Power System Fault Diagnosis Research Articles

Research on Fault Diagnosis of Power System Based on Adaptive Immune Genetic Algorithm

In this article, the fault problem of power system is investigated with an adaptive immune genetic algorithm (AIGA) for the complications of fault. By introducing new crossover rate and mutation rate, considering the general characteristics of population, vaccines are extracted with dynamic self-adaption approach, thus avoiding the disadvantage of standard genetic algorithm. On the other hand, with the idea of survival of the fittest, the antibody population with low fitness is replaced by parts of new antibody generated randomly, which allows the variety of population. A general power system is employed to show the efficiency of the new method.

Divisional fault diagnosis of large-scale power systems based on radial basis function neural network and fuzzy integral

This paper proposes an effective method for fault diagnosis of large-scale power systems based on radial basis function (RBF) neural network (NN) and fuzzy integral. It aims at effectively diagnosing the tie lines which connect different adjacent sub-networks in the context of divisional fault diagnosis. First, an overlapping network division method is proposed to divide a large-scale power system into a desired number of eligible sub-networks. Then, for each sub-network, a local RBF NN diagnostic module which is constructed by an exhaustive search-assisted forward recursive algorithm is allocated. Finally, a Choquet fuzzy integral fusion module is constructed for any pair of connected sub-networks. When a fault occurs, local RBF NN diagnostic modules will be selectively triggered according to local alarm information. If it involves a tie line, the corresponding Choquet fuzzy integral fusion module will be triggered to fuse the diagnostic outputs derived from the adjacent sub-networks which are connected by the tie line. Case studies with a 14-bus power system are presented to evaluate the feasibility and efficiency of the proposed method under various complex fault scenarios. The diagnostic results demonstrate that this proposed method is efficient in identifying faults within local sub-networks as well as those on the tie lines with strong fault tolerance and high diagnostic accuracy.

Research on Power System Fault Diagnosis Based on Bayesian Networks

Aiming at the incompleteness and uncertainty of information existing in power system fault diagnosis, a new fault diagnosis approach based on Bayesian network is proposed in this paper. Through the Bayesian network of structure learning and parameter learning, a power system fault diagnosis model based on Bayesian network has been proposed. Conditional probability table describes the connection degree between various factors in quantity. Diagnostic results of instance proved the effectiveness and superiority of the proposed method.

The Fault Diagnosis of Aircraft Power System Based on D-S Evidence Theory

Dempster’s rule of combination is commonly used in the field of information fusion, Aiming at the problem of related fault of aircraft power system , D-S theory is used to fuse multiple fault alarm information to get the only fault type accurately. Mathematical model of aircraft power system fault diagnosis, and method of fusion was established by analyzing the fault phenomena and fault causes ,for 270 V high-voltage DC power system. The accuracy of the D-S Theory data fusion is better than single sensor judged fault by simulating and testing. A example is given to show that this fusion method is feasible and rational.

Simultaneous Fault Section Estimation and Protective Device Failure Detection Using Percentage Values of the Protective Devices Alarms

This paper proposes a new approach to fault diagnosis in electrical power systems, which presents an aspect little explored in the literature that is the protective device failure detection together with the fault section estimation, since the majority of the methodologies so far proposed to fault diagnosis are limited to the fault section estimation alone. The proposed methodology makes use of operation states of protective devices as well as information related to the protection philosophy. Initially, these data undergo a preprocessing step to convert the format of 0 and 1 to percentage values. The conversion to percentage values allows the use of artificial neural networks, whose numbers of inputs do not depend on the number of alarms of the protection philosophy, or the type of bus arrangement or the number of circuit breakers. This allows the same set of neural networks to be trained and applied in different power systems with different protection schemes and bus arrangements. The proposed system has five neural networks, each containing few neurons and requiring 30 μs to perform fault diagnosis. The proposed system was trained considering the IEEE 57-bus system, containing different selective protection schemes, and subsequently tested in the IEEE 14-bus, 30-bus, and 118-bus systems, and Eletronorte 230-kV real power system.

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

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.

An Electric Power System Fault Diagnosis Method Based on PSO and FCM

For effectively analyzing electric power faults, exactly identifying failure type, and highly providing disposal measure, depending on PSO (particle swarm optimization) algorithm, a PSO-FCM (particle swarm optimization-fuzzy c-means) algorithm was constructed by the FCM improvement of fuzzy clustering to avoid get in local optimal state. On this basis, an electric power system fault diagnosis method was established by means of PSO and FCM. Finally, this method was validated by an example. Consequently, this method can intellectively diagnose and identify the fault of electric power system, and can provide a new approach to stably operation in electric power system.

OPTIMAL PLACEMENT OF PHASOR MEASUREMENT UNITS FOR POWER SYSTEM OBSERVABILITY WITHOUT CONSIDERING ZERO INJECTION BUSES

This paper present a method to find minimum number of phasor measurement units (PMUs) for complete observability of power system network for normal operating conditions .A linear algorithm is used to determine the minimum number of PMUs needed to make the system observable without considering zero injection buses. For state estimation and fault diagnosis in power system synchronized snapshot of whole system must be necessary. The proposed method is used to benchmark the optimal PMUs placement solution for the IEEE 14-bus, IEEE 18-bus, IEEE 24-bus, IEEE 30-bus and IEEE 57-bus test systems.

