ANN approach assesses system security
TL;DR: In this paper, an artificial neural network (ANN) aided method for security assessment is proposed and illustrated for a model six-bus power system, which demonstrates the feasibility of classification of load patterns for power system static security assessment using a Kohonen self-organizing feature map.
Abstract: Large interconnected power systems with dispersed and geographically isolated generators and load constitute a majority of the power network. Present-day power systems are dynamic in nature, where the network topology frequently changes with load demand. With increase in load, the power system network is loaded to its limits, making it susceptible to collapse even under minor disturbances. In order to operate the power system economically, the current operating state of the system must be identified as either secure or insecure. An artificial neural network (ANN) aided method for security assessment is proposed and illustrated for a model six-bus power system. The work demonstrates the feasibility of classification of load patterns for power system static security assessment using a Kohonen self-organizing feature map. The most important aspect of this network is its generalization property. Using 15 different line-loading patterns for training, the network successfully classifies the unknown loading patterns. This powerful and versatile feature is especially useful for power system operation. Research is in progress to include contingency analysis in the security assessment program.
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TL;DR: The comparison of the ANN models with the model based on Newton Raphson load flow analysis in terms of accuracy and computational speed indicate that the proposed model is effective and reliable in the fast evaluation of the security level of power systems.
Abstract: Fast and accurate contingency selection and ranking method has become a key issue to ensure the secure operation of power systems. In this paper multi-layer feed forward artificial neural network (MLFFN) and radial basis function network (RBFN) are proposed to implement the online module for power system static security assessment. The security classification, contingency selection and ranking are done based on the composite security index which is capable of accurately differentiating the secure and non-secure cases. For each contingency case as well as for base case condition, the composite security index is computed using the full Newton Raphson load flow analysis. The proposed artificial neural network (ANN) models take loading condition and the probable contingencies as the input and assess the system security by screening the credible contingencies and ranking them in the order of severity based on composite security index. The numerical results of applying the proposed approach to IEEE 118-bus test system demonstrate its effectiveness for online power system static security assessment. The comparison of the ANN models with the model based on Newton Raphson load flow analysis in terms of accuracy and computational speed indicate that the proposed model is effective and reliable in the fast evaluation of the security level of power systems. The proposed online static security assessment (OSSA) module realized using the ANN models are found to be suited for online application.
89 citations
Cites background from "ANN approach assesses system securi..."
...In this direction, over the past few years, several approaches using artificial neural networks (ANN) have been proposed as alternativemethods for static security assessment using both supervised and unsupervised architectures [11]–[14]....
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TL;DR: The suitability of the proposed artificial neural network (ANN)-based method for on-line security evaluation of power systems even under changing topological conditions is demonstrated.
Abstract: This paper presents an artificial neural network (ANN)-based method for on-line security evaluation of power systems. One of the important considerations in applying ANN is feature selection. A new divergence-based feature selection algorithm has been proposed and investigated. The method has been applied on an IEEE test system and the results demonstrate the suitability of the proposed method for on-line security evaluation of power systems even under changing topological conditions.
58 citations
TL;DR: A case study on a practical power system demonstrates that DT can extract operating guidelines from offline voltage stability analysis results, and helps system operators assess voltage stability status in real-time.
Abstract: This paper constructs decision tree (DT) using C4.5 algorithm for online voltage stability assessment. The entire process includes three steps: sample acquisition, attribute selection and DT construction. First, P-V curves analysis is performed to generate samples for DT. Participation factor analysis and relief algorithm are then used to select attributes for DT. C4.5 algorithm is finally applied to construct DT. The case study on a practical power system demonstrates that DT can extract operating guidelines from offline voltage stability analysis results, and helps system operators assess voltage stability status in real-time.
54 citations
TL;DR: Steady state, transient and dynamic security assessment classification and contingency ranking results are provided to highlight the overall classification accuracy and suitability of the artificial neural networks approach.
Abstract: Artificial neural networks using pattern recognition methodology for security assessment of electric power systems is presented. Conventional numerical methods are either too complex or time consuming. An alternative method using neural networks to address the security assessment problem and its effectiveness against conventional methods is discussed. Neural networks using pattern recognition techniques is a promising methodology for different types of security assessment. Feature selection and extraction are used for selecting best features having highest discriminating capabilities. An important feature of the approach is that it can be generalized for steady state, transient and dynamic security assessment, which is a desirable feature for on-line security analysis. The proposed approach has been tested on the WSCC 9-bus 3-generator system. Steady state, transient and dynamic security assessment classification and contingency ranking results are provided to highlight the overall classification accuracy and suitability of the approach.
42 citations
TL;DR: The comparison of severity obtained by the neural network models and the NRLF analysis in terms of time and accuracy, signifies that the proposed model is quick, accurate and robust for power system static security evaluation for unseen network conditions.
