Industrial Loads Research Articles

Load image inpainting: An improved U-Net based load missing data recovery method

Dealing with large percentage data missing is always a challenge for load data recovery. This paper, drawing on ideas from image inpainting, formulates load missing data recovery problem as load image inpainting and the improved U-Net is proposed to restore the load image. First, the 1-dimensional load data is constructed into 2-dimensional load image. Additionally, missing data presents as irregular black holes on the load image. Then, the improved U-Net which introduces residual network (ResNet) and convolutional block attention module (CBAM) is proposed to specifically restore the incomplete load image. Meanwhile, mean absolute error (MAE) and structural similarity (SSIM) are utilized to evaluate data recovery accuracy and load pattern recovery similarity respectively. Load missing data recovery results based on the actual industrial load dataset are presented to verify the effectiveness of the proposed method.

Sensor Effects in LCL-Type Grid-Connected Shunt Active Filters Control Using Higher-Order Sliding Mode Control Techniques.

The effects of measuring devices/sensors on improving the power quality (PQ) of electric networks are studied in this paper. In this context, improving the performance of an LCL-type grid connected to a three-phase three-wire shunt active filter (SAF) in the presence of voltage perturbations is studied. In order to ensure the high-quality performance of LCL-SAF in the presence of voltage perturbations, the robust continuous second-order sliding mode controller (2-SMC), including twisting and super-twisting controllers, and continuous higher-order sliding mode controller (C-HOSMC)-based approaches are employed. These controllers, whose outputs are processed by pulse-width modulation (PWM), allow minimization of the phase shift and prevent the generation of discontinuous chattering commands, which can severely damage the VSI components. Moreover, an integration of a generalized instantaneous power identification algorithm with an advanced phase locked loop (PLL) was proposed and experimentally tested to validate the effective performances of SAF under severe perturbations. Additionally, the studied approaches were tested via simulations taking into account a conventional nonlinear industrial load in a real textile factory environment, using measurements provided by power quality analyzers. Finally, the effects of the measuring devices, including the current and voltage sensors, on the accuracy and reliability of the SAF and, consequently, on the PQ of the electric power grid were studied via simulations and experimentally. The results of this study support the validity of the recently published patent.

Nonintrusive Load Identification for Industrial Users Integrated with LSQR and Sequential Leader Clustering

Nonintrusive load identification for industrial users can accurately acquire the operation of each load. However, it is a major challenge in the demand-side response due to the hardship of collection data for modelling, and high precision measuring equipment is required. Aiming at this situation, a nonintrusive load identification method is proposed, combining the least square QR (LSQR) with the sequential leader clustering algorithm. Firstly, regarding accurate depiction of industrial loads, some appropriate load feature indices of steady-state and transient processes are extracted, respectively. For steady-state processes, the active power, the reactive power, and the root mean square (RMS) current value are selected as the feature indices. In the case of transient processes, ten feature indices of three stages are employed: before, during, and after transient events, consisting of the duration of transient events, the RMS current value before and after transient events, the average value of active power before and after transient events, the average value of reactive power before and after transient events, the maximum RMS current value of transient events, etc. On this base, the LSQR algorithm is proposed to decompose unknown composite power to access the operation of various loads at steady-state. The sequential leader clustering algorithm is propounded to classify transient events of typical industrial loads and further identify which kind of loads had switched. Finally, to validate the effectiveness of the presented model, data of industrial loads from a concrete plant are collected, including blender, cement screw, sewage dump, and inclined belt conveyor, and simulation analysis is fulfilled. The results indicate that the model proposed can effectively achieve the nonintrusive industrial load identification, and least unified residue (LUR) is about 10−16, which is much better than the factorial hidden Markov model (FHMM) and the artificial neural network (ANN) model.

