Akash Saxena

Grey wolf optimizer based regulator design for automatic generation control of interconnected power system

Abstract This paper presents an application of grey wolf optimizer (GWO) in order to find the parameters of primary governor loop for successful Automatic Generation Control of two areas’ interconnected power system. Two standard objective functions, Integral Square Error and Integral Time Absolute Error (ITAE), have been employed to carry out this parameter estimation process. Eigenvalues along with dynamic response analysis reveals that criterion of ITAE yields better performance. The comparison of the regulator performance obtained from GWO is carried out with Genetic Algorithm (GA), Particle Swarm Optimization, and Gravitational Search Algorithm. Different types of perturbations and load changes are incorporated in order to establish the efficacy of the obtained design. It is observed that GWO outperforms all three optimization methods. The optimization performance of GWO is compared with other algorithms on the basis of standard deviations in the values of parameters and objective functions.

Automatic generation control of two area interconnected power system using Genetic algorithm

The present work proposes three intelligent Load frequency controllers to control and regulate the power output and system frequency by controlling the speed of generator. Controllers are tested on two area thermal thermal interconnected power system. A multi objective function is created by two criterions Integral Square Error (ISE), and Integral of Time Multiplied Absolute value of the Error (ITAE). All of these objective functions are optimized by using Genetic algorithm (GA). Parameters for an area are determined by using these two controllers; both controllers use GA and the third controllers parameters are calculated by an objective function which is formed by the combination of conventional objective functions. The aim of proposed controller is to restore the frequency to its nominal value in very short time. However proposed controller is tested and aimed for shortest settling time and minimum peak overshoot. It is found that the proposed controllers exhibit satisfactory over all dynamic performance when compared with conventional controllers. Two sets of observations are generated, in first observation set robustness of controller is tested by the step change in load, second observation test gives the effect of speed regulation on the frequency response. It is found that proposed controller provides a satisfactory balance between frequency deviations and transient oscillations.

Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine

With the development of society along with an escalating population, the concerns regarding public health have cropped up. The quality of air becomes primary concern regarding constant increase in the number of vehicles and industrial development. With this concern, several indices have been proposed to indicate the pollutant concentrations. In this paper, we present a mathematical framework to formulate a Cumulative Index (CI) on the basis of an individual concentration of four major pollutants (SO2, NO2, PM2.5, and PM10). Further, a supervised learning algorithm based classifier is proposed. This classifier employs support vector machine (SVM) to classify air quality into two types, that is, good or harmful. The potential inputs for this classifier are the calculated values of CIs. The efficacy of the classifier is tested on the real data of three locations: Kolkata, Delhi, and Bhopal. It is observed that the classifier performs well to classify the quality of air.

Performance Evaluation of Antlion Optimizer Based Regulator in Automatic Generation Control of Interconnected Power System

This paper presents an application of the recently introduced Antlion Optimizer (ALO) to find the parameters of primary governor loop of thermal generators for successful Automatic Generation Control (AGC) of two-area interconnected power system. Two standard objective functions, Integral Square Error (ISE) and Integral Time Absolute Error (ITAE), have been employed to carry out this parameter estimation process. The problem is transformed in optimization problem to obtain integral gains, speed regulation, and frequency sensitivity coefficient for both areas. The comparison of the regulator performance obtained from ALO is carried out with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA) based regulators. Different types of perturbations and load changes are incorporated to establish the efficacy of the obtained design. It is observed that ALO outperforms all three optimization methods for this real problem. The optimization performance of ALO is compared with other algorithms on the basis of standard deviations in the values of parameters and objective functions.

A least square support vector machine-based approach for contingency classification and ranking in a large power system

Abstract This paper proposes an effective supervised learning approach for static security assessment of a large power system. Supervised learning approach employs least square support vector machine (LS-SVM) to rank the contingencies and predict the system severity level. The severity of the contingency is measured by two scalar performance indices (PIs): line MVA performance index (PIMVA) and Voltage-reactive power performance index (PIVQ). SVM works in two steps. Step I is the estimation of both standard indices (PIMVA and PIVQ) that is carried out under different operating scenarios and Step II contingency ranking is carried out based on the values of PIs. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus (New England system). The approach can be beneficial tool which is less time consuming and accurate security assessment and contingency analysis at energy management center.

