K. Shanti Swarup

Classification and Assessment of Power System Security Using Multiclass SVM

Security assessment and classification are the major concerns in real-time operation of electric power systems. This paper proposes a multiclass support vector machine (SVM) classifier for static and transient security assessment and classification. A straightforward and quick procedure called the sequential forward selection method is used for a feature selection process. The security status of any given operating condition is classified into four modes, viz., secure, critically secure, insecure, and highly insecure, based on the computation of a security index. The proposed SVM-based pattern classifier system is implemented and tested on standard benchmark systems. The simulation results of the multiclass SVM classifier are compared with least-squares, probabilistic neural network, extreme learning machine, and extreme SVM classifiers. The feasibility of implementation of the proposed classifier system for online security evaluation is also discussed.

Evolutionary Tristate PSO for Strategic Bidding of Pumped-Storage Hydroelectric Plant

This paper develops bidding strategy for operating multiunit pumped-storage power plant in a day-ahead electricity market. Based on forecasted hourly market clearing price, the objective is to self-schedule and maximize the expected profit of the pumped-storage plant, considering both spinning and nonspinning reserve bids and meeting the technical operating constraints. Evolutionary tristate particle swarm optimization (ETPSO) based approach is proposed to solve the problem, combining basic particle swarm optimization (PSO) with tristate coding technique and genetics-based mutation operation. The discrete characteristic of a pumped-storage plant is modeled using tristate coding technique and mutation operation is used for faster convergence. The proposed model is adaptive for nonlinear 3-D relationship between the power produced, the energy stored, and the head of the associated reservoir. The proposed approach is applied for a practical utility consisting of four units. Simulation results for different operating cycles of the storage plant indicate the attractive properties of ETPSO approach with highly optimal solution and robust convergence behavior.

Neural network approach to contingency screening and ranking in power systems

Abstract An artificial neural network (ANN) approach to power system contingency analysis is proposed. Using fast voltage and line-flow contingency screening. Full AC load flow is performed for each contingency case. The off-line results of full AC load flow calculations are used to construct two kinds of performance indices, namely the real power performance index (PIMW) and voltage performance index (PIV), which reflect the degree of severity of contingencies. The results from off-line load flow calculations are used to train the “screening module”, which is a multi-layered perceptron (MLP) network, for estimating the performance indices (PIMW, PIV PIV). The MLP is trained to classify the contingencies either as critical or non-critical cases using back-propagation (BP) algorithm. The screened critical contingencies are passed on to the “ranking module” for ranking of the contingencies. The effectiveness of the proposed method is demonstrated by contingency screening and ranking on a standard 6-bus and IEEE 14-bus systems. The performance of the proposed method is compared with a traditional Newton Raphson (NR) method and the results discussed. The proposed methodology was implemented using the MATLAB Neural Network Toolbox. The generalization capability of the trained neural network was able to identify unknown contingencies with large range of operating conditions and changes in network topology. The proposed approach to contingency analysis was found to be suitable for fast voltage and line-flow contingency screening and ranking.

Artificial neural network using pattern recognition for security assessment and analysis

Artificial neural networks using pattern recognition methodology for security assessment of electric power systems is presented. Conventional numerical methods are either too complex or time consuming. An alternative method using neural networks to address the security assessment problem and its effectiveness against conventional methods is discussed. Neural networks using pattern recognition techniques is a promising methodology for different types of security assessment. Feature selection and extraction are used for selecting best features having highest discriminating capabilities. An important feature of the approach is that it can be generalized for steady state, transient and dynamic security assessment, which is a desirable feature for on-line security analysis. The proposed approach has been tested on the WSCC 9-bus 3-generator system. Steady state, transient and dynamic security assessment classification and contingency ranking results are provided to highlight the overall classification accuracy and suitability of the approach.

High Precision Frequency Estimation Using Internet-Based Phasor Measurement Unit

This paper presents a measurement technique suited for real-time transfer, monitoring and processing of state variables in power distribution networks, in terms of synchronized phasors. The proposed technique, different from commercial phasor measurement units (PMUs), is based on general purpose data acquisition hardware and uses network time protocol (NTP) for accurate time-stamping of the measurements. A least square approximation scheme for accurate frequency estimation is proposed, and with this scheme, it has been proved that the range of frequency with zero estimation error increases with the increase in number of re-samplings.

