User Load Research Articles

Analysis of DC MCB Usage Characteristics for AC and DC Load Usage

One of the most important and expensive power lines in the distribution method is the low line voltage. In case of overload or short circuit current, it protects the electrical installation. MCB is an important component in electrical installations. The formation of sparks due to a short circuit can eventually cause a fire which has the potential to cause a fire, which is an unwanted MCB nuisance. This MCB can be used as a direct current breaker with a load, either manually or automatically. When the MCB is switched from "ON" to "OFF", the mechanical part in the MCB cuts off the electric current. The manual method involves turning off the toggle switch in front of the MCB (usually blue or black). The finished tool is then tested to see how well components such as AC MCB, DC MCB, Volt meter, LED lamps, Outlets, and lamp fittings perform. The function of the analysis of the characteristics of the use of DC MCBs for the use of DC loads works to compare the use of DC MCBs on AC loads and which ones are the most effective and the circuit is given a load until the MCB trips and amperes and volts are measured in the circuit.

Imitative Collaboration: A mirror-neuron inspired mixed reality collaboration method with remote hands and local replicas

Mixed reality can overlay and display 3D digital content in the real world, convey abstract concepts to users, and promote the understanding of complex tasks. However, the abstract graphics overlaid on the physical space may cause a certain cognitive load for local users and reduce the efficiency of collaboration. To improve the efficiency of remote collaboration, we conducted an elicitation study on assembly tasks, explored the user needs for collaboration, and defined the design goals of our remote collaboration method. Inspired by the mirror-neuron mechanism, we present an imitative collaboration method that allows local users to imitate the interaction behavior of remote users to complete tasks. We also propose a series of interaction methods for remote users to select, copy, and interact with the local point clouds to facilitate the expression of collaboration intentions. Finally, the results of a user study evaluating our imitative collaboration method on assembly tasks are reported, confirming that our method improves collaboration efficiency while reducing the cognitive load of local users.

Application of the artificial bee colony algorithm to power contract capacity optimization

In recent years, the artificial intelligence of imitation human neuron operation has become an aid to human thinking. Therefore, we applied this technique to forecast medium-term load systems. Power loads represent an indispensable energy in our life, so effectively forecasting power load usage may not only decrease energy consumption but also reduce the cost of contract capacity. Currently, people have used end-use models, load density, trend analysis, and statistical methods to predict Medium-Term Load Forecasts (MTLF) and have employed artificial neural networks to predict Short-Term Load Forecasts (STLF). In this paper we propose an optimized structure to predict the power load by combining an artificial bee colony (ABC) and an artificial neural network (ANN) to forecast MTLF. After considerable trial-and-error testing, the influence of different structures, max iteration, and datasets leading to different errors all became points in our study. These structures were also put into both a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA) to test the experiment. The experimental results verified that ABC demonstrated better performance in max iteration 30 and structure 7-n-1. An appropriate contract capacity prediction can be obtained by applying this structure. To show the effectiveness of the proposed structure, we verified it with six years’ statistical data of the health care service industry from Taiwan Power Company and then discuss the results.

Performance of the variable-temperature multi-cold source district cooling system: A case study

At present, the energy consumption and CO2 emissions of traditional building cooling systems account for a large proportion of the whole society. In this context, the district cooling system (DCS) has become an emerging green cooling solution due to its efficient and clean characteristics. However, there are many factors that affect the performance of DCS, and the changing outdoor temperature has a significant impact on the cooling load of users, and it is a challenge to set an appropriate and reasonable DCS cooling temperature operation strategy to cope with the changing outdoor temperature. This study proposed a novel operating strategy for variable-temperature cooling. An existing multi-cold source DCS operating at a constant temperature in an area with hot summer and warm winter was considered as the baseline case. Moreover, the cooling temperature of the chillers with different functions in the system was reset according to the change in the outdoor temperature, and the variable-temperature cooling model was established using the transient system simulation program (TRNSYS). Subsequently, set reasonable operating strategies for chillers and the auxiliary devices of the two models and performed the simulations. The results revealed that both models could meet the cooling load demand of users. Compared with the baseline model, the newly proposed variable-temperature cooling model reduced 18.99% of energy consumption and 19.12% of CO2 emissions, and the annual cost savings was 2.29 million RMB. Additionally, the overall efficiency of the new model increased by 15.52%.

