Critical Aspects of AGC Emerging from Optimal Control to Machine Learning Techniques

With the emphasis towards renewable energy lot more advancement has been done in the field of electric energy system and it is expected that future energy system may have wind power dominating control areas or hydro power be it bulk hydro or micro hydro based power generations in order to cater the rising energy demands of the modern society. Hence, automatic generation control (AGC) plays a crucial role in the modern electrical energy system in order to maintain the frequency standards to nominal value besides maintaining the power interchange between the interconnected controls areas in order to distribute value and cost effective power. This paper presents the literature survey related to some of the critical aspects of AGC such as diverse sources power generations, hydro dominating control areas, wind power based power generations and applications of flexible alternating current transmission system (FACTS) in AGC. This paper also discusses the novel control designs based on the concept of optimal control, output vector feedback, model predictive control, robust control and finally the machine learning based AGC designs are explored and the critical gaps among the available research work are well presented and discussed

A machine learning-based control strategy for improved performance of HVAC systems in providing large capacity of frequency regulation service

Heating, ventilation and air-conditioning systems (HVAC), at demand side, have been regarded increasingly as promising candidates to provide frequency regulation service to smart power grids. In many control systems, chilled water outlet temperature setpoint is reset to change the power use of HVAC systems after the regulation capacity is determined. However, the conflict between changed power use and unchanged cooling/heating demand could become a prominent problem when a large regulation capacity is provided. This problem can deteriorate the performance of frequency regulation service provided by HVAC systems. In this study, a machine learning-based control strategy is proposed to solve this problem for improved performance of HVAC systems in providing large capacity of frequency regulation service. It adjusts the power use of HVAC systems by simultaneously resetting chilled water outlet temperature setpoint and indoor temperature setpoint. The proposed control strategy is validated on a simulation platform. Results show that the strategy can significantly increase the performance of service when an HVAC system provides different regulation capacities. Moreover, the robustness of the strategy is studied. The results show that the strategy can still work effectively even the machine learning algorithms has a relatively low prediction performance in real application due to practical difficulties

CCCTC-binding factor locks premature IgH germline transcription and restrains class switch recombination

In response to antigenic stimulation B cells undergo class switch recombination (CSR) at the immunoglobulin heavy chain (IgH) to replace the primary IgM/IgD isotypes by IgG, IgE, or IgA. CSR is initiated by activation-induced cytidine deaminase (AID) through the deamination of cytosine residues at the switch (S) regions of IgH. B cell stimulation promotes germline transcription (GLT) of specific S regions, a necessary event prior to CSR because it facilitates AID access to S regions. Here, we show that CCCTC-binding factor (CTCF)-deficient mice are severely impaired in the generation of germinal center B cells and plasma cells after immunization in vivo, most likely due to impaired cell survival. Importantly, we find that CTCF-deficient B cells have an increased rate of CSR under various stimulation conditions in vitro. This effect is not secondary to altered cell proliferation or AID expression in CTCF-deficient cells. Instead, we find that CTCF-deficient B cells harbor an increased mutation frequency at switch regions, probably reflecting an increased accessibility of AID to IgH in the absence of CTCF. Moreover, CTCF deficiency triggers premature GLT of S regions in naïve B cells. Our results indicate that CTCF restricts CSR by enforcing GLT silencing and limiting AID access to IgH

Optimal fuzzy-PID controller with derivative filter for load frequency control including UPFC and SMES

A newly adopted optimization technique known as sine-cosine algorithm (SCA) is suggested in this research article to tune the gains of Fuzzy-PID controller along with a derivative filter (Fuzzy-PIDF) of a hybrid interconnected system for the Load Frequency Control (LFC). The scrutinized multi-generation system considers hydro, gas and thermal sources in all areas of the dual area power system integrated with UPFC (unified power flow controller) and SMES (Super-conducting magnetic energy storage) units. The preeminence of the offered Fuzzy-PIDF controller is recognized over Fuzzy-PID controller by comparing their dynamic performance indices concerning minimum undershoot, settling time and also peak overshoot. Finally, the sensitiveness and sturdiness of the recommended control method are proved by altering the parameters of the system from their nominal values and by the implementation of random loading in the system

An Assessment of the Impact of Uncertainty on Automatic Generation Control Systems

This paper proposes a framework to quantify the impact of uncertainty that arises from load variations, renewable-based generation, and noise in communication channels on the automatic generation control (AGC) system. To this end, we rely on a model of the power system that includes the synchronous generator dynamics, the network, and the AGC system dynamics, as well as the effect of various sources of uncertainty. Then, we develop a method to analytically propagate the uncertainty from the aforementioned sources to the system frequency and area control error (ACE), and obtain expressions that approximate their probability distribution functions. We make use of this framework to obtain probabilistic expressions for the frequency performance criteria developed by the North American Electric Reliability Corporation (NERC); such expressions may be used to determine the limiting values of uncertainty that the system may withstand. The proposed ideas are illustrated through the Western Electricity Coordination Council (WECC) 9-bus 3-machine system and a 140-bus 48-machine system

The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output