Design of pattern recognition system for static security assessment and classification

Static security analysis is an important study carried out in the control centers of electric utilities. Static security assessment (SSA) is the process of determining whether the current operational state is in a secure or emergency (insecure) state. Conventional method of security evaluation involves performing continuous load flow analysis, which is highly time consuming and infeasible for real-time applications. This led to the application of pattern recognition (PR) approach for static security analysis. This paper presents a more efficient design of a PR system suitable for on-line SSA. The feature selection stage in the PR system uses many algorithms to select the optimal feature set. This paper proposes the use of Support Vector Machine (SVM), a recently introduced machine learning tool, in the classifier design stage of PR system. The developed PR system is implemented in IEEE standard test systems for SSA and classification. The performance of SVM classifier is compared with the conventional K-nearest neighbor, method of least squares and neural network classifiers. Simulation results prove that the SVM-PR classifier outperforms other equivalent classifier algorithms, giving high classification accuracy and less misclassification rate. The feasibility of SVM-PR classifier for on-line security assessment process is also presented.

Multiagent based particle swarm optimization approach to economic dispatch with security constraints

This paper proposes a new evolutionary approach named as multi agent particle swarm optimization (MAPSO) algorithm for solving economic dispatch with security constraints (line flow and bus voltage). This method integrates multiagent systems (MAS) and particle swarm optimization (PSO) to form a new algorithm, multiagent particle swarm optimization algorithm. In MAPSO, an agent represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice like environment, with each agent fixed on a lattice point. In order to obtain optimal solution, each agent competes and cooperates with its neighbor and it can also learn by using its knowledge. Making use of these agent-agent interactions, MAPSO realizes the purpose of minimizing the objective function value. MAPSO is applied to two representative systems i.e. IEEE 14 bus and IEEE 30 bus systems. Simulation results show that proposed approach gives better solution than earlier reported approaches.

Pumped storage bidding and its impacts in combined pool-bilateral market

This paper develops bidding strategy for operating pumped storage power plant in a combined pool-bilateral market. 1 / 0 /−1 mixed-integer programming model to account discrete tri-state operation of pumped storage plant is developed and multi-looping sequential optimization approach is used to solve the problem. Considering realistic case study, operating strategies for the pumped storage plant are evaluated. Operating pumped storage plant in an electricity market can lead to significant changes in the consumer and producer surplus, energy prices and market efficiency. This paper investigates how integration of pumped storage energy trade affects the net welfare of electricity market.

Coordinated maintenance scheduling of Gencos and Transcos in restructured power systems

This paper proposes a methodology for the development of automatic scheduling techniques, for preventive maintenance of generating units and lines, in a competitive electric energy environment, with the inclusion of transmission constraints and forced outage rates, over a specified operational period. For generator maintenance the objective of the ISO is to maintain adequate level of reliability throughout the operational period (for which Benders decomposition technique is used) and the objective of the GENCO is to maximize profit or to minimize loss in profit (for which transmission constrained price based unit commitment, TCPBUC, based on Lagrangian relaxation method is used). For line maintenance minimum cost model benders technique with adequate level of reliability is used. A coordinating technique using penalty factors is incorporated to converge the conflicting objectives. The transmission constraints are modeled using DC sensitivity factors. Case study with a 6 bus, 3 generator, 11 line system is presented and discussed

Binary SVM Approach for Security Assessment and Classification in Power Systems

Power System Security is a major concern in real time operation. Conventional method of security evaluation consists of performing continuous load flow and transient stability studies by simulation program. This is highly time consuming and infeasible for on-line application. Pattern Recognition (PR) is a promising tool for on-line security evaluation. This paper proposes a Support Vector Machine (SVM) based binary classification for static and transient security evaluation. The proposed SVM based PR approach is tested on IEEE 57 Bus and 118 Bus systems. The simulation results of SVM classifier is compared with the conventional Method of Least Squares (MLS) classifiers.