Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

Open-source and free tools are readily available to the public to process data and assist producers in making management decisions related to agricultural landscapes. On-the-go soil sensors are being used as a proxy to develop digital soil maps because of the data they can collect and their ability to cover a large area quickly. Machine learning, a subcomponent of artificial intelligence, makes predictions from data. Intermixing open-source tools, on-the-go sensor technologies, and machine learning may improve Mississippi soil mapping and crop production. This study aimed to evaluate machine learning for mapping apparent soil electrical conductivity (ECa) collected with an on-the-go sensor system at two sites (i.e., MF2, MF9) on a research farm in Mississippi. Machine learning tools (support vector machine) incorporated in Smart-Map, an open-source application, were used to evaluate the sites and derive the apparent electrical conductivity maps. Autocorrelation of the shallow (ECas) and deep (ECad) readings was statistically significant at both locations (Moran’s I, p 0.001);however, the spatial correlation was greater at MF2. According to the leave-one-out cross-validation results, the best models were developed for ECas versus ECad. Spatial patterns were observed for the ECas and ECad readings in both fields. The patterns observed for the ECad readings were more distinct than the ECas measurements. The research results indicated that machine learning was valuable for deriving apparent electrical conductivity maps in two Mississippi fields. Location and depth played a role in the machine learner’s ability to develop maps.

Role of Advanced Materials in Electrical Machines

There has been a revived and growing role for electrical machines and drives across a wide range of applications.Such applications include,hybrid/electrical traction applications,aerospace applications,and renewable energy.All these applications present different set of requirements and challenges.The common trend is that there is a need for higher-performance electrical machines in terms of higher power/torque density,and higher efficiency while keeping cost under control.There has been a lot of work done around coming up with novel machine topologies,optimizing more conventional topologies as well as improved thermal management schemes.Like many other areas of engineering/research,advanced materials can play a key role in opening up the design space for electrical machines leading to a step improvement in their performance.This paper will present an overview of some of the key advanced materials that are either recently developed or are currently under development and their potential impact on electrical machines.

Hybrid Excited Permanent Magnet Machines for Electric and Hybrid Electric Vehicles

This paper reviews various hybrid excited(HE)machines from the perspective of location of PM and DC excitation,series/parallel connection of PM and DC excited magnetic fields,and 2D/3D magnetic fields,respectively.The advantages as well as drawbacks of each category are analyzed.Since an additional control degree,i.e.DC excitation,is introduced in the HE machine,the flux weakening control strategies are more complex.The flux weakening performance as well as efficiency are compared with different control strategies.Then,the potential to mitigate the risk of uncontrolled overvoltage fault at high speed operation is highlighted by controlling the field excitation.Since additional DC coils are usually required for HE machines compared with pure PM excitation,the spatial confliction inevitably results in electromagnetic performance reduction.Finally,the technique to integrate the field and armature windings with open-winding drive circuit is introduced,and novel HE machines without a DC coil are summarized.

Numerical-Analytical Method for Magnetic Field Computation in Rotational Electric Machines

A numerical-analytical method is applied for the two-dimensional magnetic field computation in rotational electric machines in this paper. The analytical expressions for air gap magnetic field are derived. The pole pairs in the expressions are taken into account so that the solution region can be reduced within one periodic range. The numerical and analytical magnetic field equations are linked with equal vector magnetic potential boundary conditions. The magnetic field of a brushless permanent magnet machine is computed by the proposed method. The result is compared to that obtained by finite element method so as to validate the correction of the method.

Hardware,Machines & Electrical Equipment From Wenzhou

Scissors and locks are famous products from Wenzhou. Now scissors from the city including multi-purpose, tailoring, tourist and special-purpose ones and exquisite fruit and artistic knives have entered the world market. At the

The Principle and Architecture of a Hybrid System of a Neural Network and an Expert System in Intelligent CAD of Electrical Machines

Using expert systems in intelligent CAD of electrical machines have limitations such as knowledge acquisition bottlenecks and matching conflict, combinatorial explosion, and endless recursion in the reasoning process. This paper discusses the principle of a hybrid system of a neural network and an expert system (HNNES), i.e., knowledge representation, reasoning mechanism, and knowledge acquisition based on neural networks. An architecture of HNNES is presented in consideration of the feature of the design of electrical machines.

