Faults Detection And Classification Schemes For Power Transmission System Based On Enhanced Cumulative Approach

Power transmission lines are often prone to faults,making early fault detection and rapid equipment isolation critical to maintaining supply of power to consumers.An appropriate timing of fault detection can prevent extensive damage to the persons,environment and the system themselves.The issue of early faults detection and fault classification are one of the primary concerns in power protection system.However,the traditional detection and classification algorithms are still not as efficient as required for high resistance faults,noisy environment,faults during power swing,far ends fault and so on.Therefore,an effective and accurate mathematics-based program and process should be in place to detect,classify,and also isolate the faults early as possible to avoid a likelihood of disasters.This work has been suggested to establish a comprehensive and reliable fault identification schemes.Additionally,the schemes should be characterized by simplicity computational to make it more efficient and suitable for the domain applications.The proposed schemes employed an enhancement cumulative approach.The enhancement was made by employing some statistics indices to extract the fault feature from the current signals.The selected statistical indices characterized by simplicity and able to measure the relationships between the random variables.Then the cumulative technique is being used to enlarge the corresponding fault feature.Cumulative schemes are good in terms of speed and accuracy.So,the integration of cumulative approach with the selected statistical indices gives the proposed schemes the following advantages compared with the conventional techniques: 1)The proposed schemes become less sensitive to the noise or spikes that accompany the current signals;2)The selected statistical indices are sensitive in quantifying a small deviation of the data from the mean,and then the proposed scheme is very sensitive to high resistance faults;3)The proposed schemes used simple mathematical models which reflected in the high response for detecting the fault condition;4)The proposed schemes do not require huge offline data;5)The proposed schemes independent of transmission line parameters such as positivenegative sequence component.Toward this end,several schemes have been accomplished in this research;at first,the linear regression scheme is proposed for fault detection and classification in power transmission lines.The main innovation of this scheme is using the slope of each two adjacent points of current samples per cycle as a tool for distinguishing the fault condition.It is observed that,the slope of each two adjacent points during the fault period differs from that one in case of health state.Then,the cumulative sum of these slopes has been used as an apparatus for fault detection and classification in overhead power transmission lines.In the second scheme,the cumulative correlation index-based algorithm is proposed for identifying the faulted phases in power transmission line.This scheme employs the correlation between the current samples during the fault period and the current samples at pre-fault as a tool for defining the faults cases and also identifying the faulted phases.It has been noticed that,the association of the current samples at post-fault and the current samples at pre-fault is very weak association while it is very strong in the case of a safe operating condition.Therefore,this feature has been used to define the fault case from the health state.The third scheme discusses the fault detection and classification in power transmission lines based on variance index-based method.It is employed to perform the faults in two cases:(1)Conventional faults which include single line fault,double line fault and triple line faults in overhead transmission line(2)Non-conventional faults which includes cross-country faults,evolving faults,and inter-circuit fault in double circuit transmission line.In the fourth scheme a cumulative standard deviation sum-based method is presented for performing high resistance fault identification and classification in power transmission lines.It is known that the standard deviation is the best tool for quantifying the small deviation of data.Therefore,a cumulative standard deviation index has been proposed for identifying high resistance fault in power transmission line.Fault detection and classification during the power swing has been discussed in fifth scheme.It is noticed that the conventional cumulative approach is not reliable for performing faults during power swing due to uneven variation in sample-to-sample magnitude difference of the current signals.Therefore,the cumulative covariance of the current samples that obtained from two-terminals is proposed for fault identification in the presence of power swing.