Automatic Load Frequency Control Research Articles

Analysis of Load Frequency Control of Two Area Power System using Different Control Techniques

To maintain stability is a mandatory requirement of any kind of power network. Interconnected power systems have significant advantages in maintaining stability and frequency at their nominal values. The control of the interconnected power systems is performed by Automatic Generation Control (AGC), which has two parts: the Automatic Voltage Regulator (AVR) and Load Frequency Control (LFC). When a disturbance occurs in the system, Load Frequency Control (LFC) acts to re-establish the system into its steady state with zero error as well as the desired transient response characteristics. This paper exhibits the design and implementation of a PID and Fuzzy Logic Controller for two area power systems, taking the steady state error and percentage of overshoot, undershoot, and pre-shoot as comparison parameters. MATLAB/Simulink software was used to bring out the implementation and obtain results. System dynamic performance is observed for PID and Fuzzy Logic Controllers in Simulink. The comparison study indicated that the proposed Fuzzy Logic controller has better performance than the PID controller. IUBAT Review—A Multidisciplinary Academic Journal, 7(1): 62-79

Impact of ABT based control strategies on ALFC and AVR in DFIG-integrated interconnected power systems

To maintain a stable electricity supply in interconnected power grids, effective frequency and voltage regulation are required. Interconnected power systems use Automatic Load Frequency Control (ALFC) and Automatic Voltage Regulator (AVR) for this purpose. The integration of renewable sources, such as Doubly Fed Induction Generators (DFIGs), creates challenges for such control loops. This study investigates the impact of using various Frequency-Linked Pricing (FLP)-based operating strategies under Availability-Based Tariff (ABT) on combined ALFC and AVR in a two-area interconnected power system with DFIGs. FLP-based operating strategies under ABT react to frequency changes through price signals, which can act faster compared to traditional AGC methods that rely on communication between control centers. This quicker response helps maintain frequency and voltage during load disturbances. Most of the earlier research focused mostly on using only a single type of FLP-based operating strategy under ABT within the ALFC loop, neglecting the combined ALFC-AVR interaction. This work addresses this gap by evaluating three FLP-based operating strategies under ABT along with the traditional approach for their impact on frequency and voltage under step load disturbance. Simulation studies conducted in MATLAB 2022a demonstrate the effectiveness of the proposed strategies, providing valuable insights for improving frequency and voltage control in modern power systems. The investigation reveals that the strategy that considers the difference between the actual and reference values of the unscheduled interchange (UI) charge and the marginal cost of generation to produce the control command outperforms the other strategies and restores the frequency and voltage to their nominal values.

Load Frequency Control and Optimization of Load Allocation for Multi-Area Power System with Electric Vehicle Charging Load

The integration of electric vehicles (EVs) into power systems has introduced new challenges for load frequency control due to the additional charging load they impose. This research article investigates the design and analysis of automatic load frequency control in a two-area power system, considering the presence of EV charging load. The study employs Artificial Neural Network (ANN) based PI control to manage both traditional load demand and the dynamic charging requirements of EVs. Maintaining a stable power system frequency and balancing generation with the EV charging load have become crucial tasks. Automatic Generation Control (AGC) or Load Frequency Control (LFC) systems need to adapt and account for the variability and uncertainty associated with EV charging patterns. The integration of ANN-based PI control provides an intelligent and adaptive approach to address these challenges. Using MATLAB, a power system model is simulated to evaluate the effectiveness of the proposed control scheme. This investigation conducts a comparative analysis of the system's frequency responses under various scenarios, including different EV charging load profiles. It highlights the benefits and challenges of utilizing ANN-based PI control to manage the combined load of traditional demand and EV charging. Moreover, the load distribution among the distribution stations varied from 0.08% to 12.50% when compared between particle swarm optimization and genetic algorithm, respectively. By considering the dynamic interaction between power system operation and EV charging, this research aims to enhance the reliability, efficiency and sustainability of power systems in the context of evolving transportation trends and the increasing electrification of vehicles.