Application of the SSA for Optimal Reactive Power Compensation in Radial and Meshed Distribution Using D-STATCOMs

This paper deals with the problem regarding the optimal placement and sizing of distribution static compensators (D-STATCOMs) in radial and meshed distribution networks. These grids consider industrial, residential, and commercial loads within a daily operation scenario. The optimal reactive power flow compensation problem is formulated through a mixed-integer nonlinear programming (MINLP) model. The objective function is associated with the minimization of the expected energy losses costs for a year of operation by considering the investment costs of D-STATCOMs. To solve the MINLP model, the application of a master–slave optimization approach is proposed, which combines the salp swarm algorithm (SSA) in the master stage and the matricial backward/forward power flow method in the slave stage. The master stage is entrusted with defining the optimal nodal location and sizes of the D-STATCOMs, while the slave stage deals with the power flow solution to determine the expected annual energy losses costs for each combination of nodes and sizes for the D-STATCOMs as provided by the SSA. To validate the effectiveness of the proposed master–slave optimizer, the IEEE 33-bus grid was selected as a test feeder. Numerical comparisons were made against the exact solution of the MINLP model with different solvers in the general algebraic modeling system (GAMS) software. All the simulations of the master–slave approach were implemented in the MATLAB programming environment (version 2021b). Numerical results showed that the SSA can provide multiple possible solutions for the studied problem, with small variations in the final objective function, which makes the proposed approach an efficient tool for decision-making in distribution companies.

A residential user classification approach based on the graded portrait with considering the IDR adaptability and potential

In the current modern power system, extreme load peaks and valleys frequently occur due to the complicated electricity consumption behaviors. This point severely impacts the security, stability, and economy of the power system. Demand response (DR) has been proved to be one of the most effective ways to shift load to relieve the intensity of the power system. Although DR is mainly applied on the commercial and industrial loads traditionally, in recent years, the residential load has gradually attracted attentions of DR researches, especially incentive demand response (IDR) research because of its remarkable stability and flexibility in terms of load shifting. However, the difficulty of measuring the IDR adaptability and potential of a residential user according to the load curve significantly prevents the IDR from being conveniently implemented. And further, the power company is tremendously difficult to efficiently and effectively select the users with high IDR adaptabilities and potentials to participate in IDR. Therefore, to address the aforementioned issues, this paper presents a residential user classification approach based on the graded user portrait with considering the IDR adaptability and potential. Based on the portrait approach, the residential users with high IDR adaptabilities can be preliminarily selected. And then, based on the selected users, the portrait approach to delineate the users with high IDR potentials is further presented. Afterward, the achieved residential users with high adaptabilities and potentials are labeled, which are employed to train the presented variational auto encoder based deep belief network (VAE-DBN) load classification model. The experimental results show the effectiveness of the presented user portrait approaches as well as the presented load classification model. The results suggest that the presented approaches could be potential tools for power company to identify the suitable residential users for participating in the IDR tasks.

A novel two-phase load compensation method under unbalanced voltages for renewable energy integration

This paper explores new design methods for correcting unbalanced power distribution networks in order to improve the power quality and reliability for sensitive industrial loads. While traditional compensators typically need to be connected to all the three phases of the AC network, this paper proposes a novel three-phase voltage balancing compensator that can operate with a connection to only two of the three phases. The new concept is based on the phasor symmetrical component theory, which can be implemented using either mechanically switched reactors (MSRs), TCRs (thyristor-controlled reactors) or VSCs (Voltage source converters). Three methods are presented to calculate the value of the proposed two-phase compensator, i.e., discrete solution, analytical solution and novel simplified solution. The discrete solution is solved via the exhaustive search method, which was successfully used by StruthersTech to correct the power quality of an unbalanced industrial power system. The analytical solution is based on the nodal voltage of analysis method. The simplified solution derives a practical albeit approximated relationship between the negative sequence source voltage and zero sequence load voltage, thus avoiding the need to solve nonlinear equations. Dynamic simulations are implemented using MATLAB/Simulink Simscape Blockset to validate the effectiveness of the proposed two-phase voltage balancing method.

