scholarly journals An Analytic Method for Power System Fault Diagnosis Employing Topology Description

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1770 ◽  
Author(s):  
Biao Xu ◽  
Xianggen Yin ◽  
Dali Wu ◽  
Shuai Pang ◽  
Yikai Wang
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Power System ◽  
Analytic Method ◽  
Integer Programming Problem ◽  
Protective Devices ◽  
Cooperative Relationship ◽  
Abnormal Operation ◽  
Power Recovery ◽  
Relationship Of

When a fault occurs in a power system, fault section estimation is the primary premise for troubleshooting and power recovery, and an effective fault diagnosis system will play a big role in decision making. However, the topology information is not well employed in existing fault diagnosis methods, and it is complex and time consuming to analyze the relationship between the protective devices and the sections. In this paper, a novel analytic method which employs topology description is proposed for fault diagnosis. The topology descriptions of the sections and the protective devices are firstly established according to the network structure, and based on which the operating logic and the cooperative relationship of the protective devices can be easily analyzed by matrix operation. Considering the factors of logic error and communication error, the fault diagnosis problem is formulated as an integer programming problem and can be solved by intelligent algorithm. The case studies of different power systems show that the proposed method can quickly identify the fault section, even with the abnormal operation or error alarm of protective devices.

scholarly journals A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Agustín Flores ◽  
Eduardo Quiles ◽  
Emilio García ◽  
Francisco Morant ◽  
Antonio Correcher
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Power System ◽  
Transmission Line ◽  
Electric Power ◽  
Diagnostic System ◽  
Specific Area ◽  
Electric Power Systems ◽  
Electric Power System ◽  
Power Network

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

2007 ◽  
Vol 8 (4) ◽  
Author(s):  
Ravikumar B. ◽  
Thukaram Dhadbanjan ◽  
H. P. Khincha
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Power Systems ◽  
Power System ◽  
Transmission System ◽  
Fault Analysis ◽  
Support Vector ◽  
Transmission Systems ◽  
Vector Machines ◽  
The Impact

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Fault Diagnosis in Transmission Network Based on Optimization Technique

2012 ◽  
Vol 433-440 ◽  
pp. 3395-3399
Author(s):  
Hong Bo Cheng
Keyword(s):  
Genetic Algorithms ◽  
Fault Diagnosis ◽  
Power System ◽  
Network Topology ◽  
Optimization Technique ◽  
Transmission Network ◽  
Integer Programming Problem ◽  
Heuristic Knowledge ◽  
Scada System ◽  
Configuration Information

The fault diagnosis in power system is treated as a 0-1 integer programming problem use the switching and action information collected by SCADA system, combined with the analysis of protect. A comprehensive objective function has been established and genetic algorithms has been used to solve it. This approach has taken fully advantage of the characteristics of the power system's protection and the network topology configuration information. The optimized method is used to locate the fault as possible as fast. Rigorous theory of the method does not require the introduction of heuristic knowledge, and it can adapt to changes in network topology, and can used both in the single failure of power system and multiple failures in power system too.

scholarly journals A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Guojiang Xiong ◽  
Dongyuan Shi ◽  
Lin Zhu ◽  
Xianzhong Duan
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Power System ◽  
Fuzzy Reasoning ◽  
P Systems ◽  
Diagnostic Model ◽  
Graphical Modeling ◽  
Spiking Neural P Systems ◽  
Reasoning Algorithm ◽  
First Time

Fault diagnosis of power systems is an important task in power system operation. In this paper, fuzzy reasoning spiking neural P systems (FRSN P systems) are implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P systems are able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationship between candidate faulted section and protective devices is represented by the FRSN P systems, the diagnostic conclusion can be drawn by means of a simple parallel matrix based reasoning algorithm. Three different power systems are used to demonstrate the feasibility and effectiveness of the proposed fault diagnosis approach. The simulations show that the developed FRSN P systems based diagnostic model has notable characteristics of easiness in implementation, rapidity in parallel reasoning, and capability in handling uncertainties. In addition, it is independent of the scale of power system and can be used as a reliable tool for fault diagnosis of power systems.

