Industrial Loads Research Articles

Rotating Electrical Machines: Types, Applications and Recent Advances

The Rotating Electrical Machines (REMs) are classified into Motors and Generators. They powered the industrial, domestic and commercial loads. Because of their importance. This paper discussed different types of REMs, their applications and recent advances. REMs are applied in Teaching, Domestic, Mechatronics, Motorcycle, Three-wheelers, Electric Vehicle, Healthcare, Flywheel Energy Storage and Wind Energy Conversion Systems. It periscopes the advances of REMs in design, Fault diagnostic, control and condition monitoring. Its significance is to shed light on some advances made in REM.

A Modification of Magnetic Adaptive Testing: Progressive Method for Nondestructive Inspection of Microstructural Changes in Ferromagnetic Constructional Materials

Magnetic adaptive testing (MAT), thanks to its relative simplicity from the point of view of both hardware and software, appears to be very promising for non-destructive analysis of various ferromagnetic constructional materials used in many industrial applications. In order to make the inspection of tested objects faster, even more straightforward and the processing of data easier, we concentrated our effort both to improve the experimental procedure, based on specific way of the measurement of magnetization curves at piece-wise linear (triangular) exciting field with a constant field rate of change (i.e., slope) as well as to create a universal software tool for MAT data analysis. On contrary to the original implementation of MAT, the hysteresis loops are measured with decreasing maximum field values, starting at sample saturation region; therefore, time consuming sample demagnetization can be skipped completely. In addition, an advanced tool for the processing of experimentally obtained magnetization curves is presented. Using this application allows to find proper non-traditional magnetic parameters (e.g., the differential permeability) being the most sensitive to various types of industrial load (e.g., thermal, mechanical and/or neutron irradiation) and, at the same time, sufficiently correlated with other, traditional magnetic as well as non-magnetic parameters used routinely for the assessment of possible structural changes associated with applied, often long-term, load even on a microscopic scale, prior to any damage manifests on a macroscopic, visible level. The software capabilities were demonstrated on the data representing the material, whose response to acting thermal load is being difficult to analyze, since the dependence of observed parameters (differential permeability) upon defined artificial ageing was rather complicated. Nevertheless, the sensitivity of differential permeability to such a load was found being more than 8 times larger than in case of traditional hysteretic parameter, namely the remanent flux density while the correlation between them was high.

Future of sustainable renewable-based energy systems in smart city industry: Interruptible load scheduling perspective

As the industrial and household loads continue to grow in smart cities, power supply and usage balance is getting more challenging. The increase in supply-side energy production is one method of alleviating peak demand proposed by a few investigations. As peak loads last for a short time, additional installations might be required. In addition, facilities that store energy can be used to reduce peak demand. As a consequence of the previous investigation, interruptible load regulation was not taken into account in optimizing system performance. The regulation of interruptible loads can greatly reduce system peak loads in smart city and within the concept of microgrids (MGs). The study proposes an interruptible load scheduling (ILS) scheme that considers consumer subsidy rates with the goal of reducing the peak load of the MG and its operating prices. To this end, a digital twin is developed in which the consumer interruption load time and minimal per-day load decrease restrictions have been completely met in the scheme. The ILS model is solved using a combination of the Min-Conflict Algorithm and the Gray Wolf Optimization Algorithm. Furthermore, simulations demonstrate the efficiency of the suggested algorithm and the show the substantial benefits of the scheme in reducing peak loads and operating prices in smart cities.

An Industrial Load Classification Method Based on a Two-Stage Feature Selection Strategy and an Improved MPA-KELM Classifier: A Chinese Cement Plant Case

