Search

The battery State of Charge (SoC) is critical information to overcome agricultural robots' limitations related to battery and energy management. Although several SoC estimation methods have been proposed in the literature, the performance of these methods has not been validated for different battery chemistries in agricultural mobile robot applications. Compared to previous work, this paper evaluates the limits of the SoC estimation using the RC model and the Thevenin model for a Lithium Iron Phosphate (LFP) battery and a Sealed Lead Acid (SLA) battery. This evaluation used a custom agricultural robot in a controlled indoor environment. Consequently, this work assessed the limitations of two ECM-based SoC estimation methods using battery packs, low-cost sensors and discharge cycles typically used in agricultural robot applications. Finally, the results indicate that the RC model is not suitable for SoC estimation for LFP battery; however, it achieved a mean absolute error (MAE) of 2.2% for the SLA battery. On the other hand, the Thevenin model performed properly for both chemistries, achieving MAE lower than 1%. [ABSTRACT FROM AUTHOR]

This paper presents the development of a power interface for a fuel cell (FC) system supplying an ac load. The proposed system comprises a two stages conditioning system, including a switched capacitor dc–dc boost converter and a full bridge insulated-gate bipolar transistor (IGBT) single-phase inverter (dc–ac converter). A fully digital control structure is proposed to manage both power converters using the same control device. The control strategy takes advantage of the parallelism of the field programmable gate arrays (FPGA) technology to share in real-time the controller's information addressing the common problem of low-frequency ripple of FC current when supplying ac loads. The control system has been implemented and evaluated by co-simulation, and by experimentation with a proton exchange membrane fuel cell (PEM-FC) system. The results show that the proposed system allows a safe operation of the FC by limiting the current ripple under 3%. These results also confirm that the transient response of the conversion system permits to correctly supply the ac load. [ABSTRACT FROM AUTHOR]

Paralleled operation of voltage-source inverters (VSIs) is currently achieved by using voltage/frequency droop control techniques which requires the knowledge of the system parameters. Otherwise, centralized control techniques with robust communication among VSIs controllers are also used. This paper presents a new control strategy which allows the load sharing between the power sources of an ac microgrid without centralized controller or any communication among the VSIs; only local measurements of voltage and output current are used. The dispatchable sources (e.g., fuel cells) of the microgrid are operated using voltage control with a direct droop scheme, and the nondispatchables or intermittent ones (e.g., wind turbine generators) are operated using power control with a complementary inverse droop scheme (D-Droop + I-Droop). The number of operating sources can be changed online without any modification needed on the VSI controllers. The proposed VSI controllers are based on the variable frequency adaptive linear neuron with frequency-locked loop for the VSIs system synchronization, voltage/power and signal estimation. Experimental results using field-programmable gate array devices for the implementation of each VSI control in the microgrid test bench demonstrated the validity of the proposition. [ABSTRACT FROM PUBLISHER]

The novel scenario of power systems with massive utilization of distributed generation (DG) imposes that islanding detection methods for voltage source inverter (VSI)-interfaced DGs must be evaluated taking into account multi-inverter configurations. This paper presents the experimental validation of an active islanding detection algorithm for multi-inverter systems proposed by the authors in previous work. Field programmable gate array implementation of power control and islanding detection algorithms has been used to test this proposition in a multi-inverter system based on grid-connected insulated gate bipolar transistor VSIs. The proposed islanding detection algorithm offers a very low impact on power quality and rapid and efficient islanding confirmation which is demonstrated by simulation and experimentation. The performance of the algorithm was tested under critical conditions as resonant load with high quality factor and unity power factor and also for cases where load and VSI powers are matched. [ABSTRACT FROM PUBLISHER]

This paper presents the evolution of bidirectional electric vehicle charger for G2V (i.e., Grid-to-Vehicle) and V2G (i.e., Vehicle-to-Grid) utility. Electric vehicle are growing day by day in the area of transportation. As more electric vehicles hit the road to maintain the need for charging, bidirectional charging becomes essential. Bidirectional charging in electrical vehicles facilitates users to either make energy flow to the vehicle or flow from the vehicle. The proposed design allows users to make economic gains from electric vehicle with other exciting advantages. Particularly unidirectional charging solution restricts the user to use energy for charging only applications whereas, this paper proposed a control strategy for bidirectional charging of electric vehicles. Hence electric vehicles could be potentially used as a source during an emergency like power outages, grid failure, or whenever there is an excess load on grid and user needs more energy. Additionally, this paper uses an integration of buck and boost converter to develop a bidirectional vehicle charger. The performance of bidirectional converter with control algorithm is verified by simulation on MATLAB Simulink. [ABSTRACT FROM AUTHOR]

