Study of the starting current and frequency deviation of electrostatic gyromonotron

Background: Traditional thyristor-based three-phase soft starters of induction motor often suffer from high starting current and heavy harmonics. Moreover, both the trigger pulse generation and driving circuit design are usually complicated. Methods: To address these issues, we propose a novel soft starter structure using fully controlled IGBTs in this paper. Compared to approaches of traditional design, this structure only uses twophase as the input, and each phase is controlled by a power module that is composed of one IGBT and four diodes. Results: Consequently, both driving circuit and control design are greatly simplified due to the requirement of fewer controlled power semiconductor switches, which leads to the reduction of the total cost. Conclusion: Both Matlab/Simulink simulation results and experimental results on a prototype demonstrate that the proposed soft starter can achieve better performances than traditional thyristorbased soft starters for Starting Current (RMS) and harmonics.

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Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽

10.3390/en14020304 ◽

2021 ◽

Vol 14 (2) ◽

pp. 304

Author(s):

Sakthivel Ganesan ◽

Prince Winston David ◽

Praveen Kumar Balachandran ◽

Devakirubakaran Samithas

Keyword(s):

Standard Deviation ◽

Power Quality ◽

Monitoring System ◽

Condition Monitoring ◽

Classification Accuracy ◽

Induction Motors ◽

Quality Data ◽

Starting Current ◽

Quality Issues ◽

Condition Monitoring System

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

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Research on Coordinated Control Strategy of Energy Storage Participating in Primary Frequency Regulation Considering Frequency Deviation Change Rate

Journal of Physics Conference Series ◽

10.1088/1742-6596/1750/1/012034 ◽

2021 ◽

Vol 1750 ◽

pp. 012034

Author(s):

Bo Yang ◽

Liguo Weng ◽

Yuelong Li ◽

Yanghui Zhang ◽

Chen Zhou

Keyword(s):

Energy Storage ◽

Control Strategy ◽

Coordinated Control ◽

Frequency Deviation ◽

Frequency Regulation ◽

Change Rate ◽

Primary Frequency

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Load Frequency Control Using Hybrid Intelligent Optimization Technique for Multi-Source Power Systems

Energies ◽

10.3390/en14061581 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1581

Author(s):

Deepak Kumar Gupta ◽

Amitkumar V. Jha ◽

Bhargav Appasani ◽

Avireni Srinivasulu ◽

Nicu Bizon ◽

...

Keyword(s):

Power Plant ◽

Power System ◽

Frequency Control ◽

Optimization Technique ◽

Load Frequency Control ◽

Frequency Deviation ◽

Source Power ◽

Load Frequency ◽

Tie Line

The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.

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