Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.

A Novel Cascaded PID Controller for Automatic Generation Control Analysis With Renewable Sources

Present day power scenarios demand a high quality uninterrupted power supply and needs environmental issues to be addressed. Both concerns can be dealt with by the introduction of the renewable sources to the existing power system. Thus, automatic generation control(AGC) with diverse renewable sources and a modified-cascaded controller are presented in the paper.Also, a new hybrid scheme of the improved teaching learning based optimization-differential evolution(hITLBO-DE) algorithm is applied for providing optimization of controller parameters. A study of the system with a technique such as TLBO applied to a proportional integral derivative(PID), integral double derivative(IDD) and PIDD is compared to hITLBO-DE tuned cascaded controller with dynamic load change.The suggested methodology has been extensively applied to a 2-area system with a diverse source power system with various operation time non-linearities such as dead-band of, generation rate constraint and reheat thermal units. The multi-area system with reheat thermal plants, hydel plants and a unit of a wind-diesel combination is tested with the cascaded controller scheme with a different controller setting for each area. The variation of the load is taken within 1% to 5% of the connected load and robustness analysis is shown by modifying essential factors simultaneously by± 30%. Finally, the proposed scheme of controller and optimization technique is also tested with a 5-equal area thermal system with non-linearities. The simulation results demonstrate the superiority of the proposed controller and algorithm under a dynamically changing load.

Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.

MAS Based Distributed Automatic Generation Control for Cyber-Physical Microgrid System

The microgrid is a typical cyber-physical microgrid system (CPMS). The physical unconventional distributed generators (DGs) are intermittent and inverter-interfaced which makes them very different to control. The cyber components, such as the embedded computer and communication network, are equipped with DGs, to process and transmit the necessary information for the controllers. In order to ensure system-wide observability, controllability and stabilization for the microgrid, the cyber and physical component need to be integrated. For the physical component of CPMS, the droop-control method is popular as it can be applied in both modes of operation to improve the grid transient performance. Traditional droop control methods have the drawback of the inherent trade-off between power sharing and voltage and frequency regulation. In this paper, the global information (such as the average voltage and the output active power of the microgrid and so on) are acquired distributedly based on multi-agent system (MAS). Based on the global information from cyber components of CPMS, automatic generation control (AGC) and automatic voltage control (AVC) are proposed to deal with the drawback of traditional droop control. Simulation studies in PSCAD demonstrate the effectiveness of the proposed control methods. © 2014 Chinese Association of Automation.

A hybrid stochastic fractal search and pattern search technique based cascade PI-PD controller for automatic generation control of multi-source power systems in presence of plug in electric vehicles

A hybrid Stochastic Fractal Search plus Pattern Search （hSFS-PS） based cascade PI-PD controller is suggested in this paper for Automatic Generation Control （AGC） of thermal, hydro and gas power unit based power systems in presence of Plug in Electric Vehicles （PEV）. Firstly, a single area multi-source power system consisting of thermal hydro and gas power plants is considered and parameters of Integral （I） controller is optimized by Stochastic FractaI Search （SFS） algorithm. The superiority of SFS algorithm over some recently proposed approaches such as optimal control, differential evolution and teaching learning based optimization techniques is demonstrated by comparing simulation results for the identical power system. To improve the system performance further, Pattern Search （PS） is subsequently employed. The study is further extended for different controllers like PI, PID, and cascaded PI-PD controller and the superiority of cascade PI-PD controller over conventional controllers is demonstrated. Then, cascade PI- PD controller parameters of AGC searched using the proposed hSFS-PS algorithm in presence of plug in electric vehicles. The study is also extended to an interconnected power system. It is seen from the comparative analysis that hSFS-PS tuned PI-PD controller in single and multi-area with multi sources improves the system frequency stability in complicated situations. Lastly, a three area interconnected system with PEVs with dissimilar cascade PI-PD controller in each area is considered and proposed hSFS- PS algorithm is used to tune the controller parameters in presence of nonlinearities like rate constraint of units, dead zone of governor and communication delay.

