Research and Simulation of IBPSO-based Fault Location in Power Distribution Network adopting DG

Due to the local optimum and the inaccurate fault location of distribution network when DG (distributed generation) access using the traditional BPSO (binary particle swarm optimization), an IBPSO (improved binary particle swarm optimization) to locate the fault places is proposed. Firstly, the locating model of distribution network fault is established, which mainly includes the improved coding mode, the improved switching function and the improved fitness function. Then, the BPSO is improved, in which the inertial weight in the algorithm has the adaptive ability, so that the particle can maintain better. At last, this algorithm is used to simulate and locate the fault of distribution network with DG. The results prove that the algorithm and the improved function can accurately locate fault places when the single point fault and multi point fault in the distribution network with DG.

PurposeAn electrical power distribution network is expected to deliver uninterrupted power supply to the customers. The disruption in power supply occurs whenever there is a fault in the system. Therefore, fast fault detection and its precise location are necessary to restore the power supply. Several techniques are proposed in the past for fault location in distribution network but they have limitations as their fault location accuracy depends on system conditions. The purpose of this paper is to present a travelling wave-based fault location method, which is fast, accurate and independent of system conditions.Design/methodology/approachThis paper proposes an effective method for fault detection, classification and location using wavelet analysis of travelling waves for a multilateral distribution network embedded with distributed generation (DG) and electric vehicle (EV) charging load. The wavelet energy entropy (WEE) is used for fault detection and classification purpose, and wavelet modulus maxima (WMM) of aerial mode component is used for faulted lateral identification and exact fault location.FindingsThe proposed method effectively detects and classifies the faults, and accurately determines the exact fault location in a multilateral distribution network. It is also found that the proposed method is robust and its accuracy is not affected by the presence of distributed generation and electric vehicle charging load in the system.Originality/valueTravelling wave based method for fault location is implemented for a multilateral distribution network containing distributed generation and electric vehicle load. For the first time, a fault location method is tested in the presence of EV charging load in distribution network.

At present, the fault location technology of distribution network mainly relies on FTU, which is divided into direct method and indirect method. The direct method is mainly matrix method, and the indirect method is mainly based on AI algorithm. The matrix method can not locate the fault correctly when the FTU is disturbed and other reasons cause the failure information to be missed. So there are some problems in fault location of distribution network using matrix method (direct method). To solve this problem, based on the traditional particle swarm optimization (PSO) algorithm, this paper constructs a binary particle swarm optimization (PSO) algorithm model to realize fault location of distribution network, and uses MATLAB to verify the simulation example. The results show that the algorithm has the advantages of good convergence, high stability and excellent fault tolerance.

The accuracy of DNA computing highly depends on the DNA strands used in solving complex computations. As such, many approaches are proposed to design DNA oligonucleotides that are stable and unique. In this paper, an improved binary particle swarm optimization (IBPSO) algorithm is proposed and implemented. Four objective functions which are H-measure, similarity, hairpin and continuity are employed to define the uniqueness of designed sequences. The DNA words are constrained within a predefined range of GC-content and melting temperature. The performances and the ability of the algorithm to enhance the characteristics of generated DNA code words are analyzed. The results obtained show that this algorithm executes better sequences and did perform better compared to other optimization techniques. Moreover, it converges faster than the previously suggested binary particle swarm optimization algorithm.

With the access of distributed generators (DG), the power flow direction and structure of distribution networks have changed, and therefore many traditional fault location methods are no longer applicable. Single-phase grounding fault is a common fault in power distribution network, which may bring secondary fault and even blackout. Starting from the relationship between the wavelet eigenvalues of traveling waves and the fault section of distribution lines with DG, the optimal fault features were selected by dimension reduction of linear discriminant analysis (LDA), and then the Bayesian classification model based on kernel distribution was used to realize the new method of Single phase grounding fault location. The IEEE 33-bus model with DGs was constructed to test the faults in different sections of active distribution network, the locating accuracy of the optimal three-dimensional feature sample is 97.9%. The result shows the method can achieve accurate fault location.

Feature selection, a combinatorial optimization problem, remains broadly applied in the area of Computational Learning with the aim to construct a model with reduced features so as to improve the performance of the model. Feature selection algorithm aims to identify admissible subgroup of features without sacrificing the accuracy of the model. This research works uses Improved Binary Particle Swarm Optimization (IBPSO) to optimally identify subset of features. The problem of stagnation, trapping in local optima and premature convergence of Binary Particle Swarm Optimization (BPSO) for solving discrete feature selection dispute has been tackled using IBPSO. IBPSO prevents the model from overfitting and also takes less computational time for constructing the model because of reduced feature subset. The sine function, cosine function, position of the random particle and linear decrement of inertial weight are integrated in IBPSO, which balances between exploration and exploitation to identify optimal subset of features. The linear decrement of inertial weight tends to do good level of exploration at the starting phase, whereas at the end it tends to exploit solution space to find the optimal subset of features that are more informative and thereby discarding redundant and irrelevant features. Experimentation is carried out on seven benchmarking datasets obtained from University of California, Irvine repository, which includes various real-world datasets for processing with machine learning algorithms. The proposed IBPSO is compared with conventional metaheuristic algorithms such as BPSO, Simulated Annealing, Ant Colony Optimization, Genetic Algorithm and other hybrid metaheuristic feature selection algorithms. The result proves that IBPSO maximizes the accuracy of the classifier together with maximum dimensionality reduction ratio. Also, statistical tests such as T-test, Wilcoxon signed-pair test are also carried out to demonstrate IBPSO is better than other algorithms taken for experimentation with confidence level of 0.05.

