IEEE

AI - BASED POWER SYSTEM TRANSIENT SECURITY ASSESSMENT Dr. Hossam Talaat Dept. of Electrical Power & Machines Faculty of Engineering - Ain Shams University

Presentation Outlines b 1. Basic Definitions b 2. Methods for Transient Security Assessment b 3. AI - Based Approaches b 4. Performance Evaluation b 5. Conclusions

b Power System Operating States Restorative Secure Insecure Emergency preventive corrective Emergency Restorative Control State 1. Basic Definitions

Power System Security ( IEEE definition ) b An instantaneous time-varying condition reflecting the robustness of the system relative to imminent disturbances. b The concept was Introduced by Dy Liacco in 1967 as a result of the 1965 northeast blackout.

Modes of Power System Security b Steady State Security Assessment: Checking overloads of components (OPF) b Dynamic Security Assessment: Examining the eigenvalues of a linearized model. b Transient Security Assessment: Checking transient stability subjected to a large disturbance.

Domains of PS Security b Planning Stage: response to a set of contingencies. b Operation Stage: Response to a given configuration and load condition.

2 METHODS OF TRANSIENT SECURITY ASSESSMENT b OFF-LINE: Time Domain Simulation (Runge- Kutta, Predictor Corrector,…) b ON-LINE: b 1. Direct methods b 2. Probability methods b 3. Parallel processing simulation b 4. AI - based methods

2.1 Lyapunov’s Direct Method b Description: The value of Energy function V is calculated at the instant of last switching, if V<V cr stable. b Drawbacks: 1. Conservative (V>V cr may happen for stable cases) --> Many false alarms1. Conservative (V>V cr may happen for stable cases) --> Many false alarms 2. Time consuming2. Time consuming

2.2 Probabilistic Methods b Probability distributions for fault type, fault location, and fault clearing time are determined. b Stability indices are then calculated for each component and for overall system. b Drawbacks: Classification (secure/insecure) accuracy is moderate (Suitable for planning rather than operation)Classification (secure/insecure) accuracy is moderate (Suitable for planning rather than operation)

2.3 Parallel Processing b Needs algorithms applying “pipelining”. b Pipelining means the division of each sequential task into a sequence of elementary sequential independent sub- tasks (e.g. system equations) b Applicable for small systems. b A promising method for near future (rate of growth of VLSI is much greater than rate of growth of power systems.

3AI - BASED APPROACHES b Learning Concept: Use Extensive off-line calculations (training set) relating key variables to calculated stability condition to design a classifier. b On-line classification: Use the available data of key variables (features) to detect stability condition.

3.1 K-means Clustering Pattern Recognition b It uses distance functions as a classification tool. b Parameters: no. of features = n, no. of samples = N, no. of cluster domains = k.

Algorithm of k-means 1. Set initial cluster centers. 2. Calculate N*k distances between sample i and cluster j using

Algorithm of k-means b 3. Distribute the samples among k clusters b 4. Compute the new cluster centers b 5. Continue until convergence

Detection of Stability Condition b 1. Each cluster should be marked as Stable or Unstable depending on the majority of samples belonging to this cluster. b 2. For on-line applications, the values of features are calculated then distances to all cluster distances are calculated. b 3. The stability condition of the sample is determined from the cluster to which it belongs.

3.2Artificial Neural Network b multi-Perceptron b (back-propagation) Output Layer Hidden Layer Input Layer

Characteristics of ANN b Training Algorithm: Back-Propagation b 3 input neurons / 1 output neuron b Activation Function: Sigmoid

Back-Propagation Procedure b At each iteration the input pattern flows through the Network to find the output. b The current output is compared to desired output to evaluate the error. b The error is propagated backward through the network to modify the weights.

3.3 Fuzzy Rule-Based Classification b Objective: generate fuzzy rules dividing the pattern space into two decision areas.

Constitution of Fuzzy subspaces

Induction of Fuzzy If-then Rules b 1. Find memberships to subspaces b 2. If subspace A ij is stable (CL ij = 1) otherwise it is unstable. b 3. Determine Certainty Factors

Classification of a New Pattern b Given the new pattern (x n, y n ), find b If the pattern is stable,otherwise it is unstable

Selected Features   1 =(po-p1)/M   2 =(po-p2)/M   =(  1 -  2 )   av  =(  1 +  2 )/2

Real-Time Considerations

4. Performance Evaluation b Generation of 162 training samples and 384 test samples by changing fault location and loading condition. b Time domain simulation is performed for all samples. b The sample is considered unstable if the rotor angle reaches 180 o within 2 sec.

System Under Study

Summary of Results

5 CONCLUSIONS b Simulation using parallel algorithms presents a promising approach for near future. b Best approaches for present application are those based on AI. b Fuzzy classification offers fast assessment with simple structure.

REFERENCES b A. El-Arabaty, H. Talaat, M. Mansour & A. Abd-Elaziz, “ Out-of-step Detection Based on Pattern Recognition”, Int. J. Electr. Power & Energy Systems, Vol.16, No.4, b A. Abdelaziz, M. Irving, A. El-Arabaty, M. Mansour, ”Out- of-step Prediction Based on Artificial Neural Networks”, Electric Power systems Research, Vol.34, No.1, b H. Talaat, “ Predictive Out-of-step Relaying Using Fuzzy Rule-based Classification”, Electric Power systems Research, Vol.48, No.3, 1999, To appear.

ON-LINE SECURITY ANALYSIS b Security Monitoring: measurements b Security Assessment: evaluation of data (secure / insecure) b Preventive Control: transition from insecure state to secure state b Emergency/Corrective Control: transition from emergency state to normal state b Restorative Control: restore all loads