Chromium adsorption on surface activated biochar made from tannery liming sludge: A waste-to-wealth approach

In a beamhouse, liming plays a key role in the removal of hair/wool and epidermis, but problems are created when waste liming sludge is discharged to the environment. The treatment of tannery wastewater is another major challenge to the industry. In this study, thermally-activated biochars derived from liming sludge were studied for their effective adsorption of chromium (Cr) from the tannery wastewater. The thermally activated biochars (B500, B550, B600, and B650) were prepared at different temperatures from the liming sludge. Their characteristics before and after the treatment were investigated using Fourier transform infrared spectroscopy, energy dispersive X-ray spectroscopy, Brunauer–Emmett–Teller, and scanning electron microscopy analyses. The related functional groups (C–H, O–H, C–N, and =C–O) and chromium adsorption capacity were determined according to the surface morphology, element contents (C, O, Ca, Na, Al, Mg, and Si), surface area (5.8–9.2 m2/g), pore size (5.22–5.53 nm), and particle size (652–1 034 nm) of the experimental biochars. The biochar originated at 600°C from the tannery liming sludge (B600) had a greater surface area with a chromium adsorption capacity of 99.8% in comparison to B500, B550, and B650 biochars. This study developed an innovative way of utilizing liming sludge waste to minimize the pollution load and wastewater treatment cost in the tannery industry.

Thermal energy storage for industrial thermal loads and electricity demand side management

Thermal energy storage offers a solution to mitigate the intermittency of electricity generation in a grid powered by renewable energy and an opportunity to arbitrage on the temporal variability in the cost of the grid supplied energy. This study investigates the potential of a thermal energy storage system used for thermal load and electricity demand management at the industrial scale. A latent heat thermal energy storage system in conjunction with an advanced control and forecast algorithm have been integrated into an existing industrial food processing plant. This demonstration system also incorporates electricity from onsite PV and the grid to power the refrigeration system. The control and forecast code provide a schedule to charge the thermal energy storage from the grid during low or negative prices; otherwise, it instructs to charge from solar PV or discharge to cover the refrigeration load. The data collected from the demonstration system clearly showed the role of the integrated system in demand side management leading to electricity cost reduction during the ten months of operation. The net estimated electricity cost savings was 10,435 AUD in ten months, while doubling the capacity of thermal energy storage could save about 48,700 AUD per annum. Data analysis of energy demand acquired from all the modules of the plant provided valuable insight into further optimization of the control code for higher realization of its potential in the reduction of electricity demand charges. Moreover, such a control system combined with thermal energy storage can be used for the management of any variable load and renewable energy sources.

Industrial Power Monitoring and Load Controlling System

Industrial Power Monitoring and Load Controlling System

Research on the Influence of Load Difference for Photovoltaic Micro-grid System Performance

Abstract: In this paper, make use of PVSOL software studied the generation performance and Economic benefits of PV system at various inclinations. And then analyzed residential load, industrial load and commercial load system performance and self-sufficiency under different battery capacity. The results show that for Shanghai area, when the inclination angle of PV panel is about 23o, the annual total power generation of PV system reaches the maximum. The self-sufficiency rate of PV systems with industrial load is higher than commercial load, and the self-sufficiency rate of PV systems with residential load is the lowest. The self-sufficiency rate of PV systems with batteries are higher than PV systems without batteries, and the increment of installation power can increase self-sufficiency rate of the PV system.

