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In this paper, three element-oriented models based on simplified Bayesian networks with Noisy-Or and Noisy-And nodes are proposed to estimate the faulty section of a transmission power system. The three models are used to test if any transmission line, transformer, or busbar within a blackout area is faulty. They can deal with uncertain or incomplete data and knowledge relating to power system diagnosis, so they are flexible. The structures and initial parameters of the Bayesian networks depend on the prior knowledge of the domain experts. The parameters can be revised by using an error back propagation algorithm similar to the back-propagation algorithm for artificial neural networks. The fault diagnosis models do not vary with the change of the network structure, so they can be applied to any transmission power system. Furthermore, they have clear semantics, rapid reasoning, powerful error tolerance ability, and no convergence problem during the diagnosing procedure. Experimental tests show that the approach is feasible and efficient, so the prototype program based on the approach is promising to be used in a large transmission power system for online fault diagnosis.

This paper describes a new power network topology processor which has been successfully used in two practical power system application programs. Artificial intelligence (AI) techniques have been used and this provides several advantages. AI's frame representation method is used to represent electric network configuration. This avoids manual numbering on a large amount of circuit breaker terminals/circuits. Rule-based method has been adopted for substation's bus configuration analysis instead of the search method. This makes the analysis simple and adaptable to new types of bus configurations just by adding corresponding rules. AI's search techniques have been applied for network graph search making the search algorithm simpler and easier to be programmed. Some results from the application of the proposed topology processor are also included in the paper.

Because matter-element theory's shortcoming in ignoring the uncertainty of boundary values when setting up a fault diagnosis model, the diagnosing results are deviated from the actual situation. Considering cloud model theory can be applied to represent the uncertainty of the boundary, we combine cloud model theory with matter-element to propose a new diagnosing model. Meanwhile, by using data such as the concentration of dissolved gases, gas production rate and the ratio between gases in oil, the proposed diagnosing model is tested. Compared with other techniques for transformer diagnosing with case analysis, the new proposed model has a higher diagnosing precision.

As sparse decomposition method can improve the efficiency of data compression, it's effective to reduce data storage and transmission costs in insulator monitoring system by compressing non-stationary leakage current. There are large computation and high reconstruction error during Fast Fourier Transform (FFT) decomposing non-stationary leakage current. To solve these problems, this paper presents sparse decomposition method of leakage current based on EMD. Decomposed non-stationary leakage current into a finite number of stationary intrinsic mode functions (IMF), then sparse decomposition of FFT processed every IMFs. The experiment results indicate that this method not only enhances the speed of process but also the whole compression ratio in the case that FFT processes leakage current by EMD.

In power systems, the power transformer is seen to be the core equipment, so the fault diagnosis has been subject to the academic and engineering concern. It is very important to find incipient faults as early as possible. Due to the randomness and uncertainty of power transformer fault diagnosis data, Bayesian network satisfactory capacity of knowledge representation and strong solving ability to deal with uncertain facts, represent knowledge flexibility and it has strong advantage of dealing with uncertainty and associated problems, in this paper transformer fault diagnosis method based on Bayesian network classifiers is proposed. In the paper, a variety of Bayesian classifiers have been studied separately, and the corresponding models have been established, then their advantages and disadvantages have been described. The experimental results show that the methods improve the accuracy of the transformer fault diagnosis.

The main goal of IEC 61850 standard is interoperability, i.e. the ability for intelligent electronic devices (IEDs) from one or several vendor to exchange information and use the information for their own functions. If IEC 61850 client can't acquire IEC 61850 data model of IEC 61850 server, the following real-time data communication will become impossible. Based on the research and analysis of substation configuration description language (SCL) and manufacturing message specification (MMS), two methods are put forward to exchange IEC 61850 data model, including the offline exchange based on SCL configuration tools and the online exchange based on MMS. In this paper, a prototype system based on the second method is designed, and interoperability and function tests prove the feasibility of the proposed scheme.

In this paper, an approximate inference algorithm on Bayesian networks (BN) for reliability assessment of power systems by time-sequence simulation is presented. Based on uniformly distributed pseudo random numbers, the duration time sampling for a component state can be obtained through inverse transform method. According to the logical relationship of the power system to be assessed, the sampling of minimal state cut sets of the system and the system node's sampling are obtained respectively. Thus, the corresponding sample sequence in forward sampling is available. When the amount of sample sequence is large enough, marginal and conditional statistic values are close to marginal and conditional probability of the node. Then the approximate inference results of the BN can be obtained. Numerical simulating results show the effectiveness of the proposed algorithm. It can easily identify the weaker components of a system in reliability, calculate the system reliability indices, and is suitable for a large-scale power system.

The tripping of transmission lines for overload or failure will cause transference of vast load, then further cause a series of lines and generators to trip. It is greatly relevant to serious failures, particular to the cascading outages. This paper investigates the mechanism of cascading outages based on the characters of overload in lines, and proposes a method for forecasting sequential events of cascading outages. In this method, overload severity index is defined to judge the loading status of transmission lines, and identify the set of overload lines. Furthermore, the paper uses the drawing theory to zone networks topology according to load flow, thereby finds the set of hidden failure lines. Finally the correction of this method is verified through real example.

