Evolving Artificial Neural Network Models for voltage contingency ranking (original) (raw)

Fast voltage contingency screening and ranking using cascade neural network

Electric Power Systems Research, 2000

Voltage contingency selection and ranking is performed to choose the contingencies that cause the worst voltage problems. In this paper, a cascade neural network based approach is proposed for fast voltage contingency screening and ranking. The developed cascade neural network is a combination of a filter module and a ranking module. All the selected contingency cases are applied to the filter module, which is trained to classify them either in critical contingency class or in non-critical contingency class using a modified BP algorithm. The screened critical contingencies are passed to the ranking module (four-layered feed-forward ANN) for their further ranking. The effectiveness of the proposed method is demonstrated by applying it for contingency screening and ranking at different loading conditions for IEEE 30-bus system and a practical 75-bus Indian system. Once trained, the cascade neural network gives fast and accurate screening and ranking for unknown patterns and will be suitable for on-line applications at energy management centre.

Artificial neural network model for voltage security based contingency ranking

Applied Soft Computing, 2007

The continual increase in demand for electrical energy and the tendency towards maximizing economic benefits in power transmission system has made real-time voltage security analysis an important issue in the operation of power system. The most important task in real time security analysis is the problem of identifying the critical contingencies from a large list of credible contingencies and rank them according to their severity. This paper presents an artificial neural network (ANN)-based approach for contingency ranking. A set of feed forward neural networks are developed to estimate the voltage stability level at different load conditions for the selected contingencies. Maximum L-index of the load buses in the system is taken as the indicator of voltage instability. A mutual information-based method is proposed to select the input features of the neural network. The effectiveness of the proposed method has been demonstrated through contingency ranking in IEEE 30-bus system. The performance of the developed model is compared with the unified neural network trained with the full feature set. Simulation results show that the proposed method takes less time for training and has good generalization abilities. #

Voltage Contingency Ranking of a Practical Power Network Using Hybrid Neuro-Fuzzy System

2008 Joint International Conference on Power System Technology and IEEE Power India Conference, 2008

Maintaining power system security is a challenging task for power system engineers. The idea is to shortlist critical contingencies from a large list of contingencies and to rank the contingencies expected to drive the system towards instability. Corrective measures can then be planned to save the system from collapse and blackout. This paper presents a simple multi-output fuzzy-neural network for contingency ranking in a power system. A fuzzy overall performance index (FOPI), formulated by combining i) voltage violations and ii) voltage stability margin is being employed in this paper for composite ranking of contingencies. The proposed approach is very effective in handling contingencies lying on the boundary between two severity classes. Feature selection using fuzzy curves has been employed to reduce the dimension of the network. The performance of the proposed method has been tested on a 69-bus practical Indian power system.

Cascade fuzzy neural network based voltage contingency screening and ranking

Electric Power Systems Research, 2003

A method based on cascade fuzzy neural network (CFNN) comprising of a filter module and ranking module is proposed for online voltage contingency screening and ranking under known but uncertain loads. A new fuzzy performance index, which combines voltage violations and voltage stability margin following a contingency, is proposed for effective voltage security ranking. All the selected contingency cases are first applied to a filter module, which filters out the non-critical contingencies and passes on the critical ones to the ranking module for on-line ranking. The uncertainty associated with loads is modeled by representing them as fuzzy quantities using non-linear membership functions. The performance index is also translated into fuzzy set notations to ensure a flexible and more realistic ranking. Due to the fuzzy nature of the performance index, the proposed method is particularly useful for ranking contingencies, which lie on the boundary between two severity classes. The potential of the CFNN to provide insight into the ranking process, without having to go through the complicated task of rule framing has been demonstrated on IEEE 30-bus test system and a practical 75-bus Indian system.

Knowledge-based neural network for voltage contingency selection and ranking

Iee Proceedings-generation Transmission and Distribution, 1999

Voltage contingency screening and ranlung is performed to choose the contingencies that cause the worst voltage problems. A knowledge-based conceptual neural network (KBCNN) is developed for fast voltage contingency selection and ranking. A recognised shortcoming of the ANN based approach is that the problem solving knowledge of ANN is represented in the connection weights, and hence is dlffcult for a human user to comprehend. One way to provide an understanding of the behaviour of neural networks is to extract rules that can be provided to the users. The rules extracted are used to build a knowledge-based connectionist network for learning and revision of rules. The knowledge-based neural network is applied for voltage contingency selection and ranlung in an IEEE 30-bus system and a practical 75-bus Indian system. Once trained, the KBCNN gives accurate selection and ranking for unknown patterns. At the same time, the system user is able to validate the output of the ANN under all possible input conditions. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution.