Automatic Generation Control of Multigeneration Power System

Load frequency Control (LFC) is used for many years as part of Automatic Generation Control (AGC) in power system around the world. In a mixed power system, it is usual to find an area regulated by hydro generation interconnected to another area regulated by thermal generation or in combination of both. In the following study, performance of AGC for Thermal, Hydro and Thermal turbine based power system is examined, including how frequency bias setting influences AGC response and inadvertent interchange. Control performance analysis of three area interconnected systems is simulated and studied through Matlab Simulink software. Integral square error and Integral time absolute error has been used as performance criterion. It is shown that integral time absolute error (ITAE) as performance index leads to faster optimization of controller gain.

CMOS Automatic Gain Control Circuit with DC Offset Cancellation for FM/cw Ladar

This paper presented an automatic gain control （AGC） circuit suitable for FM/cw ladar. The proposed architecture was based on two-stage variable gain amplifier （VGA） chain with a novel DC offset canceller circuit, which contained an improved Gilbert cell and a Gm-C feedback loop. To keep the VGA with a linearity in dB characteristic, an improved exponential gain control circuit was introduced. The AGC was implemented in 0.18 gm standard CMOS process. Simulation and measurement results verified that its gain ranged from -20 dB to 30 dB, and band- width ranged from 100 kHz to 10 MHz. Its power consumption was 19.8 mW under a voltage supply of 3.3 V.

Automatic Generation Control in a Distributed Power Grid Based on Multi-step Reinforcement Learning

The increasing use of renewable energy in the power system results in strong stochastic disturbances and degrades the control performance of the distributed power grids.In this paper,a novel multi-agent collaborative reinforcement learning algorithm is proposed with automatic optimization,namely,Dyna-DQL,to quickly achieve an optimal coordination solution for the multi-area distributed power grids.The proposed Dyna framework is combined with double Q-learning to collect and store the environmental samples.This can iteratively update the agents through buffer replay and real-time data.Thus the environmental data can be fully used to enhance the learning speed of the agents.This mitigates the negative impact of heavy stochastic disturbances caused by the integration of renewable energy on the control performance.Simulations are conducted on two different models to validate the effectiveness of the proposed algorithm.The results demonstrate that the proposed Dyna-DQL algorithm exhibits superior stability and robustness compared to other reinforcement learning algorithms.

Improved Smith prediction monitoring AGC system based on feedback-assisted iterative learning control

The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.

Research on Wind Farm Participation in Power Grid AGC Control

Since wind power has the features of being intermittent and unpredictable, and usually needs transmission over long distances, grid integration of large-scale wind power will exert signif icant influence on power grid planning and construction, and will make a heavy impact on the safe and reliable operation of power systems. To deal with the diff iculties of large scale wind power dispatch, this paper presents a new automatic generation control (AGC) scheme that involves the participation of wind farms. The scheme is based on ultra-short-term wind power forecast. The author establishes a generation output distribution optimization mode for the power system with wind farms and verif ies the feasibility of the scheme by an example.

Automated and controlled patch generation for enhanced fixing of communication software vulnerabilities

Software is a crucial component in the communication systems,and its security is of paramount importance.However,it is susceptible to different types of attacks due to potential vulnerabilities.Meanwhile,significant time and effort is required to fix such vulnerabilities.We propose an automated program repair method based on controlled text generation techniques.Specifically,we utilize a fine-tuned language model for patch generation and introduce a discriminator to evaluate the generation process,selecting results that contribute most to vulnerability fixes.Additionally,we perform static syntax analysis to expedite the patch verification process.The effectiveness of the proposed approach is validated using QuixBugs and Defects4J datasets,demonstrating significant improvements in generating correct patches compared to other existing methods.