scholarly journals Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2565 ◽  
Author(s):  
Huizhong Song ◽  
Ming Dong ◽  
Rongjie Han ◽  
Fushuan Wen ◽  
Md. Salam ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Programming ◽  
Power Systems ◽  
Power System ◽  
Programming Approach ◽  
Chance Constrained Programming ◽  
Circuit Breakers ◽  
On Line ◽  
Protective Relays ◽  
Constrained Programming

When a fault occurs in a section or a component of a given power system, the malfunctioning of protective relays (PRs) and circuit breakers (CBs), and the false and missing alarms, may manifestly complicate the fault diagnosis procedure. It is necessary to develop a methodologically appropriate framework for this application. As a branch of stochastic programming, the well-developed chance-constrained programming approach provides an efficient way to solve programming problems fraught with uncertainties. In this work, a novel fault diagnosis analytic model is developed with the ability of accommodating the malfunctioning of PRs and CBs, as well as the false and/or missing alarms. The genetic algorithm combined with Monte Carlo simulations are then employed to solve the optimization model. The feasibility and efficiency of the developed model and method are verified by a real fault scenario in an actual power system. In addition, it is demonstrated by simulation results that the computation speed of the developed method meets the requirements for the on-line fault diagnosis of actual power systems.

Markov Jump Linear System Analysis of a Microgrid Operating in Islanded and Grid Tied Modes

2020 ◽  
Author(s):  
Gilles Mpembele ◽  
Jonathan Kimball
Keyword(s):  
Monte Carlo ◽  
Power Systems ◽  
Power System ◽  
System Analysis ◽  
Differential Algebraic Equations ◽  
Algebraic Equations ◽  
Markov Jump ◽  
Numerical Application ◽  
Conditional Moments ◽  
Markov Jump Linear Systems

<div>The analysis of power system dynamics is usually conducted using traditional models based on the standard nonlinear differential algebraic equations (DAEs). In general, solutions to these equations can be obtained using numerical methods such as the Monte Carlo simulations. The use of methods based on the Stochastic Hybrid System (SHS) framework for power systems subject to stochastic behavior is relatively new. These methods have been successfully applied to power systems subjected to</div><div>stochastic inputs. This study discusses a class of SHSs referred to as Markov Jump Linear Systems (MJLSs), in which the entire dynamic system is jumping between distinct operating points, with different local small-signal dynamics. The numerical application is based on the analysis of the IEEE 37-bus power system switching between grid-tied and standalone operating modes. The Ordinary Differential Equations (ODEs) representing the evolution of the conditional moments are derived and a matrix representation of the system is developed. Results are compared to the averaged Monte Carlo simulation. The MJLS approach was found to have a key advantage of being far less computational expensive.</div>

Grasshopper Algorithm Optimized Fractional Order Fuzzy PID Frequency Controller for Hybrid Power Systems

2019 ◽  
Vol 12 (6) ◽  
pp. 519-531
Author(s):  
Deepak Kumar Lal ◽  
Ajit Kumar Barisal
Keyword(s):  
Power Systems ◽  
Power System ◽  
Fractional Order ◽  
Pid Controller ◽  
Load Frequency Control ◽  
Pid Controllers ◽  
Fuzzy Pid ◽  
Fuzzy Pid Controller ◽  
Hybrid Power ◽  
Increasing Demand

