In this paper, harmonic analysis and passive filter design for an industrial installation is presented. A system is designed under MATLAB / Simulink platform that consist of universal bridge with pulse generator to supply a DC load. Harmonic distortion levels of system currents and voltages are obtained by using FFT analysis and also waveform graphs are given to show distortion. Then passive harmonic filters are designed step by step to eliminate harmonics on system. As the designed filters are applied on system, results show that harmonic distortion decreased. FFT analysis is performed for filtered design to obtain effect of designed filters on system. Also current and voltage waveform graphs are given for comparison.
This paper presents harmonic analysis and passive filter design for an industrial installation. A system is designed under MATLAB / Simulink platform that consist of universal bridge with pulse generator to supply a DC load. Harmonic distortion levels of system currents and voltages are obtained by using Fast Fourier Transform (FFT) analysis and also waveform graphs are given to show distortion. Then passive harmonic filters are designed step by step to eliminate harmonics on system. As the designed filters are applied on system, results show that harmonic distortion decreased. FFT analysis is performed for filtered design to obtain effect of designed filters on system. Also current and voltage waveform graphs are given for comparison.
Computer based methods used to analysis the power systems are developed instead of the steady state of mathematical methods. By the development of computer technology, solution of the network problems gets easier. Increment of the necessity to electrical energy by the development of technology, whereas the increment rate of raw energy sources doesn't enough, it have made it mandatory to use the energy sources efficiently. Interconnected networks formed by the connection between not only the domestic sources and customers, but also between the different countries for the optimization and for the efficient use of the sources. Electrical engineers faced by the planning and optimization problems of developing interconnected networks. By this way, the requirement of the use of intelligent systems and computer analysis of power systems has become inevitable. In this study, power flow analysis the of a power system that consist five busbars performed by designed neural network. Results are compared by the results that gained by the analysis with classic Gauss-Seidel method of the same system, then the success of the neural network is investigated.
This paper, using an inverse signal approach, presents a novel deep learning algorithm based on a convolutional neural network (CNN) and bidirectional long short‐term memory (Bi‐LSTM) together with spectrograms for the classification of power quality disturbances (PQDs). The proposed method focuses on the region where the PQD event occurs, using a deep learning‐based spectrogram with the aim of increasing classification success rates. In the proposed approach, the time shift of the signal relative to the pure sine signal was found and relative to this time shift, an inverse sine wave was generated according to the original signal. By collecting this sine wave together with the original signal, spectrograms of both signals were obtained and converted into red/green/blue (RGB) images that were then combined. Finally, classification was carried out via CNN/Bi‐LSTM. In this context, 29 different disturbance events in both single and combined structures were used. The proposed model was applied to the disturbance events and 99.33% classification accuracy was obtained.
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