Efficient Throttled load balancing algorithm to improve the response time and processing time in data center

SummaryIn today's world, cloud computing is an emerging service, and it has proved to be a profit‐oriented business model. A drastic growth was observed in cloud computing during the last decade because of its easy access to services. As the number of users are increasing dynamically, load balancing is required to handle the user's load. Load balancing algorithms minimize the data center processing time and increase the throughput. Therefore cloud service providers need the best dynamic strategies. In this work, the proposed Efficient Throttled load balancing algorithm improves the performance in the data center environment. Our experimental results indicate that the Efficient Throttled load balancing algorithm improves the average overall response time by 6.47% and average data center processing time by 20.74%, compared to the Throttled load balancing algorithm.

Power Quality Improvement using Parallel Connected FACTS Device with Simplified d-q Control

INTRODUCTION: Distribution network is mainly affected by the end user load nature. Ruinous influence on power system operation based on the type of load used by the end user and due to the presence of non-linear loads causes Harmonics in power system. The concept of reducing the harmful effect of harmonics on power system attracted the research attentiveness. OBJECTIVES: To reduce these harmonics and reactive power problems FACTS devices are found reliable. Out of various FACTS devices, Active Power Filter (APF) is one FACTS device which identifies and controls the harmonic contamination in power system with non-linear loads. This work mainly presents a parallel APF concept for the compensation of harmonics to improve the reliability of the system. METHODS: The main contribution of this work is proposal of single control strategy for compensation of Power quality. Corrective back currents (for parallel APF) to compensate the identified harmonics are generated using modified d-q reference-based control methodology. RESULTS: The models are developed, and analysis is presented using MATLAB/SIMULINK software CONCLUSION: d-q reference theory control strategy is explained in detail. Load current, Source Voltage and DC link voltage which are measured using various sensors and send to processor. modified d-q reference the signals to be measured are same

Stochastic geometry analysis of coverage probability in energy efficient dense heterogeneous network with sleep control mechanism

The upsurge of data traffic in the macrocell networks has led to the massive deployment of small cells (SCs) for load sharing. Though small cell power consumption is low individually, significant aggregate power consumption, together with the MBS transmission power that is exponentially increasing over its served user load, deteriorates the network energy efficiency (EE). Consequently, EE is seen as a critical requirement for wireless network design in future. The challenge of EE maximization in dense heterogeneous networks is investigated in this research through a strategic small cell sleeping technique. A heuristic method based on distance and load awareness strategy in which the small cells with fewer users near the macrocell will be put into sleep mode. User equipments (UEs) under the service of the sleep small cells will be offloaded to the MBS. A dense heterogeneous network is considered with overlapped small cell coverage regions. All the edge area SCs will be kept ON to avoid QoS degradation. Simulation analysis on the stochastic geometry model indicates that the proposed sleep strategy significantly improves the network EE than the prevailing sleep control strategies while assuring seamless, efficient coverage in the sleep cell areas.

Compressed Air Energy Storage Capacity Configuration and Economic Evaluation Considering the Uncertainty of Wind Energy

The random nature of wind energy is an important reason for the low energy utilization rate of wind farms. The use of a compressed air energy storage system (CAES) can help reduce the random characteristics of wind power generation while also increasing the utilization rate of wind energy. However, the unreasonable capacity allocation of the CAES system results in high capital investment and a long payback period. In order to improve the economic benefits of energy storage, this paper studies the capacity configuration of compressed air energy storage systems under the condition of wind energy uncertainty. First, the typical hourly power distribution of wind power generation was obtained using historical data. Factors such as user load demand, time-of-use price of the power grid, system investment cost, power shortage cost, and power sales revenue were considered. Then, a model was built with the charging and discharging power and gas storage capacity of the CAES system as constraints, and the maximum return on investment and the minimum volume of the gas storage tank as targets. NSGA-II and TOPSIS optimal selection methods were used to solve the problem. Finally, the model was used to optimize a power operation case. The results show that in the case of an hourly load power demand of a factory using 3.2 MW, a wind farm would need to keep four wind turbines running every day, and a compressed air energy storage system with a rated power of 1 MW and a rated capacity of 7 MW would ensure the best project benefit. In this mode, 1.24 × 103 MWh of wind abandoning power could be reduced annually, 2.6 × 104 kg of carbon emissions could be reduced by increasing energy storage within the operation cycle, and the payback period of investment would only be 4.8 years.