Accurately identifying industrial loads helps to accelerate the construction of new power systems and is crucial to today’s smart grid development. Therefore, this paper proposes an industrial load classification method based on two-stage feature selection combined with an improved marine predator algorithm (IMPA)-optimized kernel extreme learning machine (KELM). First, the time- and frequency-domain features of electrical equipment (active and reactive power) are extracted from the power data after data cleaning, and the initial feature pool is established. Next, a two-stage feature selection algorithm is proposed to generate the smallest features, leading to superior classification accuracy. In the initial selection phase, each feature weight is calculated using ReliefF technology, and the features with smaller weights are removed to obtain the candidate feature set. In the reselection stage, the k-nearest neighbor classifier (KNN) based on the MPA is designed to obtain the superior combination of features from the candidate feature set concerning the classification accuracy and the number of feature inputs. Third, the IMPA-KELM classifier is developed as a load identification model. The MPA improvement strategy includes self-mapping to generate chaotic sequence initialization and boundary mutation operations. Compared with the MPA, IMPA has a faster convergence speed and more robust global search capability. In this paper, actual data from the cement industry within China are used as a research case. The experimental results show that after two-stage feature selection, the initial feature set reduces the feature dimensionality from 58 dimensions to 3 dimensions, which is 5.17% of the original. In addition, the proposed IMPA-KELM has the highest overall recognition accuracy of 93.39% compared to the other models. The effectiveness and feasibility of the proposed method are demonstrated.

Optimal dynamic multi-microgrid structuring for improving distribution system resiliency considering time-varying voltage-dependent load models

This paper presents an innovative approach for enhancing distribution network resiliency against severe weather-related faults. Given the importance of loads in the resiliency concept, an exact and realistic load model is developed along with the consumption pattern and voltage-dependency of each load type. By defining two technical and economic objective functions, a unique method is used to combine and optimize the objective functions through Imperialist Competitive Algorithm (ICA). Two main strategies, namely the dynamic arrangement of microgrid regions via hourly network reconfiguration as well as grid voltage control through Automatic Voltage Regulation (AVR) of distributed generation facilities, are utilized for increased load restoration. The effect of each of these strategies on grid resiliency is evaluated along with the overall approach efficiency based on simulations on a sample 33-bus network comprising renewable and non-renewable resources and commercial, residential and industrial loads. Using this approach, the strategy to encounter each particular type of failure is obtained.

Quasi-dynamic hosting capacity in radial distribution feeder

The target of massive installation of renewable energy is the focus of this research. Several industrial sectors continue to install photovoltaic rooftop to support green energy. One of the main objectives of this research is to see the maximum impact of installing a photovoltaic rooftop at 1 point of customer and spread capacity for each customer. This research uses a radial distribution network system that closely resembles the distribution network in Indonesia, where the load profile considers the load characteristics of industrial, commercial, and residential loads. This study uses the line equation theorem method to calculate the voltage rises by considering two current measurement points: the current at the end and the current at the base. The obtained voltage rise is then accumulated to be summed up with the customer afterward. The results are obtained by considering three scenarios: 1) voltage limits, 2) voltage limits and line loading, and 3) voltage limits, thermal, and harmonics in accordance with regulations. The obtained results are closely aligned with the simulations performed on the hosting capacity software such as DIgSILENT.

Comparative analysis of control techniques using a PV-based SAPF integrated grid system to enhance power quality

The deterioration of power quality due to the increased adoption of Power Electronic Switching Devices is a major concern for electrical engineers. In addition to industrial loads, household loads cause numerous disturbances on the utility side, such as sags, ripples, flickers, and harmonics. It is possible to create power quality problems in a system or to have them arise due to a faulty consumer. This paper implements a renewable energy system with a Shunt Active Power Filter (SAPF) that injects PV energy into a power grid in the presence of nonlinear loads and under various load conditions to maintain power quality and improve power grid capacity. The Perturb and Observe (P&O) algorithm adjusts boost converter duty cycles to track the Maximum Power Point (MPP). The boost converter output is connected to the grid via a SAPF. The effectiveness of SAPF in reducing total harmonic distortion has been investigated using various reference current generation strategies of control methods and improved maximum power factor. The voltage source inverter switches are controlled through hysteresis current control. Moreover, this paper explains how to regulate the dc link capacitor of a three-phase SAPF with PI, fuzzy, and Adaptive Fuzzy Neuro Inference System (ANFIS) controllers to enhance power quality and compensate for reactive power and harmonic current caused by nonlinear loads. This system is developed under balanced and unbalanced nonlinear loads in MATLAB-Simulink. Furthermore, SRF-based reference current generation techniques with ANFIS controller to achieve better performance tested for control performance in proto-type hardware model to validate the proposed performance of this system.

