Adaptive Power System Stabilizer Design Using Optimal Support Vector Machines Based on Harmony Search Algorithm

Abstract—This article presents the application of support vector machines to adaptive power system stabilizer design in a multi-machine power system based on the harmony search algorithm. Data from a multi-machine power system are the input features of the support vector machines. Support vector machine parameters and power system features are simultaneously optimized by harmony search based on the k-fold cross-validation technique. The proposed algorithm is trained by the optimal support vector machine parameters and optimal power system features. Power system stabilizer parameters produced by the proposed algorithm can be adapted by various operating conditions when the power system operates either inside or outside the training ranges. Simulation studies in the IEEJ Western Japan ten-machine power system demonstrate that the proposed algorithm is far superior to conventional power system stabilizers with fixed parameters and those designed by a robust coupled vibration model under various operating conditions and severe disturbances.     

Weighted least absolute value power system state estimation using rectangular coordinates and equivalent measurement functions

This paper presents a new efficient computation technique for robust power system state estimation based on weighted least absolute value (WLAV) criterion. The proposed method employs rectangular form of state variables and equivalent measurements technique in order to obtain the linear measurement functions with linear constraints of state variables. The state estimation problem is then formulated as an optimization problem with a set of equality and inequality constraints. A solution method based on interior point algorithm is also proposed. Tests with several IEEE standard systems have been performed to investigate the performance of the proposed algorithm. Results indicate that the proposed state estimator gives promising performance compared with weighted least square (WLS) based estimation algorithms using state variables in polar forms. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Application of electrolyzer system to enhance frequency stabilization effect of microturbine in a microgrid system

It is well known that the power output of microturbine can be controlled to compensate for load change and alleviate the system frequency fluctuations. Nevertheless, the microturbine may not adequately compensate rapid load change due to its slow dynamic response. Moreover, when the intermittent power generations from wind power and photovoltaic are integrated into the system, they may cause severe frequency fluctuation. In order to study the fast dynamic response, this paper applies electrolyzer system to absorb these power fluctuations and enhance the frequency control effect of microturbine in the microgrid system. The robust coordinated controller of electrolyzer and microturbine for frequency stabilization is designed based on a fixed-structure H_∞ loop shaping control. Simulation results exhibit the robustness and stabilizing effects of the proposed coordinated electrolyzer and microturbine controllers against system parameters variation and various operating conditions.     

A robust centralized SMES controller design based on WAMS considering system and communication delay uncertainties

It is well known that the communication delay due to the phasor measurement in wide area monitoring system (WAMS) as well as various system operating conditions such as heavy line flows and unpredictable network structures, may deteriorate the wide-area stabilizing control effect. To overcome this problem, the inverse input and output multiplicative model is proposed to represent unstructured uncertainties due to system operations and communication delay in the robust centralized damping controller design of superconducting magnetic energy storage (SMES) based on WAMS. The structure of centralized controller for SMES is the practical 1st-order lead/lag compensator. To automatically tune the control parameters, the optimization problem based on the enhancement of damping performance and system robust stability margin is achieved by particle swarm optimization. Simulation studies in the West Japan six-area interconnected system confirm that the proposed robust SMES centralized controller is superior to the conventional SMES centralized controller in terms of damping performance and robustness against system and time delay uncertainties.     

WLAV state estimation for power system containing multitype FACTS devices

This paper presents the state estimation of power system in which not only the bus voltages but also the states of the flexible AC transmission system (FACTS) are considered as the state variables. By using the rectangular form of state variables and equivalent measurement techniques, a linear measurement model with constraints of FACTS device is obtained. The predictor–corrector interior point method based on the weighted least absolute value criterion is developed for solving the optimization problem. Simulation results on the modified IEEE 14‐bus and 118‐bus test systems are provided. The numerical results indicate the effectiveness of the proposed approach. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Robust controller design of heat pump and plug-in hybrid electric vehicle for frequency control in a smart microgrid based on specified-structure mixed H2/H∞ control technique

This paper proposes a new robust controller design of heat pump (HP) and plug-in hybrid electric vehicle (PHEV) for frequency control in a smart microgrid (MG) system with wind farm. The intermittent power generation from wind farm causes severe frequency fluctuation in the MG. To alleviate frequency fluctuation, the smart control of power consumption of HP and the power charging of PHEV in the customer side can be performed. The controller structure of HP and PHEV is a proportional integral derivative (PID) with single input. To enhance the performance and robustness against system uncertainties of the designed controller, the particle swarm optimization based-mixed H₂/H_∞ control is applied to design the PID controllers of HP and PHEV. Simulation studies confirm the superior robustness and frequency control effect of the proposed HP and PHEV controllers in comparison to the conventional controller.     

