Electromagnetic field and artificial intelligence based fault detection and classification system for the transmission lines in smart grid
03 Jul 2021-Energy Sources Part A-recovery Utilization and Environmental Effects (Taylor & Francis)-pp 1-15
TL;DR: An electromagnetic field and artificial intelligence-based transmission line protection system for smart grid and Bayesian regularization neural network are proposed in this paper.
Abstract: An electromagnetic field and artificial intelligence-based transmission line protection system for smart grid is proposed in this paper. Bayesian regularization neural network is used as an intelli...
Citations
More filters
TL;DR: In this article , Haar wavelet feature extraction-based firefly optimized fault detection (HWFE-FFOFD) method has been introduced to overcome the issues and identify faults in electrical power transmission lines.
Abstract: ABSTRACT Through transmission lines (TLs), an electric power transmission system has been able to transmit power from generating stations to consumers. During transmission, various kinds of malfunctions take place and they are termed as a fault. Although fault is undesirable, it is unavoidable event hampering the smooth functioning of the power system. In power transmission systems, and a large number of voltage and current signal, distortions take place due to faults. Faults occur in power TL causing power supply interruption. Several fault detection techniques have been presented by researchers to detect a fault in TL. However, the time required to locate the fault remained higher and the power loss rate (PLR) was not reduced. To overcome these issues and identify faults in electrical power TL, Haar wavelet feature extraction-based firefly optimized fault detection (HWFE-FFOFD) method has been introduced. The power TL signal sample has been taken as an input. Zero-mean normalization is the pre-processing approach that converts the transmission signal sample into a specified range. To extort features (i.e. voltages and current values) with higher accuracy, the normalized signal has been given to Haar Wavelet Transform. Then, the extracted features at different time instants have been given to the firefly optimized fault detection (FFOFD) algorithm. In the FFOFD algorithm, extracted features have been considered as firefly populations. The FFOFD algorithm functions with the flashing behavior of a firefly. At last, the firefly position has been updated and ranked according to light intensity to detect a fault in electrical power TL. In this manner, the fault detection time (FDT) gets reduced using HWFE-FFOFD method. HWFE-FFOFD method is evaluated in FEA, FDT, and PLR. From the experimental results obtained, it can be confirmed that the HWFE-FFOFD method has been able to enhance accuracy by 14% and minimize time by 26% and PLR by 62% when compared to conventional methods.
TL;DR: Based on the analysis of the quality structure of bi-innovation talents, the authors put forward reasonable ideas on the quality evaluation system of Bi-innvention talents from four aspects: evaluation principles, evaluation objectives, evaluation indicators and evaluation methods.
Abstract: AbstractBuilding an innovative country and promoting employment through entrepreneurship is China’s major development strategy for the future. Building an innovative country needs to vigorously cultivate Bi-innovation talents. Based on the analysis of the quality structure of Bi-innovation talents, this paper puts forward reasonable ideas on the quality evaluation system of Bi-innovation talents from four aspects: evaluation principles, evaluation objectives, evaluation indicators and evaluation methods.KeywordsInnovation and entrepreneurshipQuality of personnel trainingEvaluation system
References
More filters
TL;DR: A review of the literature related to the HIF phenomenon can be found in this paper, where the authors categorized, evaluated, and compared the existing HIF detection techniques and HIF location techniques.
200 citations
TL;DR: Results demonstrate that the combination of EMD and SVM can be an efficient classifier with acceptable levels of accuracy.
103 citations
TL;DR: In this paper, three separate fuzzy inference systems are designed for complete protection scheme for transmission line, which is able to accurately detect the fault (both forward and reverse), locate and also identify the faulty phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault inception angle, fault resistances and fault location.
Abstract: This study aims to improve the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy system. Three separate fuzzy inference system are designed for complete protection scheme for transmission line. The proposed technique is able to accurately detect the fault (both forward and reverse), locate and also identify the faulty phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault inception angle, fault resistances and fault location. The proposed method needs current and voltage measurements available at the relay location and can perform the fault detection and classification in about a half-cycle time. The proposed fuzzy logic based relay has less computation complexity and is better than other AI based methods such as artificial neural network, support vector machine, and decision tree (DT) etc. which require training. The percentage error in fault location is within 1 km for most of the cases. Fault location scheme has been validated using χ2 test with 5% level of significance. Proposed scheme is a setting free method and is suitable for wide range of parameters, fault detection time is less than half cycle and relay does not show any reaching mal-operation so it is reliable, accurate and secure.
92 citations
TL;DR: The novel real time voltage sag and swell detection, classification scheme using artificial neural network is presented and the suitability, robustness and adaptability to monitor power quality issues is claimed.
66 citations
TL;DR: In this paper, an artificial neural-network (ANN)-based digital fault classification and location algorithm for medium voltage (MV) overhead power distribution lines with load taps and embedded remote-end source was presented.
Abstract: This study presents an artificial neural-network (ANN)-based digital fault classification and location algorithm for medium voltage (MV) overhead power distribution lines with load taps and embedded remote-end source. The algorithm utilizes frequency spectra of voltage and current samples which are recorded by the digital relay at the substation. In the algorithm, to extract useful information for ANN inputs, the frequency spectral analysis of voltage and current waveforms has been carried out using Fast Fourier Transform. To classify and locate the shunt faults on an MV distribution system, a multilayer perceptron neural network (MLPNN) with the standard back-propagation technique has been used. A 34.5 kV overhead distribution system has been simulated using MATLAB/Simulink, and the results are used to train and test the ANNs. The technique takes into account all the practical aspects of real distribution system, such as errors, originated from instrument transformers and interface. The ANN-based fault location technique has been extensively tested for a realistic model and gives satisfactory results for radial overhead distribution systems with load taps and in the presence of remote-end source connection.
34 citations