Robust fuzzy PSS design using ABC

This paper presents an Artificial Bee Colony (ABC) algorithm to tune optimal rule-base of a Fuzzy Power System Stabilizer (FPSS) which leads to damp low frequency oscillation following disturbances in power systems. Thus, extraction of an appropriate set of rules or selection of an optimal set of rules from the set of possible rules is an important and essential step toward the design of any successful fuzzy logic controller. Consequently, in this paper, an ABC based rule generation method is proposed for automated fuzzy PSS design to improve power system stability and reduce the design effort. The effectiveness of the proposed method is demonstrated on a 3-machine 9-bus standard power system in comparison with the Genetic Algorithm based tuned FPSS under different loading condition through ITAE performance indices

Multi particle swarm optimisation algorithm applied to supervisory power control systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPower quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the stability of electrical power networks using artificial intelligence techniques. An intelligent controller was designed to control and optimise simulated models for electrical system power stability. Fuzzy logic controller controlled the power generation, while particle swarm Optimisation (PSO) techniques optimised the system’s power quality in normal operation conditions and after faults. Different types of PSO were tested, then a multi-swarm (M-PSO) system was developed to give better Optimisation results in terms of accuracy and convergence speed.. The developed Optimisation algorithm was tested on seven benchmarks and compared to the other types of single PSOs. The developed controller and Optimisation algorithm was applied to power system stability control. Two power electrical network models were used (with two and four generators), controlled by fuzzy logic controllers tuned using the Optimisation algorithm. The system selected the optimal controller parameters automatically for normal and fault conditions during the operation of the power network. Multi objective cost function was used based on minimising the recovery time, overshoot, and steady state error. A supervisory control layer was introduced to detect and diagnose faults then apply the correct controller parameters. Different fault scenarios were used to test the system performance. The results indicate the great potential of the proposed power system stabiliser as a superior tool compared to conventional control systems

Design of Hybrid Intelligent Power System Stabilizer for a Multi-Machine System

In this project a coordinated design of Fuzzy Power System Stabilizer (FPSS) and TCSC based power oscillation damping (POD) controller to improve power system small-signal stability need to be designed. Two controllers are used for optimizing the system for a better result. Conventional power system stabilizer is replaced by a Fuzzy PSS and the Particle Swarm Optimization (PSO) algorithm tries to minimize an eigenvalue-based multi-objective function by optimizing the parameters of the POD controller. Time domain simulations in MATLAB/SIMULINK performed on a two area four machine (2A4M) power system reveals that superior enhancement in damping of oscillations is achieved by employing coordinated control of FPSS-POD controller in comparison with conventional PSS-POD controller

Advanced and Innovative Optimization Techniques in Controllers: A Comprehensive Review

New commercial power electronic controllers come to the market almost every day to help improve electronic circuit and system performance and efficiency. In DC–DC switching-mode converters, a simple and elegant hysteretic controller is used to regulate the basic buck, boost and buck–boost converters under slightly different configurations. In AC–DC converters, the input current shaping for power factor correction posts a constraint. But, several brilliant commercial controllers are demonstrated for boost and fly back converters to achieve almost perfect power factor correction. In this paper a comprehensive review of the various advanced optimization techniques used in power electronic controllers is presented

Optimal Location and Design of TCSC controller For Improvement of Stability

Power system stability improvement by a coordinate Design ofThyristor Controlled Series Compensator (TCSC) controller is addressed in this paper.Particle Swarm Optimization (PSO) technique is employed for optimization of the parameterconstrained nonlinear optimization problem implemented in a simulation environment. The proposed controllers are tested on a weakly connected power system. The non-linear simulation results are presented. The eigenvalue analysis and simulation results show the effectiveness and robustness of proposed controllers to improve the stability performance of power system by efficient damping of low frequency oscillations under various disturbances

Using Particle Swarm Optimization for Power System Stabilizer and energy storage in the SMIB system under load shedding conditions