A fault diagnosis method of Smart Grid based on rough sets combined with genetic algorithm and tabu search

Most of the existing methods of fault diagnosis in Smart Grid focus primarily on the information of the protective relays and switches. There is still some incomplete or uncertain information in the process of receiving data. Usually, relying only on the information may obtain wrong conclusions. Large numbers of methods have been applied in the fault diagnosis of power system. An improving accuracy of fault diagnosis method in Smart Grid is put forward in this paper using rough sets combined with genetic algorithm (GA) and Tabu search (TS). The reduction in continuous attributes and their value reduction are the major application of rough sets. The proposed algorithm can combine the parallel global searching capability of genetic algorithm with the local searching ability of Tabu search and significantly improve the efficiency of execution and ensure the optimal result. The effectiveness of the proposed algorithm has been demonstrated using Changchun south substation and its distribution grid. To validate the proposed approach adequately, simulation studies have also been carried out on the simulated Smart Grid model. A series of tests are conducted toward three fault categories: the single faults, the multiple faults, and the loss information faults. All the results demonstrate that the proposed method in this paper is better than the preceding algorithms.

A new analytic approach for power system fault diagnosis employing the temporal information of alarm messages

Traditional analytic models for power system fault diagnosis are usually formulated as an unconstrained 0–1 integer programming problem. The key issue of the models is to seek the fault hypothesis that minimizes the discrepancy between the actual and the expected states of the concerned protective relays and circuit breakers. The temporal information of alarm messages has not been well utilized in these methods, and as a result, the diagnosis results may be not unique and hence indefinite, especially when complicated and multiple faults occur. In order to solve this problem, this paper presents a novel analytic model employing the temporal information of alarm messages along with the concept of related path. The temporal relationship among the actions of protective relays and circuit breakers, and the different protection configurations in a modern power system can be reasonably represented by the developed model, and therefore, the diagnosed results will be more definite under different circumstances of faults. Finally, an actual power system fault was served to verify the proposed method.

A Distributed State Estimation Approach to Condition Monitoring of Nonlinear Electric Power Systems

AbstractThis paper analyzes distributed state estimation methods for condition monitoring of electric power transmission and distribution systems. When a fault occurs in such large‐scale systems, it is usually difficult to detect it and to determine its exact position. Moreover, due to the cost of installation and maintenance of measurement devices and due to the excessive size of the electric power grid, the complete monitoring of the associated infrastructure is impractical. Therefore, to monitor the condition of the power grid, some form of estimation is required. As suitable approaches for distributed state estimation this paper proposes the extended information filter (EIF) and the unscented information filter (UIF). The Extended Information Filter is actually an implementation of distributed extended Kalman filtering while the unscented information filter is an implementation of distributed unscented Kalman filtering. With the use of the aforementioned filtering algorithms on processing units located at different parts of the power grid, one can produce local estimates of the system's state vector which in turn can be fused into an aggregate state estimation. The produced global state estimate enables continuous monitoring of the condition of the electric power system and early fault diagnosis if used by a suitable fault detection and isolation algorithm.

A circuit approach to fault diagnosis in power systems by wide area measurement system

Fault diagnosis following a disturbance in a power system is of great importance for the operators in the control center as a prerequisite for system restoration. In this article, for the first time, an analytic method for fault diagnosis, using the wide area measurement system (WAMS) and employing circuit rules, is developed. Instead of conventional information about status of protective relays and circuit breakers, voltage and current phasors at different points of the power network after fault occurrence are utilized. Because most of the power systems will be equipped with WAMS consisting of high-speed sampling features, the proposed method introduces a new application of WAMS for fault diagnosis without the need for additional hardware. In this method, with the aid of network impedance matrix, analytic voltage and current phasors resulted after a fault in each of the fault candidate points are compared with those monitored by WAMS. The faulted point is where the best fitting coincides with the monitored voltage and current phasors. The proposed method is applied on IEEE 118-bus test system where the results confirm its ability in wide-area fault diagnosis of large power networks. Copyright © 2012 John Wiley & Sons, Ltd.

Application of Multiple Level Rules Set in Fault Diagnosis of the Wind Power Generation System

For the incompletion problem of sensors’ collected data in fault diagnosis of the wind power system, this article puts forward a kind of multiple level rules set based on rough set. First, let the sensors’ collected data go through Fourier transform and extract its feature attributes as well as discrete them. Establish the decision table of fault diagnosis according to attribute values. Then set out from the decision table to establish a multiple level set of nodes with diverse reduced levels and deduce the rules of each node, which has a corresponding belief level. When in reasoning and decision-making of the new data using the multiple level rules set, match the information of the new data with the rule of its corresponding node. Finally, achieve the fault diagnosis of wind power generation system by choosing comprehensive evaluation algorithm. The result of the diagnosis example shows the reliability and accuracy of this method in the diagnosis of fault types for wind power generation system.