Background: Due to the increasing demand for the electrical power and limitations of conventional energy to produce electricity. Methods: Now the Microgrid (MG) system based on alternative energy sources are used to provide electrical energy to fulfill the increasing demand. The power system frequency deviates from its nominal value when the generation differs the load demand. The paper presents, Load Frequency Control (LFC) of a hybrid power structure consisting of a reheat turbine thermal unit, hydropower generation unit and Distributed Generation (DG) resources. Results: The execution of the proposed fractional order Fuzzy proportional-integral-derivative (FO Fuzzy PID) controller is explored by comparing the results with different types of controllers such as PID, fractional order PID (FOPID) and Fuzzy PID controllers. The controller parameters are optimized with a novel application of Grasshopper Optimization Algorithm (GOA). The robustness of the proposed FO Fuzzy PID controller towards different loading, Step Load Perturbations (SLP) and random step change of wind power is tested. Further, the study is extended to an AC microgrid integrated three region thermal power systems. Conclusion: The performed time domain simulations results demonstrate the effectiveness of the proposed FO Fuzzy PID controller and show that it has better performance than that of PID, FOPID and Fuzzy PID controllers. The suggested approach is reached out to the more practical multi-region power system. Thus, the worthiness and adequacy of the proposed technique are verified effectively.

Analysis between graph-based and Power Transfer Distribution Factors (PTDF)-based model reduction methods in Electric Power Systems

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Diego A. Monroy-Ortiz ◽  
Sergio A. Dorado-Rojas ◽  
Eduardo Mojica-Nava ◽  
Sergio Rivera
Keyword(s):  
Power Systems ◽  
Power System ◽  
Model Reduction ◽  
Electrical Power ◽  
Schur Complement ◽  
Power Transfer ◽  
Electrical Power System ◽  
Admittance Matrix ◽  
Test Beds ◽  
Power Transfer Distribution Factors

Abstract This article presents a comparison between two different methods to perform model reduction of an Electrical Power System (EPS). The first is the well-known Kron Reduction Method (KRM) that is used to remove the interior nodes (also known as internal, passive, or load nodes) of an EPS. This method computes the Schur complement of the primitive admittance matrix of an EPS to obtain a reduced model that preserves the information of the system as seen from to the generation nodes. Since the primitive admittance matrix is equivalent to the Laplacian of a graph that represents the interconnections between the nodes of an EPS, this procedure is also significant from the perspective of graph theory. On the other hand, the second procedure based on Power Transfer Distribution Factors (PTDF) uses approximations of DC power flows to define regions to be reduced within the system. In this study, both techniques were applied to obtain reduced-order models of two test beds: a 14-node IEEE system and the Colombian power system (1116 buses), in order to test scalability. In analyzing the reduction of the test beds, the characteristics of each method were classified and compiled in order to know its advantages depending on the type of application. Finally, it was found that the PTDF technique is more robust in terms of the definition of power transfer in congestion zones, while the KRM method may be more accurate.

scholarly journals Series Compensation of Transmission Systems: A Literature Survey

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1717
Author(s):  
Camilo Andrés Ordóñez ◽  
Antonio Gómez-Expósito ◽  
José María Maza-Ortega
Keyword(s):  
Steady State ◽  
Power Systems ◽  
Power System ◽  
Case Studies ◽  
Electronic Devices ◽  
Literature Survey ◽  
Power Electronic ◽  
Transmission Systems ◽  
Series Compensation ◽  
Near Future

This paper reviews the basics of series compensation in transmission systems through a literature survey. The benefits that this technology brings to enhance the steady state and dynamic operation of power systems are analyzed. The review outlines the evolution of the series compensation technologies, from mechanically operated switches to line- and self-commutated power electronic devices, covering control issues, different applications, practical realizations, and case studies. Finally, the paper closes with the major challenges that this technology will face in the near future to achieve a fully decarbonized power system.

scholarly journals Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

2021 ◽  
Vol 13 (12) ◽  
pp. 6953
Author(s):  
Yixing Du ◽  
Zhijian Hu
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transient Stability ◽  
Short Term Memory ◽  
Risk Function ◽  
Stability Margin ◽  
Risk Level ◽  
Stability Assessment ◽  
Lstm Network ◽  
Hierarchical Assessment

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

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