Optimization of micro milling electrical discharge machining of Inconel 718 by Grey-Taguchi method

The optimization of micro milling electrical discharge machining（EDM） process parameters of Inconel 718 alloy to achieve multiple performance characteristics such as low electrode wear,high material removal rate and low working gap was investigated by the Grey-Taguchi method.The influences of peak current,pulse on-time,pulse off-time and spark gap on electrode wear（EW）,material removal rate（MRR） and working gap（WG） in the micro milling electrical discharge machining of Inconel 718 were analyzed.The experimental results show that the electrode wear decreases from 5.6×10-9 to 5.2×10-9 mm3/min,the material removal rate increases from 0.47×10-8 to 1.68×10-8 mm3/min,and the working gap decreases from 1.27 to 1.19 μm under optimal micro milling electrical discharge machining process parameters.Hence,it is clearly shown that multiple performance characteristics can be improved by using the Grey-Taguchi method.

Partial Discharge Measurement of OCP-Tapes in Rotating Electrical Machines

Nowadays, PD （partial discharge） measurements are a crucial part of the preventive maintenance of electrical equipment within high voltage engineering. Especially for electrical machines, both the supplier and the user are interested in the results of PD measurements. However, PDs hardly represent the cause of the failure, more likely they are claimed as the outcome of a failure. This paper deals with the insulation of a 6 kV electrical machine, whereas PD measurements were carried out at a single stator from wound coils. During manufacturing, these coils were equipped with different materials for the OCP （outer corona protection）. Using different PD measurement systems and different bandwidths, investigations of the PD behavior of the coils were carried out. Additionally, the surface resistivity of the corona protection was determined. As a result, conclusions for the correlations between the resistance of the OCP as well as the PD behavior are stated. Furthermore, the influence of using different measurement systems, different measuring circuits, and different bandwidths is shown.

Evaluation and Prediction of Groundwater Quality in the Municipality of Za-Kpota (South Benin) Using Machine Learning and Remote Sensing

Accessing drinking water is a global issue. This study aims to contribute to the assessment of groundwater quality in the municipality of Za-Kpota (southern Benin) using remote sensing and Machine Learning. The methodological approach used consisted in linking groundwater physico-chemical parameter data collected in the field and in the laboratory using AFNOR 1994 standardized methods to satellite data (Landsat) in order to sketch out a groundwater quality prediction model. The data was processed using QGis (Semi-Automatic Plugin: SCP) and Python (Jupyter Netebook: Prediction) softwares. The results of water analysis from the sampled wells and boreholes indicated that most of the water is acidic (pH varying between 5.59 and 7.83). The water was moderately mineralized, with conductivity values of less than 1500 μs/cm overall (59 µS/cm to 1344 µS/cm), with high concentrations of nitrates and phosphates in places. The dynamics of groundwater quality in the municipality of Za-Kpota between 2008 and 2022 are also marked by a regression in land use units (a regression in vegetation and marshland formation in favor of built-up areas, bare soil, crops and fallow land) revealed by the diachronic analysis of satellite images from 2008, 2013, 2018 and 2022. Surveys of local residents revealed the use of herbicides and pesticides in agricultural fields, which are the main drivers contributing to the groundwater quality deterioration observed in the study area. Field surveys revealed the use of herbicides and pesticides in agricultural fields, which are factors contributing to the deterioration in groundwater quality observed in the study area. The results of the groundwater quality prediction models (ANN, RF and LR) developed led to the conclusion that the model based on Artificial Neural Networks (ANN: R2 = 0.97 and RMSE = 0) is the best for groundwater quality changes modelling in the Za-Kpota municipality.

Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric vehicles.The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge.The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue.Nevertheless,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of service.Modelling the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging infrastructure.It is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic patterns.When vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering system.Initially,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature selection.Subsequently,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic congestion.Based on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is provided.The results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algorithm(GA),to the theoretical median that would be expected that there is no difference.

Conformal Human–Machine Integration Using Highly Bending‑Insensitive,Unpixelated,and Waterproof Epidermal Electronics Toward Metaverse

Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals,which is critical to the breakthrough development of metaverse.Interactive electronics face common dilemmas,which realize highprecision and stable touch detection but are rigid,bulky,and thick or achieve high flexibility to wear but lose precision.Here,we construct highly bending-insensitive,unpixelated,and waterproof epidermal interfaces(BUW epidermal interfaces)and demonstrate their interactive applications of conformal human–machine integration.The BUW epidermal interface based on the addressable electrical contact structure exhibits high-precision and stable touch detection,high flexibility,rapid response time,excellent stability,and versatile“cut-and-paste”character.Regardless of whether being flat or bent,the BUW epidermal interface can be conformally attached to the human skin for real-time,comfortable,and unrestrained interactions.This research provides promising insight into the functional composite and structural design strategies for developing epidermal electronics,which offers a new technology route and may further broaden human–machine interactions toward metaverse.