A multi-use framework of energy storage systems using reinforcement learning for both price-based and incentive-based demand response programs

This study proposes a multi-use energy storage system (ESS) framework to participate in both price-based and incentive-based demand response programs with reinforcement learning (RL) on the demand side. We focused on industrial customers, to provide them the opportunity to obtain additional profits through market participation in addition to managing their load. Since industrial customers pay their electricity bills according to the time of use tariff structure, they can benefit if they can shift their electricity usage from high-price hours to low-price hours. Furthermore, they are able obtain additional incentives by fulfilling a dispatch signal from the system operator by using the ESS. To model the multi-use ESS by industrial users, we used the RL framework to make customer decisions. The RL approach uses a control action policy by interacting with an environment with no prior knowledge. For this, we formulated the ESS operation as a Markov decision process so that the environmental information obtained by the customers provides RL, which takes optimal actions for the current environment considering customer benefits. We developed several RL agents to identify an acceptable control agent. We utilized the actual industrial load profile in South Korea to train the RL agents. The experimental results demonstrated that the proposed framework can make near-optimal decisions for using ESS in multiple ways.

Hydrogeochemical processes and causative pollution sources in the highly urbanized crystalline aquifer system in Southern India

Groundwater contamination due to the disposal of untreated or partially treated domestic and industrial sewage is posing a severe threat to urban groundwater reserves in many parts of the world. The efforts made for the modelling of contaminant transport to understand these pollution transport and hydrogeochemical processes in urban areas are often challenging due to the difficulty in selecting source boundary condition locations mainly in the presence of Multiple Point Sources (MPS). The present research used Multivariate Statistical analysis (MSA) to classify the major pollution sources and to demarcate its area in the highly urbanized Hussain Sagar (HS) lake catchment situated in Hyderabad, South India. The observed monitoring data of groundwater levels and groundwater quality (pH, TDS, Ca2+, Mg2+, Na+, K+, HCO32−, Cl−, SO42−, NO3-N, total hardness) at 108 locations for eight different seasons over four years have been used for modelling. The MSA has revealed that the first component is associated with industrial pollution loads that are restricted to Kukatpally and Dulapally streams and HS lake with a total variance of up to 46% to the total pollution load. Component 2 and component 3 are associated with domestic pollution loads (HCO32−, Na+, and NO3-N) that spread across the lake catchment with a total variance of up to 37%. The approach used in this study can be applied to any local to regional scale river basins for understanding hydrogeochemical processes and to provide inputs to simulate contaminant transport.

A two‐level hybrid control strategy to augment transient stability and tracking accuracy of hybrid microgrid coupled with industrial loads

AbstractThe hybrid microgrid (HMG) is an arrangement for empowering the society by using the renewable energy sources (RESs) connected with common DC bus terminal. The effective operation of the HMG relies on the transient response of the DC bus as well as the stability of the system state that may change due to the stochastic behavior of RESs and industrial loads. This work is motivated to develop a two‐level decentralized control strategy consisting of a non‐linear and linear control scheme to augment the transient stability and tracking performance of an HMG. Firstly, a non‐linear partial feedback linearization control framework combined with a PI regulator (PFLC‐PI) is designed for the transient stability enhancement of the common bus. Secondly, a linear unified controller (UC) is developed to guarantee the constant load voltages over the industrial loads through the control of the load‐side inverter. The UC effectiveness is measured by varying the load parameters associated with single‐ and three‐phase load networks. The obtained results are then compared with the existing controllers to guarantee the augmented performance of proposed two‐level controller. Additionally, an external parametric uncertainty is intentionally added to the single‐phase load network dynamics and studied to ensure the robustness of the controller.

Stress-strain analysis of the tow hook of a commercial truck by numerical simulations

Numerical simulations have been widely used in the industry, although with some limitations. One of these limitations is the fact that the thermal effects derived from component manufacturing processes are normally disregarded in the analysis. Thus, the main goal of this study is to evaluate strains near the weld beads of a geometric model for a truck tow hook using numerical simulations by finite element method, considering thermal loads applied before the field loading. To validate the simulation results, an experiment was performed using a tow hook found in a commercial truck. Strain gauge rosettes were placed on the body base plate, and a load of 181.050 N was applied to the hook. This loading was the result of a commercial truck being pulled on level terrain, simulating a normal operation condition of the tow hook. The Von Mises stress found in simulations was approximately 302.23 MPa at the same position where the strain gauges were glued, when the load was applied. The difference between the simulated and experimental values of the equivalent specific strains in the area of the weld beads was 19.2%, and the difference between the permanent displacement values, resulting from plastic strain, in the X-direction was 0.1%. These results justify conducting new studies involving numerical simulations and considering thermal effects on static and dynamic loads in the automotive industry.

