Fully Evolvable Optimal Neurofuzzy Controller Using Adaptive Critic Designs

A near-optimal neurofuzzy external controller is designed in this paper for a static compensator (STATCOM) in a multimachine power system. The controller provides an auxiliary reference signal for the STATCOM in such a way that it improves the damping of the rotor speed deviations of its neighboring generators. A zero-order Takagi-Sugeno fuzzy rule base constitutes the core of the controller. A heuristic dynamic programming (HDP) based approach is used to further train the controller and enable it to provide nonlinear near-optimal control at different operating conditions of the power system. Based on the connectionist systems theory, the parameters of the neurofuzzy controller, including the membership functions, undergo training. Simulation results are provided that compare the performance of the neurofuzzy controller with and without updating the fuzzy set parameters. Simulation results indicate that updating the membership functions can noticeably improve the performance of the controller and reduce the size of the STATCOM, which leads to lower capital investment

Design of a prototype personal static var compensator

The focus of this thesis is the design and implementation of a personal static var compensator (PSVC) for distributed var control through load power factor correction. The PSVC demonstrates the two key benefits of power factor correction, which include decreased power costs and increased system capacity. The PSVC prototype consists of two types of branches---a TSC branch and a TCR branch. A microprocessor is responsible for calculating the load displacement power factor (PFD) and for executing the fuzzy logic control scheme for the two branches. The PSVC was found to reduce the RMS current drawn by a 55-watt AC motor by 25% while raising its PFD by 40% to 0.99 lagging. The expected quick rate of return of installation costs is attributed to the PSVC\u27s low initial cost and its ability to reduce tariffs for reactive power consumption

PERFORMANCE COMPARISON BETWEEN SVC AND STATCOM FOR REACTIVE POWER COMPENSATION BY USING FUZZY LOGIC CONTROLLER

The performances comparison between SVC and STATCOM controller for compensating the reactive power by using the control technique i.e. with fuzzy controller and without fuzzy controller to improve the voltage stability. Improving the system reactive power handling capacity via Flexible AC transmission System (FACTS) devices is a remedy for prevention of voltage instability. This paper compares the SVC and STATCOM in static voltage stability improvement and the performance of the STATCOM is better compare with that of the conventional SVC.A Simulations using MATLAB / SIMULINK are carried out to verify the performance of the proposed controllers and paper deals only with power-factor correction mode and show the Total Harmonic Distortion

Optimal Neuro-Fuzzy External Controller for a STATCOM in the 12-Bus Benchmark Power System

An optimal neuro-fuzzy external controller is designed in this paper for a static compensator (STATCOM) in the 12-bus benchmark power system. The controller provides an auxiliary reference signal for the STATCOM in such a way that it improves the damping of the rotor speed deviations of its neighboring generators. A Mamdani fuzzy rule base constitutes the core of the controller. A heuristic dynamic programming-based approach is used to further train the controller and enable it to provide nonlinear optimal control at different operating conditions of the power system. Simulation results are provided that indicate the proposed neuro-fuzzy external controller is more effective than a linear external controller for damping out the speed deviations of the generators. In addition, the two controllers are compared in terms of the control effort generated by each one during various disturbances and the proposed neuro-fuzzy controller proves to be more effective with smaller control effort

Adaptive Critic Design Based Neuro-Fuzzy Controller for a Static Compensator in a Multimachine Power System

This paper presents a novel nonlinear optimal controller for a static compensator (STATCOM) connected to a power system, using artificial neural networks and fuzzy logic. The action dependent heuristic dynamic programming, a member of the adaptive Critic designs family, is used for the design of the STATCOM neuro-fuzzy controller. This neuro-fuzzy controller provides optimal control based on reinforcement learning and approximate dynamic programming. Using a proportional-integrator approach the proposed controller is capable of dealing with actual rather than deviation signals. The STATCOM is connected to a multimachine power system. Two multimachine systems are considered in this study: a 10-bus system and a 45-bus network (a section of the Brazilian power system). Simulation results are provided to show that the proposed controller outperforms a conventional PI controller in large scale faults as well as small disturbance

Enhancing the performance of flexible AC transmission systems (FACTS) by computational intelligence

