Application of Multi-Objective Evolutionary Optimization Algorithms to Reactive Power Planning Problem

This paper presents a new approach to treat reactive power (VAr) planning problem using multi-objective evolutionary algorithms. Specifically, Strength Pareto Evolutionary Algorithm (SPEA) and Multi-Objective Particle Swarm Optimization (MOPSO) approaches have been developed and successfully applied. The overall problem is formulated as a nonlinear constrained multi-objective optimization problem. Minimizing the total incurred cost and maximizing the amount of Available Transfer Capability (ATC) are defined as the main objective functions. The proposed approaches have been successfully tested on IEEE 14 bus system. As a result a wide set of optimal solutions known as Pareto set is obtained and encouraging results show the superiority of the proposed approaches and confirm their potential to solve such a large scale multi-objective optimization problem

An Adaptive Load Frequency Control Based on Least Square Method

Modern power system becomes a complex system consisted with various load and power stations. Therefore, it may spread into some areas of power system in neighborhood, and so a load frequency control (LFC) is a necessity device to regulate the frequency of the power system by distributing the load to the generating units and controlling tie-line power interchange between areas. The integration of renewable energy sources (RES) into a power grid has presented important issues about stability and security of power system. In such conditions, the use of conventional LFC may not be sufficient to protect the power system against the power changes. In this chapter, an adaptive LFC controller is proposed using the least square method (LSM). The controller adopts an internal model control (IMC) structure in two scenarios, i.e., static controller gain with adaptive internal model and both the adaptive controller gain and adaptive internal model. A two-area power system is used to test and to validate both performance and the effectiveness of this controller through some case studies

Critical Clearing Time prediction within various loads for transient stability assessment by means of the Extreme Learning Machine method

The Critical Clearing Time (CCT) is a key issue for Transient Stability Assessment (TSA) in electrical power system operation, security, and maintenance. However, there are some difficulties in obtaining the CCT, which include the accuracy, fast computation, and robustness for TSA online. Therefore, obtaining the CCT is still an interesting topic for investigation. This paper proposes a new technique for obtaining CCT based on numerical calculations and artificial intelligence techniques. First, the CCT is calculated by the critical trajectory method based on critical generation. Second, the CCT is learned by Extreme Learning Machine (ELM). This proposed method has the ability to obtain the CCT with load changes, different fault occurrences, accuracy, and fast computation, and considering the controller. This proposed method is tested by the IEEE 3-machine 9-bus system and Java-Bali 500 kV 54-machine 25-bus system. The proposed method can provide accurate CCTs with an average error of 0.33% for the Neural Network (NN) method and an average error of 0.06% for the ELM method. The simulation result also shows that this method is a robust algorithm that can address several load changes and different locations of faults occurring. There are 29 load changes used to obtain the CCT, with 20 load changes included for the training process and 9 load changes not included

Voltage Control Capability Analysis Based on the Steady State performance of SVC

Static Var Compensator(SVC) is a recent power electronics device that can provide reactive power to control power system voltage. The response speed of SVC, detailed operation characteristics of the SVC must be taken into consideration to improve steady-state stability and steady-state performance. In this paper, the system strength to voltage control is represented as linear contribution of slow-response Var devices to the change of shunt susceptance, which is applied to operation point control while keeping desirable voltage profile. The controllable voltage variation and the feasible slope setting avoiding violation of control limit are quantified based on available control margin at current operation point. The quantitative analysis provides an effective control method of SVC that improves the utilization of its control margin. The paper also discusses coordination among multiple controls of local SVCs

SVMを用いたPSS制御に関する研究

電力系統が大規模・重負荷になるにつれて,系統の安定度もローカルな電力動揺だけでなく,系統全体に影響する長周期動揺が問題となっている.このため大規模電力系統の安定化に対し,本研究室でも電力系統安定化装置(PSS)のロバスト性を考慮した設計法を提案してきた.しかし,一つのPSSでは系統状態によっては所望の制御性能が得られないという課題が生じていた.そこで本稿では,パターン認識手法の一つであるサポートベクターマシン(SVM)を適用し,系統状態を判別することによって,二つのPSSを切り替える制御系を提案する.これにより適切な制御系を構成し,安定性の改善を図る