面向多源协调互济的AGC指令两阶段分配方法

高比例新能源电力系统的惯量支撑能力弱、随机波动性强,电网频率质量难以得到保障。引入不同品类调频资源共同参与自动发电控制(automatic generation control,AGC)是应对上述问题的有效手段,但这也要求AGC指令分配能够适配不同品类资源的差异化调节特性。对此,提出面向多源协调互济的AGC指令两阶段分配方法。首先,将不同资源分别安排至与自身调节特性相匹配的超前分配阶段和实时分配阶段。其次,在实时分配阶段提出基于资源调节优先级的AGC指令分配策略,根据不同品类资源的调节特性以及电网频率状况对资源调节优先级进行设定,以互补利用各类资源的调节优势。然后,在超前分配阶段,建立优化分配模型以实现不同品类资源的协调最优利用。同时,嵌入与资源剩余调节裕度相关的惩罚项,以保障资源持续调节能力。最后,基于电网实际数据的算例仿真验证了所提方法的有效性。

19.8MW分布式光伏电站AGC/AVC系统的应用研究

AGC/AVC系统在光伏电站中的应用,可有效提升电站的运行性能,让电站在不同运行条件下都能稳定输出电力,有效优化电网的电压水平。文章阐述了分布式光伏电站的基本构成和工作原理,确定了AGC/AVC系统的基本功能,分析了AGC/AVC系统在光伏电站中的具体应用。研究结果表明,合理配置AGC/AVC系统,19.8 MW分布式光伏电站在复杂多变的电网环境下保持高效稳定的运行,显著提高电站的运行效率和经济效益,为未来分布式光伏电站优化设计和运行管理提供实践指导。

基于AGC系统的光伏电站一次调频控制系统设计

在光伏电站的发电过程中,由于太阳光的间歇性和不可预测性,如何保障电网频率的稳定性是一个重要问题。文章详细探讨了基于自动发电控制(Automatic Generation Control,AGC)系统的光伏电站一次调频控制系统的设计方法。首先概述了AGC系统的工作原理和组成,其次提出一个针对光伏电站特定需求的一次调频控制系统设计方案,包括系统架构设计、数据收集与分析、控制算法与模型的开发以及系统稳定性与效率的评估,最后通过系统测试验证所提出设计的有效性与实用性。

AGC调频指标提升关键技术研究

通过重新优化汽机主控、锅炉主控逻辑及自主研发的AGC信号智能自动校正控制装置,消除AGC的调节量、被调量与调度能量管理系统信号之间的误差,实时对其进行动态校正,提高调节速率、调节精度、响应时间等指标,提升AGC综合性能指标。同时在电力辅助服务市场上,通过调节精度的提升,可有效增加机组的AGC调节范围,争取到更多的补偿收益。

新能源电力系统AGC调频容量监测设计

文章提出了用于新能源电力系统自动发电控制(Automatic Generation Control,AGC)的实时调频容量评估方法,以满足控制区的自动发电控制调频能力分析需求。根据AGC调频容量考核标准,整理了影响调频容量的主要因素,并运用历史数据预测调频标准差。以贵州黔源电力镇宁某水电站和光伏站作为例,评估AGC调频容量。实验结果证实,应用所提方法能有效地分配调频容量。

A Two-stage Kalman Filter for Cyber-attack Detection in Automatic Generation Control System

Communication plays a vital role in incorporating smartness into the interconnected power system.However,historical records prove that the data transfer has always been vulnerable to cyber-attacks.Unless these cyber-attacks are identified and cordoned off,they may lead to black-out and result in national security issues.This paper proposes an optimal two-stage Kalman filter(OTS-KF)for simultaneous state and cyber-attack estimation in automatic generation control(AGC)system.Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics.Five types of cyber-attacks,i.e.,false data injection(FDI),data replay attack,denial of service(DoS),scaling,and ramp attacks,are injected into the measurements and estimated using OTS-KF.As the load variations of each area are seldom available,OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system.The proposed technique is validated on the benchmark two-area,three-area,and five-area power system models.The simulation results under various test conditions demonstrate the efficacy of the proposed filter.

