Hybrid classifier for fault location in active distribution networks

Abstract

This paper presents a fast hybrid fault location method for active distribution networks with distributed generation (DG) and microgrids. The method uses the voltage and current data from the measurement points at the main substation, and the connection points of DG and microgrids. The data is used in a single feedforward artificial neural network (ANN) to estimate the distances to fault from all the measuring points. A k-nearest neighbors (KNN) classifier then interprets the ANN outputs and estimates a single fault location. Simulation results validate the accuracy of the fault location method under different fault conditions including fault types, fault points, and fault resistances. The performance is also validated for non-synchronized measurements and measurement errors.

A Travelling Wave-Based Fault Location Strategy Using the Concepts of Metric Dimension and Vertex Covers in a Graph

“…A set of possible fractions corresponding to the section on line 12 − 13 from bus 12 to the fault can be found from Equation (9), which in turn can be used to find a set of predicted reflected travelling wave arrival times (specifically travelling waves from the fault to bus 13, and back to 12). It is possible to find these using Equation (15). As an example 𝑥 12,13 12 shows the WC of the travelling waves at bus 12, with a matching arrival time of 10.055, which can also be confirmed from Table 6 as T2.…”

“…Then calculate and match all distances corresponding to the arrival times in 𝑈 + . If it is possible to find a 𝑢 𝑖 ∈ 𝑈 + and 𝑢 𝑗 ∈ 𝑈 + , that satisfies Equation (15), that is, 𝑡 𝑗 = 𝑡 0 + 3(𝑡 𝑢 − 𝑡 0 ) as well as 𝑡 0 = 1 2 (3𝑡 𝑢 − 𝑢 𝑖 ), then the distance from the fault to 𝑢 will be 𝑚 𝑖 = (𝑢 𝑖 −𝑢 1 )𝑣…”

“…These include impedance-based techniques, requiring voltage, current and the impedance sequence to estimate the location of the fault [13], [14]. In [15], hybrid classifiers were investigated for fault location in distribution networks. S-transform-based feed forward neural networks were investigated in [16] for distribution grid networks, which are able to detect faults independent of the physical limitations associated with traditional TWFL detection schemes.…”

Application Status and Prospects of 5G Technology in Distribution Automation Systems

“…The fault location of additional equipment is mainly determined by the sectionalizer and recloser method [22] and the fault indicator method [23]. In the second method, the accuracy of the fault location is based on the measurement information of the distribution line [24]. The decisive factor is the need for sufficient measurement information for analysis to achieve accurate fault location determination.…”

A New Locating Method of Break Faults in an Active Distribution Network Based on Distributed Generator Monitoring

“…Given their weak characteristics, accurately locating ADN break faults under the randomness and polymorphism of DGs presents a challenge. With the excellent controllability of ADNs [21], this paper proposes a break fault location method for ADNs based on DG monitoring. Unlike traditional methods that take DG fault output as disturbance in fault identification, this paper uses rich DG fault output information to quickly and accurately locate break faults.…”