The aim of this work is to develop a prediction method for renewable energy sources in order to achieve an intelligent management of a microgrid system and to promote the utilization of renewable energy in grid connected and isolated power systems. The proposed method is based on the multi-resolution analysis of the time-series by means of Wavelet decomposition and artificial neural networks. The analysis of predictability of each component of the input data using the Hurst coefficient is also proposed. In this context, using the information of predictability, it is possible to eliminate some components, having low predictability potential, without a negative effect on the accuracy of the prediction and reducing the computational complexity of the algorithm. In the evaluated case, it was possible to reduce the resources needed to implement the algorithm of about 29% by eliminating the two (of seven) components with lower Hurst coefficient. This complexity reduction has not impacted the performance of the prediction algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a methodology for optimal decision making for electrical systems is addressed. This methodology seeks to identify and to prioritize the replacement and maintenance of a power asset fleet optimizing the return of investment. It fulfills this objective by considering the risk index, the replacement and maintenance costs, and the company revenue. The risk index is estimated and predicted for each asset using both its condition records and by evaluating the consequence of its failure. The condition is quantified as the probability of failure of the asset, and the consequence is determined by the impact of the asset failure on the whole system. Failure probability is estimated using the health index as scoring of asset condition. The consequence is evaluated considering a failure impact on the objectives of reliability (energy not supplied -ENS), environment, legality, and finance using Monte Carlo simulations for an assumed period of planning. Finally, the methodology was implemented in an open-source library called PywerAPM for assessing optimal decisions, where the proposed mathematical optimization problem is solved. As a benchmark, the power transformer fleet of the New England IEEE 39 Bus System was used. Condition records were provided by a local utility to compute the health index of each transformer. Subsequently, a Monte Carlo contingency simulation was performed to estimate the energy not supplied for a period of analysis of 10 years. As a result, the fleet is ranked according to risk index, and the optimal replacement and maintenance are estimated for the entire fleet. [ABSTRACT FROM AUTHOR]

Home Energy Management systems are in a rapid development curve, supported by the advancements in computational intelligence, smart appliances, and new smart-grid frameworks. These systems are a fundamental part to implement demand-side management strategies and to shift local energy demand to periods of lower consumption effectively. In this paper, we explore the application of distributed co-evolutionary optimization algorithms and an agent-based architecture to reduce the consumption profile signature of the heating system during the critical peak demand periods, by reducing costs and respecting the comfort constraints of the occupants. The proposed control architecture targets the typical baseboard space heating systems and electrical thermal storage systems, as these represent a large portion of the energy usage in Nordic countries and are commonly controlled by room independent thermostats, which could be easily replaced by smart devices running an algorithm as the one presented in this work. Results prove the strategy proposed getting a cost reduction of up to 23% and a peak-to-average ratio decrease of up to 25% for reference scenarios. Also, an emulation Simulink model is developed to recreate a house and the different heating loads studied in this paper and an experimental test bed is built to model a real ETS system, two different complexity degree RC models are proposed to describe such systems. [ABSTRACT FROM AUTHOR]

