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2. PHD Thesis / Tesis PHD
3. Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina

Title:	Risk based security assessment of power systems using statistical computational intelligent techniques
Authors:	Marayati Marsadek (P42036)
Supervisor:	Azah Mohamed, Prof. Dr.
Keywords:	Risk based security assessment Power systems Statistical computational intelligent techniques Electric power systems--Security measures
Issue Date:	8-Dec-2011
Description:	Power system operation is currently becoming more complex due to financial and environmental constraints that limit the expansion of transmission networks. Thus, power transmission networks become heavily loaded and a great importance has to be given to power system security assessment. Traditionally, power system security is assessed using deterministic approach in which power system operation is deemed to be secure if it operates within the pre-defined security margin. However, in the current competitive environment, there is a drawback with the deterministic approach because it does not reflect the stochastic or probabilistic nature of the system in terms of load profiles, component availability, failures and other uncertainties. To incorporate probabilistic or stochastic techniques, power system security assessment based on the concept of risk is required. Risk assessment is an approach that quantitatively captures the factors that determine security level, namely likelihood and the severity of events. The objective of this research is to develop a comprehensive and fast risk based security assessment in power systems which consider risk based static security assessment (RBSSA) that includes risk of low voltage (LV) and line overload (LO), and risk based voltage collapse assessment (RBVCA). The probability of transmission line outage is modeled using the Poisson distribution function. The non-sequential Monte Carlo simulation is also implemented and the result is compared with the Poisson distribution function so as to investigate its effectiveness in calculating the probability of contingency. To consider the effect of weather on the likelihood of outage, an improved failure rate model using the data pooling method is proposed. The outage history data obtained for six years period is used in the data pooling procedure. Severity function is another important attribute in risk assessment and therefore three types of severity function are considered in the implementation of RBSSA. For RBVCA, a new severity function utilizing a voltage collapse prediction index is considered. To reduce the number of power flow simulation that needs to be performed, transmission line outage screening using risk factor is proposed. In order to develop fast and accurate RBSSA and RBVCA methods, two computational intelligent techniques based on support vector machine (SVM) and generalized regression neural network (GRNN) are used because of their robustness and fast training time. A feature extraction technique based on the principle component analysis is applied for the purpose of reducing the SVM and GRNN training times. The proposed techniques for RBSSA and RBVCA are implemented on the IEEE 24 bus test system and the 87 bus practical power system. Simulations are carried out using the Power System Analysis Toolbox and the development of the computational intelligent techniques are implemented in the MATLAB version 7 environment. Results showed that the GRNN give better risk prediction performance compared to SVM because it provides the lowest mean average error, mean square error and root mean square error (RMSE). For low voltage RBSSA, line overload RBSSA and RBVCA, the accuracy obtained using GRNN model in terms of RMSE are 0.019, 0.018 and 0.026, respectively.,PhD
Pages:	127
Call Number:	TK1025 .M337 2011 3
Publisher:	UKM, Bangi
Appears in Collections:	Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina

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