Multi-BP expert system for fault diagnosis of powersystem

Fault diagnosis and assessment is a crucial and difficult problem for power system. Back propagation neural network expert system (BPES) is an often used method in fault diagnosis. However, with the layer numbers increasing, BPES becomes time consuming and even hard to converge. To solve this problem, we divide the whole networks into many sub-BP groups within a short depth and then propose a novel Multi-BP expert system (MBPES) based method for power system fault diagnosis. We use two real power system data sets to test the effectiveness of MBPES. Experimental results show that MBPES obtains higher accuracy than two commonly used methods.

Fault Diagnosis in Transmission Network Based on Optimization Technique

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.

Fuzzy Timing Petri Net for Fault Diagnosis in Power System

A model‐based system for fault diagnosis in power system is presented in this paper. It is based on fuzzy timing Petri net (FTPN). The ordinary Petri net (PN) tool is used to model the protective components, relays, and circuit breakers. In addition, fuzzy timing is associated with places (token)/transition to handle the uncertain information of relays and circuits breakers. The received delay time information of relays and breakers is mapped to fuzzy timestamps, π(τ), as initial marking of the backward FTPN. The diagnosis process starts by marking the backward sub‐FTPNs. The final marking is found by going through the firing sequence, σ, of each sub‐FTPN and updating fuzzy timestamp in each state of σ. The final marking indicates the estimated fault section. This information is then in turn used in forward FTPN to evaluate the fault hypothesis. The FTPN will increase the speed of the inference engine because of the ability of Petri net to describe parallel processing, and the use of time‐tag data will cause the inference procedure to be more accurate.

Application of ENN‐1 for Fault Diagnosis of Wind Power Systems

Maintaining a wind turbine and ensuring secure is not easy because of long‐term exposure to the environment and high installation locations. Wind turbines need fully functional condition‐monitoring and fault diagnosis systems that prevent accidents and reduce maintenance costs. This paper presents a simulator design for fault diagnosis of wind power systems and further proposes some fault diagnosis technologies such as signal analysis, feature selecting, and diagnosis methods. First, this paper uses a wind power simulator to produce fault conditions and features from the monitoring sensors. Then an extension neural network type‐1‐ (ENN‐1‐) based method is proposed to develop the core of the fault diagnosis system. The proposed system will benefit the development of real fault diagnosis systems with testing models that demonstrate satisfactory results.

The Fault Location Approach for Grid Network Base on MAS of Rough Set Theory

With distributed power access, Network reconfiguration and other technology, Traditional power system fault diagnosis and fault location of large-scale grid has some limitations. This paper proposes a fault location method for Smart grid base on MAS of Rough Set theory. To solve the issues that the fault diagnosis decision-making needs load computational capacity and flexible network structure mutations. The method uses the group decision-making feature of Agent; divides the network area into the associated regions by the matrix for described the network area, then using rough set theory to a small area fault location in the node sets. The method combines the characteristics of a distributed network that the parallel processing of MAS and easy to expand and modify. Fault location by it has better flexibility and faster reaction speed.

Avances en el diagnóstico de fallas en sistemas eléctricos de transporte mediante redes neuronales: Un enfoque modular.

En este trabajo se propone un nuevo metodo para el diagnostico de fallas en sistemas electricos de potencia basado en modulos neuronales. El metodo realiza el diagnostico mediante la asignacion de un modulo neuronal generico para cada tipo de componente que conforma al sistema electrico de potencia, ya sea linea de transporte, bus o transformador. En total se disenan tres modulos neuronales genericos, uno para cada tipo de componente. Los modulos neuronales para buses y transformadores se componen de dos niveles de diagnostico tomando en cuenta los estados logicos de interruptores y relevadores tanto propios como de respaldo, a excepcion del modulo neuronal para lineas de transporte, que ademas de los dos niveles de diagnostico con los que cuentan los modulos neuronales para buses y transformadores, cuenta con un tercer nivel de diagnostico que toma en consideracion los oscilogramas de voltajes y corrientes de falla, asi como los espectros de frecuencia de estos oscilogramas, para verificar si la linea de transporte realmente estuvo sujeta a una falla y a la vez determinar el tipo de esta (L-g, LL-g, LL, LLL, LLL-g), esto a traves de una estructura neuronal. Este tercer nivel de diagnostico, es posible llevarlo a cabo ya que toda linea de transporte que es sometida a una falla presentara corrientes y voltajes de falla, antes de que esta sea liberada del sistema por la accion de sus esquemas de proteccion.

The Study of SA with Improved GA

Aimed at the blindness of operation parameter and the shortage of local part search ability, an improved algorithm was put forward in this paper. By adapting operation parameter and combining GA with SA, a mixed excellent algorithm was formed. Emulation result shows that this mixed algorithm has a good ability for searching and a good application in fault diagnosis of electric power system.