ICTACT Journal on Soft Computing

K. Suresh ¹, N. Kumarappan ²

Affiliations
1 Department of Electrical and Electronic Engineering, Sri Manakula Vinayagar Engineering College, IN
2 Department of Electrical Engineering, Annamalai University, IN

Abstract

This paper presents a methodology for maintenance scheduling (MS) of generators using binary particle swarm optimization (BPSO) based probabilistic approach. The objective of this paper is to reduce the loss of load probability (LOLP) for a power system. The capacity outage probability table (COPT) is the initial step in creating maintenance schedule using the probabilistic levelized risk method. This paper proposes BPSO method which is used to construct the COPT. In order to mitigate the effects of probabilistic levelized risk method, BPSO based probabilistic levelized risk method is embarked on a MS problem. In order to validate the effectiveness of the proposed algorithm, case study results for simple five unit system can accomplish a significant levelization in the reliability indices that make possible to evaluate system generation system adequacy in the MS horizon of the power system. The proposed method shows better performance compared with other optimization methods and conventional method with improved search performance.

Keywords

Maintenance Scheduling, Probabilistic Levelized Risk Method, Binary Particle Swarm Optimization, Capacity Outage Probability Table.

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R. Ashok Bakkiyaraj ¹, N. Kumarappan ¹

Affiliations
1 Department of Electrical Engineering, Annamalai University, IN

Abstract

This paper presents a new approach for state adequacy evaluation of sampled system state in composite power system reliability analysis. Generalized regression neural network (GRNN) is used in conjunction with non-sequential Monte Carlo simulation (MCS) to evaluate the loss of probability and the power indices. GRNN approach predicts the test functions for all the sampled states after sufficient training patterns are obtained in the initial MCS sampling with dc load flow based load curtailment minimization model. This model predicts the test functions for both success and failure states. The sampled system states are used to evaluate annualized system and load point indices. The indices evaluated are loss of load probability, loss of load expectation, expected demand not served and expected energy not supplied. The results obtained in this approach are compared with the conventional non-sequential MCS which uses load curtailment minimization model for state adequacy evaluation. An error analysis for different reliability levels is also carried out to check applicability of GRNN approach for calculating the test functions in reliability optimization, where several reliability levels are analyzed. The application of the proposed GRNN approach is illustrated through case studies carried out using RBTS and IEEE-RTS test systems and annualized indices are presented. It is found that the proposed approach estimates indices nearer to the conventional non-sequential MCS.

Keywords

Generalized Regression Neural Network, Composite Power System, State Adequacy Evaluation, Reliability Indices, DC Load Flow Based Load Curtailment Model.

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S. Anbazhagan ¹, N. Kumarappan ¹

Affiliations
1 Department of Electrical Engineering, Annamalai University, IN

Abstract

There is a general consensus that the movement of electricity price is crucial for electricity market. The binary electricity price classification method is as an alternative to numerical electricity price forecasting due to high forecasting errors in various approaches. This paper proposes a binary classification of day-ahead electricity prices that could be realized using discrete cosine transforms (DCT) based neural network (NN) approach (DCT-NN). These electricity price classifications are important because all market participants do not to know the exact value of future prices in their decision-making process. In this paper, classifications of electricity market prices with respect to pre-specified electricity price threshold are used. In this proposed approach, all time series (historical price series) are transformed from time domain to frequency domain using DCT. These discriminative spectral co-efficient forms the set of input features and are classified using NN. The binary classification NN and the proposed DCT-NN were developed and compared to check the performance. The simulation results show that the proposed method provides a better and efficient method for day-ahead deregulated electricity market of mainland Spain.

Keywords

Price Forecasting, Discrete Cosine Transforms, Neural Network, Binary Electricity Price Classification, Electricity Market.

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