Artificial neural network model for voltage security based contingency ranking
Introduction
The intensive loading of existing generation and transmission facilities, due to difficulties in building new generation in load areas and drawing power from remotely-located generation has resulted in voltage related problems in many power systems. Moreover, lavish use of shunt capacitor banks, while extending transfer limits, makes the power system to move closer to the voltage instability point. A system enters a state of voltage instability when a disturbance, increase in load or change in system conditions, cause a progressive and uncontrollable deterioration in the voltage profile. Studies have been performed to study voltage instability with both static and dynamic approaches [1]. Traditional methods of voltage stability investigations have relied on static analysis using the conventional power flow method. This approach has been practically viable because of the fact that the voltage collapse is a relatively slow process. Computed P–V/V–Q curves are the most widely used method for evaluating the voltage stability of a power system. Kessel et.al. [2] developed a voltage stability index called L-index based on the power flow solution. This index value ranges from 0 (no load) to 1 (voltage collapse). The bus with the highest L-index will be the most vulnerable bus in the system. Tiranuchit [3] proposed the minimum singular value of the Jacobian of the load flow equation as a voltage stability index. In [4], continuation power flow method was applied to compute the exact collapse point and the voltage stability margin. Gao et al. [5] used the modal analysis technique to compute the voltage stability level of the system. The aforementioned techniques require large computations and are not efficient for on-line applications.
On-line voltage security analysis requires the evaluation of the effects of all possible contingency cases and ordering them based on their severity. The most severe contingency is ranked one and the least severe is ranked last. Recently, artificial neural networks (ANN) have been proposed as a tool for contingency ranking [6], [7], [8]. In most of the published literature on ANN-based contingency ranking, a single large network is trained to map the system operating state to the post-contingency voltage stability level for all the contingencies in the contingency list. The problem with this approach is that, as the size of the system grows, the number of variables to be considered and the number of contingencies to look at to estimate the voltage stability will also increase. This may leads to difficulty in training the network in a limited amount of time. In this paper separate neural networks dedicated to handle specific contingencies are developed. While training the neural network, by selecting only the relevant attributes of the data as input features and excluding redundant ones, higher performance is expected with smaller computational effort. In this work, we propose “mutual information” [9] between the input variables and the output as the criterion to select the input features of the networks. The motivation for considering the mutual information is its capability to measure a general dependence between two variables. The effectiveness of the proposed method is demonstrated through contingency ranking in IEEE 30-bus test system.
The remainder of this paper is organized as follows: In Section 2, the use of L-index in voltage stability analysis is reviewed. The details of artificial neural network are given in Section 3. The methodology followed to configure the ANN from the input–output data is explained in Section 4. Section 5 gives the details of the application of the proposed model for contingency ranking in IEEE 30-bus test system.
Section snippets
Voltage stability index
The static approach for voltage stability analysis involves determination of an index known as voltage collapse proximity indicator. This index is an approximate measure of closeness of the system operating point to voltage collapse. There are various methods of determining the voltage collapse proximity indicator. One such method is the L-index of the load buses in the system proposed in [2]. It is based on load flow analysis and its value ranges from 0 (no load condition) to 1 (voltage
Review of artificial neural network
Artificial neural networks [10], [11] can be viewed as parallel and distributed processing systems, which consists of a large number of simple and massively connected processors. There are a number of architectures proposed to solve different pattern recognition problems. A multilayer feed forward network trained by back propagation is the most popular and versatile form of neural network for pattern mapping or function approximation problem. The structure of a multilayer feed forward network
Development of neural network model
The proposed methodology for contingency ranking is based on feed forward neural networks for L-index estimation for different operating conditions. The neural network approach has two phases: training and exploitation. During the training phase, a set of neural networks each dedicated to one contingency are trained to capture the underlying relationship between pre-contingency system state and the post-contingency L-index value. This is done in view of the diversified nature of the different
Simulation results
This section presents the details of the simulation carried out on IEEE 30-bus system for contingency ranking using the proposed approach. IEEE 30-bus system consists of 6 generators, 24 load buses and 41 transmission lines. The transmission line parameters and the generator cost coefficients are given in [12]. Thirty eight single line outages were considered for voltage stability-based contingency ranking. Based on the procedure given in Section 4.1, a total of 1000 input–output pairs were
Conclusion
In this paper, an artificial neural network-based approach is presented for voltage stability-based contingency ranking. A set of feed forward neural networks have been trained to map the non-linear relationship between the pre-contingency operating conditions and the post-contingency stability index. The problem of feature selection is addressed through mutual information between the input variables and the output stability index. With the incorporation of the feature selection method,
References (12)
Power Systems Voltage Stability
(1993)- et al.
Estimating the voltage stability of power systems
IEEE Trans. Power Syst.
(1986) - et al.
A Posturing strategy against voltage instability in electric power systems
IEEE Trans. Power Syst.
(1988) - et al.
The continuation power flow: a tool for steady state voltage stability analysis
IEEE Trans. Power Syst.
(1992) - et al.
Voltage stability evaluation using modal analysis
IEEE Trans. Power Syst.
(1992) - et al.
Applications of artificial neural networks in voltage stability assessment
IEEE Trans. Power Syst.
(1995)
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