Nuwan Perera

Isolation of Faults in Distribution Networks With Distributed Generators

This paper presents an agent-based protection scheme for distribution networks with distributed generators. The distribution network is divided into several network segments. The relay agents, which are located at the boundaries of these segments, can determine the direction of fault currents using the transients generated by the fault. Fault directions determined by the relay agents located at different points of the network are combined to determine the faulted segment. A fault direction identification technique, which uses the wavelet coefficient of the measured line currents, was developed for relay agents. The performance of the proposed protection scheme is investigated through simulation of a benchmark medium-voltage distribution system.

Investigation of a fast islanding detection methodology using transient signals

A novel approach for fast detection of power islands in a distribution network using the transient signals generated during the islanding event is investigated. Performance of several pattern recognition techniques in classifying the transient generating events as islanding or non-islanding was examined. Discrete wavelet transform of the transient current signals are utilized to extract feature vectors for the classifiers. Samples of the feature vectors corresponding to various islanding and non-islanding events are applied to train (i) a decision tree classifier, (ii) a probabilistic neural network classifier, and (iii) a support vector machine classifier for recognizing the transient patterns originating from the islanding events. The trained classifiers were then tested with unseen test current waveforms. The test results demonstrated that the investigated technique can potentially provide a new way for identification of islanding in distribution systems.

Recognition of Fault Transients Using a Probabilistic Neural-Network Classifier

This paper investigates the applicability of decision tree, hidden Markov model, and probabilistic neural-network (PNN) classification techniques to distinguish the transients originating from the faults from those originating from normal switching events. Current waveforms due to different types of events, such as faults, load switching, and capacitor bank switching were generated using a high-voltage transmission system simulated in PSCAD/EMTDC simulation software. Simulated transients were used to train and test the classifiers offline. The wavelet energies calculated using three-phase currents were used as input features for the classifiers. The results of the study showed the potential for developing a highly reliable transient classification system using the PNN technique. An online classification model for PNN was fully implemented in PSCAD/EMTDC. This model was extensively tested under different scenarios. The effects of the fault impedance, signal noise, current-transformer saturation, and arcing faults were investigated. Finally, the operation of the classifier was verified using actual recorded waveforms obtained from a high-voltage transmission system.

Series-Compensated Double-Circuit Transmission- Line Protection Using Directions of Current Transients

This paper presents the development of a new protection method for series-compensated double-circuit transmission lines using current transients. Using the proposed method, the faulted circuit can be identified locally, by comparing the polarities of wavelet coefficients of the branch currents. Applicability of the proposed method is demonstrated using a 500-kV transmission system simulated in an electromagnetic transient simulation program. Comparisons with the conventional distance and phase comparison protection schemes show that the proposed method can provide faster and more reliable protection for the series-compensated double-circuit transmission systems. The security of the relay can be further enhanced if the fault direction information is exchanged between the relays at two ends.

Agent-based protection scheme for distribution networks with distributed generators

This paper describes a novel protection scheme for a distribution network with distributed generators. The network is divided into several segments to facilitate fault isolation. Relay agents protecting the system are installed at points of interconnection between different network segments. These relay agents communicate with the neighboring agents through an asynchronous communication network. A wavelet transform coefficients based technique is proposed to identify the direction of a fault with respect to a node in the network by measuring the transient currents in the branches connected to the node. Relay agents, which use this technique to determine the fault directions with respect to their locations, then collaboratively determine the faulted zone. Simulation studies using PSCAD/EMTDC reveals that this scheme is capable of identifying and locating faults with very high accuracy even for high impedence faults

Development and Hardware Implementation of a Fault Transients Recognition System

This paper presents the development and hardware implementation of a classification scheme to distinguish the transients originated by faults from other types of transients. In the proposed scheme, a set of Hidden Markov Model-based classifiers is employed to recognize the fault transients. Input features for the classifiers are the energy contained in wavelet coefficients of the measured current waveforms. A laboratory prototype of the fault recognition system was implemented on a floating-point digital-signal-processor (DSP)-based hardware platform. The classification system was tested using the transient signals generated by a real-time waveform playback unit. The test waveforms were generated by simulating an actual extra-high-voltage transmission system on an electromagnetic transient simulation program. The operation of the classification system was further verified using waveforms obtained from an actual fault recorder. The performance of the classifier was investigated under different practical scenarios, such as current transformer saturation, measurement noise, and lightning faults.

Hardware implementation of an islanding detection approach based on current and voltage transients

A pattern classification technique for fast detection of power islands in a distribution network is implemented and tested. It utilizes voltage and current transient signals generated during an islanding event to detect the formation of the island. A Decision Tree classifier is trained to categorize the transient generating events as ‘islanding’ or ‘non-islanding’. It involves two basic stages of signal processing to extract the required feature vectors for the classification. The first stage involves signal filtering and in the second stage signals are processed by rectifying, summing, and low-pass filtering to get the energy content in the three phases during a selected time-frame. Analog filters, rectifiers, adders and micro-controllers complete the implementation. The performance of the design was tested with signals generated using a real-time waveform playback instrument. A simple radial medium voltage distribution system with single distributed generator was simulated in PSCAD/EMTDC to obtain the transient waveforms. The experimental and simulation results give comparable results showing high accuracy in detecting islanding events very fast.

Electromagnetic Transients Simulation for Renewable Energy Integration Studies

Grid interface of renewable energy based distributed generation requires satisfying the interconnection requirements stipulated by the local distribution utility. Detailed testing of protection and control systems is required to ensure conformity to wide ranging interconnection requirements such as fault ride through capability, protection against islanding, harmonics and voltage flicker limits. Electromagnetic transient simulation is a powerful tool for performing such studies with very detailed models of power electronics converters, controllers and protection systems. Furthermore, the simulated transient waveforms can be recorded and played back into actual hardware using real time signal playback equipment to verify correct operation.

Recognition of fault transients using a probabilistic neural-network classifier

This paper investigates the applicability of decision tree, hidden Markov model, and probabilistic neural-network(PNN) classification techniques to distinguish the transients originating from the faults from those originating from normal switching events. Current waveforms due to different types of events, such as faults, load switching, and capacitor bank switching were generated using a high-voltage transmission system simulated in PSCAD/EMTDC simulation software. Simulated transients were used to train and test the classifiers offline. The wavelet energies calculated using three-phase currents were used as input features for the classifiers. The results of the study showed the potential for developing a highly reliable transient classification system using the PNN technique. An online classification model for PNN was fully implemented in PSCAD/EMTDC. This model was extensively tested under different scenarios. The effects of the fault impedance, signal noise, current-transformer saturation, and arcing faults were investigated. Finally, the operation of the classifier was verified using actual recorded waveforms obtained from a high-voltage transmission system.

Real-time implementation of discrete wavelet transform for transient type protection applications

This paper presents the real-time implementation of discrete wavelet transform for transient type protection applications. The multi-resolution properties of wavelet transform makes it efficient for spectral representation of high frequency power system transients. This property is exploited to rapidly detect and accurately classify power network disturbances. This information can then be used for high speed relays that could operate network switches for remedial actions. A digital signal processor platform was used to implement the algorithm. The accuracy of the implementation was verified using MATLAB wavelet toolbox. Example case was presented to demonstrate the applicability of wavelet implementation for transient based protection applications.