Artificial ecosystem optimization for optimizing of position and operational power of battery energy storage system on the distribution network considering distributed generations

Installing Distributed Generation (DG) on the Distribution Network (DN) is one of the effective solutions to reduce the cost of electricity supplied from the system. However, the mismatch between DG capacity and load demand may lead to wasted energy. This paper demonstrates the problem of optimizing the location and power of the battery energy storage system (BESS) to reduce energy cost on the DN considering DGs. The survey period is a typical day that is divided into off-peak, normal and peak hours corresponding to different electricity prices. Artificial Ecosystem Optimization (AEO) is first adapted to determine the BESS’s location and power during the survey period. The approach for reducing energy cost and the proposed AEO method are applied to three test DNs integrated photovoltaic and wind turbine DGs. Wherein, the considered types of loads of the DNs are residential, commercial and industrial loads. The performance of the AEO is compared with the well-known Particle Swarm Optimization (PSO) and the recent Harris Hawks Optimizer (HHO). The results show that the BESS placement helps to reduce significantly electricity cost in the survey period for the DN with and without DGs. Furthermore, AEO is the more effective approach than PSO and HHO for the BESS placement problem.

Governor controller improvement to enhance SSSG performance in presence of electrical arc furnace

Electrical Arc Furnaces (EAFs) consume time-varying power. This causes mechanical torque variations in the nearby small scale synchronous generators (SSSGs) and reduces their life expectancy. In this paper, an appropriate EAF model is implemented and verified by using field measurements. Moreover, an actual distribution system to which an SSSG is connected to supply industrial loads including an EAF is investigated based on time-domain simulation studies. To enhance dynamic performance of the SSSG adjacent to an EAF load, two novel approaches are presented in this paper. The first method is utilized for proper adjustment of governor controller parameters. Analytical and simulation-based studies are accomplished to determine proper parameters of the SSSG conventional governor controller to mitigate the mechanical oscillations caused by EAF. These comprehensive investigations reveal that the governor PI controller proportional parameter should be adjusted as small as possible to mitigate the SSSG oscillations; while, the oscillations are fairly independent of integrator parameter. Besides, the integrator parameter should be set as high as possible to improve the SSSG dynamic performance. Moreover, a new modified SSSG governor controller including steady-state and transient control loops is developed and a novel control strategy is proposed to enhance steady-state and transient performance of SSSG. Finally, robustness and effectiveness of the proposed controller and its parameters are investigated using field measured EAF current signals for various transients. It should be noted that the proposed strategies of this paper can be also applied to similar cases where a nonlinear time varying load is connected to an SSSG.

Структурні показники популяції плоскирки (Blicca bjoerkna Linnaeus, 1758) Кременчуцького водосховища

Purpose. Analysis of structural indicators of the population of Blicca bjoerkna L. in the Kremenchuk reservoir in the world increasing its importance in the composition of industrial catches and development of methodological approaches to its use to reduce the industrial load on the catch of major industrial species. Methodology. Primary materials were collected at the monitoring sites of the Institute of Fisheries of NAAS, performed in the surface area of the Kremenchuk reservoir during 2016–2021. Findings. The article establishes the general basic population indicators of the population of Blicca bjoerkna L. in the Kremenchuk reservoir. Indicators of natural, commercial and general mortality of the population are established. The originality: the findings of the work serve the sustainable use of aquatic bioresources of natural reservoirs as a basis of scientifically sound principles for sustainable fisheries. Practical Value: the population indicators conducted in the work can be used to calculate the limits of the commercial catch of silver bream in the Kremenchuk reservoir to ensure its sustainable use. Key words: silver bream, Kremenchuk Reservoir, age structure, mortality, commercial stock