A heuristic‐based design of robust SMES controller taking system uncertainties into consideration

In this paper, a heuristic‐based design of robust superconducting magnetic energy storage (SMES) controller is proposed taking system uncertainties into consideration. The SMES model with active and reactive power controllers is used. In addition, the effect of SMES coil current is also included in the model. The power system and the SMES unit with the designed controller are formulated as an optimization problem. The proposed objective function considers both the damping performance index and the robust stability index. In particular, the robust SMES controller is designed to enhance the system damping performance and robustness against system uncertainties such as various load and system parameter changes. The robust stability margin is guaranteed in terms of the multiplicative stability margin (MSM). In the proposed method, the robust SMES active and reactive power controllers are designed systematically by using hybrid tabu search and evolutionary programming, so that the desired damping performance and the best obtainable MSM are acquired. Finally, the designed SMES controller is examined under different situations to evaluate and confirm the effectiveness and robustness via eigenvalue analysis and nonlinear simulations. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Alleviation of power fluctuation in a microgrid by electrolyzer based on optimal fuzzy gain scheduling PID control

This paper presents an application of the electrolyzer (EZ) to alleviate the power fluctuation in a microgrid with hybrid power generations from wind, photovoltaic array, fuel cell, and diesel engine. In this microgrid, the intermittent power generations from wind and photovoltaic arrays cause severe power fluctuation. With the fast response of EZ, the power absorbed by EZ can be controlled to compensate for the power fluctuation, in addition to the hydrogen production for fuel cell. The structure of the active and reactive power controllers of EZ is the fuzzy gain scheduling of a proportional‐integral‐derivative (FGS‐PID) controller. Without trial and error, the scale factors, membership functions, and control rules of the FGS‐PID controller are automatically optimized by bee colony optimization. A simulation study confirms that the proposed EZ with optimal FGS‐PID controller is much superior to the optimal PID controller in terms of damping effect, robustness against disturbances, and hydrogen production. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Multiple tabu search algorithm for economic dispatch problem considering valve-point effects

This paper presents a multiple tabu search (MTS) algorithm to solve the economic dispatch (ED) problem by taking valve-point effects into consideration. The practical ED problem with valve-point effects is represented as a non-smooth optimization problem with equality and inequality constraints that make the problem of finding the global or near global optimum difficult. The proposed MTS algorithm is the sequential execution of individual tabu search (TS) algorithm simultaneously by only one personal microcomputer. The MTS algorithm introduces additional techniques for improvement of search process, such as initialization, adaptive searches, multiple searches, replacing and restarting process. To show its effectiveness, the MTS is applied to test two studied systems consisting of 13 and 40 power generating units with valve-point effects. The optimized results by MTS are compared with those of conventional approaches, such as simulated annealing (SA), genetic algorithm (GA), TS algorithm and particle swarm optimization (PSO). Studied results confirm that the proposed MTS approach is capable of obtaining higher quality solution efficiently and lowest computational time.     

A heuristic training-based least squares support vector machines for power system stabilization by SMES

This paper presents the application of least squares support vector machines (LS-SVMs) to design of an adaptive damping controller for superconducting magnetic energy storage (SMES). To accelerate LS-SVMs training and testing, a large amount of training data set of a multi-machine power system is reduced by the measurement of similarity among samples. In addition, the redundant data in the training set can be significantly discarded. The LS-SVM for SMES controllers are trained using the optimal LS-SVM parameters optimized by a particle swarm optimization and the reduced data. The LS-SVM control signals can be adapted by various operating conditions and different disturbances. Simulation results in a two-area four-machine power system demonstrate that the proposed LS-SVM for SMES controller is robust to various disturbances under a wide range of operating conditions in comparison to the conventional SMES.