomposition --- discrete cosine transform --- discrete Haar transform --- discrete wavelet transform --- transient stability assessment --- home energy management --- binary-coded genetic algorithms --- optimal power scheduling --- demand response --- Data Injection Attack --- machine learning --- critical infrastructure --- smart grid --- water treatment plant --- power system

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries.

virtual power plant (VPP) --- power quality (PQ) --- global index --- distributed energy resources (DER) --- energy storage systems (ESS) --- power systems --- long-term assessment --- battery energy storage systems (BESS) --- smart grids --- conducted disturbances --- power quality --- supraharmonics --- 2–150 kHz --- Power Line Communications (PLC) --- intentional emission --- non-intentional emission --- mains signalling --- virtual power plant --- data mining --- clustering --- distributed energy resources --- energy storage systems --- short term conditions --- cluster analysis (CA) --- nonlinear loads --- harmonics, cancellation, and attenuation of harmonics --- waveform distortion --- THDi --- low-voltage networks --- optimization techniques --- different batteries --- off-grid microgrid --- integrated renewable energy system --- cluster analysis --- K-means --- agglomerative --- ANFIS --- fuzzy logic --- induction generator --- MPPT --- neural network --- renewable energy --- variable speed WECS --- wind energy conversion system --- wind energy --- frequency estimation --- spectrum interpolation --- power network disturbances --- COVID-19 --- time-varying reproduction number --- social distancing --- load profile --- demographic characteristic --- household energy consumption --- demand-side management --- energy management --- time series --- Hidden Markov Model --- short-term forecast --- sparse signal decomposition --- supervised dictionary learning --- dictionary impulsion --- singular value decomposition --- discrete cosine transform --- discrete Haar transform --- discrete wavelet transform --- transient stability assessment --- home energy management --- binary-coded genetic algorithms --- optimal power scheduling --- demand response --- Data Injection Attack --- machine learning --- critical infrastructure --- smart grid --- water treatment plant --- power system --- n/a --- 2-150 kHz