Analysis and Assessment of STATCOM-Based Damping Stabilizers for Power System Stability Enhancement

Power system stability enhancement via STATCOM-based stabilizers is thoroughly investigated in this paper. This study presents a singular value decomposition (SVD)-based approach to assess and measure the controllability of the poorly damped electromechanical modes by STATCOM different control channels. The coordination among the proposed damping stabilizers and the STATCOM internal ac and dc voltage controllers has been taken into consideration. The design problem of STATCOM-based stabilizers is formulated as an optimization problem. For coordination purposes, a time domain-based multiobjective junction to improve the system stability as well as ac and dc voltage regulation is proposed. Then, a real-coded genetic algorithm (RCGA) is employed to search for optimal stabilizer parameters. This aims to enhance both rotor angle stability and voltage regulation of the power system. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. The nonlinear simulation results show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions. It is also observed that the proposed STATCOM-based damping stabilizers extend the critical clearing time (CCT) and enhance greatly the power system transient stability

Analysis and Assessment of STATCOM-Based Damping Stabilizers for Power System Stability Enhancement

Power system stability enhancement via STATCOM-based stabilizers is thoroughly investigated in this paper. This study presents a singular value decomposition (SVD)-based approach to assess and measure the controllability of the poorly damped electromechanical modes by STATCOM different control channels. The coordination among the proposed damping stabilizers and the STATCOM internal ac and dc voltage controllers has been taken into consideration. The design problem of STATCOM-based stabilizers is formulated as an optimization problem. For coordination purposes, a time domain-based multiobjective junction to improve the system stability as well as ac and dc voltage regulation is proposed. Then, a real-coded genetic algorithm (RCGA) is employed to search for optimal stabilizer parameters. This aims to enhance both rotor angle stability and voltage regulation of the power system. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. The nonlinear simulation results show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions. It is also observed that the proposed STATCOM-based damping stabilizers extend the critical clearing time (CCT) and enhance greatly the power system transient stability

Analysis and Assessment of STATCOM-Based Damping Stabilizers for Power System Stability Enhancement

Power system stability enhancement via STATCOM-based stabilizers is thoroughly investigated in this paper. This study presents a singular value decomposition (SVD)-based approach to assess and measure the controllability of the poorly damped electromechanical modes by STATCOM different control channels. The coordination among the proposed damping stabilizers and the STATCOM internal ac and dc voltage controllers has been taken into consideration. The design problem of STATCOM-based stabilizers is formulated as an optimization problem. For coordination purposes, a time domain-based multiobjective junction to improve the system stability as well as ac and dc voltage regulation is proposed. Then, a real-coded genetic algorithm (RCGA) is employed to search for optimal stabilizer parameters. This aims to enhance both rotor angle stability and voltage regulation of the power system. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. The nonlinear simulation results show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions. It is also observed that the proposed STATCOM-based damping stabilizers extend the critical clearing time (CCT) and enhance greatly the power system transient stability

Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem

The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems

Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem

The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems

On the Control Strategies of Shunt FACTS Devices for the Improvement of Transient Stability

To enhance power system transient stability, shunt FACTS devices can be controlled in discontinuous mode or in a combination of discontinuous and continuous mode. This paper investigates the latter discontinuous then continuous control strategy in a view to improve angle and speed response. In continuous mode, it is found that proper selection of controller gain plays an important role on proportional controller performance. Nonlinear timedomain simulation with various ratings of SVC and STATCOM shows that controller gain-setting depends on FACTS device rating. Gain of the controller is optimized for minimum settling time and overshoot using Particle Swam Optimization (PSO) technique and results are verified using time-domain simulation

Neural network-based diagnostic tool for detecting stator inter-turn faults in line start permanent magnet synchronous motors

Three-phase line-start permanent magnet synchronous motors are considered among the most promising types of motors in industrial applications. However, these motors experience several faults, which may cause significant financial losses. This paper proposed a feed-forward neural network-based diagnostic tool for accurate and fast detection of the location and severity of stator inter-turn faults. The input to the neural network is a group of representative statistical and frequency-based features extracted from the steady-state three-phase stator current signals. The current signals with different numbers of shorted turns and loading conditions are captured using the developed finite element JMAG ™ model for interior mount LSPMSM. In addition, an experimental set-up was built to validate the finite element model and the proposed diagnostics tool. The simulation and experimental test results showed an overall accuracy of 93.125% in detecting the location and the size of inter-turn, whereas, the accuracy in detecting the location of the fault is 100%

Optimal VAR Dispatch Using a Multiobjective Evolutionary Algorithm

In this paper, a novel multiobjective evolutionary algorithm for optimal reactive power (VAR) dispatch problem is presented. The optimal VAR dispatch problem is formulated as a nonlinear constrained multiobjective optimization problem where the real power loss and the bus voltage deviations are to be minimized simultaneously. A new Strength Pareto Evolutionary Algorithm based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and non-commensurable objectives. A hierarchical clustering algorithm is imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Moreover, fuzzy set theory is employed to extract the best compromise solution over the trade-off curve. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal solutions of the multiobjective VAR dispatch problem in one single run. The results demonstrate the superiority of the proposed approach and confirm its potential to solve the multiobjective VAR dispatch problem