Smart Load Controller Research Articles

Progressive energy management technique for smart load control

Progressive energy management technique for smart load control

An integrated system of energy generation, storages, and appliances consumption based on machine learning techniques and internet of things

In the swiftly evolving micro-grid landscape, this paper explores the pivotal role of residential consumers and their active engagement. The primary focus is on revolutionizing customer management through the integration of smart homes, with an emphasis on facilitating demand response and bolstering operational efficiency. Also, the innovative Power Generation, Electricity Storages, and Energy Consumption in Smart Homes (PG–ES–ECSH) approach is introduced, meticulously designed for next-generation technologies, and delve into an smart home load control system. This system, employing Extreme Machine Learning (EML) and an imprecise reasoning framework, aims to enhance computational efficiency in managing diverse electrical energy sources and small-scale power generation appliances. The decision-making process of the framework incorporates real-time monitoring of environmental data, power generation, and battery storage. Our research significantly contributes to the evolution of smart homes and micro-grids by embracing technologies crucial for the future, such as machine learning and the Internet of Things (IoT). In this paper, the application of IoT and EML are used to enhance the connection between users in the microgrid and the learning rate of the proposed system, respectively. Emphasizing the burgeoning significance of energy management in smart homes, our study underscores the profound impact of energy resources on overall quality of life. With the increasing prevalence of smart homes, there is a growing demand for efficient energy utilization and management. Noteworthy is the PG–ES–ECSH method's impressive accuracy ratio of 96 % and a precision rate of 92 %. These findings highlight the imperative need for cost-effective energy management solutions, particularly in the face of escalating electricity consumption and the integration of novel power resources.

Device-Free Human Motion Detection Using Single Link WiFi Channel Measurements for Building Energy Management

Human motion is a vital parameter signalling indoor occupancy in occupant centric building energy management systems. In this letter, a WiFi based device free sensing model employing ESP32-a low cost embedded IoT device, is proposed as a suitable retrofit for human motion sensing in new and existing buildings. A low complexity feature set derived from WiFi received signal strength indicator and channel state information samples collected by ESP32 is utilized for training ensemble machine learning models. The proposed model shows an accuracy of up to 99.4% in classifying human motion in 3 different rooms of a residential building, with a single WiFi access point. The proposed model can be employed for motion driven smart load control and for a typical residential occupancy scenario, this approach exhibits a potential monthly electricity savings of up to 66.26% and a three times reduction in CO2 emissions.

Multiobjective Metaheuristic Load Control Algorithm for Interaction between Smart Home and Humans

Technology has boosted electric power consumption both locally and worldwide, resulting in a substantial increase in demand for electric power. A multiobjective smart house human-computer interaction load control method is suggested to meet the goal of lowering power usage and pricing in smart home load control. It presents a model that includes marginal costs and establishes an electricity price model that takes the load rate into account. It identifies switch appliances and temperature control appliances and gathers human activities, indoor and outdoor temperature, and light intensity using intelligent equipment to create a multiparameter comfort model. It creates a multiobjective model of comfort and electricity price with the purpose of reducing electricity price and multiparameter comfort, and it improves particle swarm performance by using a distance ratio based on fitness value. The optimization algorithm solves the model, determines the best smart home human-computer interaction load control scheme, creates the smart home remote control system’s functional modules, and optimizes the multiobjective smart home human-computer interaction load control algorithm using a frequency-duration parameter tracking learning model. The testing findings suggest that the proposed algorithm can cut power prices in a reasonable manner, as well as regulating 40% reduced electricity usage and load in smart homes.

Performance analysis of adaptive smart controllers for islanded microgrid

Abstract This paper presents implementation of control method for dual control of Voltage and Current control and Power sharing coupled with Demand Response for the islanded microgrid. These three controller viz Voltage and current controller, Power sharing controller and smart load controller while acting in unison can able to maintain long layoff from the connection requirement of the main grid. The paper uses state space inverter based generator model with close loop Voltage and Current control and Power sharing controller will enhance microgrid stability and transient response where smart load controller which uses state space model for voltage,current and Phase Locked Loop units to estimate reserve power available from smart load for better voltage and frequency regulation. Voltage current Controller and Power Sharing Controller will also assist the microgrid for smooth disconnection of microgrid from main grid while smart load controller will use its point of load voltage control for smooth islanding of microgrid. This control technique henceforth called tripartite control technique, compared with classical controller connected to microgrid in maintaining frequency, voltage with power sharing of the inverter driven by renewable generator. This paper investigate the operation performance issues of test Microgrid using Tripartite Control Technique. Simulation results indicate close approximation with experimental results

