Electronic Load Modeling for VRM

2021 ◽  
Author(s):  
Filip Stoimenov ◽  
Vladimir Dimitrov ◽  
Dimitar Arnaudov
Keyword(s):  
Load Modeling ◽  
Electronic Load

SVM-Based Parameter Identification for Composite ZIP and Electronic Load Modeling

2019 ◽  
Vol 34 (1) ◽  
pp. 182-193 ◽  
Author(s):  
Chong Wang ◽  
Zhaoyu Wang ◽  
Jianhui Wang ◽  
Dongbo Zhao
Keyword(s):  
Parameter Identification ◽  
Load Modeling ◽  
Electronic Load

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

scholarly journals Effect of Symmetrically Switched Rectifier Topologies on the Frequency Regulation of Standalone Micro-Hydro Power Plants

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3201
Author(s):  
Henry Bory ◽  
Jose L. Martin ◽  
Iñigo Martinez de Alegria ◽  
Luis Vazquez
Keyword(s):  
Power Factor ◽  
Power Plants ◽  
Reactive Power ◽  
Alternating Current ◽  
Frequency Regulation ◽  
Hydro Power ◽  
Major Energy Source ◽  
Hydro Power Plants ◽  
Electronic Load ◽  
Ac Converters

Micro-hydro power plants (μHPPs) are a major energy source in grid-isolated zones because they do not require reservoirs and dams to be built. μHPPs operate in a standalone mode, but a continuously varying load generates voltage unbalances and frequency fluctuations which can cause long-term damage to plant components. One method of frequency regulation is the use of alternating current-alternating current (AC-AC) converters as an electronic load controller (ELC). The disadvantage of AC-AC converters is reactive power consumption with the associated decrease in both the power factor and the capacity of the alternator to deliver current. To avoid this disadvantage, we proposed two rectifier topologies combined with symmetrical switching. However, the performance of the frequency regulation loop with each topology remains unknown. Therefore, the objective of this work was to evaluate the performance of the frequency regulation loop when each topology, with a symmetrical switching form, was inserted. A MATLAB® model was implemented to simulate the frequency loop. The results from a μHPP case study in a small Cuban rural community called ‘Los Gallegos’ showed that the performance of the frequency regulation loop using the proposed topologies satisfied the standard frequency regulation and increased both the power factor and current delivery capabilities of the alternator.

Composite load modeling by spatial-temporal deep attention network based on wide-area monitoring systems

2021 ◽  
pp. 1-12
Author(s):  
Omid Izadi Ghafarokhi ◽  
Mazda Moattari ◽  
Ahmad Forouzantabar
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Wide Area ◽  
Load Modeling ◽  
Wide Area Monitoring ◽  
Training Performance ◽  
Spatial Features ◽  
Temporal Features ◽  
Frequency Domains ◽  
Area Monitoring

With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods.

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