A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Modelling of a protective scheme for AC 330 kV transmission line in Nigeria

Transmission lines play a vital role in the reliable and efficient delivery of electrical power over long distances, and these lines are affected by faults that occur due to lightning strikes, equipment failures, human, animal or vegetation interference, environmental factors, ageing equipment, voltage sag or grid faults adverse effects on the line. Therefore, protecting these transmission lines becomes crucial with the increasing demand for electricity and the need to ensure grid stability. The modelling process involves the development of a comprehensive protection scheme utilising modern technologies and advanced algorithms. The protection scheme encompasses various elements, including fault detection, fault classification, fault location, and fault clearance. It incorporates intelligent devices, such as protective relays and communication systems, to enable rapid and accurate fault identification and isolation. First, a 330 kV, 500 km three-phase Delta transmission line is modelled using MATLAB/SIMULINK. A section of the Delta network in Delta State Nigeria was used since the entire Nigeria 330 kV network is large. Faulty current and voltage data were generated for training using the CatBoost, 93340 data sizes comprising fault data from three-phase current and voltage extracted from the Delta transmission line model in Nigeria were designed, and twelve fault conditions were used. The CatBoost classifier was employed to classify the faults after different machine language algorithm was used to train the same data with other parameters. The trainer achieved the best accuracy of 99.54%, with an error of 0.46%, at 748 iterations out of 1000 compared to GBoost, XBoost and other classification techniques. Second, the Artificial Neural Network technique was used to train this data, and an accuracy of 100% was attained for fault detection and about 99.5% for fault localisation at different distances with 0.0017 microseconds of detection and an average error of 0% to 0.5%. This model performs better than Support Vector Machine and Principal Component Analysis with a higher fault detection time. The effect of noise signal on the ANN model was studied, and the discrete wavelet technique was used to de-noise the signal for better performance and to enhance the model’s accuracy during transient. Third, the wavelet transforms as a data extraction model to detect the threshold value of current and voltage and the coordination time for the backup relay to trip if the primary relay does not operate or clear the fault on time. The difference between the proposed model and the model without the threshold value was analysed. The simulated result shows that the trip time of the two relays demonstrates a fast and precise trip time of 60% to 99.87% compared to other techniques used without the threshold values. The proposed model can eliminate the trial-and-error in programming the instantaneous overcurrent relay setting for optimal performance. Fourth, the PSO-PID controller algorithm was used to moderate the load frequency of the transmission network. Due to the instability between the generation and distribution, there is always a switch in the stability of the transmission or load frequency; therefore, the PSO-PID algorithm was used to stabilise the Delta power station as a pilot survey from the Nigerian transmission network. Also, a hybrid system with five types of generation and two load centres was used in this model. It has been shown that the proposed control algorithm is effective and improves system performance significantly. As a result, the suggested PSO-PID controller is recommended for producing high-quality, dependable electricity. Moreover, the PSO-PID algorithm produces 0.00 seconds settling time and 0.0005757 ITAE. It’s essential to carefully consider potential drawbacks like complexity and computational overhead, sensitivity to algorithm parameters, potential parameter convergence and limited interpretability and assess their impact on the specific LFC application before implementing a PSO-PID controller in a power system. When implemented with the model in this research, the Delta transmission line network will reduce the excessive fault that occurs in the transmission line and improve the energy efficiency of the entire network when replicated with the Nigerian network. Generally, for the effective design and implementation of the protection scheme of the 330 kV transmission line, the fault must be detected and classified, and the exact location of the fault must be ascertained before the relay protection and load frequency control will be applied for effective fault management and control system