Two-Stage Robust Optimal Scheduling of “Nearly-Zero-Carbon Park” Considering the Synergy of Waste Disposal and Carbon Capture Gas-Fired Power Plants

Aiming at the demand characteristics of “Nearly-zero Carbon Parks" (NZCP) powered by all renewable energy for low-carbon emissions and waste reduction, a NZCP topology and a two-stage robust optimal scheduling model based on the Carbon Capture Gas-fired Power Plant (CCGPP)-Power to Gas(P2G)-Waste Disposal (WD) collaborative model are proposed in this paper. First, the relationship between WD, CCGPP and energy supply and demand that can be generated from renewable energy is studied, and according to the system energy regulation requirements, the energy flexibility of residential loads, small industrial loads and electric vehicles are considered, and the NZCP mathematical model based on the CCGPP-P2G-WD collaborative model is established. Secondly, NZCP energy supply and demand characteristics are studied, and an energy coordination model for NZCP energy conversion and storage powered by 100% renewable energy is established. Then, wind power, photovoltaic power, and combined demand response uncertainties are considered, and the NZCP two-stage robust hybrid optimal dispatch model is established based on second-order cone relaxation and dual theory. Finally, the simulation analysis is carried out based on the operation data of the integrated energy system of a park in a certain area in northern my country. The simulation results show that the proposed two-stage robust optimal scheduling model of NZCP coordinated with WD and CCGPP can effectively improve the economy of the system at a higher energy balance level, improve the absorption capacity of renewable energy, and provide better multi-time scale energy regulation characteristics for NZCP with a larger energy supply scale.

Optimal Selection of Reactive Power For Single Tuned Passive Filter Based on Curve Fitting Technique

This research presents the Optimal Reactive Power (Qc) selection for a single-tuned passive filter. DC drives are very popular in the industrial zone due to their high performance, flexibility, easy control, and low cost. DC drives operate by giving supply from an AC utility and AC to DC can be converted using the AC-DC converter. But this conversion introduces harmonics in the input supply current that affect the performance of the DC drive and also cause serious problems for the overall power quality of the system. Many researchers are searching for the appropriate solutions to mitigate this cause. A passive filter is one solution to minimize or avoid harmonics from entering the electrical system. The key aspect of the passive filter design has been a difficult task. The parameters of the passive filter largely depends upon selecting the suitable value of reactive power (Qc). In this paper, the Simulink model of an AC-DC converter based on a separately excited dc motor is used as an industrial load, and a curve fitting technique has been used to select the optimal value of reactive power (Qc) for the passive filter. The simulation results and analysis show that optimal selection of reactive power for single tuned passive filter using the proposed technique is very effective by taking international standards limits for harmonic distortion.

Multi-objective energy management for modern distribution power systems considering industrial flexibility mechanisms

Increasing energy generation from variable and uncertain renewable resources leads to high demand for flexibility procurement from different energy sectors to maintain supply/demand balance in distribution power systems. Industrial energy systems are rapidly emerging as the primary contributor in providing the bulk of said flexibility to the electrical power systems due to their energy capacity, reliable infrastructure, and potential financial benefits. However, there is a lack of available energy management models that help integrate industrial flexibility into the fold and can solve for an optimal operation of the distribution power system. This paper proposes an efficient energy management model that solves a distributed multi-objective optimal power flow for a modern distribution grid, including industrial prosumers and distributed generation resources. We generate two distinct kinds of flexible industrial load profiles: one uses optimal peak shaving with an energy storage system, and the other employs process optimization in the industrial grid. The obtained results show that the proposed energy management model can solve for optimal operation of all involved participants while maintaining data privacy between them. Industrial prosumers save up to 4.85% in financial expenses and 18.6% in energy exchange with the grid. Further, the grid operator can reduce its CO2 emissions by 4.6%.