Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers' parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods

Enhancement of Power System Dynamic Performance by Coordinated Design of PSS and FACTS Damping Controllers

Due to environmental and economical constraints, it is difficult to build new power lines and to reinforce the existing ones. The continued growth in demand for electric power must therefore to a great extent be met by increased loading of available lines. A consequence of this is reduction of power system damping, leading to a risk of poorly damped power oscillations between generators. To suppress these oscillations and maintain power system dynamic performance, one of the conventional, economical and effective solutions is to install a power system stabilizer (PSS). However, in some cases PSS may not provide sufficient damping for the inter-area oscillations in a multi-machine power system. In this context, other possible solutions are needed to be exposed. With the evolution of power electronics, flexible AC transmission systems (FACTS) controllers turn out to be possible solution to alleviate such critical situations by controlling the power flow over the AC transmission line and improving power oscillations damping. However, coordination of conventional PSS with FACTS controllers in aiding of power system oscillations damping is still an open problem. Therefore, it is essential to study the coordinated design of PSS with FACTS controllers in a multi-machine power system. This thesis gives an overview of the modelling and operation of power system with conventional PSS. It gives the introduction to emerging FACTS controllers with emphasis on the TCSC, SVC and STATCOM controllers. The basic modelling and operating principles of the controllers are explained in this thesis, along with the power oscillations damping (POD) stabilizers. The coordination design of PSS and FACTS damping controllers over a wide range of operating conditions is formulated as an optimization problem. The objective function of this optimization problem is framed using system eigen values and it is solved using AAPSO and IWO algorithms. The optimal control parameters of coordinated controllers are obtained at the end of these optimization algorithms. A comprehensive approach to the hybrid coordinated design of PSS with series and shunt FACTS damping controllers is proposed to enhance the overall system dynamic performance. The robustness and effectiveness of proposed hybrid coordinated designs are demonstrated through the eigen value analysis and time-domain simulations. The proposed hybrid designs provide robust dynamic performance under wide range in load condition and providing significant improvement in damping power system oscillations under severe disturbance. The developed hybrid coordinated designs are tested in different multimachine power systems using AAPSO and IWO algorithms. The IWO based hybrid designs and AAPSO based hybrid designs are more effective than other control designs. In addition to this, the proposed designs are implemented and validated in real-time using Opal-RT hardware simulator. The real-time simulations of different test power systems with different proposed designs are carried out for a severe fault disturbance. Finally, the proposed controller simulation results are validated with real-time results

Power System Simulation, Control and Optimization

This Special Issue “Power System Simulation, Control and Optimization” offers valuable insights into the most recent research developments in these topics. The analysis, operation, and control of power systems are increasingly complex tasks that require advanced simulation models to analyze and control the effects of transformations concerning electricity grids today: Massive integration of renewable energies, progressive implementation of electric vehicles, development of intelligent networks, and progressive evolution of the applications of artificial intelligence

電力系統の静的および動的セキュリティ評価増強のための同期位相計測装置の最適配置

九州工業大学博士学位論文 学位記番号：工博甲第490号 学位授与年月日：令和2年3月25日1 INTRODUCTION|2 PMU-BASED POWER SYSTEM MONITORING AND CONTROL|3 OPTIMAL PMU PLACEMENT PROBLEM AND STATE ESTIMATION|4 MULTI OBJECTIVE PMU PLACEMENT WITH CURRENT CHANNEL SELECTION|5 INFLUENCE OF MEASUREMENT UNCERTAINTY PROPAGATION IN PMU PSEUDO MEASUREMENT|6 PHASOR-ASSISTED VOLTAGE STABILITY ASSESSMENT BASED ON OPTIMALLY PLACED PMUS|7 PMU PLACEMENT FOR DYNAMIC VULNERABILITY ASSESSMENT|8 CONCLUSIONS九州工業大学令和元年

Advanced Communication and Control Methods for Future Smartgrids

Proliferation of distributed generation and the increased ability to monitor different parts of the electrical grid offer unprecedented opportunities for consumers and grid operators. Energy can be generated near the consumption points, which decreases transmission burdens and novel control schemes can be utilized to operate the grid closer to its limits. In other words, the same infrastructure can be used at higher capacities thanks to increased efficiency. Also, new players are integrated into this grid such as smart meters with local control capabilities, electric vehicles that can act as mobile storage devices, and smart inverters that can provide auxiliary support. To achieve stable and safe operation, it is necessary to observe and coordinate all of these components in the smartgrid

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field