• Analyzing the essential prerequisites of a NILM system to enable its diagnosis capacity. • A database construction of household appliances' load models. • A flexible time-variant load modeling, designed by a set of straightforward algorithms. • A recurrent pattern recognition process, able to detect and maintain load models. • An adaptive system capable of actualizing an on-line load model learning procedure. Enabling diagnosis capabilities of Appliance Load Monitoring (ALM) necessitates providing in-operation information of appliances' behavior. Due to both appliances' time-varying model parameters and operations, household aggregated consumption has a dynamic structure. Existing time-invariant load models, built of off-line datasets with static information, are not sufficient to capture the actual behavior of the power consumption. In fact, these models, generally obtained from exhaustive training phases are intended to satisfy load monitoring goals. Therefore, a time-variant load modeling is more practical to capture such a dynamic property of the power consumption. Accordingly, this paper presents an adaptive on-line appliance-level load modeling approach, to design a load monitoring structure for diagnosis purposes. By using the aggregated power consumption of individual households, our proposed structure results in an autonomous household database construction. The modeling procedure begins with a designed recurrent pattern recognition system that is capable of detecting and maintaining load models. This load model structure is determined by using a hidden Markov model (HMM) with dynamic parameters, that are extracted from aggregated signal and trained within an on-line learning process. Our proposed approach can detect time-varying power consumption behavior and estimate the robust load models of appliances. Additionally, our novelty in employing a set of straightforward algorithms, suggests the practicality of our database construction approach. [ABSTRACT FROM AUTHOR]

Highlights • Recognition of PEVs activities at a charging point can lead to fruitful anticipation. • First, online PEV recognition is performed by means of an artificial neural network. • Second, a statistical modeling of the charging habits for each PEV is constructed. • The proposed method relies solely on the measurement of the charging current on the grid side. Abstract In this paper, a novel plug-in electric vehicle (PEV) modeling approach is proposed for residential charging stations. This methodology can be used in the design of autonomous energy management systems (EMS) with the purpose of providing the charging schedules using minimum input measured data. The proposed method is composed of two main steps. In the first step, unlike other similar works, online PEV recognition is performed by means of an artificial neural network as a supervised classification method. The required feature space for classification is provided using the power spectral density estimation and the statistical analysis of individual PEVs charging current. The second step deals with the statistical modeling of the charging habits to facilitate the scheduling by predicting the charging demand using the current measurements on the grid side. In this respect, each PEV charging habit is modeled based on the correlation among the plug-in time, departure time, required energy, and weekdays using kernel density estimation. The performance of the suggested method is validated using real data collected from a charging station. The final results confirm the applicability of the proposed methodology with a satisfactory precision. The effectiveness of the method is demonstrated using a comparative analysis in terms of the recognition performance. [ABSTRACT FROM AUTHOR]

Abstract The household residual component of total power consumption can be considered as a portion of load demand describing the non-temperature-related factors. This component can be decomposed to irregular and predictable energy demands. The predictable component of the residual load include consumptions which are likely to have a periodic behavior. The modeling of this periodic part of the residual load can help to enhance the overall forecasting accuracy. This paper intends to model the periodic part of the residual component by capturing the behavioral patterns of overestimated and underestimated residuals as the divisions of the main residual component. Accordingly, in order to achieve its ambition, this work proposes an Adaptive Circular Conditional Expectation (ACCE) method on the basis of circular analysis to define the sub-residuals operation schedules. Consequently, an adaptive Linear Model (LM) procedure is employed to predict the residual component demand using the results of the ACCE process at each time window. Subsequently, the predicted residual is utilized to adaptively improve the performance of total electricity demand forecasting. The accuracy of the forecasting results is evaluated using Normalized Mean Absolute Error (NMAE). As a result, the proposed approach of the periodic residual demand modeling in a daily horizon leads to a promising accuracy increase of 23%. Furthermore, the proposed residual modeling method, in combination with the temperature-related component forecasting, can increase the total power consumption prediction performance by 7%. The efficacy of the proposed approach is examined via numerical analysis of real data. [ABSTRACT FROM AUTHOR]

Large-scale deployment of advanced smart grid technologies bolsters load forecasting as a vital requirement for deregulated power systems. In this regard, providing an accurate short-term load forecasting (STLF) can facilitate demand response applications and real-time electricity dispatch. STLF is mainly influenced by meteorological conditions among which investigating the relationship between temperature and household total electricity consumption is notably important due to their strong correlation. Accordingly, in this paper, we estimate the total electricity consumption to explore the impact of temperature in terms of a non-linear relationship with electricity demand. We propose the adaptive conditional density estimation (ACDE) method on the basis of kernel density estimation (KDE) to enhance the load forecast accuracy. The aim of the suggested approach is to decompose and examine the mentioned relationship in the context of both temperature-related, and residual components of the total consumption. The performance of the model to forecast the electricity demand is evaluated using a comparison study. The results prove that an ACDE model can significantly improve the recognition capability of the temperature-related component of aggregated power. Finally, the efficacy of the ACDE method is examined via numerical analysis of real data. [ABSTRACT FROM AUTHOR]

