|dc.description.abstract||The widespread deregulation and restructuring of electric power utilities throughout the world and the surge in competition amongst utility companies has brought about the desire for improved economic efficiency of electric utilities and the provision of better service to energy consumers. These end users are usually connected to the distribution network. Thus, there is a growing research interest in distribution network fault detection and diagnosis algorithms for reducing the down-time due to faults. This is done so as to improve the reliability indices of utility companies and enhance the availability of power supply to customers.
The application of signal processing and computational intelligence techniques in power systems protection, automation, and control cannot be overemphasized. This research work focuses on power system distribution network and is aimed at the development of versatile algorithms capable of accurate fault detection and diagnosis of all fault types for operation in balanced/unbalanced distribution networks, under varying fault resistances, fault inception angles, load angles, and system operating conditions.
Therefore, different simulation scenarios encompassing various fault types at several locations with different load angles, fault resistances, fault inception angles, capacitor switching, and load switching were applied to the IEEE 34 Node Test Feeder in order to generate the data needed. In particular, the effects of system changes were investigated by integrating various Distributed Generators (DGs) into the distribution feeder. The length of the feeder was also extended and investigations carried out. This was implemented by modelling the IEEE 34-node benchmark test feeder in DIgSILENT PowerFactory (DPF).
In the course of this research, a hybrid combination of Discrete Wavelet Transform (DWT), decision-taking rule-based algorithms, and Artificial Neural Networks (ANNs) algorithms for electric power distribution network fault detection and diagnosis was developed. The integrated algorithms were capable of fault detection, fault type classification, identification of the faulty line segment, and fault location respectively.
Several scenarios were simulated in the test feeder. The resulting waveforms were exported as ASCII or COMTRADE files to MATLAB for DWT signal processing. Experiments with various DWT mother wavelets were carried out on the waveforms obtained from the simulations. In particular, Daubechies db-2, db-3, db-4, db-5, and db-8 were considered. Others are Coiflet-3 and Symlet-4 mother wavelets respectively. The energy and entropy of the detail coefficients for each decomposition level based on a sampling frequency of 7.68 kHz were analysed. The best decomposition level for the diagnostic tasks was then selected
based on the analysis of the wavelet energies and entropy in each level of decomposition. Consequently, level-1 db-4 detail coefficients were selected for the fault detection task, while level-5 db4 detail coefficients were used to compute the wavelet entropy per unit indices which were then used for fault classification, fault section identification, and fault location tasks respectively.
Decision-taking rule-based algorithms were used for the fault detection and fault classification tasks respectively. The fault detection task verifies if a fault did indeed occur or not, while the fault classification task determines the fault class and the faulted phase(s). Similarly, Artificial Neural Networks (ANNs) were used for the fault section identification and fault location tasks respectively. For the fault section identification task, the ANNs were trained for pattern classification to identify the lateral or segment affected by the fault. Conversely, the fault location ANNs were trained for function approximation to predict the location of the fault from the substation in kilometres.
Also, the IEEE 13 Node Benchmark Test Feeder was modelled in RSCAD software and batch mode simulations were carried out using the Real-Time Digital Simulator (RTDS) as a ‘proof of concept’ for the proposed method, in order to demonstrate the scalability, and to further validate the developed algorithms. The COMTRADE files of disturbance records retrieved from an external IED connected in closed-loop with the RTDS and the runtime simulation waveforms were used as test inputs to the developed Hybrid Fault Detection and Diagnosis (HFDD) method.
Comparison of the method based on entropy with statistical methods based on standard deviation and Mean Absolute Deviation (MAD) has shown that the method based on entropy is very reliable, accurate, and robust. Results of preliminary studies carried out showed that the proposed HFDD method can be applied to any power system network irrespective of changes in the operating characteristics. However, certain decision indices would change and the decision-taking rules and ANN algorithms would need to be updated.
The HFDD method is promising and would serve as a useful decision support tool for system operators and engineers to aid them in fault diagnosis thereby helping to reduce system down-time and improve the reliability and availability of electric power supply.
Key words: Artificial neural network, discrete wavelet transform, distribution network, fault simulation, fault detection and diagnosis, power system protection, RTDS.||en_US