Energy loss evaluation of a battery buffered smart load controller

This paper aims to evaluate the energy and power loss of a single-phase battery-buffered smart load (BBSL) under demand-side primary frequency control (PFC). The BBSL consists of a battery and a power & energy management system (PEMS), the evaluation in this work entails the experimental measurement of the energy lost in the battery during the charge and discharge procedures dictated by the PFC and the measurement of the power lost in the PEMS. A method is developed to measure the battery energy loss using the smart load strategy by maintaining the battery state of charge (SOC) constant over the PFC regulation period. The effects of the PFC droop characteristics and dead-bands on battery energy loss are studied. The experimental measurements of the energy loss are confirmed by simulation of a battery model in which the total voltage drop of the battery is represented by a nonlinear current-dependent resistance. Then, the power loss associated with the power electronic converters of the PEMS is experimentally determined under different PFC droop and dead-band settings.

A transmission optimization algorithm for smart load controllers

This paper introduces a transmission optimization algorithm for wireless transmissions between smart load controllers and a corresponding gateway in wireless personal area networks, where smart load controllers connect several electrical appliances through their corresponding load interfaces, measure the power consumption from each electrical appliance connected to the load controller, and control on/off switching through its load interface. The aim of this paper is to reduce the traffic load of power consumption data in electrical appliances used in the building area network and the smart grid network. The proposed algorithm allows the smart load controller to efficiently reduce traffic load even if watt-hours calculated in the smart load controllers are the same as the watt-hours in the gateway and the errors of the present values of power consumption interpreted in the gateway fall under the typical tolerance allowed in the specifications of manufacturers of watt-hour meters used in the electrical power industry.

A Coordinated Power Control Strategy for Urban Micro-Grid

Abstract In modern urban energy system, micro-grid can be an effective solution to improve the power system reliability. However, power balance and voltage stability are everlasting issues in micro-grid. In this paper, a coordinated power control strategy based on demand response (DR) technology for urban micro-grid is introduced. The key components of this novel control scheme are the smart load (SL) controller of flexible load and the grid-tie inverter of renewable energy (RE) source. Both the SL controller and the dynamic grid-tie inverter are operated as series reactive power compensator (RPC), on one hand, the SL controller offers active power variation in the flexible load for energy storage capacity reduction, on the other hand, the dynamic grid-tie inverter of RE can regulate the out-put voltage of the inverter. Detailed mathematical derivations of the proposed control paradigm are provided. Results validate the effectiveness of adopting this coordinated power control scheme for an isolated urban micro-grid with renewable energy source and energy storage system.

Smart control of fatigue loads on a floating wind turbine with a tension-leg-platform

In this paper, the smart fatigue load control of a representative floating wind turbine (FWT) with a tension-leg-platform was numerically investigated using our newly integrated aero-hydro-servo-elastic code. The control was achieved by introducing a local, fast and efficient closed-loop system based on deformable trailing edge flap (DTEF) activation near the tip of each blade, and as a result, it was able to effectively suppress the unstable loads on the critical components, as well as improve the general performances of FWT. The control effectiveness lies in the great impairment of original synchronized wind-rotor relationship by controllable DTEFs. This significantly decreased the combined effects of wind, waves and primary modes of blade, driving-chain components, tower and TLP platform, and subsequently attenuated the main fatigue loads on them. The effect of the control mechanism became more drastic as the investigation focused on the case beyond the rated wind velocity. In addition, the research findings showed the crucial role that the wind-wave-rotor relationship played during the development of smart rotor controller.