Statistical Description of Debris Particle Size Distribution in Electrical Discharge Machining

Gap debris as discharge product is closely related to machining process in electrical discharge machining（EDM）. A lot of recent researches have focused on the relationship among debris size, surfaces texture, remove rate, and machining stability. The study on statistical distribution of debris size contributes to the research, but it is still superficial currently. In order to obtain the distribution law of the debris particle size, laser particle size analyzer（LPSA） combined with scanning electron microscope（SEM） is used to analyze the EDM debris size. Firstly, the heating dried method is applied to obtain the debris particles. Secondly, the measuring range of LPSA is determined as 0.5–100 μm by SEM observation, and the frequency distribution histogram and the cumulative frequency distribution scattergram of debris size are obtained by using LPSA. Thirdly, according to the distribution characteristic of the frequency distribution histogram, the statistical distribution functions of lognormal, exponentially modified Gaussian（EMG）, Gamma and Weibull are chosen to achieve curve fitting of the histogram. At last, the distribute law of the debris size is obtained by fitting results. Experiments with different discharge parameters are carried out on an EDM machine designed by the authors themselves, and the machining conditions are tool electrode of red-copper material, workpiece of ANSI 1045 material and working fluid of de-ionized water. The experimental results indicate that the debris sizes of all experiment sample truly obey the Weibull distribution. The obtained distribution law is significantly important for all the models established based on the debris particle size.

Surface Modification Process by Electrical Discharge Machining with Ti Powder Green Compact Electrode

This paper describes a new method of surface modification by Electrical Discharge Machining (EDM). By using ordinary EDM machine tool and kerosene fluid, a hard ceramic layer can be created on the workpiece surface with Ti or other compressed powder electrode in a certain condition. This new revolutionary method is called Electrical Discharge Coating (EDC). The process of EDC begins with electrode wear during EDM,then a kind of hard carbide is created through the thermal and chemical reaction between the worn electrode material and the carbon particle decomposed from kerosene fluid under high temperature. The carbide is piled up on a workpiece quickly and becomes a hard layer of ceramic about 20 μm in several minutes. This paper studies the principle and process of EDC systemically by using Ti powder green compact electrode. In order to obtain a layer of compact ceramic film, it is very important to select proper electric pulse parameters, such as pulse width, pulse interval, peak current. Meantime, the electrode materials and its forming mode will effect the machining surface quality greatly. This paper presents a series of experiment results to study the EDC process by adopt different technology parameters. Experiments and analyses show that a compact TiC ceramic layer can be created on the surface of metal workpiece. The hardness of ceramic layer is more 3 times higher than the base body, and the hardness changes gradiently from surface to base body. The method will have a great future because many materials can be easily added to the electrode and then be coated on the workpiece surface. Gearing the parameters ceramic can be created with different thickness. The switch between deposition and removal process is carried out easily by changing the polarity, thus the gear to the thickness and shape of the composite ceramic layer is carried out easily. This kind of composite ceramic layer will be used to deal with the surface of the cutting tools or molds possibly, in order to lengthen their life. It also can be found wide application in the fields of surface repairing and strengthening of the ship or aircraft.

Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit

External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batteries under ESC conditions.Experiments were systematically performed under different battery initial state of charge and ambient temperatures.Based on the experimental results,we employed an extreme learming machine(ELM)-based thermal(ELMT)model to depict battery temperature behavior under ESC,where a lumped-state thermal model was used to replace the activation function of conventional ELMs.To demonstrate the effectiveness of the proposed model,wecompared the ELMT model with a multi-lumped-state thermal(MLT)model parameterized by thegenetic algorithm using the experimental data from various sets of battery cells.It is shown that the ELMT model can achieve higher computa-tional efficiency than the MLT model and better fitting and prediction accuracy,where the average root mean squared error(RMSE)of the fitting is 0.65℃ for the ELMT model and 3.95℃ for the MLT model,and the RMES of the prediction under new data set is 3.97℃ for the ELMT model and 6.11℃ for the MLT model.

Micro Electrical Discharge Machining Deposition in Air for Fabrication of Micro Spiral Structures

Micro electrical discharge machining（EDM） deposition process is a new micro machining method for fabrication of metal micro structures. In this process, the high level of tool electrode wear is used to achieve the metal material deposition. Up to now, the studies of micro EDM deposition process focused mainly on the researches of deposition process, namely the effects of discharge parameters in deposition process on the deposition rate or deposition quality. The research of the formation of micro structures with different discharge energy density still lacks. With proper conditions and only by the z-axis feeding in vertical direction, a novel shape of micro spiral structure can be deposited, with 0.11 mm in wire diameter, 0.20 mm in outside diameter, and 3.78 mm in height. Then some new deposition strategies including angular deposition and against the gravity deposition were also successful. In order to find the forming mechanism of the spiral structures, the numerical simulation of the transient temperature distribution on the discharge point was conducted by using the finite-element method（FEM）. The results show that there are two major factors lead to the forming of the spiral structures. One is the different material removal form of tool electrode according with the discharge energy density, the other is the influenced degree of the movement of the removed material particles in the discharge gap. The more the energy density in single discharge is, the smaller the mass of the removed material particles is, and the easier the movements of which will be changed to form an order tendency. The fine texture characteristics of the deposited micro spiral structures were analyzed by the energy spectrum analysis and the metallographic analysis. It shows that the components of the deposited material are almost the same as those of the tool electrode. Moreover the deposited material has the brass metallic luster in the longitudinal profile and has compact bonding with the base material. This research is useful to understand the micro-process of micro EDM deposition better and helpful to increase the controllability of the new EDM method for fabrication of micro structures.

