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Transient Stability Assessment using Neural Networks

Published on Nov 17, 2015


A high degree of security for normal operation of larger inter connected power system is required one of the requirements of reliable service in electrical power system is to maintain the synchronous machines running in parallel with adequate capacity to meat the low demand with the growing stress on present days power systems, the potential impact of faults and other disturbances on their security is increasing. Protective relays in the power system detect faults and trigger the opening of circuit to isolate the fault.

The power system can be considered to go through changes in configuration in three stages, from a prefault, faulted to a post faults system. The analysis required knowing whether following a contingency, the power system will "survive" the transients and moving to a stable operating condition is referred to as dynamic security assessment. The transient stability assessment of power system is done to appraise the system capability to with stand major contingencies and to suggest remedial actions i.e., means to enhance this capability.

Conventional methods for transient stability assessment.

Several approaches for transient stability assessment of power systems such as numerical integration second method of Lyapunov, pattern recognition.

Numerical integration :

In this approach transient stability analysis if performed by simulation. For given operating condition and special and specified large disturbance a time solution obtained for the generator rotor angles, speeds, terminal voltages etc., by examining the swing curves, separation of one or more generators from the rest of the system indicating loss of synchronism is detected. Even for small power network and simple mathematical model possible this method is slow and cumbersome.

The second method of Lyapunov:

In this method, the integration off post fall system equation is replaced by stability criterion. The value of a suitably designed lyapunov function v is calculated at the instant of last switching in the system and compared with the previously determined critical value Vcr of this function if V is smaller than Vcr the system will reach a stable equilibrium point.

Pattern recognition :

The main objective of the pattern recognition method is transient stability assessment is to reduce computational requirements to minimum this is done at the expense of elaborate off line computations. The methodology of pattern recognition consists of defining a pattern vector x whose components contain all significant variables of the system. This vector is evaluated at many representative-operating conditions to generate "training set". If some component of the pattern vector or strongly correlated with one another a process of dimensionality reduction is performing to identified significant and hopefully uncorrelated set of components.

This is process is called feature extraction. The final step is to determine a function s (x) such that s(x) = { >=0 for a secure x) {<=0 for an insecure x) this function is called a classifier, at once the classifier obtained, for sample x one can classify the sample as stable or unstable very rapidly. The most important task in the application of pattern recognition is the selection of primary variables because the lower limit for the classification error depends on the primary variables.

Draw backs of conventional methods

1. The online transient stability assessment of the electrical power systems is an extremely difficult task with the available techniques.

2. Each contingency (fault) must be treated separately.

3. Smaller time step intervals are needed to ensure numerical stability.

4. Electro motive force and mechanical power inputs are assumed constant during the transient.

5. Very elaborate off line computations give scope to errors.

The Advantages:

1. this technique has the potential of faster transient stability assessment than the other other conventional methods.

2. This technique provides the online transient stability assessment.

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