Numerical investigation of azimuth dependent smart rotor control on a large-scale offshore wind turbine

The smart fatigue load control of a large-scale wind turbine blade was numerically investigated on our newly integrated aero-servo-elastic platform with emphasis on the effect of azimuth angles. It was found that the smart control effectively reversed the phases of the flapwise aerodynamic force or the acceleration through the controllable deformable trailing edge flap (DTEF) activation within most of rotor azimuth angle range, turning in-phased flow-blade interaction into an anti-phased one at primary 1P mode, significantly enhancing the damping of the fluid-structure system and subsequently contributing to the greatly attenuatedflapwise fatigue loads on the blade and turbine performances. This aero-elastic control physics was most drastic as the investigations were focused on the case beyond the rated wind velocity, leading to the maximum reduction percentages in the time-averaged and azimuth-averaged fatigue loads up to about 30.0%, in contrast to the collective pitch control method. In addition, the finding pointed to a crucial role that the suppression of the coupled flow-blade system dependent on azimuth angles played in the smart blade control, which might guarantee better effectiveness if it would be considered in the development of the DTEF controller.

Smart fatigue load control on the large-scale wind turbine blades using different sensing signals

This paper presented a numerical study on the smart fatigue load control of a large-scale wind turbine blade. Three typical control strategies, with sensing signals from flapwise acceleration, root moment and tip deflection of the blade, respectively, were mainly investigated on our newly developed aero-servo-elastic platform. It was observed that the smart control greatly modified in-phased flow-blade interaction into an anti-phased one at primary 1P mode, significantly enhancing the damping of the fluid-structure system and subsequently contributing to effectively attenuated fatigue loads on the blade, drive-chain components and tower. The aero-elastic physics behind the strategy based on the flapwise root moment, with stronger dominant load information and higher signal-to-noise ratio, was more drastic, and thus outperformed the other two strategies, leading to the maximum reduction percentages of the fatigue load within a range of 12.0–22.5%, in contrast to the collective pitch control method. The finding pointed to a crucial role the sensing signal played in the smart blade control. In addition, the performances within region III were much better than those within region II, exhibiting the benefit of the smart rotor control since most of the fatigue damage was believed to be accumulated beyond the rated wind speed.

Smart load control of the large-scale offshore wind turbine blades subject to wake effect

The smart fatigue load control of a large-scale wind turbine blade subject to wake effect was numerically investigated in this paper. The performances were evaluated and compared at selected typical wind speeds within the whole operational region under three turbine layout strategies, i.e., column, row and array arrangements, together with a single turbine case as reference, utilizing our newly developed aero-servo-elastic platform. It was observed that not only the blade fatigue loads but the stabilities of power and collective pitch angle were effectively controlled for all cases, especially at the highest studied hub velocity of 20m/s, leading to the averaged reduction percentages in the standard deviations of the flapwise root moment, the flapwise tip deflection and the root damage equivalent load, of about 30.0%, 20.0% and 20.0%, respectively. Furthermore, the control effectiveness gradually lessened in the sequences of single, column, row and array cases, with successively increasing effective turbulence intensity, within regions II and III. The performances in region III, associated with the impaired flow separation on the blade by the effective pitching action, were much better than those in region II, related to enhanced flow detachment. In addition, at the rated wind velocity, the control for the array case was superior over other three cases, which was thought to be originated from the more pitch activities to impair the uncontrolled flow separation on the blade surface.

Designing a Model to Implement Healthcare Team Work through Coaching and Navigation

The smart fatigue load control of a large-scale wind turbine blade subject to wake effect was numerically investigated in this paper. The performances were evaluated and compared at selected typical wind speeds within the whole operational region under three turbine layout strategies, i.e., column, row and array arrangements, together with a single turbine case as reference, utilizing our newly developed aero-servo-elastic platform. It was observed that not only the blade fatigue loads but the stabilities of power and collective pitch angle were effectively controlled for all cases, especially at the highest studied hub velocity of 20 m/s, leading to the averaged reduction percentages in the standard deviations of the flapwise root moment, the flapwise tip deflection and the root damage equivalent load, of about 30.0%, 20.0% and 20.0%, respectively. Furthermore, the control effectiveness gradually lessened in the sequences of single, column, row and array cases, with successively increasing effective turbulence intensity, within regions II and III. The performances in region III, associated with the impaired flow separation on the blade by the effective pitching action, were much better than those in region II, related to enhanced flow detachment. In addition, at the rated wind velocity, the control for the array case was superior over other three cases, which was thought to be originated from the more pitch activities to impair the uncontrolled flow separation on the blade surface.