POWER QUALITY AND RELIABILITY ENHANCEMENT BY DSTATCOM

Over the recent period of time, power quality problems have been marked as a buzzing issue with regards to promote delicate or miniature power electronic devices where Voltage sag problem is the most frequently occurring as well as detrimental power quality problems. Thus, the purpose of this study is to assuage voltage sag problem using proposed Distribution Static Synchronous Compensator (D-STATCOM). Utility distribution networks, sensitive industrial loads and critical commercial operations suffer from various types of outages and service interruptions which can cost significant financial losses. In developing countries such as India, where the power frequency and many such other determinants of power quality are themselves a serious question. The DSTATCOM is a most effective device which is based on the VSC principle. In this paper DSTATCOM, under different faults is analyzed. The modeling and simulation of the proposed shunt compensator was implemented in MATLAB Simulink environment. Simulation results showed that the proposed shunt compensator was efficient in mitigating voltage sags power system. Keywords: Power Quality, Distribution Static Synchronous Compensator (DSTATCOM), Voltage Source Converter (VSC), MATLAB.

Reviewing flexibility in industrial electrification: Focusing on green ammonia and steel in the United States

The renewable energy transition in the power sector involves a paradigm shift for flexibility. Flexible demand is more attainable than before, while supply flexibility faces new constraints due to the increased share of variable renewable resources. Increased demand flexibility can allow less use of peaking power plants and avoid need for additional capacity. Industrial flexibility could be especially interesting in this regard, as industrial customers are larger on average than other customers and have typically provided the largest share of demand response in the United States. We consider industrial demand, studying characteristics of flexible industrial loads. We then examine the dynamics of change occurring around industrial load flexibility by focusing on two case studies: green ammonia and steel production via electric arc furnaces. Electric arc furnace steel production is an important component of current demand response programs, whereas green ammonia and green fuels offer new paradigms for flexibility. We analyze the structure and functions of the technological innovation systems of load flexibility in those two industries via interviews with twenty-two stakeholders. We conclude that in the United States these technological innovation systems are not well-functioning for industry in general or for steel, but do seem to be present for green ammonia. Additionally, explicit connections between scope two greenhouse gas emissions (those from purchased energy) and flexibility are lacking, and likewise industry stakeholders do not appear to make a connection between decarbonization and load flexibility, thus flexible demand is not viewed as a tool in industrial decarbonization.

Probabilistic integrated flexible regions of multi-energy industrial parks: Conceptualization and characterization

Multi-energy industrial parks, composed of the district energy supply system and terminal industrial loads, are dominant energy consumers with over 50% occupation of total energy consumption. Considering the energy conversion in the district energy supply system and adjustment of production subtasks in terminal industrial loads, the industrial parks could provide considerable flexibility for utility multi-energy systems to deal with contingencies. However, industrial parks may have multiple uncertainties originating from the production subtasks' variability, distributed renewable energy's fluctuation, and random failures of equipment. These uncertainties could make the flexibility assessment of industrial parks unreliable, which may further exacerbate utility system risks. To address the problem, this paper innovatively proposes the concept of probabilistic integrated flexible regions and corresponding characterization approaches, which can effectively describe the credible multi-energy adjustment ability of industrial parks. First, the energy-material flows in the industrial park are modeled considering multiple uncertainties while subject to constraints such as operating limits of equipment, energy and material balancing, and production targets. Then, the modeling and calculation methods of probabilistic integrated flexible regions are given. These regions are labeled with certain probability requirements and visualized in the input multi-energy space. Using a real-world test system that produces air conditioning equipment in China, the results show that the proposed model can effectively reflect the probability characteristics of the integrated flexibility under multiple uncertainties. For this test system, the area of a highly credible integrated flexibility, represented by the probabilistic integrated flexible region with 99% probability, is about half of that obtained by the deterministic model. That is, half of the integrated flexibility evaluated by the deterministic method in the test system may not be practically utilized considering the impacts of uncertainties. Therefore, the proposed method could provide a credible and comprehensive evaluation of the multi-energy industrial park's flexibility considering multiple uncertainties.

