Contingency ranking for voltage collapse using parallel self-organizing hierarchical neural network (original) (raw)
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Computers & Electrical Engineering, 2003
Increased interconnections and loading of power systems, sometimes, lead to insecure operation. Since insecure cases often represent the most severe threats to secure system operation, it is important that the user be provided with a measure for quantifying the severity of the cases both in planning and operational stages of a power system. The Euclidean distance to the closest secure operating point has been used as a measure of the degree of insecurity. Recently, artificial neural networks are proposed increasingly for complex and time-consuming problems of power system. This paper presents a parallel self-organised hierarchical neural network based approach for estimation of the degree of voltage insecurity. Angular distance based clustering is used to select the input features. The proposed method has been tested on IEEE 30-bus system and a practical 75-bus Indian system and found to be suitable for real time implementation in 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. #
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.
International Journal of Electrical Power Energy Systems, 2012
This paper presents the application of cascade neural network (CANN) based approach for integrated security (voltage and line flow security) assessment. The developed cascade neural network is a combination of one screening module and two ranking modules, which are Levenberg-Marquardt Algorithm based neural networks (LMANNs). All the single line outage contingency cases are applied to the screening module, which is 3-layered feed-forward ANN having two outputs. The screening module is trained to classify them either in critical contingency class or in non-critical contingency class from the viewpoint of voltage/line loading. The screened critical contingencies are passed to the corresponding ranking modules, which are developed simultaneously by using parallel computing. Parallel computing deals with the development of programs where multiple concurrent processes cooperate in the fulfillment of a common task. For contingency screening and ranking, two performance indices: one based on voltage security of power system (VPI) and other based on line flow (MWPI) are used. Effectiveness of the proposed cascade neural network based approach has been demonstrated by applying it for contingency selection and ranking at different loading conditions for IEEE 30-bus and a practical 75-bus Indian system. The results obtained clearly indicate the superiority of the proposed approach in terms of speedup in training time of neural networks as compared to the case when the two ranking neural networks were developed sequentially to estimate VPI and MWPI.
ANN based integrated security assessment of power system using parallel computing
International Journal of Electrical Power & Energy Systems, 2012
This paper presents the application of cascade neural network (CANN) based approach for integrated security (voltage and line flow security) assessment. The developed cascade neural network is a combination of one screening module and two ranking modules, which are Levenberg-Marquardt Algorithm based neural networks (LMANNs). All the single line outage contingency cases are applied to the screening module, which is 3-layered feed-forward ANN having two outputs. The screening module is trained to classify them either in critical contingency class or in non-critical contingency class from the viewpoint of voltage/line loading. The screened critical contingencies are passed to the corresponding ranking modules, which are developed simultaneously by using parallel computing. Parallel computing deals with the development of programs where multiple concurrent processes cooperate in the fulfillment of a common task. For contingency screening and ranking, two performance indices: one based on voltage security of power system (VPI) and other based on line flow (MWPI) are used. Effectiveness of the proposed cascade neural network based approach has been demonstrated by applying it for contingency selection and ranking at different loading conditions for IEEE 30-bus and a practical 75-bus Indian system. The results obtained clearly indicate the superiority of the proposed approach in terms of speedup in training time of neural networks as compared to the case when the two ranking neural networks were developed sequentially to estimate VPI and MWPI.
Supervised learning approach to online contingency screening and ranking in power systems
International Journal of Electrical Power & Energy Systems, 2012
This paper proposes a supervised learning approach to fast and accurate power system security assessment and contingency analysis. The severity of the contingency is measured by two scalar performance indices (PIs): Voltage-reactive power performance index, PI VQ and line MVA performance index, PI MVA. In this paper, Feed-Forward Artificial Neural Network (FFNN) is employed that uses pattern recognition methodology for security assessment and contingency analysis. A feature selection technique based on the correlation coefficient has been employed to identify the inputs for the FFNN. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus New England system at different loading conditions corresponding to single line outage. The overall accuracy of the test results for unknown patterns highlights the suitability of the approach for online applications at Energy Management Center.
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.
Evolving Artificial Neural Network Models for voltage contingency ranking
Voltage performance index (PI) was calculated to determine the severity of the contingency using full AC load flow. PI can be given as The functionfi, for ith voltage violated bus, is defined as f i = ui -U~P~' for ui > Urpper f i = upwe' -ui for ui < upwe'. Wi = 1 and M = 4 were heuristically chosen so that masking effect was removed for the sample power systems. A number of line outages having no bus voltage
Comparison of feature selection techniques for ANN-based voltage estimation
Electric Power Systems Research, 2000
Fast estimation of bus voltage magnitude is essential for security monitoring and analysis of power system. An approach based on a parallel self-organising hierarchical neural network (PSHNN) is proposed to estimate bus voltage magnitudes at all the PQ buses of a power system in an efficient manner. PSHNN is a multi-stage neural network in which stages operate in parallel rather than in series during testing. The revised back-propagation algorithm is used for learning input non-linearities along with forward-backward training of stage neural networks. A method based on Euclidean distance clustering is proposed for feature selection. Effectiveness of the proposed method is compared with two existing methods of feature-selection entropy based and angular distance based clustering methods for bus voltage magnitude estimation at different loading conditions in the IEEE 30-bus system and a practical 75-bus Indian system. The PSHNN based on Euclidean distance based clustering method is found to be superior in terms of training time and error performance.
ONLINE POWER SYSTEM CONTINGENCY SCREENING AND RANKING METHODS USING RADIAL BASIS NEURAL NETWORKS
This paper presents a supervising learning approach using Multilayer Feed Forward Neural Network(MFFN) and Radial Basis Fuction Neural Network(RBFN) to deal with fast and accurate static security assessment (SSA) and contingency analysis of a large electric power systems. The degree of severity of contingencies is measured by two scalar performance indices (PIs): Voltage-reactive power performance index, PIVQ and line MVA performance index, PIMVA. For each (N-1) contingency, thePerformance Index (PI) is computed using the Newton Raphson (NR) method. A correlation coefficient feature selection technique has been utilized to identify the inputs for the MFFN and RBFN. The proposed method has been applied on an IEEE 39-bus New England test system at different operating conditions comparing to single line outage and the results demonstrate the suitability of the methodology for on-line power system security assessment at Energy Management Center. The performace of the proposed ANN models is compared withNewton Raphson (NR) method and the results shows that the proposed model is effective and reliable in terms of static security assessment of power systems.