STUDI TENTANG PERBAIKAN JATUH TEGANGAN DI TIANG UJUNG JARINGAN TEGANGAN RENDAH PADA PT.PLN UP3 AREA SAMARINDA

Abstract— Electric power distribution system is one of the systems in electric power that has an important role that is directly related to the user of electric energy. In the distribution system must have the appropriate reliability of the standards applied but in reality the distribution of electric power distribution is not appropriate, such as falling voltage commonly called drop voltage that reaches the consumer. This is caused by several factors so that the quality of existing electricity is not optimally beneficial and can even cause damage to electrical equipment. In phase S, from the analysis of calculations and measurements made by the author based on basic theory, conclusions can be drawn as follows. In calculations and measurements in phaseS R and T the voltage fall is caused by the length of the delivery. In phase S, when viewed from the calculation and measurement of voltage fall due to the length of the delivery and other factors, namely the cable connection is not perfect. From the data obtained by the author of the voltage fall at the GNK 383 Distribution Substation is not constant because the load changes the use of electrical energy by different consumers The voltage fall at the GNK 383 Distribution Substation occurs at night because the load usage activity by consumers is very high.

Internal Characterization-Based Prognostics for Micro-Direct-Methanol Fuel Cells under Dynamic Operating Conditions.

Micro-direct-methanol fuel cells (DMFCs) use micro-electro mechanical system (MEMS) technology, which offers high energy density, portable use, quick replenishment, and free fuel reforming and purification. However, the DMFC is limited by a short effective service life due to the membrane electrode’s deterioration in electrochemical reactions. This paper presents a health status assessment and remaining useful life (RUL) prediction approach for DMFC under dynamic operating conditions. Rather than making external observations, an internal characterization is used to describe the degradation indicator and to overcome intrusive influences in operation. Then, a Markov-process-based usage behavior prediction mechanism is proposed to account for the randomness of real-world operation. The experimental results show that the proposed degradation indicator alleviates the reduction in DMFC output power degradation behavior caused by the user loading profile. Compared with the predictions of RUL using traditional external observation, the proposed approach achieved superior prognostic performance in both accuracy and precision.

Cooperated Online Learning and Optimization Operation of the Low Voltage Distribution System Considering User Electrical Characteristics

With the acceleration of energy reform, photovoltaic, energy storage, electric vehicles, and other new loads in low-voltage distribution networks have been rapidly developed. However, the distribution network with distributed power supply has some problems, such as imprecise power flow modeling and difficult coordination between various energy sources and loads, which bring challenges to the online optimization of the distribution network load curve. In this study, a multi-time scale online load optimal operation scheme of the distribution network is proposed by using the Bayesian online learning method. This scheme transforms the online power optimization of the distribution network into a Markov decision process. The output time of different energy sources is different, and the load with different user load characteristics is optimized. The scheme can track the state of the distribution network in real time and make the optimization scheme of multi-energy output online. Finally, an example is given to verify the effectiveness of the proposed method, which has theoretical significance for promoting the diversified development of low-voltage user-side load.

Dynamic pricing strategy and regional energy consumption optimization based on different stakeholders

This paper focuses on the pricing strategy of agents and the optimization of business performance of different stakeholders. The pricing strategy of agents directly affects the energy consumption cost on the demand side. It is an important planning problem to formulate a reasonable pricing strategy to meet the energy consumption needs of different stakeholders. In order to increase the profit of agents and reduce the energy consumption cost on the demand side, a multi-objective bilevel optimization model is established based on the principle of Stackelberg game. The upper layer takes the maximum day ahead real-time profit of agents as the optimization objective, and the lower layer is the microgrid area with the lowest operation cost. By introducing Karush-Kuhn-Tucker (KKT) condition, the bilevel problem is simplified, and the optimization model is converted into a mixed integer linear programming model by combining duality theorem, linear relaxation, etc., which solves the difficult problem of solving the bilevel nesting problem. Wind, photovoltaic and user load forecasting scenarios are formed in the form of opportunity constraints. The simulation process is carried out without considering wind, photovoltaic and user loads forecasting errors and forecasting errors respectively. The simulation results show that demand-side dynamic response pricing strategy can greatly improve the profit of distribution network agents and reduce the operating costs of microgrid operators. Achieve win–win results; finally, the influencing factors of optimization objective are analyzed to provide reference for power market decision-making.