Sliding Mode Without Reaching Phase Design for Automatic Load Frequency Control of Multi-Time Delays Power System

Sliding Mode Without Reaching Phase Design for Automatic Load Frequency Control of Multi-Time Delays Power System

Employment of renewable based sources in amalgamated frequency-voltage control restructured system with TSA trained IPD(1+I) controller

The existing effort explores automatic load frequency control learning and automatic voltage regulator for manifold areas and sources under restructured situations. Sources in Area_1 are bio-diesel and thermal; diesel and thermal plants in Area_2. Thus, each area consists of two generating companies and two distributing companies. A unique attempt has been undergone to perform a deuce phase controller integral-proportional-derivative with one plus integral (IPD(1 + I)) controller. Various examination expresses that IPD(1 + I) is an excellent controller when compared with proportional-derivative (PD) and proportional-integral-derivative-filter (PIDN) from outlook regarding crest overshoot, grade-of-fluctuations, crest undershoot as well as the subsiding period in case of bilateral contacts. In an attempt to secure the controller's attributes bio-enthused meta-heuristic tunicate search algorithm (TSA) is applied. Along with that, renewable source solar energy in the form of solar photovoltaic has been merged in both the operating areas. Action in existence of hydrogen aqua electrolyser – fuel cell (HAE (FC)) has also been examined using IPD(1 + I) controller for bilateral contact, in order to obtain noteworthy outcomes in a dynamic presentation. It is pragmatic that the best responses are obtained for the scheme with the inclusion of both PV and HAE (FC). Also, IPD(1 + I) parameters values at nominal condition has been analyzed to confirm its suitability for varied loading conditions without the need for optimization.

Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller

This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation.

Load Frequency Control and Automatic Voltage Regulation in Four-Area Interconnected Power Systems Using a Gradient-Based Optimizer

Existing interconnected power systems (IPSs) are being overloaded by the expansion of the industrial and residential sectors together with the incorporation of renewable energy sources, which cause serious fluctuations in frequency, voltage, and tie-line power. The automatic voltage regulation (AVR) and load frequency control (LFC) loops provide high quality power to all consumers with nominal frequency, voltage, and tie-line power deviation, ensuring the stability and security of IPS in these conditions. In this paper, a proportional integral derivative (PID) controller is investigated for the effective control of a four-area IPS. Each IPS area has five generating units including gas, thermal reheat, hydro, and two renewable energy sources, namely wind and solar photovoltaic plants. The PID controller was tuned by a meta-heuristic optimization algorithm known as a gradient-based optimizer (GBO). The integral of time multiplied by squared value of error (ITSE) was utilized as an error criterion for the evaluation of the fitness function. The voltage, frequency, and tie-line power responses of GBO-PID were evaluated and compared with integral–proportional derivative (GBO-I-PD), tilt integral derivative (GBO-TID), and integral–proportional (GBO-I-P) controllers with 5% step load perturbation (SLP) provided in each of the four areas. Comprehensive comparisons between GBO-PID and other control methodologies revealed that the proposed GBO-PID controller provides superior voltage, frequency, and tie-line power responses in each area. The reliability and efficacy of GBO-PID methodology were further validated with variations in the turbine time constant and speed regulation over a range of  ± 25%. It is evident from the outcomes of the sensitivity analysis that the proposed GBO-PID control methodology is very reliable and can successfully stabilize the deviations in terminal voltage, load frequency, and tie-line power with a shorter settling time in a four-area IPS.

Dandelion Optimizer-Based Combined Automatic Voltage Regulation and Load Frequency Control in a Multi-Area, Multi-Source Interconnected Power System with Nonlinearities

Frequency, voltage, and power flow between different control zones in an interconnected power system are used to determine the standard quality of power. Therefore, the voltage and frequency control in an IPS is of vital importance to maintaining real and reactive power balance under varying load conditions. In this paper, a dandelion optimizer (DO)-based proportional-integral-proportional-derivative (PI-PD) controller is investigated for a realistic multi-area, multi-source, realistic IPS with nonlinearities. The output responses of the DO-based PI-PD were compared with the hybrid approach using artificial electric field-based fuzzy PID algorithm (HAEFA), Archimedes optimization algorithm (AOA)-based PI-PD, learning performance-based behavior optimization (LPBO)-based PI-PD and modified particle swarm optimization (MPSO)-based PI-PD control schemes in a two-area network with 10% step load perturbation (SLP). The proposed strategy was also investigated in a two- and three-area IPS in the presence of different nonlinearities and SLPs. The simulation results and the comprehensive comparison between the different control schemes clearly confirm that the proposed DO-based PI-PD is very effective for realistic, multi-area multi-source IPS with nonlinearities.