Coherency-based fast voltage contingency ranking employing counterpropagation neural network

Engineering Applications of Artificial Intelligence, 2007

Power system security is one of the major concerns in competitive electricity markets driven by trade demands and regulations. If the system is found to be insecure, timely corrective measures need to be taken to prevent system collapse. This paper presents an approach based on a counterpropagation neural network (CPNN) to identify and rank the contingencies expected to reduce or eliminate the steady-state loadability margin of the system, making it prone to voltage collapse. It has been shown that unlike other artificial neural networks (ANN) paradigms, which start with random weights, CPNN is very sensitive to initial weights. To reduce the dimension and training time, a novel feature selection method, based on the coherency existing between load buses with respect to voltage dynamics, is employed to select significant input features for the CPNN. Once trained, the CPNN is found to rank voltage contingencies accurately for previously unknown system conditions very fast. Due to its fast training, the proposed CPNN will be particularly useful for power system planning studies, as a number of combinations can be tried within a small time frame. The effectiveness of the proposed approach has been demonstrated on IEEE 30-bus test system and a 75-bus practical Indian system.

Contingency ranking for voltage collapse using parallel self-organizing hierarchical neural network

International Journal of Electrical Power & Energy Systems, 2001

On-line monitoring of the power system voltage security has become a vital factor for electric utilities. This paper proposes a voltage contingency ranking approach based on parallel self-organizing hierarchical neural network (PSHNN). Loadability margin to voltage collapse following a contingency has been used to rank the contingencies. PSHNN is a multi-stage neural network where the stages operate in parallel rather than in series during testing. The number of ANNs required is drastically reduced by adopting a clustering technique to group contingencies of similar severity into one cluster. Entropy based feature selection has been employed to reduce the dimensionality of the ANN. Once trained, the proposed ANN model is capable of ranking the voltage contingencies under varying load conditions, on line. The effectiveness of the proposed method has been demonstrated by applying it for contingency ranking of IEEE 30-bus system and a practical 75-bus Indian system. q

Contingency Evaluation of Electrical Power System Using Artificial Neural Network

2010

This paper presents application of Artificial Neural Network (ANN) based contingency analysis of power system. The ANN has been chosen because of its high adaptation parallel information processing capability. Another feature that makes the ANN more suitable for this type of problems is its ability to augment new training data without the need for retraining. In this Multilayer Feed Forward network is used for contingency analysis in planning studies where the goal is to evaluate the ability of a power system to support a projected range of peak demand under all foreseeable contingencies. This work involves selection of network design, preparation of input patterns, training & testing. In order to generate the training patterns three system topologies were considered. Training data are obtained by load flow studies (NR Method) for different system topologies over a range of load levels using software simulation package (Mipower) and the results are compiled to form the training set....

Fast voltage contingency selection using fuzzy parallel self-organizing hierarchical neural network

IEEE Transactions on Power Systems, 2003

A fuzzy neural network comprising of a screening module and ranking module is proposed for online voltage contingency screening and ranking. A four-stage multioutput parallel self-organizing hierarchical neural network (PSHNN) has been presented in this paper to serve as the ranking module to rank the screened critical contingencies online based on a static fuzzy performance index formulated by combining voltage violations and voltage stability margin. Compared to the deterministic crisp ranking, the proposed approach provides a more informative and flexible ranking and is very effective in handling contingencies lying on the boundary between two severity classes. Angular distance-based clustering has been employed to reduce the dimension of the fuzzy PSHNN. The potential of the fuzzy PSHNN to provide insight into the ranking process, without having to go through the complicated task of rule framing is demonstrated on IEEE 30-bus system and a practical 75-bus Indian system.

Review Paper on Power System Contingency Analysis Using Artificial Neural Network

Contingency analysis is the important function of the power system security. The security assessment is an important task because it offers the information regarding the system state within the event of a contingency. Contingency analysis method is being widely used to predict the result of outages like failures of apparatus, conductor etc, and to require necessary actions to stay the facility system secure and reliable. The off line analysis to predict the impact of individual contingency could be a tedious task as a power system contains large number of parts. Various strategies of contingency analysis have been given in this review paper and additionally the utilization of artificial neural network for comntingency analysis has been cited.