Enhancing the performance of smart electrical grids using data mining and fuzzy inference engine

This paper is about enhancing the smart grid by proposing a new hybrid feature-selection method called feature selection-based ranking (FSBR). In general, feature selection is to exclude non-promising features out from the collected data at Fog. This could be achieved using filter methods, wrapper methods, or a hybrid. Our proposed method consists of two phases: filter and wrapper phases. In the filter phase, the whole data go through different ranking techniques (i.e., relative weight ranking, effectiveness ranking, and information gain ranking) The results of these ranks are sent to a fuzzy inference engine to generate the final ranks. In the wrapper phase, data is being selected based on the final ranks and passed on three different classifiers (i.e., Naive Bayes, Support Vector Machine, and neural network) to select the best set of the features based on the performance of the classifiers. This process can enhance the smart grid by reducing the amount of data being sent to the cloud, decreasing computation time, and decreasing data complexity. Thus, the FSBR methodology enables the user load forecasting (ULF) to take a fast decision, the fast reaction in short-term load forecasting, and to provide a high prediction accuracy. The authors explain the suggested approach via numerical examples. Two datasets are used in the applied experiments. The first dataset reported that the proposed method was compared with six other methods, and the proposed method was represented the best accuracy of 91%. The second data set, the generalization data set, reported 90% accuracy of the proposed method compared to fourteen different methods.

A Two-Level Energy Management Strategy for Multi-Microgrid Systems With Interval Prediction and Reinforcement Learning

Setting retail electricity prices is one of the significant strategies for energy management of multi-microgrid (MMG) systems integrated with renewable energy. Nevertheless, the need of privacy preservation, the uncertainties of renewable energy and loads, as well as the time-varying scenarios, bring challenges for pricing problems. In this paper, a two-level pricing framework is proposed based on interval predictions and model-free reinforcement learning to address these challenges. In particular, at the higher level, the distribution system operator (DSO) is viewed as an agent, which sets retail electricity prices without detailed user information for privacy protection to maximize the total revenue from selling energy with reinforcement learning. For time-varying scenarios with intermittent photovoltaic power generation and diverse loads, a differentiable trust region layer is considered in reinforcement learning to improve the robustness of the policy updating process. While at the lower level, operators in microgrids solve three-phase unbalanced optimal power flow (OPF) problems to minimize generation cost and network power loss. Additionally, to deal with the challenges from the uncertainties of renewable power generation and user loads, interval predictions are chosen to quantify prediction errors and improve the flexibility of pricing policies. Finally, a set of experiments are conducted to validate the effectiveness of the proposed method for pricing problems in MMG systems.

Probability prediction of short-term user-level load based on random forest and kernel density estimation

With the development of smart grids and the popularization of smart meters, grid companies have obtained a large amount of fine-grained user electricity consumption data, making it possible to forecast individual users’ electricity load. Traditional deterministic forecasting cannot measure the uncertainty of users’ future electricity consumption behavior. This paper aims to establish a short-term load probability forecasting model for individual users, which can output the probability prediction interval and the probability density curve. Firstly, historical data is applied to train a deterministic prediction model based on random forest. Then, when predicting the user load at a future time, the output of each tree in the forest is formed into a prediction set, and the kernel density estimation method is used to obtain the probability prediction result of the user load. The proposed RF-KDE method obtains promising results on a public dataset. It also shows the advantages of easy parameters adjustment and fast training speed. Besides, tests conducted on the public dataset confirm that the proposed method can be applied to users with different electricity consumption behaviors.

Performance of Impedance Control-Based Strategies in Power-Assisted Wheelchairs: A Predictive Simulation Study.