Fuzzy Logic Based Load Frequency Control of Hybrid Power System Integrated with SMES

The use of a Fuzzy logic-based controller for automatic generation control (AGC) and load frequency control (LFC) of two area power plants in an interconnected power system is proposed in this paper. In addition, a superconducting magnetic Energy Storage (SMES) unit is being considered in two areas. After a sudden load disturbance, the implementation of SMES combination arrests the initial fall in frequency deviation and tie-line power deviation. The simulation results show that the proposed SMES unit improves system performance significantly. The system's sensitivity analysis is carried out by altering the parameters and operating load conditions from their nominal values. Even when the system is subjected to wide variations in loading conditions and system parameters, the optimised gains of the proposed fuzzy logic- based controller and fuzzy controller along with SMES controllers do not need to be reset. Finally, different types of load patterns are used to test the effectiveness of the proposed controller design. The simulation results show that the Fuzzy PID controller combined with SMES outperforms the Fuzzy PID controller alone. The simulations were run using the MATLAB software.

Automatic Load Frequency Control of Renewable Energy Integrated Hybrid Power System

Frequency aberration, power quality, and system instability may be caused by the general perception of Renewable Energy (RE). To control the frequency with the tolerable limit, load frequency control is being performed. Automatic Load Frequency Control (ALFC) must be provided with a proper controller. Ziegler-Nichols method is being used to tune the parameters of the Proportional-Integral-Derivative (PID) controller for Load Frequency Control of Hybrid Power System (HPS). Traditional PID controllers are capable of handling a larger varieties of rapid changes in load variations in renewable energy hybrid power systems. This work considers the HPS of 2000 MW power system including RE resources. The OP4510 is utilized for hardware-in-loop (HIL) simulation to test the accomplished controller's real-time applicability. The MATLAB simulation and the Real-Time simulator provide identical results.

Coordinated Control of ALFC-AVR in Multiarea Multisource Systems Integrated with VRFB and TCPS Using CFPDN-PIDN Controller

The article discusses the coordinated effect of the vanadium redox flow battery (VRFB) and thyristor control phase shifter (TCPS) on combined automatic load frequency control and automatic voltage regulator of three area hydrothermal systems incorporating renewable sources. Solar thermal power plant, dish-Stirling solar thermal system, and geothermal power plant are integrated into Area-I, Area-II, and Area-III, respectively. Suitable generation rate constraint and governor dead-band are considered for both thermal and hydro plants. A new fuzzy logic-based cascaded controller named fuzzy proportional-derivative with filter coefficient cascaded with proportional-integral-derivative with filter coefficient (CFPDN-PIDN) is proposed as a secondary controller and its performance is compared with the PIDN and CPDN-PIDN controllers. Various controller parameters are optimized by the artificial flora algorithm. Simulation results reveal that the CFPDN-PIDN controller outperforms the PIDN and CPDN-PIDN controllers. Two types of control schemes are used to regulate the TCPS's phase angle variation: Type-I is a first-order lag transfer function, while Type-II includes a washout filter and lead-lag compensator along with Type-I. Analysis shows that the Type-I control strategy is better than Type-II. VRFB charging/discharging is regulated using high-pass filter (HPF) and dead-band (DB) control schemes. Investigation reveals that the BD scheme provided better system dynamics than the HPF scheme. Moreover, the system performance degrades with very high or low state-of-charge values. Further, using the best control schemes for both VRFB and TCPS, the system's dynamic performance was superior to that of the individual one. Finally, the robustness of the proposed control approach is validated through sensitivity analysis.