The enhanced utilization of Appliance Load Monitoring (ALM) in customer sites enabled by Home Energy Management Systems (HEMS) technologies, offers customized services and enables demand side flexibility in power systems. The significant integration of advanced electrical and computer engineering tools makes the nonintrusive approach of ALM a technically feasible solution to improve demand side energy utilization in the context of HEMS. This paper presents a comprehensive study conducted to reveal significant inevitabilities of a well organized Non-intrusive Load Monitoring (NILM) that aids Smart Home (SH) idea to be implemented. In fact, the viewpoint of this study is to discuss critical issues related to NILM prerequisite necessities, hindered the practical implication of this approach despite improvements during over 30 years. Accordingly, this work presents actual analyses in order to elucidate some arguments using state of the art procedures and results of a semi-synthetic data generator tool. In addition, with the aim of an achievable NILM, we analyze NILM applications from the stakeholders’ perspective to assist the choice of employed techniques. Consequently, by investigating crucial intentions of an effective NILM considering current standstill and future progression, the authors propose the Advanced NILM (ANILM) concept and describe its properties to provide an enhanced energy usage system in demand side. In order to meet its ambition, the paper uses a realistic point of view to pinpoint major obstacles toward NILM and elaborate various factors that will make it effectively feasible. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an alkaline electrolyser simulation tool (AEST). This new approach is based on a physics model of an electrolytic cell and an electrical analogy of the physical effects taking place in the electrolyser. The physics model has been developed taking into account the electrodes' electrochemical properties, the thermodynamics of the reaction, and the electrical properties of the electrode plates, membrane and electrolyte. The electrical analogy is used to integrate the physics model into the electrical simulation systems. The AEST is able to describe the electrolyser's physical and electrical performance and estimates the operating current as function of the voltage, temperature, pressure and electrolyte concentration. The technical information on the Hydrogen Research Institute (HRI) electrolyser has been used to set up the AEST. The start-up phase of the electrolyser has been analyzed for various operating temperatures, pressures and voltages. The steady-state response of the electrolyser (at constant temperature, pressure and concentration of electrolyte) has been simulated as an element of a possible hybrid system. Simulations have been carried out using MATLAB/Simulink/SimPowerSystems®. [Copyright &y& Elsevier]

An efficient participation of prosumers in power system management depends on the quality of information they can obtain. Prosumers actions can be performed by automated agents that are operating in time-changing environments. Therefore, it is essential for them to deal with data stream problems in order to make reliable decisions based on the most accurate information. This paper provides an in-depth investigation of data and concept drift issues in accordance with residential prosumer agents. Additionally, the adaptation techniques, forgetting mechanisms, and learning strategies employed to handle these issues are explored. Accordingly, an approach is proposed to adapt the prosumer agent models to overcome the gradual and sudden concept drift concurrently. The suggested method is based on triggered adaptation techniques and performance-based forgetting mechanism. The results obtained in this study demonstrate that the proposed approach is capable of constructing efficient prosumer agents models with regard to the concept drift problem. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the improvement of synchronization technique for single‐phase inverter. Specifically, the paper proposes a modified structure of second‐order generalized integrator with frequency‐locked loop (SOGI‐FLL) with FLL gain normalization. The proposed structure enhances the frequency detection, which makes it a powerful technique under distorted grid voltage. The validation of the proposed synchronization method includes simulations and experimental tests using Xilinx field programmable gate array (FPGA) as the target device. Moreover, time domain simulations using the direct power control (DPC) with the proposed structure are performed. The decoupled active and reactive powers are controlled using the artificial neural networks (ANNs) trained by the mean of a metaheuristic algorithm. In this paper, the grey wolf optimizer (GWO) is proposed to train the multilayer perceptron (MLP). The proposed approach shows better generation of synchronization signals and smooth power quality, making it suitable for grid‐tied and microgrids (MGs) power systems control. [ABSTRACT FROM AUTHOR]