Experimental Research on Effects of Process Parameters on Servo Scanning 3D Micro Electrical Discharge Machining

Servo scanning 3D micro electrical discharge machining (3D SSMEDM) is a novel and effective method in fabricating complex 3D micro structures with high aspect ratio on conducting materials. In 3D SSMEDM process, the axial wear of tool electrode can be compensated automatically by servo-keeping discharge gap, instead of the traditional methods that depend on experiential models or intermittent compensation. However, the effects of process parameters on 3D SSMEDM have not been reported up until now. In this study, the emphasis is laid on the effects of pulse duration, peak current, machining polarity, track style, track overlap, and scanning velocity on the 3D SSMEDM performances of machining efficiency, processing status, and surface accuracy. A series of experiments were carried out by machining a micro-rectangle cavity (900 μm×600 μm) on doped silicon. The experimental results were obtained as follows. Peak current plays a main role in machining efficiency and surface accuracy. Pulse duration affects obviously the stability of discharge state. The material removal rate of cathode processing is about 3/5 of that of anode processing. Compared with direction-parallel path, contour-parallel path is better in counteracting the lateral wear of tool electrode end. Scanning velocity should be selected moderately to avoid electric arc and short. Track overlap should be slightly less than the radius of tool electrode. In addition, a typical 3D micro structure of eye shape was machined based on the optimized process parameters. These results are beneficial to improve machining stability, accuracy, and efficiency in 3D SSMEDM.

Machining performance on hybrid process of abrasive jet machining and electrical discharge machining

To develop a hybrid process of abrasive jet machining (AJM) and electrical discharge machining (EDM),the effects of the hybrid process parameters on machining performance were comprehensively investigated to confirm the benefits of this hybrid process.The appropriate abrasives delivered by high speed gas media were incorporated with an EDM in gas system to construct the hybrid process of AJM and EDM,and then the high speed abrasives could impinge on the machined surface to remove the recast layer caused by EDM process to increase the efficiency of material removal and reduce the surface roughness.In this study,the benefits of the hybrid process were determined as the machining performance of hybrid process was compared with that of the EDM in gas system.The main process parameters were varied to explore their effects on material removal rate,surface roughness and surface integrities.The experimental results show that the hybrid process of AJM and EDM can enhance the machining efficiency and improve the surface quality.Consequently,the developed hybrid process can fit the requirements of modern manufacturing applications.

MICRO ELECTRICAL DISCHARGE MACHINING DEPOSITION IN AIR

A new deposition method is described using micro electrical discharge machining （EDM） to deposit tool electrode material on workpiece in air. The basic principles of micro electrical discharge deposition （EDD） are analyzed and the realized conditions are predicted. With an ordinary EDM shaping machine, brass as the electrode, high-speed steel as the workpiece, a lot of experiments are carried out on micro EDD systematically and thoroughly. The effects of major processing parameters, such as the discharge current, discharge duration, pulse interval and working medium, are obtained, As a result, a micro cylinder with 0.19 mm in diameter and 7.35 mm in height is deposited. By exchanging the polarities of the electrode and workpiece the micro cylinder can be removed selectively. So the reversible machining of deposition and removal is achieved, which breaks through the constraint of traditional EDM. Measurements show that the deposited material is compact and close to workpiece base, whose components depend on the tool electrode, material.

High Specific Power Electrical Machines: A System Perspective

There has been a growing need for high specific power electrical machines for a wide range of applications.These include hybrid/electric traction applications,and aerospace applications.A lot of work has been done to accomplish significantly higher specific power electrical machines especially for aerospace applications.Several machine topologies as well as thermal management schemes have been proposed.Even though there has been a few publications that provided an overview of high-speed and high specific power electrical machines[1-3],the goal of this paper is to provide a more comprehensive review of high specific power electrical machines with special focus on machines that have been built and tested and are considered the leading candidates defining the state-of-the art.Another key objective of this paper is to highlight the key“system-level”tradeoffs involved in pushing electrical machines to higher specific power.Focusing solely on the machine specific power can lead to a sub-optimal solution at the system-level.