Automatic Generation Control of Multi-area Power System with Network Constraints and Communication Delays

Newly proposed power system control methodologies combine economic dispatch(ED) and automatic generation control(AGC) to achieve the steady-state cost-optimal solution under stochastic operation conditions. However, a real power system is subjected to continuous demand disturbance and system constraints due to the input saturation, communication delays and unmeasurable feed-forward load disturbances. Therefore, optimizing the dynamic response under practical conditions is equally important. This paper proposes a state constrained distributed model predictive control(SCDMPC)scheme for the optimal frequency regulation of an interconnected power system under actual operation conditions, which exist due to the governor saturation, generation rate constraints(GRCs), communication delays, and unmeasured feed-forward load disturbances. In addition, it proposes an algorithm to handle the solution infeasibility within the SCDMPC scheme, when the input and state constraints are conflicting. The proposed SCDMPC scheme is then tested with numerical studies on a three-area interconnected network. The results show that the proposed scheme gives better control and cost performance for both steady state and dynamic state in comparison to the traditional distributed model predictive control(MPC) schemes.

Automatic Generation Control with Virtual Synchronous Renewables

As synchronous generators(SGs)are gradually displaced by renewable energy sources(RESs),the frequency stability of power systems deteriorates because RESs,represented by utility-scale solar and wind power sources,do not provide the inertial response,primary frequency response,secondary frequency response,and tertiary frequency regulation.As a result,the remaining SGs may not be sufficient to maintain the power balance and frequency stability.The concept and control strategies of virtual synchronous generators(VSGs)enable the inverter-based wind and solar power sources to emulate the outer characteristics of traditional SGs and participate in the active power and frequency control of power systems.This paper focuses on the automatic generation control(AGC)with virtual synchronous renewables(VSRs).First,the VSR strategy that enables the RESs to participate in AGC is introduced.Second,based on the interval representation of uncertainty,the output of RES is transformed into two portions,i.e.,the dispatchable portion and the stochastic portion.In the dispatchable portion,the RESs can participate in AGC jointly with SGs.Accordingly,a security-constrained economic dispatch(SCED)model is built considering the RESs operating in VSR mode.Third,the solution strategy that employs the slack variables to acquire deterministic constraints is introduced.Finally,the proposed SCED model is solved based on the 6-bus and 39-bus systems.The results show that,compared with the maximum power point tracking(MPPT)mode,VSRs can participate in the active power and frequency control jointly with SGs,increase the maximum penetration level of RESs,and decrease the operating cost.

Automatic Generation Control Based on Multiple-step Greedy Attribute and Multiple-level Allocation Strategy

The strong stochastic disturbance caused by largescale distributed energy access to power grids affects the security,stability and economic operations of the power grid.A novel multiple-step greedy policy based on the consensus Qlearning(MSGP-CQ)strategy is proposed in this paper,which is an automatic generation control(AGC)for distributed energy incorporating multiple-step greedy attribute and multiple-level allocation strategy.The convergence speed and learning efficiency in the MSGP algorithm are accelerated through the predictive multiple-step iteration updating in the proposed strategy,and the CQ algorithm is adopted with collaborative consensus and selflearning characteristics to enhance the adaptability of the power allocation strategy under the strong stochastic disturbances and obtain the total power commands in the power grid and the dynamic optimal allocations of the unit power.The simulations of the improved IEEE two-area load-frequency control(LFC)power system and the interconnected system model of intelligent distribution network(IDN)groups incorporating a large amount of distributed energy show that the proposed strategy can achieve the optimal coordinated control and power allocation in the power grid.The algorithm MSGP-CQ has stronger robustness and faster dynamic optimization speed and can reduce generation costs.Meanwhile it can also solve the strong stochastic disturbance caused by large-scale distributed energy access to the grid compared with some existing intelligent algorithms.