Decreasing surface chlorophyll in the tropical ocean as an indicator of anthropogenic greenhouse effect during 1998–2020

Abstract Available satellite data reveal a decreasing trend in surface chlorophyll (SChl) over the entire tropical ocean until 2020. Where contributions by internal variability and external forcing remain unclear. Here, state-of-the-art climate model simulations are analyzed to show that external forcing significantly contributes to the decreasing SChl trend. In contrast, internal variability plays a weak or even offsetting role. As for the underlying processes, anthropogenic greenhouse emissions lead to a remarkable reduction in SChl over the tropical oceans, whereas industrial aerosol load facilitates a considerable increase in SChl in the western tropical Pacific. In addition, the negative phase of the interdecadal Pacific variability during 1998–2020 contributes to an increase in SChl, while the impact from the Atlantic multidecadal variability is relatively weak in facilitating a decrease in SChl. Overall, these results imply that the impact of anthropogenic forcing has emerged as indicated in the tropical marine ecosystem.

Optimal sliding mode control for frequency stabilization of hybrid renewable energy systems

AbstractThe constant changes in the load power lead permanently to a power mismatch between the power generation and the power consumption. So, the system frequency due to the power imbalance deviates from the nominal value. Consequently, a control loop should be implemented to stabilize the system frequency whenever a load change occurs. This paper presents a new super‐twisting sliding mode control methodology for obtaining an optimal frequency performance in a multi‐pool system. The paper presents a three‐pool system for frequency deviation problems using an optimal gain Super Twisting Sliding Mode Controller (STSMC), which regulates the frequency change and the line power change to zero in a minimal time. The extent of the excellence of the study proposed is evaluated by comparing it with three Benchmark classical controllers, which are the Tilt‐Integral‐Derivative (TID), Proportional‐Integral‐Derivative (PID), and Fractional‐Order PID (FOPID). The parameters of the four controllers are determined by a proposed physical meta‐heuristic optimization technique called Transient Search Optimizer (TSO), inspired by the dynamic behaviour in the electrical circuits comprising storage elements such as capacitors and inductors during the switching actions. The system simulation is performed, and the STSMC proved overwhelming superiority over other controllers, as it deals better with the transient interval of the system response. Renewable Energy sources (RESs) like photovoltaic and wind energy systems are established, and the STSMC is tested with industrial and residential load models. Finally, energy storage devices such as batteries and superconducting magnetic energy storage are implemented to suppress the rapid fluctuations in the system response, and it succeeded in doing that as the system oscillations are greatly damped with the energy storage devices.

Performance Analysis of Harmonic-Reduced Modified PUC Multi-Level Inverter Based on an MPC Algorithm

Renewable and distributed energy generation includes wind turbines, fuel cells, solar cells, and batteries. These distributed energy sources need special power converters in order to connect them to the grid and make the generated power available for public use. Solar energy is the most readily available energy source; hence, if utilized properly, it can power up both domestic and industrial loads. Solar cells produce DC power, and this should be converted to an AC source with the help of inverters. A multi-level inverter for an application is selected based on a trade-off between cost, complexity, losses, and total harmonic distortion (THD). A packed U-cell (PUC) topology is composed of power switches and voltage sources connected in a series-parallel fashion. This basic unit can be extended to a greater number of output voltage levels. The significance of this design is the reduced use of power switches, gate drivers, protection circuits, and capacitors. The converter presented in this paper is a 31-level topology switched by a variable switching frequency-based model predictive controller that helps in achieving optimal output with reduced harmonics to a great extent. The gate driver circuit is also optimized in terms of power consumption and size complexity. A comparison of the 9-level and the 31-level PUC inverters is carried out to study the impact of the number of levels on the total harmonic distortion. The simulation results depict that the total harmonic distortion (THD) for a nominal modulation index of 0.8 is 11.54% and 3.27% for the 9-level multi-level inverter (MLI) and the 31-level modified packed U-cell multi-level inverter (MPUC-MLI), respectively. The reduction in THD is attributed to the increased number of steps in the output when using the model predictive controller.