Manual wheelchair propulsion is known to be inefficient and causes upper extremity pain, fatigue, and injury. Power-assisted wheelchairs can mitigate these effects through motors that reduce users' effort and load during propulsion. Among the different control strategies proposed to govern the user-wheelchair interaction, impedance control-based ones appear to be the most natural and effective. It can change the apparent dynamical properties of the wheelchair, particularly mass and friction, and automatically compensate for external disturbances such as terrain conditions. This study investigates the advantages and disadvantages of this control strategy employing predictive simulations of locomotion with power-assisted wheelchairs in different scenarios. The simulations are generated using a biomechanically realistic model of the upper extremities and their interaction with the power-assisted wheelchair by solving an optimal control problem. Investigated scenarios include steady-state locomotion vs. a transient maneuver starting from rest, movement on a ramp vs. a level surface, and different choices of reference model parameters. The results reveal that the investigated impedance control-based strategy can effectively reproduce the reference model and reduce the user's effort, with a more significant effect of inertia in the transient maneuver and of friction in steady-state locomotion. However, the simulations also show that imposing a first-order, linear reference model with constant parameters can produce disadvantageous locomotion patterns, particularly in the recovery phase, leading to unnecessary energy dissipation and consequent increase in energy consumption from the batteries. These observations indicate there is room for improvement, for instance, by exploring energy regeneration in the recovery phase or by switching reference model nature or parameters along the cycle. To the best of our knowledge, this is the first investigation of impedance control-based strategies for power-assisted wheelchairs using predictive simulations and a realistic, nonlinear model of the user-wheelchair system.

WFH, not WTH? The security challenges of working-from-home

Under the coronavirus pandemic, governments and corporations around the world have adopted a work-from-home (WFH) mode of operations to continue governing and operating. Over a two year into the COVID-19 pandemic, many of us continue to work from home and a large majority have few plans to return to the office. Early on, governments and companies scrambled to increase Virtual Private Network (VPN) licenses and bandwidth capacities to take on the additional user load at a technical level. This allowed a near seamless continua of communications for common government unclassified information and corporate sensitive information of non-national interest using commercial software encryption. But what about information of national interest? A smaller number of individuals in key government departments, sometimes under staff rotations, continued to work in the office to serve these needs. Within weeks, government departments began deploying assets to access classified Secret systems from home. This paper discusses the WFH use of classified (e.g., Secret) IT systems while considering multiple security areas (physical, operational, personnel, IT, communication, and electromagnetic and radio-frequency emission) with focus on insider threats and foreign state actors, to describe the impact to the WFH public servant, the citizens, and the government. It describes the severe security challenges and risks governments have accepted under the pandemic, raising the question “what the heck were governments thinking?”

A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles

The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV’s information, such as the battery size, current state-of-charge, and arrival & departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery’s desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems.

A short-term load forecasting model based on mixup and transfer learning

When the amount of historical load data is insufficient, the use of deep learning for load forecasting is prone to overfitting. This paper proposes a short-term electric load forecasting model based on mixup and transfer learning. Mixup is introduced to achieve data enhancement, which improves the generalization ability of the forecasting model by expanding the distribution of training samples. Transfer the historical load of other users with similar consumption patterns to supplement data samples and avoid overfitting of the forecasting model. The similarity between the target load sequence and the source load sequence is measured by the maximal information coefficient (MIC). Finally, the expanded load data adopts a long short-term memory (LSTM) neural network that can learn the long-term dependence in the time series for load forecasting. Experimental results on the residential data set demonstrate that the model proposed in this paper improves the accuracy of load forecasting in scenarios where historical data is scarce.

Design of an Improved Impedance Controller for Powered Transtibial Prosthesis

With the rapid development of powered transtibial prostheses, the control of motorized actuators is a research hotspot. This study designs a double-loop impedance controller to improve the performance of the traditional controller. Including time-varying parameters, the designed controller is implemented on a novel elastic actuator. Under different loads of users’ weights, simulation tests are conducted in a working condition of walking, and the results show that the designed control system has outstanding performances in the control of an entire gait cycle. The error between output and reference of ankle joint position during walking is less than 0.013rad in each sample time. A position disturbance test demonstrates that the control also has a high tolerance of any position disturbance at the ankle joint. The double-loop impedance controller can replace traditional finite-state impedance control in the field of the powered transtibial prosthesis and has the potential to be applied in other relative fields.