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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 --- n/a --- 2-150 kHz

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

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This book presents an interesting sample of the latest advances in optimization techniques applied to electrical power engineering. It covers a variety of topics from various fields, ranging from classical optimization such as Linear and Nonlinear Programming and Integer and Mixed-Integer Programming to the most modern methods based on bio-inspired metaheuristics. The featured papers invite readers to delve further into emerging optimization techniques and their real application to case studies such as conventional and renewable energy generation, distributed generation, transport and distribution of electrical energy, electrical machines and power electronics, network optimization, intelligent systems, advances in electric mobility, etc.

n/a --- Stackelberg game --- MILP --- optimal congestion threshold --- magnetic field mitigation --- simulation --- multi-objective particle swarm optimization --- virtual power plant --- internal defect --- day-ahead load forecasting --- neural network --- modular predictor --- multi-objective particle swarm optimization algorithm --- stochastic optimization --- dragonfly algorithm --- unit commitment --- metaheuristic --- multi-population method (MP) --- optimization --- tabu search --- considerable decomposition --- loss minimization --- active distribution system --- islanded microgrid --- dynamic solving framework --- feature selection --- electric energy costs --- power factor compensation --- dependability --- interactive load --- overhead --- energy internet --- evolutionary computation --- wind power --- developed grew wolf optimizer --- underground --- ETAP --- fuzzy algorithm --- electric vehicles --- Schwarz’s equation --- evolutionary algorithms --- electric power contracts --- congestion management --- optimizing-scenarios method --- building energy management system --- particle encoding method --- ringdown detection --- HOMER software --- DC optimal power flow --- prosumer --- constrained parameter estimation --- distributed generations (DGs) --- strong track filter (STF) --- transient stability --- calibration --- cost minimization --- radiance --- decentralized and collaborative optimization --- hybrid renewable energy system --- renewable energy sources --- rural electrification --- distribution network reconfiguration --- interval variables --- optimization methods --- particle swarm optimization --- hierarchical scheduling --- micro grid --- AC/DC hybrid active distribution --- consensus --- artificial bee colony --- CCHP system --- data center --- support vector machine --- affinity propagation clustering --- extended Kalman filter --- affine arithmetic --- linear discriminant analysis (LDA) --- current margins --- heterogeneous networks --- Cameroon --- hybrid method --- distributed heat-electricity energy management --- discrete wind driven optimization --- fitness function --- cross-entropy --- GenOpt --- wind energy --- demand uncertainty --- UC --- off-design performance --- genetic algorithm --- energy storage --- the biomimetic membrane computing --- power system optimization --- electric vehicle --- power architectures --- economic load dispatch problem (ELD) --- runner-root algorithm (RRA) --- Cable joint --- battery energy storage system --- load curtailment --- integration assessment --- power system unit commitment --- artificial lighting --- power flow --- hybrid membrane computing --- two-point estimation method --- low-voltage networks --- demand bidding --- non-sinusoidal circuits --- energy flow model --- power transfer distribution factors --- sustainability --- HVAC system --- voltage deviation --- street light points --- radial basis function --- energy storage system --- charging/discharging --- power systems --- intelligent scatter search --- MV/LV substation --- optimal power flow --- stochastic state estimation --- eight searching sub-regions --- chaos optimization algorithm (COA) --- mutual information theory --- inter-turn shorted-circuit fault (ISCF) --- C&I particle swarm optimization --- multiobjective optimization --- passive shielding --- sub-Saharan Africa --- micro-phasor measurement unit --- geometric algebra --- bio-inspired algorithms --- adaptive consensus algorithm --- energy management --- PCS efficiency --- multi-stakeholders --- generalized generation distribution factors --- the genetic algorithm based P system --- JAYA algorithm --- thermal probability density --- power optimization --- pumped-hydro energy storage --- smart grid --- two-stage feature selection --- piecewise linear techniques --- photovoltaic --- SOCP relaxations --- switched reluctance machine (SRM) --- optimal reactive power dispatch --- optimal operation --- controllable response --- off-grid --- active shielding --- transformer-fault diagnosis --- IEEE Std. 80-2000 --- principal component analysis --- demand response --- Schwarz's equation