Generator instability, which manifests as oscillations in frequency and rotor angle, is brought on by sudden disruptions in the power supply. Power System Stabilizer (PSS) and Energy Storage are additional controllers that enhance generator stability. Energy storage types include superconducting magnetic (SMES) and capacitive (CES) storage. If the correct settings are employed, PSS, SMES, and CES coordination can boost system performance. It is necessary to use accurate and effective PSS, SMES, and CES tuning techniques. Artificial intelligence techniques can replace traditional trial-and-error tuning techniques and assist in adjusting controller parameters. According to this study, the PSS, SMES, and CES parameters can be optimized using a method based on particle swarm optimization (PSO). Based on the investigation's findings, PSO executes quick and accurate calculations in the fifth iteration with a fitness function value of 0.007813. The PSO aims to reduce the integral time absolute error (ITAE). With the addition of a load-shedding instance, the case study utilized the Single Machine Infinite Bus (SMIB) technology. The frequency response and rotor angle of the SMIB system are shown via time domain simulation. The analysis's findings demonstrate that the controller combination can offer stability, reducing overshoot oscillations and enabling quick settling times.

Particle swarm optimization and Taguchi algorithm-based power system stabilizer-effect of light loading condition

A robust design of particle swarm optimization (PSO) and Taguchi algorithm-based power system stabilizer (PSS) is presented in this paper. It incorporates a novel concept in which Taguchi and PSO techniques are integrated for stabilization of single machine infinite bus (SMIB). The system tolerates uncertainty and imprecision to a maximum extent. The proposed controller's effectiveness is proved through experiments covering light load condition using MATLAB/Simulink platform. The performance of the system is compared without PSS and with a conventional PSS. The settling time of the optimal PSS is decreased by more than 75% to conventional PSS. The study reveals that the proposed hybrid controller offers enhanced performance with respect to settling time as well as peak overshoot of the system

Controlling Techniques for STATCOM using Artificial Intelligence

The static synchronous compensator (STATCOM) is a power electronic converter designed to be shunt-connected with the grid to compensate for reactive power. Although they were originally proposed to increase the stability margin and transmission capability of electrical power systems, there are many papers where these compensators are connected to distribution networks for voltage control and power factor compensation. In these applications, they are commonly called distribution static synchronous compensator (DSTATCOM). In this paper we have focussed on STATCOM and the controlling techniques which are based on artificial intelligence

Wide Area Signals Based Damping Controllers for Multimachine Power Systems

Nowadays, electric power systems are stressed and pushed toward their stability margins due to increasing load demand and growing penetration levels of renewable energy sources such as wind and solar power. Due to insufficient damping in power systems, oscillations are likely to arise during transient and dynamic conditions. To avoid undesirable power system states such as tripping of transmission lines, generation sources, and loads, eventually leading to cascaded outages and blackouts, intelligent coordinated control of a power system and its elements, from a global and local perspective, is needed. The research performed in this dissertation is focused on intelligent analysis and coordinated control of a power system to damp oscillations and improve its stability. Wide area signals based coordinated control of power systems with and without a wind farm and energy storage systems is investigated. A data-driven method for power system identification is developed to obtain system matrices that can aid in the design of local and wide area signals based power system stabilizers. Modal analysis is performed to characterize oscillation modes using data-driven models. Data-driven models are used to identify the most appropriate wide-area signals to utilize as inputs to damping controller(s) and generator(s) to receive supplementary control. Virtual Generators (VGs) are developed using the phenomena of generator coherency to effectively and efficiently control power system oscillations. VG based Power System Stabilizers (VG-PSSs) are proposed for optimal damping of power system oscillations. Herein, speed deviation of VGs is used to generate a supplementary coordinated control signal for an identified generator(s) of maximum controllability. The parameters of a VG-PSS(s) are heuristically tuned to provide maximum system damping. To overcome fallouts and switching in coherent generator groups during transients, an adaptive inter-area oscillation damping controller is developed using the concept of artificial immune systems - innate and adaptive immunity. With increasing levels of electric vehicles (EVs) on the road, the potential of SmartParks (a large number of EVs in parking lots) for improving power system stability is investigated. Intelligent multi-functional control of SmartParks using fuzzy logic based controllers are investigated for damping power system oscillations, regulating transmission line power flows and bus voltages. In summary, a number of approaches and suggestions for improving modern power system stability have been presented in this dissertation