Optical Developments in Concentrator Photovoltaic Systems—A Review

Energy needs have increased with global advancements and industrial revolutions. Electrical energy utilization shares a huge amount of energy with residential and industrial loads. Traditional energy resources are expensive and polluting, producing greenhouse gasses, which is a major environmental concern. Solar energy utilization is a cost-effective, sustainable, and green solution to meet the ongoing energy demand. Concentrator photovoltaic (CPV) systems are developed for energy conversion by providing high efficiency using multi-junction solar cells. This paper provides an overview of the recent optical developments in CPV systems and emerging technologies that are likely to shape the future of CPV systems. The objective of this article is to provide an overview of the issues that need to be resolved to improve the geometrical concentration, acceptance angle, uniformity, and optical efficiency of CPV systems. A comprehensive comparison is also presented on different types of solar concentrators. In addition, future research directions are presented to facilitate the continued growth and success of CPV systems. Furthermore, this review article gives an up-to-date and widespread overview of CPV technology, assesses its potential for various applications, and distinguishes the challenges and opportunities for future research and development.

Optimal Dispatch Strategy for Virtual Power Plants with Adjustable Capacity Assessment of High-Energy-Consuming Industrial Loads Participating in Ancillary Service Markets

Amid the context of a sustainable development strategy, there is a growing interest in renewable energy as an alternative to traditional energy sources. However, as the penetration rate of clean energy gradually increases, its inherent features, such as randomness and uncertainty, have led to a surging demand for flexibility and regulation in power systems, highlighting the need to enhance the flexibility of power systems in multiple dimensions. This paper proposes a method for evaluating the adjustable power capacity of a virtual power plant (VPP), which considers the high-energy-consuming industrial load in the day-ahead to real-time stages and establishes an optimization scheduling model for auxiliary service markets based on this method. Firstly, within the day-ahead phase, the VPP is categorized and modeled based on its level of load flexibility regulation. The assessable capacity is then evaluated to establish the adjustable power range of the VPP, and the capacity of the VPP is subsequently reported. Secondly, the adjustable loads inside the VPP are ranked using the performance indicator evaluation method to obtain the adjustment order of internal resources. Finally, on the real-time scale, an optimization scheduling model to minimize the net operating cost of the VPP is established based on real-time peak-shaving and frequency regulation instructions from the auxiliary service market and solved using the CPLEX solver. The case study results show that the proposed method effectively reduces the net operating cost of the VPP and improves the stability of its participation in the auxiliary service market, which verifies the effectiveness of the proposed method.

Modeling and Operation of Series-Parallel Resonant Load in Industrial RF Dielectric Heating Application

Radio Frequency (RF) based heating application is used in multiple industries like drying cross-laminated woods, food and packaging, melting silicon etc. These industrial RF heating plants operate at frequencies of 6.78 MHz, 13.56 MHz, or 27.12 MHz (ISM band). Historically, the high frequency requirement has forced the industry to rely on vacuum tube based RF generator, which are only 60% efficient. Modern semiconductors like wide bandgap (WBG) devices have achieved the high frequency operation that is required to replace the inefficient vacuum tubes. However, a comprehensive study of load network for an industrial RF heating plant has not been undertaken such that an existing plant can be retrofitted with semiconductor based turn-key solution. In this paper the authors present design equations for an industrial RF dielectric heating load structure and suggest necessary modifications required to replace vacuum tube RF generator with voltage source converter (VSC). A new figure of merit (FOM) is defined that impacts the load structure efficiency. Finally, a small-scale prototype load is manufactured as a case study. The prototype demonstrated an output power of 750 W at 6.76 MHz with an overall efficiency of 90% from DC input to RF load which can not be achieved with a vacuum tube technology.