Abstract: Efficient contingency screening and ranking method has gained importance in modern power systems for its secure operation. This paper proposes two artificial neural networks namely multi-layer feed forward neural network (MFNN) and radial basis function network (RBFN) to realize the online power system static security assessment (PSSSA) module. To assess the severity of the system, two indices have been used, namely active power performance index and voltage performance index, which are computed using Newton–Raphson load flow (NRLF) analysis for variable loading conditions under N − 1 line outage contingencies. The proposed MFNN and RBFN models based PSSSA module, are fed with power system operating states, load conditions and N − 1 line outage contingencies as input features to train the neural network models, to predict the performance indices for unseen network conditions and rank them in descending order based on performance indices for security assessment. The proposed approaches are tested on standard IEEE 30-bus test system, where the simulation results prove its performance and robustness for power system static security assessment. The comparison of severity obtained by the neural network models and the NRLF analysis in terms of time and accuracy, signifies that the proposed model is quick, accurate and robust for power system static security evaluation for unseen network conditions. Thus, the proposed PSSSA module implemented using MFNN and RBFN models are found to be feasible for online implementation.
38 citations
References
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01 Sep 1990
TL;DR: The self-organizing map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications, and an algorithm which order responses spatially is reviewed, focusing on best matching cell selection and adaptation of the weight vectors.
Abstract: The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed. >
7,883 citations
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Abstract: Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech and image recognition. These models are composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. Computational elements or nodes are connected via weights that are typically adapted during use to improve performance. There has been a recent resurgence in the field of artificial neural nets caused by new net topologies and algorithms, analog VLSI implementation techniques, and the belief that massive parallelism is essential for high performance speech and image recognition. This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification. These nets are highly parallel building blocks that illustrate neural net components and design principles and can be used to construct more complex systems. In addition to describing these nets, a major emphasis is placed on exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components. Single-layer nets can implement algorithms required by Gaussian maximum-likelihood classifiers and optimum minimum-error classifiers for binary patterns corrupted by noise. More generally, the decision regions required by any classification algorithm can be generated in a straightforward manner by three-layer feed-forward nets.
7,798 citations
01 Dec 1987
TL;DR: This paper reviews present formulations and methods, and tries to point out areas of difficulty that constitute the main challenges for successful practical on-line implementations over the coming years.
Abstract: An operationally "secure" power system is one with low probability of blackout or equipment damage. The power system control processes needed to maintain a designated security level at minimum operating cost are extremely complicated. They increasingly depend upon on-line computer security analysis and optimization. This on-line technology is still relatively new, with enormous further potential. Since security and optimality are normally conflicting requirements of power system control, it is inappropriate to treat them separately. Therefore, they are slowly becoming coalesced into a unified hierarchical mathematical problem formulation: one that is, however, far too complex to afford anything but an approximate, near-optimal solution. The practical validity of this unifying trend relies on being able to incorporate all significant security constraints within the process. The main two current computational tools in this field are contingency analysis and special operations-oriented versions of optimal power flow (OPF). Contingency analysis identifies potential emergencies through extensive "what if?." simulations on the power system network. OPF is a major extension to the conventional dispatch calculation. It can respect system static security limits, and can schedule reactive as well as active power. Moreover, the advanced versions of OPF include or interface with contingency analysis. This paper reviews present formulations and methods, and tries to point out areas of difficulty that constitute the main challenges for successful practical on-line implementations over the coming years.
533 citations
TL;DR: A fast technique has been developed for the automatic ranking and selection of contingency cases for a power system contingency analysis study and results of this technique applied to different test systems are presented.
Abstract: A fast technique has been developed for the automatic ranking and selection of contingency cases for a power system contingency analysis study. A contingency list is built containing line and generator outages which are ranked according to their expected severity as reflected in voltage level degradation and circuit overloads. An adaptive contingency processorcan be set up by performing sequential contingency tests starting with the most severe contingencies at the top of the list and proceeding down the list, stopping when the severity goes below a threshold. Computational results of this technique applied to different test systems are presented.
466 citations
01 Feb 1992
TL;DR: A broad overview of on-line power system security analysis is provided in this paper, with the intent of identifying areas needing additional research and development, such as external system modeling and external system analysis.
Abstract: A broad overview of on-line power system security analysis is provided, with the intent of identifying areas needing additional research and development. Current approaches to state estimation are reviewed and areas needing improvement, such as external system modeling, are discussed. On-line contingency selection has become practical, particularly for static security. Additional work is necessary to identify better indices of power system stress to be used in on-line screening filters for both static and dynamic security analysis. Use of optimal power flow schemes to recommend optimal preventive and corrective strategies is presented on a conceptual level. Techniques must be further developed to provide more practical contingency action plans, which include real-world operating considerations and use a reasonably small number of control actions. Techniques must be developed for costing operating variables which are not easily quantified in dollars. Soft or flexible constraints and time variables must be included in the preventive and corrective strategy formulation. Finally, the area of on-line transient and dynamic security analysis is presented. >
229 citations