Intelligente Regelung für Stromspeichersysteme/Intelligent control for energy storage systems

2021 ◽  
Vol 111 (03) ◽  
pp. 167-171
Author(s):  
Can Kaymakci ◽  
Raoul Laribi ◽  
Alexander Sauer
Keyword(s):  
Intelligent Control ◽  
Storage System ◽  
Load Forecasting ◽  
Measurement Data ◽  
Third Party ◽  
Mechatronic System ◽  
Electricity Storage ◽  
Storage Devices ◽  
Intelligent Control System ◽  
Forecasting Models

In zahlreichen mechatronischen Anwendungen kann der Einsatz von Stromspeichern die notwendige Anschlussleistung reduzieren und die Energieeffizienz durch die Nutzung von Bremsenergie erhöhen. Ein Stromspeichersystem in eine Fremdmaschine zu integrieren stellt eine Herausforderung bei der Inbetriebnahme von Hardware und Software dar. Eine automatische Systemidentifikation und Lastprognose anhand von Messdaten erleichtert den Regelungsentwurf für das mechatronische System. Dieser Beitrag erläutert eine Vorgehensmethodik für die Vorauswahl von geeigneten Methoden für die Modellbildung zur Lastprognose.   In numerous mechatronic applications it is possible to apply electricity storage devices for reducing the necessary installed load and increasing energy efficiency and thus to make use of the braking energy. The integration of an electricity storage system into a third-party machine is challenging with regard to the commissioning of hardware and software. An intelligent control system facilitates system identification based on measurement data and load forecasting within the mechatronic system. This article explains a procedure for pre-selecting suitable methods for load forecasting models.

scholarly journals Automation of microclimate in greenhouses

2020 ◽  
Vol 210 ◽  
pp. 05004
Author(s):  
Marina Ganzhur ◽  
Alexey Ganzhur ◽  
Andrey Kobylko ◽  
Denis Fathi
Keyword(s):  
Intelligent Control ◽  
Experimental Validation ◽  
Fuzzy Controller ◽  
Asynchronous Motor ◽  
Measurement Data ◽  
Indoor Climate ◽  
Intelligent Control System ◽  
Dynamic Simulation Model ◽  
Simulation Results ◽  
Favorable Conditions

An agricultural greenhouse is a complex system with many input features. Taking these features into consideration creates favorable conditions for the production of plants. The parameters are temperature and internal humidity, which have a significant impact on the yield. The aim of this study was to propose a dynamic simulation model in the MATLAB/Simulink environment for experimental validation. In addition, a fuzzy controller for the indoor climate of the greenhouse with an asynchronous motor for ventilation, heating, humidification, etc. has been designed. The model includes an intelligent control system for these drives in order to ensure optimal indoor climate. The dynamic model was validated by comparing simulation results with experimental measurement data. These results showed the effectiveness of the control strategy in regulating the greenhouse indoor climate.

scholarly journals The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 345
Author(s):  
Janusz Sowinski
Keyword(s):  
Load Forecasting ◽  
Large Set ◽  
Exogenous Variables ◽  
Anfis Model ◽  
Load Forecast ◽  
Forecasting Models ◽  
Power Company ◽  
Ex Post ◽  
Daily Load ◽  
Selection Of

Forecasting of daily loads is crucial for the Distribution System Operators (DSO). Contemporary short-term load forecasting models (STLF) are very well recognized and described in numerous articles. One of such models is the Adaptive Neuro-Fuzzy Inference System (ANFIS), which requires a large set of historical data. A well-recognized issue both for the ANFIS and other daily load forecasting models is the selection of exogenous variables. This article attempts to verify the statement that an appropriate selection of exogenous variables of the ANFIS model affects the accuracy of the forecasts obtained ex post. This proposal seems to be a return to the roots of the Polish econometrics school and the use of the Hellwig method to select exogenous variables of the ANFIS model. In this context, it is also worth asking whether the use of the Hellwig method in conjunction with the ANFIS model makes it possible to investigate the significance of weather variables on the profile of the daily load in an energy company. The functioning of the ANFIS model was tested for some consumers exhibiting high load randomness located within the area under supervision of the examined power company. The load curves featuring seasonal variability and weekly similarity are suitable for forecasting with the ANFIS model. The Hellwig method has been used to select exogenous variables in the ANFIS model. The optimal set of variables has been determined on the basis of integral indicators of information capacity H. Including an additional variable, i.e., air temperature, has also been taken into consideration. Some results of ex post daily load forecast are presented.

Short-term Load Forecasting by Using Improved GEP and Abnormal Load Recognition

2021 ◽  
Vol 21 (4) ◽  
pp. 1-28
Author(s):  
Song Deng ◽  
Fulin Chen ◽  
Xia Dong ◽  
Guangwei Gao ◽  
Xindong Wu
Keyword(s):  
Gene Expression ◽  
Probability Distribution ◽  
Detection Rate ◽  
Cross Validation ◽  
Normal Load ◽  
Gene Expression Programming ◽  
Load Forecasting ◽  
Short Term ◽  
Forecasting Models ◽  
Short Term Load Forecasting

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved Gene Expression Programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved Gene Expression Programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE, MAPE, RSME, and R 2 .

scholarly journals Supporting SLA via Adaptive Mapping and Heterogeneous Storage Devices in Ceph

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 847
Author(s):  
Sopanhapich Chum ◽  
Heekwon Park ◽  
Jongmoo Choi
Keyword(s):  
Positive Impact ◽  
Distributed Storage ◽  
Storage System ◽  
Hard Disk Drives ◽  
Service Level Agreement ◽  
Service Level ◽  
Cost Ratio ◽  
Solid State Drives ◽  
Storage Devices ◽  
Management Scheme

This paper proposes a new resource management scheme that supports SLA (Service-Level Agreement) in a bigdata distributed storage system. Basically, it makes use of two mapping modes, isolated mode and shared mode, in an adaptive manner. In specific, to ensure different QoS (Quality of Service) requirements among clients, it isolates storage devices so that urgent clients are not interfered by normal clients. When there is no urgent client, it switches to the shared mode so that normal clients can access all storage devices, thus achieving full performance. To provide this adaptability effectively, it devises two techniques, called logical cluster and normal inclusion. In addition, this paper explores how to exploit heterogeneous storage devices, HDDs (Hard Disk Drives) and SSDs (Solid State Drives), to support SLA. It examines two use cases and observes that separating data and metadata into different devices gives a positive impact on the performance per cost ratio. Real implementation-based evaluation results show that this proposal can satisfy the requirements of diverse clients and can provide better performance compared with a fixed mapping-based scheme.

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