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In this paper a new method for fault detection in microgrids is proposed considering uncertainties of their networks topologies. The new method is realized by wavelet and S transforms. The properties of three phase components and positive, negative and zero sequences of current and voltage waveforms which are used in fault detection, fault location and to determining the occurred fault type are mined by wavelet and S transforms. For this reason that the process of the fault detection in this method is independent from the network topology, so this method is capable to detect occurred faults in all dynamic states of the studied microgrid. To check the ability of the proposed method in fault detection, this method is implemented on a sample microgrid. The simulation results show that the proposed method is capable to distinguish between the occurred faults and the transient distortions in the network. Also the comparison of S transform and wavelet transform shows that although wavelet transform usage exceeds the speed of maintaining a decision, but the accuracy of S transform in differentiate between the occurred states in microgird is the premier property of this transform.

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Electricity demand forecasting is one of the most important factors in the planning, design, and operation of competitive electrical systems. However, most of the load forecasting methods are not accurate. Therefore, in order to increase the accuracy of the short-term electrical load forecast, this paper proposes a hybrid method for predicting electric load based on a deep neural network with a wavelet transform and input selection. Based on the entropy function. Also, in order to demonstrate the strength of the proposed method, the PJM electricity market and one of the Kerman substations load data in 1395 were used and the results of which emphasized the efficiency of the proposed method in predicting the electric load for production planning And distribution.

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Dissolved gases in transformer oil indicate the occurrence of a fault in the transformer. Many standards use the ratio of dissolved gases in transformer oil to detect faults in transformers. These traditional methods cannot, however, be used in cases in which the transformer needs to be immediately removed from service in case of a serious error such as electric arcs to prevent the error from spreading. For this purpose, this paper uses signal processing methods that analyze the signal online. The paper assumes that CO and CO2 dissolved gases in transformer oil are measured online by a sensor and then some signal processing methods are applied to the measurement data. These methods include Fast Fourier Transform, Discrete Wavelet Transform, Hilbert Transform, Gabor Transform, the combination of discrete wavelet and Hilbert transform, the combination of discrete wavelet and Gabor transform, and spectrogram. These methods are continuously applied to these two gases that are soluble in transformer oil to determine their variations in the transformer oil at a certain time or a certain frequency. The gases are also modified and then the performance of each of the processing methods mentioned in these changes is investigated. Fault detection reference is the CIGER 761-2019 standard. The purpose of this paper is to find out the samples of gases change in a frequency interval or timeframe together irrespective of the faults in the transformer and changes in the volume of dissolved gases in transformer oil, analyze the signal processing methods, and detect the type of fault using CIGRE 761-2019 standard. The fast Fourier transform method analyzes signal power by frequency. The discrete wavelet transform method extracts high-frequency components of gases and detects faults based on the largest components. The Hilbert transform method converts the signal into two real and imaginary parts. Then, it uses the imaginary part that represents the signal phase to detect faults. The Gabor transform method extracts instantaneous frequencies in the time-frequency plane and uses this method to detect faults. In the methods that combine discrete wavelet transform and Hilbert or Gabor transform, high-frequency components are extracted by discrete wavelet transform, and then Hilbert or Gabor transform methods are applied. The spectrogram method also indicates the size of the short-time Fourier transform, which is used to analyze the signal. These signal processing methods are compared in several features, and the discrete wavelet transform method is introduced as the best method for fault detection.
 

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Power transformers are the most important components of a power system, so their protection is a critical issue. This paper proposes a novel and efficient algorithm based on the high-frequency components of the differential current signal to discriminate between the magnetizing inrush currents and the internal faults. After detecting the over-current in the differential current signals, samples of a quarter of a cycle of the signal are recorded. Then, discrete wavelet transform (DWT) is applied to the recorded signals, and the details of the wavelet transform output are extracted. Because of the existence of the high-frequency transients in the internal fault current signals, the wavelet transform outputs of the internal fault signals have more fluctuations than that of the inrush current signals. By calculating the standard deviation of the wavelet transform output, the fluctuations can be quantified. Therefore, the standard deviation of the wavelet transform output can be used as a criterion to discriminate between the internal faults and the magnetizing inrush currents. The proposed algorithm has a very low computational burden, and it uses only a quarter of a cycle of the differential current signals. This guarantees the high speed of the proposed algorithm. The proposed algorithm is tested by different conditions of the internal faults and the inrush situations, and it successfully identifies the true situation with high accuracy in all conditions. The simulation results show the superior specifications of the proposed algorithm.