Support Vector Machine based approach for accurate contingency ranking in power system

This paper presents an effective supervised learning approach for static security assessment. The approach proposed in this paper employs Least Square Support Vector Machine (LS-SVM) to rank the contingencies and predict the severity level for a standard IEEE -39 Bus power system. SVM works in two stage, in stage 1st estimation of a standard index line MVA Performance Index PIMVA is carried out under different operating scenarios and in stage II (based on the values of PIMVA) contingency ranking is carried out. The test results are compared with some recent approaches reported in literature. The overall comparison of test results is based on the, regression performance and accuracy levels through confusion matrix. Results obtained from the simulation studies advocate the suitability of the approach for online applications. The approach can be a beneficial tool to fast and accurate security assessment and contingency analysis at energy management centre.

Supervised Learning Paradigm Based on Least Square Support Vector Machine for Contingency Ranking in a Large Power System

In modern emerging power system many contingencies and critical operating conditions present a potential threat to system’s stability. An intelligent designer at energy management center requires a paradigm which can not only predict such cases but also suggests an effective strategy for preventive control. This paper presents a least square support vector machine (LS-SVM)-based classifier to identify and rank the critical contingencies in a standard IEEE-39 bus Network (New England). This paradigm works in two stages. In first stage, the identification of two indices, i.e., voltage reactive performance index PIVQ and MVA line loading index PIMVA is carried out and in next stage the classification of contingencies is carried out. The proposed approach shows promising results when compared with recent contemporary techniques.

Design of damping controller for multimachine power system by using simplified swarm optimization

This paper presents design of Power System Stabilizer (PSS) by using Simplified Swarm Optimization (SSO). PSS is a useful device to enhance the system damping and small signal stability of the power system. PSS design problem is an optimization problem. From the optimization process proper set of gain and time constants are calculated. Proper tuning of the parameters provides adequate amount of damping to the system. This paper presents a swarm based algorithm to address the above said optimization process. Objective function based on speed deviations is employed to calculate the parameters of PSSs of New England System (10 Generator & 39 Bus). To present a meaningful analysis the comparison of SSO is done with conventional optimization algorithms namely Particle Swarm optimization (PSO) and Genetic Algorithm (GA). Robustness of proposed method is tested over different type of perturbations, fault locations and loading conditions.

Minimax approximation synthesis in PSS design by embedding gravitational search algorithm

This paper presents a new approach to formulate the conventional objective function into multi order polynomials. This work also gives an emphasis on a novel designing and tuning methodology of power system stabilizers (PSSs) over New England System (10 Generator, 39 bus). The intelligent technique based on Newtons Law of gravitation and law of motion, Gravitation Search algorithm (GSA) is applied to find the optimal parameters of PSSs. A new minimax approach is employed to design the PSSs, where several run of eigenvalue analysis is performed and in each iteration maximum deviation between ideal phase lag and proposed design is calculated. Approach is to minimize the maximum deviation between both frequency responses. For each set of PSSs parameters, the correlation between settling time, over shoot of the rotor swing curves is formulated by using minimax approach. The efficacy of the proposed design is tested over a wide range of contingencies, perturbation and different system configurations. Comparative analysis of all polynomial designs are presented while solving each with GSA. The results show those polynomials of order one (linear) are found to be the best fit and show a robust response under all operating conditions.

Assessment of Global Voltage Stability Margin through Radial Basis Function Neural Network

Dynamic operating conditions along with contingencies often present formidable challenges to the power engineers. Decisions pertaining to the control strategies taken by the system operators at energy management centre are based on the information about the system’s behavior. The application of ANN as a tool for voltage stability assessment is empirical because of its ability to do parallel data processing with high accuracy, fast response, and capability to model dynamic, nonlinear, and noisy data. This paper presents an effective methodology based on Radial Basis Function Neural Network (RBFN) to predict Global Voltage Stability Margin (GVSM), for any unseen loading condition of the system. GVSM is used to assess the overall voltage stability status of the power system. A comparative analysis of different topologies of ANN, namely, Feedforward Backprop (FFBP), Cascade Forward Backprop (CFB), Generalized Regression (GR), Layer Recurrent (LR), Nonlinear Autoregressive Exogenous (NARX), ELMAN Backprop, and Feedforward Distributed Time Delay Network (FFDTDN), is carried out on the basis of capability of the prediction of GVSM. The efficacy of RBFN is better than other networks, which is validated by taking the predictions of GVSM at different levels of Additive White Gaussian Noise (AWGN) in input features. The results obtained from ANNs are validated through the offline Newton Raphson (N-R) method. The proposed methodology is tested over IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems.