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

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

Novel metaheuristic hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation

© 2014 Elsevier B.V. All rights reserved. This paper presents hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation and their application to control of a flexible manipulator system. Spiral dynamic algorithm (SDA) has faster convergence speed and good exploitation strategy. However, the incorporation of constant radius and angular displacement in its spiral model causes the exploration strategy to be less effective hence resulting in low accurate solution. Bacteria chemotaxis on the other hand, is the most prominent strategy in bacterial foraging algorithm. However, the incorporation of a constant step-size for the bacteria movement affects the algorithm performance. Defining a large step-size results in faster convergence speed but produces low accuracy while de.ning a small step-size gives high accuracy but produces slower convergence speed. The hybrid algorithms proposed in this paper synergise SDA and bacteria chemotaxis and thus introduce more effective exploration strategy leading to higher accuracy, faster convergence speed and low computation time. The proposed algorithms are tested with several benchmark functions and statistically analysed via nonparametric Friedman and Wilcoxon signed rank tests as well as parametric t-test in comparison to their predecessor algorithms. Moreover, they are used to optimise hybrid Proportional-Derivative-like fuzzy-logic controller for position tracking of a flexible manipulator system. The results show that the proposed algorithms significantly improve both convergence speed as well as fitness accuracy and result in better system response in controlling the flexible manipulator

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency

Controller tuning by means of evolutionary multiobjective optimization: current trends and applications

Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach for calculating a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective and search for a set of potentially preferable solutions; the designer may then analyse the trade-offs among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are discussed. Throughout this paper it is noticeable that EMO research has been developing towards different optimisation statements, but these statements are not commonly used in controller tuning. Gaps between EMO research and EMO applications on controller tuning are therefore detected and suggested as potential trends for research.The first author is grateful for the hospitality and availability of the UTC at the University of Sheffield during his academic research stay at 2011; especially to Dr. P.J. Fleming for his valuable comments and insights in the development of this paper. This work was partially supported by Grant FPI-2010/19 and project PAID-2011/2732 from the Universitat Politecnica de Valencia and projects TIN2011-28082 and ENE2011-25900 from the Spanish Ministry of Economy and Competitiveness.Reynoso Meza, G.; Blasco Ferragud, FX.; Sanchís Saez, J.; Martínez Iranzo, MA. (2014). Controller tuning by means of evolutionary multiobjective optimization: current trends and applications. Control Engineering Practice. 28:58-73. https://doi.org/10.1016/j.conengprac.2014.03.003S58732

Evolution of Controllers for the Speed Control in Thyristor Fed Induction Motor Drive

Induction Motors (IMs) are now becoming the pillar of almost all the motoring applications related to the industry and household. The practical applications of IMs usually require constant motoring speed. As a result, different types of control systems for IM's speed controlling have been shaped. One of the important techniques is the utilization of thyristor fed drive. Although, the thyristor fed induction motor drive (TFIMD) offers stable speed performance, the practical speed control demand is much more precise. Hence, this drive system utilizes additional controllers to attain precise speed for practical applications. This paper offers a detailed review of the controllers utilized with the thyristor fed IM drive in the past few decades to achieve good speed control performance. The clear intent of the paper is to provide a comprehensible frame of the pros and cons of the existing controllers developed for the TFIMD speed control requirements. Keywords: Thyristor Fed Drives, Induction Motors, Speed Controller, Conventional Controllers, and Soft Computing Techniques

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Hybrid pitch angle controller approaches for stable wind turbine power under variable wind speed

The production of maximum wind energy requires controlling various parts of medium to large-scale wind turbines (WTs). This paper presents a robust pitch angle control system for the rated wind turbine power at a wide range of simulated wind speeds by means of a proportional–integral–derivative (PID) controller. In addition, ant colony optimization (ACO), particle swarm optimization (PSO), and classical Ziegler–Nichols (Z-N) algorithms have been used for tuning the PID controller parameters to obtain within rated stable output power of WTs from fluctuating wind speeds. The proposed system is simulated under fast wind speed variation, and its results are compared with those of the PID-ZN controller and PID-PSO to verify its effeteness. The proposed approach contains several benefits including simple implementation, as well as tolerance of turbine parameters and several nonparametric uncertainties. Robust control of the generator output power with wind-speed variations can also be considered a significant advantage of this strategy. Theoretical analyses, as well as simulation results, indicate that the proposed controller can perform better in a wide range of wind speed compared with the PID-ZN and PID-PSO controllers. The WT model and hybrid controllers (PID-ACO and PID-PSO) have been developed in MATLAB/Simulink with validated controller models. The hybrid PID-ACO controller was found to be the most suitable in comparison to the PID-PSO and conventional PID. The root mean square (RMS) error calculated between the desired power and the WT’s output power with PID-ACO is found to be 0.00036, which is the smallest result among the studied controllers