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.
Voltage contingency ranking using fuzzified multilayer perceptron
Electric Power Systems Research, 2001
A fuzzified multi-layer perceptron (FMLP) trained by back-propagation algorithm is proposed for on line voltage contingency analysis and ranking. The input vector consists of fuzzy membership values of loads to different linguistic categories, while the output vector is defined in terms of fuzzy membership values of a voltage performance index in different severity classes. Fuzzifying the loads into linguistic categories using non-linear membership functions enables efficient modeling of uncertainty associated with loads. Angular distance based clustering has been used to determine significant inputs to the fuzzified neural network. Due to the incorporation of fuzzy logic, the method is capable of handling even those contingencies that belong to more than one class. The effectiveness of the method has been shown on IEEE 30-bus test system and 75-bus Indian system and it is found to classify and rank the contingencies quite accurately for unknown load patterns.
Power system contingency classification using machine learning technique
Bulletin of Electrical Engineering and Informatics, 2022
One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighbor machine learning strategy is presented to categorize these patterns. The proposed machine learning classifiers are trained on the IEEE 30 bus system before being evaluated on the IEEE 14, IEEE 57, and IEEE 118 bus systems. The suggested k-nearest neighbor (KNN) classifier increases the accuracy of power system security assessments categorization. A fuzzy logic approach was also investigated and implemented for the IEEE 14 bus test system to forecast the aforementioned five classifications.
An efficient approach for contingency ranking based on voltage stability
International Journal of Electrical Power & Energy Systems, 2004
This paper presents an efficient approach for line outage contingency ranking based on voltage stability concerns. This is achieved through non-iterative fast calculation of reactive support and voltage security indices using Norton's equivalent. Earlier approach required full AC load flow for every contingency simulation. Results for IEEE, 30 bus, 57 bus and an Indian 91 Bus Power Systems have been obtained to demonstrate the effectiveness of the algorithm.
Power System Contingency Ranking Using Fast Decoupled Load Flow Method
Voltage instability is the phenomena associated with heavily loaded power systems. It is normally aggravated due to large disturbance. The Power system security is one of the significant aspects, where the proper action needs to be taken for the unseen contingency. In the event of contingency, the most serious threat to operation and control of power system is insecurity. Therefore, the contingency analysis is a key for the power system security. The contingency ranking using the performance index is a method for the line outages in a power system, which ranks the highest performance index line first and proceeds in a descending manner based on the calculated PI for all the line outages. This helps to take the prior action to keep the system secure. In this paper Fast Decoupled power flow method is used for the power system contingency ranking for the line outage based on the Active power and Voltage performance index. The ranking is given by considering the overall performance index, which is the summation of Active power and voltage performance index. The proposed method is implemented on a IEEE-14 bus system.
GSC Advanced Engineering and Technology, 2022
Power System Security and Contingency analysis is one of the most important tasks in power systems. In operation, contingency analysis assists engineers to operate the power system at a secure and safe operating point where equipment are loaded within their safe operating area (SOA). Power is dispatched to customers with acceptable quality standards. The results of off-line load flow calculations are used to estimate performance indices (PI flow, PI V). MATLAB toolbox was the proposed methodology used for the implementation. The proposed approach for contingency analysis was found to be appropriate for screening and ranking fast voltage and line flow contingencies.