A novel unified constraint parameter based on plastic strain energy

Constraint, defined as the resistance of a component against plastic deformation, has a significant effect on the materials’ fracture toughness. A loss of constraint can mean a higher fracture toughness. The standard test geometries such as C(T) specimens are designed to have high levels of constraint thus ensuring that a lower bound toughness is measured. To get a more accurate estimate of the integrity of low constraint components, it is necessary to study an approach of quantifying the constraint level and evaluating it on failure load. Currently, widely accepted crack-tip constraint parameters are validated using specimens with uniaxial loading such as C(T) and SEN(B). However, a large number of industrial equipment experiences loading modes other than uniaxial. The multi-axiality has been considered an important effect in various standards. Load multiaxiality can cause a change in constraint thus fracture toughness. In this paper, a novel unified constraint parameter λ based on the plastic strain energy was proposed. A series of uniaxial and biaxial bending experiments made of BS1501-224 28B steel were conducted at −160℃. A large amount of previous fracture test data of C(T) and SEN(B) specimens were used to support the study. The elastic–plastic parameter Q and the unified parameters φ, Ad* were calculated as a comparison. It is shown that the newly suggested parameter λ not only can characterize the constraint of C(T) and SEN(B) specimens but is effective in characterizing the multi-axiality effect while traditional constraint parameters fail to do so.

Total Harmonics Distortion Prediction at the Point of Common Coupling of industrial load with the grid using Artificial Neural Network

Power quality challenges have generated a lot of disputes between utilities, customers, network operators, and equipment manufacturers around the world as regards the share of responsibility for power quality solutions, this results in different levels of financial and technical losses for both the network operators and the customers. One of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling (PCC). In order to quantify the harmonics contribution at the PCC by industrial consumers, this paper presents three-phase total harmonics distortion of current (THDi) prediction model at the PCC. The proposed artificial neural network (ANN) models use a multilayer perceptron neural network (MLPN) to predict three-phase total harmonic distortion. The input parameter used in the models is easily measured with basic power meters. The model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five (5) different steel manufacturing plants. Eight (8) different models were designed, trained, validated, and tested with different combinations of input parameters, number of hidden layers, and number of neurons in the hidden layer. The results show that the model with two hidden layers which uses four major power parameters (Current, apparent power, reactive and active power) as input parameters in the training model had the best performance with a 95.5% coefficient of correlation between the measured THDi and the predicted THDi.

Physics-Informed Time-Aware Neural Networks for Industrial Nonintrusive Load Monitoring

Nonintrusive load monitoring enables the situational awareness of appliance-level energy consumption without installing appliance-specific sensors. It has been researched for over thirty years, with deep learning methods being the state-of-the-art solutions. However, current works mainly focus on the residential scenario, and industrial load disaggregation as a more challenging problem from the appliance type perspective is much less investigated. Nevertheless, industrial loads play an important role in energy savings and climate change mitigation and adaptation. Therefore, this paper focuses on the industrial nonintrusive load monitoring problem and proposes a physics-informed time-aware neural network method for it. Herein, multiple features of industrial loads are considered, and the physics relationship among them is leveraged to improve the learning process explicitly. In addition, a two-dimensional convolutional layer is further proposed to encode the timestamp for feature enhancement. Experiments on real-world industrial data from ten appliances will verify the effectiveness of the proposed method.

Effect of PLTS Power Factor Settings on Power Losses and Voltage Conditions in 20 kV Medium Voltage Networks

As technology develops, the rapid increase in PV capacity will affect the grid. This makes it important to determine the PV limit that can be injected into the grid defined as the PV Hosting Capacity (PVHC). In this paper, the author evaluates and analyses the impact of PV connections on medium voltage distribution lines (20kv) using ETAP 19.01 simulation. A medium voltage Grid with a capacity of 30MVA, has an industrial load varies from 650 kVA to 25 kVA with the total load capacity of 2225 KVA. The loads are supplied from two sources, grid and PV system. The simulation results state that the best of grid performance which provide the best losses and voltage condition when connecting 100% capacity of PV to the existing grid network is by setting the inverter power factor to 0.9. This setting will provide lowest power losses of 0,7 kW and improve the voltage value.