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

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

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

On the contribution of wind farms in automatic generation control: Review and new control approach

© 2018 by the authors. Wind farms can contribute to ancillary services to the power system, by advancing and adopting new control techniques in existing, and also in new, wind turbine generator systems. One of the most important aspects of ancillary service related to wind farms is frequency regulation, which is partitioned into inertial response, primary control, and supplementary control or automatic generation control (AGC). The contribution of wind farms for the first two is well addressed in literature; however, the AGC and its associated controls require more attention. In this paper, in the first step, the contribution of wind farms in supplementary/load frequency control of AGC is overviewed. As second step, a fractional order proportional-integral-differential (FOPID) controller is proposed to control the governor speed of wind turbine to contribute to the AGC. The performance of FOPID controller is compared with classic proportional-integral-differential (PID) controller, to demonstrate the efficacy of the proposed control method in the frequency regulation of a two-area power system. Furthermore, the effect of penetration level of wind farms on the load frequency control is analyzed

Load frequency control for multi-area interconnected power system using artificial intelligent controllers

Power system control and stability have been an area with different and continuous challenges in order to reach the desired operation that satisfies consumers and suppliers. To accomplish the purpose of stable operation in power systems, different loops have been equipped to control different parameters. For example, Load Frequency Control (LFC) is introduced to maintain the frequency at or near its nominal values, this loop is also responsible for maintaining the interchanged power between control areas interconnected via tie-lines at scheduled values. Other loops are also employed within power systems such as the Automatic Voltage Regulator (AVR). This thesis focuses on the problem of frequency deviation in power systems and proposes different solutions based on different theories. The proposed methods are implemented in two different power systems namely: unequal two-area interconnected thermal power system and the simplified Great Britain (GB) power system. Artificial intelligence-based controllers have recently dominated the field of control engineering as they are practicable with relatively low solution costs, this is in addition to providing a stable, reliable and robust dynamic performance of the controlled plant. They professionally can handle different technical issues resulting from nonlinearities and uncertainties. In order to achieve the best possible control and dynamic system behaviour, a soft computing technique based on the Bees Algorithm (BA) is suggested for tuning the parameters of the proposed controllers for LFC purposes. Fuzzy PID controller with filtered derivative action (Fuzzy PIDF) optimized by the BA is designed and implemented to improve the frequency performance in the two different systems under study during and after load disturbance. Further, three different fuzzy control configurations that offer higher reliability, namely Fuzzy Cascade PI − PD, Fuzzy PI plus Fuzzy PD, and Fuzzy (PI + PD), optimized by the BA have also been implemented in the two-area interconnected power system. The robustness of these fuzzy configurations has been evidenced against parametric uncertainties of the controlled power systems Sliding Mode Control (SMC) design, modelling and implementation have also been conducted for LFC in the investigated systems where the parameters are tuned by the BA. The mathematical model design of the SMC is derived based on the parameters of the testbed systems. The robustness analysis of the proposed SMC against the controlled systems’ parametric uncertainties has been carried out considering different scenarios. Furthermore, to authenticate the excellence of the proposed controllers, a comparative study is carried out based on the obtained results and those from previously introduced works based on classical PID tuned by the Losi Map-Based Chaotic Optimization Algorithm (LCOA), Fuzzy PID Optimized by Teaching Learning-Based Optimization (TLBO

Fuzzy logic controller equilibrium base to enhance AGC system performance with renewable energy disturbances

Owing to the various sources of complexity in the electrical power system, such as integrating intermittent renewable energy resources and widely spread nonlinear power system components, which result in sudden changes in the power system operating conditions, the conventional PID controller fails to track such dynamic challenges to mitigate the frequency deviation problem. Thus, in this paper, a fuzzy PI controller is proposed to enhance the automatic generation control system (AGC) against step disturbance, dynamic disturbance, and wind energy disturbance in a single area system. The proposed controller is initialized by using Equilibrium Optimization and proved its superiority through comparison with a classical PI optimized base. Results show that the fuzzy PI controller can reduce the peak-to-peak deviation in the frequency by 30–59% under wind disturbance, compared to a classical PI optimized base. Moreover, a fuzzy PID controller is also proposed and EO initialized in this paper to compare with the PIDA optimized by several techniques in the two-area system. Results show that the fuzzy PID controller can reduce the peak-to-peak deviation in the frequency of area 1 by 30–50% and the deviation of frequency in area 2 by 13–48% under wave disturbance, compared to the classical PIDA optimized base

Fractional order PID controller design for LFC in electric power systems using imperialist competitive algorithm

AbstractIn this paper, fractional order PID (FOPID) controller was proposed for load frequency control (LFC) in an interconnected power system. This controller had five parameters to be tuned; thus, it provided two more degrees of freedom in comparison with the conventional PID. For proper tuning of the controller parameters, imperialist competitive algorithm (ICA) was used. ICA is a new evolutionary algorithm with proved efficiency. In this study, simulation investigations were carried out on a three-area power system with different generating units. These results showed that FOPID controller was robust to the parameter changes in the power system. Also, the simulation results certified much better performance of FOPID controller for LFC in comparison with conventional PID controllers