Because traditional transformer diagnosing approaches are over-rigidity and need almost complete and accurate testing data, a NB (naive Bayesian) classifier based model to diagnose transformer faults is presented and constructed in the paper. As the diagnosing performance is depressed by incomplete testing data, SVM regression approach is used to estimate the missing data. Thus a new diagnosis model, which integrates SVM regression and NB classifier, is constructed. The diagnosing experiments of different transformer testing scenarios show that the constructed NB diagnosis model has a good performance given complete testing data, and the proposed SVM regression approach can raise the accuracy of transformer diagnosing even if a certainty quantity of data or important data are missed.

Fast modification of node impedance matrix of an interconnected power network is pivotal to realize the real-time adaptive relay setting. Because the network has several adjacent networks, getting the boundary equivalents of each adjacent network is necessary to calculate the complete impedance matrix of the studied network for short circuit analysis. A parallel algorithm on blocking storage and modification of node impedance matrix based on PC cluster is proposed in this paper. The proposed parallel algorithm combines large grained blocking storage with message passing, it can fast deal with the network containing mutually coupled circuits. Moreover, the formula to calculate boundary equivalents based on node impedance matrix is derived. According to the boundary equivalents of the adjacent networks and the modified impedance matrix of the network-self, the complete impedance matrix of the studied network can be calculated. Furthermore, an optimization strategy on remote memory access (RMA) function is utilized to overlap the parallel computation and the communication between the scheduling processor and the sub-processors. The experimental results demonstrate the proposed parallel computing approaches on the modification and boundary equivalents of an interconnected network has obvious advantage in computation efficiency over traditional serial computing approaches. The proposed approaches are effective to meet the speed requirement of calculating adaptive relay settings for a large interconnected electric network.

This paper presents a multi-agent and WAN based adaptive coordinated protection system (MAWBACPS). The system utilizes IP intranet to realize real-time wide area information exchange of a power system. Multi-agent system's several good qualities to solve distributing problems are used to realize the coordination of the operation settings of protective relays. A new idea of effortful adaptation for power system protection is proposed, and a new method to determine adaptive coordinated relay settings of lines are proposed in this paper. Moreover, the structure, communication mechanism and coordination control strategy of MAWBACPS, and relating agents' design are presented. Experiment results illustrate that MAWBACPS can realize the coordination of protections, enable the back-up relays to use their adaptive settings in most situations, and efficiently improve the security of power system

Three element-oriented Bayesian networks models are built to estimate the fault section of a power system. Each of them is composed of noisy-or and noisy-and nodes. The three models are used to locate three types of fault elements: transmission lines, transformers and bus bars respectively. The learning algorithm for network parameters is analogous to the back propagation algorithm of neural networks. Taking the sum of the mean-squared error between the expected values and the computed results of target variables as the minimizing optimization function, it adjusts the network's parameters continuously. According to the operation information of protective relays and circuit breakers, fault credibility of elements in the blackout area is calculated based on the structure of the Bayesian network. By comparing the resultant beliefs of possible fault elements, the fault element(s) is identified. The proposed approach can deal with uncertainties in fault section diagnosis, and the models have clear semantics, rapid reasoning, etc. The testing results for a real power system have shown that the fault diagnosis models are correct, efficient and are promising to be used in a large power system for on-line fault diagnosis.

This paper presents an application method of Bayesian networks (BN) to the reliability assessment of power systems. Bayesian networks provide a flexible framework to represent probabilistic information and to make inference on it. Uncertainty and dependency of the components' information in a system are easily incorporated in the analysis. The flexibility of the probabilistic inference algorithms in Bayesian networks permit to compute both the system's reliability indices and the mutual affection on reliability indices of all components. However, a BN cannot be constructed easily based on the topology of the relating power system. The paper gives a new method to construct a Bayesian network based on the assessed system's fault tree or its minimal path set. The method is efficient and can compute components failure probabilities on the condition of the system failure. Its advantages are demonstrated through two examples.

In this paper, a regional power supply system is taken as a radial system with redundancy reservation and a spinning reserve system. Reservation types are defined and described. Furthermore, a new reliability analysis method for the simple loop network structure of the regional power supply system is developed. The reliability analysis model for a simple loop network's power supply system is built up based on the state-space analysis technique by considering failure and repair rates of wires. In the proposed model, through drawing the state transfer chart of the redundancy reservation system, the corresponding transfer density matrix and linear equation group are put forward, and the solution of placidity state probability is obtained. The power supply unreliability degree and reliability degree of the substation and the average time of the power supply lines in all different states are calculated. The conclusion is extended to state of the complex loop network. It is the first time that the state space method is applied to the power supply reliability evaluation of the regional power supply systems with higher calculation accuracy. The effectiveness of the proposed method is verified by an example.

In this paper, the principle of expert systems is described, it has been applied to writing operation tickets at Tieling Substation of North-east China Electric Network. This expert System can be taught and adapted conveniently.

In this paper, the principle of expert systems is described, it has been applied to writing operation tickets at Tieling Substation of North-east China Electric Network. This expert System can be taught and adapted conveniently.