Smart load control on large-scale wind turbine blades due to extreme coherent gust with direction change

The control of a typical extreme load with varying velocity and direction, i.e., International Electro-technical Commission Extreme Coherent Gust with Direction Change (ECD) load, was investigated on an Upwind/National Renewable Energy Laboratory 5 MW reference wind turbine using a newly developed smart blade system. The control action was implemented through the local perturbation of the Deformable Trailing Edge Flap (DTEF) on the blade surface and thus flow-blade system. The investigations were separately conducted within four time zones, depending on the complex yaw or/and pitching functions of the turbine. It was found that, without DTEF control, the flapwise root moment and tip deflection of the blades experienced rather complicated fluctuations due to the ECD load, together with the influence of blade yaw and pitching. On the other hand, the smart rotor control was very effective to reduce both blade flapwise root moment and tip deflection up to 30% and even more. The good control performance lied in the altered nature of the flow-blade interactions by the local controllable DTEF perturbation to change the in-phased fluid-structure synchronization into anti-phased collision at dominant load frequencies, thus significantly enhancing the damping of fluid-structure system and impairing their correlations. Consequently, the ECD load on the rotor and even drive-chain components would be greatly suppressed.

Aerodynamic physics of smart load control for wind turbine due to extreme wind shear

This paper presents a numerical investigation of the smart load control on an Upwind/NREL 5 MW reference wind turbine under the IEC extreme wind shear (EWS) condition utilizing newly developed aero-servo-elastic platform. The control action was implemented through the local perturbation of a deformable trailing edge flap (DTEF) per blade, which was driven by a smart rotor system, based on the FAST/Aerodyn and Matlab/Simulink codes. Results showed that, compared with the original collective pitch control method, the aerodynamic load in terms of blade flapwise root moment and tip deflection were effectively reduced. Furthermore, the smart rotor control also positively affected other components of the drive-chain as well as generator power and pitch system. It was found that the smart control effect altered the nature of the flow-blade interactions and changed the in-phased fluid-structure synchronization into much weaker couplings. As a result, the damping of the fluid-blade system was significantly enhanced, leading to great attenuation in the EWS load on both rotor and other drive-chain components.

Demand response by dynamic demand control using frequency linked real‐time prices

PurposeThe purpose of this paper is to propose a novel demand response method for real‐time (RT) balancing markets, which relies on dynamic demand control (DDC) based on frequency linked RT prices.Design/methodology/approachA RT balancing market, where the RT price varies inversely with the system frequency, is described. In such a market, producers and consumers can get the RT price, simply by monitoring the frequency deviations themselves. Air conditioning load, the main contributor of summer peaks, is selected for DDC. A physically based model of air conditioner load is taken to simulate the load control. A smart load controller is proposed that changes the thermostat setting with deviations in grid frequency/RT price. Simple examples are simulated to show the load reductions achieved by DDC in a single air conditioner, a group of large number of air conditioners and a RT market consisting of four generation companies and four distribution companies.FindingsThe result of simulation on a single air conditioner shows that significant reduction in energy consumption can be achieved during severe frequency dips in a RT market. The results of simulation in an example of a RT market show that such load control not only results in better frequency control but also lowers the RT price of power.Practical implicationsThe demand control mechanism suggested in this paper can be applied to Indian market setting but its applicability to other markets might be limited due to differing, reliability standards, market rules, and regulatory framework.Originality/valueThis paper finds that DDC by distribution companies based on RT price/frequency signal not only results in a significant benefit to these companies and consumers but also helps the system operator in frequency regulation. By making the demand‐side participate in frequency regulation, such control can potentially facilitate an increase in renewable energy portfolio.

Utilising Robustness of Positive Buck-Boost Converter Against Input Voltage and Load Current Disturbances

A positive buck-boost (PBB) converter is a known DC-DC converter that can operate in step-up and step-down modes. Unlike buck, boost and inverting buck-boost converters, the inductor current of a PBB can be controlled independently of its voltage conversion ratio. In other words, the inductor of PBB can be utilised as an energy storage unit in addition to its main function of energy transfer. In this paper, the capability of PBB to store energy has been utilised to achieve robustness against input voltage fluctuations and output current changes. The control strategy has been developed to keep accuracy, affordability and simplicity acceptable. To improve the efficiency of the system, a smart load controller (SLC) has been suggested. Applying SLC, extra current storage occurs when there is a sudden load change, otherwise little extra current is stored.