A new method for fault location of a power distribution network based on Improved Cuckoo Search Algorithm is proposed. Cuckoo Search Algorithm uses Levy flight to simulate the global parasitic propagation mechanism of the cuckoo population. Therefore, the algorithm avoids falling into local optimum easily. According to the requirements of quickness and accuracy for fault location, the algorithm is improved by combining the search step size of the algorithm and the number of iterations. It improves the algorithm's early iteration speed and subsequent search accuracy. In this paper, the improved Cuckoo Search Algorithm is used to generate random status for all line segments. Then, convert it to the expected status for each switch. And next, update the line segment status by iteration of the algorithm, which makes the expected status of the switches approach to status uploaded by the FTU. Finally, the model outputs the fault location. In this way, a new generic switching function is proposed. It is suitable for single or multiple faults under single and multiple power conditions, which greatly extends the range of applications and versatility. In the evaluation function, the anti-false positive factor and the assumed fault number are introduced. Both of them improve effectiveness and adaptability. And, the validity of these ideas was proved by simulation. Compared with Cuckoo Search Algorithm, Binary Particle Swarm Optimization Algorithm and Genetic Algorithm, Improved Cuckoo Search Algorithm turns out to be good at finding an optimal solution to multiple faults location problems with a faster convergence speed and higher accuracy.

This paper presents the design and simulation of a method for phase to phase fault location for distribution network with distributed generation (DG). In the last years, fault location on power distribution networks is receiving especial attention due to the importance of a fast actuation to clear the fault causes and the resulting reduce outage time and significantly improve system reliability. This paper presents the design and simulation of a method for phase to phase fault location for distribution network with distributed generation (DG). In this method, the distribution network is divided into two parts and then with voltage and current measurements at the substations of main system supply and DG supply fault location is found. Moreover, a new method is proposed for sections between main substation and DG in order to reduce residual error. Results show that the method can efficiently improve the process of fault location and is well suited for identifying phase to phase fault location in different fault resistances. Also this method has greatly simplified the derivation of complicated fault location equation especially in sections before DG and therefore can superiorly enhance the fault location process. (4 pages)

Fault location in power distribution network with presence of distributed generation resources using impedance based method and applying π line model

Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access.

UTF: Upgrade transfer function for binary meta-heuristic algorithms

Precise fault location in distribution network is one of the most important applications among intelligent monitoring and outage management tasks used for realization of self-healing networks. The data gathered from various intelligent sensors installed throughout the power system could be utilized for smart approaches to locating faults, helping the system restoration, reducing outage time and improving system reliability. Since the distribution network is radial, with multiple laterals connected to the main feeder, faults at various locations may lead to the same voltages and currents observed at the substation. In other words, using the substation measurements to calculate the fault location, multiple failure states are possible. In this paper, Markov decision process is used as a tool for the determination of the faulted feeder section and its isolation from the grid. The algorithm is based on transition probabilities among states obtained from intelligent sensors and tested on a radial distribution network with 3 sectionalizers.

Increasing power demand is usually met by the expansion of generation capacity. The transmission network should be expanded in tandem to ensure power is evacuated from generation points to the load centres. Inadequate power capacity causes congestion. Congestion results due to under-voltages and violation of transmission lines’ loading limits. Constructing additional transmission lines is required to alleviate the congestion after measures of increasing the transmission line’s transfer capability are exploited. Transmission network expansion planning (TNEP) determines the transmission lines to be added to a power system at minimal construction cost, without violating network constraints. In this research, voltage limit violations are penalized in a constrained dynamic TNEP problem for a 10-year planning horizon. The optimal location and number of new transmission lines required at minimal construction cost, and transmission losses associated with the transmission network operations are determined. Improved binary particle swarm optimization (IBPSO) algorithm is applied to optimize the dynamic transmission network expansion planning (DTNEP) results. The developed model is tested on Garver’s 6-bus system using MATLAB. The construction cost for new transmission lines is minimized, and transmission losses reduced when compared to other published works without violating voltage limits (±5%) and transmission lines’ thermal capacities. The transmission network system adequacy is improved.

It is very interesting to note that single line outage may lead to overloading of certain lines which may cause the cascading phenomenon of multiple line outage. Most of the available methods can identify the limited number of line outages due to increasing computational complexity with a large number of line outages. In this paper, an improved version of the binary particle swarm optimization (BPSO) technique is applied to solve the multiple line outage identification (LOI) problem. The proposed improved version of BPSO (IBPSO) is developed considering adaptive inertia weight throughout the iterations. Further, the acceleration co-efficient is also varied throughout the iterations to achieve the global optima. Moreover, to avoid the stagnation a single point mutation is also applied to explore the better search space. The proposed IBPSO is tested on IEEE 14 bus and 57- bus systems and results are compared with the standard BPSO algorithm.

Power distribution (PD) networks have many laterals and load taps. Accurate fault locating in PD systems causes to improve reliability indices and its efficiency. In this paper, a new combined method is proposed for locating single phase fault to earth in PD networks. In this article, an impedance based fault-location algorithm is used to find the possible fault locations. Then the new method is proposed for determining the faulty section using voltage sag matching algorithm. In this method, after occurring single phase fault to ground, the possible locations of fault are determined using the impedance based fault-location algorithm. The same fault is simulated in possible locations, separately. Then the voltage at the beginning of feeder is saved and the amplitude and angle of the voltage differences are determined and online data bank is generated. This data bank is compared with the obtained and recorded amplitude and angle of the voltage differences (at the beginning of feeder) for actual fault. The real location of fault is determined by the matching value of each possible fault location.