The thesis studies and analyzes UPFC technology concerns the management of active and reactive power in the power networks to improve the performance aiming to reach the best operation criteria. The contributions of the thesis start with formatting, deriving, coding and programming the network equations required to link UPFC steady-state and dynamic models to the power systems. The thesis derives GA applications on UPFC to achieve real criteria on a real world sub-transmission network. An enhanced GA technique is proposed by enhancing and updating the working phases of the GA including the objective function formulation and computing the fitness using the diversity in the population and selection probability. The simulations and results show the advantages of using the proposed technique. Integrating the results by linking the case studies of the steady-state and the dynamic analysis is achieved. In the dynamic analysis section, a new idea for integrating the GA with ANFIS to be applied on the control action procedure is presented. The main subject of the thesis deals with enhancing the steady-state and dynamics performance of the power grids by Flexible AC Transmission System (FACTS) based on computational intelligence. Control of the electric power system can be achieved by designing the FACTS controller, where the new trends as Artificial Intelligence can be applied to this subject to enhance the characteristics of controller performance. The proposed technique will be applied to solve real problems in a Finnish power grid. The thesis seeks to deal, solve, and enhance performances until the year 2020, where the data used is until the conditions of year 2020. The FACTS device, which will be used in the thesis, is the most promising one, which known as the Unified Power Flow Controller (UPFC). The thesis achieves the optimization of the type, the location and the size of the power and control elements for UPFC to optimize the system performance. The thesis derives the criteria to install the UPFC in an optimal location with optimal parameters and then designs an AI based damping controller for enhancing power system dynamic performance. In this thesis, for every operating point GA is used to search for controllers' parameters, parameters found at certain operating point are different from those found at others. ANFISs are required in this case to recognize the appropriate parameters for each operating point

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

Application of Fuzzy Logic for Performance Enhancement of Drives

Fuzzy logic shows enormous potential for advancing power electronics technology. Its application to DC and AC drives control is discussed here. Initially, a phase-controlled bridge converter DC drive was considered. Analysis of converter performance at continuous and discontinuous conduction modes was first conducted. Fuzzy control was used to linearize the transfer characteristics of the converter in discontinuous conduction mode. It was then extended to current and speed loops, replacing the conventional proportional-integral controllers. The control algorithms were developed in detail, and verified by PC-SIMNON (developed by Lund Institute of Technology Sweden) digital simulation. Significant performance improvement was achieved over conventional control methods. Efficiency optimization of an indirect vector controlled induction motor drive was next considered. An accurate loss model of the converter induction machine system was first developed. Steady-state fundamental and harmonics loss characteristics, besides the dynamic of the machine were analyzed and incorporated in the model, resulting in a new synchronous frame dynamic De-Qe equivalent circuit. The converter system has been modeled accurately for conduction and switching losses. The lossy models were then used in the validation of the fuzzy logic based on-line efficiency optimization control. At steady-state, the fuzzy controller adaptively changes the excitation current on the basis of measured input power, until the maximum efficiency point is reached. The pulsating torque, due to flux reduction, has been compensated by an ingenious feedforward scheme. During transients, rated flux is established, to get the best transient response. After a comprehensive simulation study, an experimental 5 hp drive system was tested, with the proposed controller implemented on a Texas Instrument TMS320C25 digital signal processor, and the theoretical development was fully validated. Finally, fuzzy logic was applied in combination with model-reference adaptive control (MRAC) technique to slip gain tuning of an indirect vector controlled induction motor drive. The MRAC methods based on reactive power and D-axis voltage were combined through a weighting factor, generated by a fuzzy controller, that ensures the use of the best method for any point in the torque-speed plane. A second fuzzy controller tunes the slip gain based on combined detuning error and its slope. The drive performance was extensively investigated through simulations and experiments. The results confirmed the validity of the proposed method

IMPROVEMENT OF POWER QUALITY OF HYBRID GRID BY NON-LINEAR CONTROLLED DEVICE CONSIDERING TIME DELAYS AND CYBER-ATTACKS

Power Quality is defined as the ability of electrical grid to supply a clean and stable power supply. Steady-state disturbances such as harmonics, faults, voltage sags and swells, etc., deteriorate the power quality of the grid. To ensure constant voltage and frequency to consumers, power quality should be improved and maintained at a desired level. Although several methods are available to improve the power quality in traditional power grids, significant challenges exist in modern power grids, such as non-linearity, time delay and cyber-attacks issues, which need to be considered and solved. This dissertation proposes novel control methods to address the mentioned challenges and thus to improve the power quality of modern hybrid grids.In hybrid grids, the first issue is faults occurring at different points in the system. To overcome this issue, this dissertation proposes non-linear controlled methods like the Fuzzy Logic controlled Thyristor Switched Capacitor (TSC), Adaptive Neuro Fuzzy Inference System (ANFIS) controlled TSC, and Static Non-Linear controlled TSC. The next issue is the time delay introduced in the network due to its complexities and various computations required. This dissertation proposes two new methods such as the Fuzzy Logic Controller and Modified Predictor to minimize adverse effects of time delays on the power quality enhancement. The last and major issue is the cyber-security aspect of the hybrid grid. This research analyzes the effects of cyber-attacks on various components such as the Energy Storage System (ESS), the automatic voltage regulator (AVR) of the synchronous generator, the grid side converter (GSC) of the wind generator, and the voltage source converter (VSC) of Photovoltaic (PV) system, located in a hybrid power grid. Also, this dissertation proposes two new techniques such as a Non-Linear (NL) controller and a Proportional-Integral (PI) controller for mitigating the adverse effects of cyber-attacks on the mentioned devices, and a new detection and mitigation technique based on the voltage threshold for the Supercapacitor Energy System (SES). Simulation results obtained through the MATLAB/Simulink software show the effectiveness of the proposed new control methods for power quality improvement. Also, the proposed methods perform better than conventional methods