Power System Security Assessment Application of Learning Algorithms
نویسنده
چکیده
The last years blackouts have indicated that the operation and control of power systems may need to be improved. Even if a lot of data was available, the operators at different control centers did not take the proper actions in time to prevent the blackouts. This depends partly on the reorganization of the control centers after the deregulation and partly on the lack of reliable decision support systems when the system is close to instability. Motivated by these facts, this thesis is focused on applying statistical learning algorithms for identifying critical states in power systems. Instead of using a model of the power system to estimate the state, measured variables are used as input data to the algorithm. The algorithm classifies secure from insecure states of the power system using the measured variables directly. The algorithm is trained beforehand with data from a model of the power system. The thesis uses two techniques, principal component analysis (PCA) and support vector machines (SVM), in order to classify whether the power system can withstand an (n−1)-fault during a variety of operational conditions. The result of the classification with PCA is not satisfactory and the technique is not appropriate for the classification problem. The result with SVM is much more satisfying and the support vectors can be used on-line in order to determine if the system is moving into a dangerous state, thus the operators can be supported at an early stage and proper actions can be taken. In this thesis it is shown that the scaling of the variables is important for successful results. The measured data includes angle difference, busbar voltage and line current. Due to different units, such as kV, kA and MW, the data must be preprocessed to obtain classification results that are satisfactory. A new technique for finding the most important variables to measure or supervise is also presented. Guided by the support vectors the variables which has large influence on the classification are indicated.
منابع مشابه
A Novel Index for Online Voltage Stability Assessment Based on Correlation Characteristic of Voltage Profiles
Abstract: Voltage instability is a major threat for security of power systems. Preserving voltage security margin at a certain limit is a vital requirement for today’s power systems. Assessment of voltage security margin is a challenging task demanding sophisticated indices. In this paper, for the purpose of on line voltage security assessment a new index based on the correlation characteristic...
متن کاملAssessment Methodology for Anomaly-Based Intrusion Detection in Cloud Computing
Cloud computing has become an attractive target for attackers as the mainstream technologies in the cloud, such as the virtualization and multitenancy, permit multiple users to utilize the same physical resource, thereby posing the so-called problem of internal facing security. Moreover, the traditional network-based intrusion detection systems (IDSs) are ineffective to be deployed in the cloud...
متن کاملLow-Area/Low-Power CMOS Op-Amps Design Based on Total Optimality Index Using Reinforcement Learning Approach
This paper presents the application of reinforcement learning in automatic analog IC design. In this work, the Multi-Objective approach by Learning Automata is evaluated for accommodating required functionalities and performance specifications considering optimal minimizing of MOSFETs area and power consumption for two famous CMOS op-amps. The results show the ability of the proposed method to ...
متن کاملResearch and Developments in the Area of Power System Security
This note gives a survey of some research activities in the Stochastic Methods Group of the University of Liège, “Montefiore” Institute, in the area of power systems security assessment and control, those in which the author is directly involved. Current research activities in the field are conducting through several research projects supported by the European Union and include those within: (a...
متن کاملMachine-Learning Approaches to Power-System Security Assessment
This paper describes ongoing research and development of machine learning and other complementary automatic learning techniques in a framework adapted to the specific needs of power system security assessment. In the proposed approach, random sampling techniques are considered to screen all relevant power system operating situations, while existing numerical simulation tools are exploited to de...
متن کاملApplication to Adaptive Control to Synchronous Machine Excitation
Self-tuning adaptive control technique has the advantage of being able to track the system operating conditions so that satisfactory control action can always be produced. Self-tuning algorithms can be implemented easily. Because the power systems are usually time varying non-linear systems and their parameters vary, adaptive controllers are very suitable for power systems. Characteristics of a...
متن کامل