A Chaotic Search-Based Hybrid Optimization Technique for Automatic Load Frequency Control of a Renewable Energy Integrated Power System

In this work, a chaotic search-based hybrid Sperm Swarm Optimized-Gravitational Search Algorithm (CSSO-GSA) is proposed for automatic load frequency control (ALFC) of a hybrid power system (HPS). The HPS model is developed using multiple power sources (thermal, bio-fuel, and renewable energy (RE)) that generate power to balance the system’s demand. To regulate the frequency of the system, the control parameters of the proportional-integral-derivative (PID) controller for ALFC are obtained by minimizing the integral time absolute error of HPS. The effectiveness of the proposed technique is verified with various combinations of power sources (all sources, thermal with bio-fuel, and thermal with RE) connected into the system. Further, the robustness of the proposed technique is investigated by performing a sensitivity analysis considering load variation and weather intermittency of RE sources in real-time. However, the type of RE source does not have any severe impact on the controller but the uncertainties present in RE power generation required a robust controller. In addition, the effectiveness of the proposed technique is validated with comparative and stability analysis. The results show that the proposed CSSO-GSA strategy outperforms the SSO, GSA, and hybrid SSO-GSA methods in terms of steady-state and transient performance indices. According to the results of frequency control optimization, the main performance indices such as settling time (ST) and integral time absolute error (ITAE) are significantly improved by 60.204% and 40.055% in area 1 and 57.856% and 39.820% in area 2, respectively, with the proposed CSSO-GSA control strategy compared to other existing control methods.

Simulation framework for automatic load frequency control studies of VSC-based AC/DC power grids

This paper introduces an efficient simulation framework for automatic load frequency control (ALFC) studies of VSC-based AC/DC power grids. It is aimed at slow transients caused by large megawatt imbalances occurring either in the AC systems or DC grid. These studies involve the coordinated action of the primary and secondary control loops of power plants, ones requiring long simulation times ranging from various tens of seconds to minutes. To address these overly challenging AC/DC power system simulations, the developed modelling framework resorts to suitable linear models, i.e., VSC dynamic controls are devised to conform to the slow dynamics featuring the ALFC. The most fundamental dynamics of AC systems, DC grid and VSC stations are compiled with reasonable precision. This is demonstrated using a three-terminal, multilevel VSC-based HVDC system implemented in the PSCAD simulation package for comparative purposes; both solutions are shown to concur very well because root mean squared errors inferior to 3% are obtained. The practicality of this effective simulation framework is also demonstrated by studying a five-terminal VSC-based transmission grid facing large power imbalances imposed by load disconnections and VSC outages.

A Novel Grey Wolf Optimizer Based Load Frequency Controller for Renewable Energy Sources Integrated Thermal Power Systems

The frequency value should be kept constant to ensure and maintain synchronization in power systems. When the balance between generation and load is interrupted, the frequency value increases or decreases. This frequency deviation may lead to serious problems in the power system. Therefore, a design of a controller is required to keep the system frequency and tie-line power variations within specified limits, which is called automatic generation control (AGC) or load frequency control (LFC). This paper aims to determine the optimal controller parameters used in the LFC for a two-area non-reheat thermal power system integrated with various renewable energy sources (RES) such as photovoltaic (PV) and wind energy systems. The proposed controller is a PI–(1 + DD) controller which is a combination of proportional, integral, and double derivative controllers. The optimal gains of the proposed controller are determined by the Grey Wolf Optimization (GWO) algorithm. Moreover, the performance of the PI–(1 + DD) controller is tested under various scenarios such as different step load perturbations, random load changes, system parameters and RES variation. The results show that the PI–(1 + DD) controller provides an improvement of about 40% in system frequency overshoot and about 45% in settling time compared to other controllers.

Renewable sources-based automatic load frequency control of interconnected systems using chaotic atom search optimization

This paper proposes an improved form of chaotic based atom search optimization (IASO) algorithm by adapting one-dimensional (1D) chaotic map (tent, sine and logistic) to improve the search ability by intensifying the exploration and exploitation phase. The IASO avoids premature convergence and trapping into local optima. Initially, the proposed IASO is validated using a classical benchmark function and its performance is compared with ASO algorithm. Test results indicate that the proposed algorithm outperforms in terms of mean, standard deviation, and best values. Further, the proposed technique is used to design the parameters of fractional-order proportional integral derivative controller for automatic load frequency control (ALFC) of multi-area, multi-source hybrid power system (HPS) by minimizing the integral time absolute error. The results obtained show that the proposed control scheme improves the frequency response of the system by 48 %, 70 %, 15 % and 69 % in terms of settling time, peak undershoot, steady state error value and control effort, respectively compared to ASO. Moreover, the sensitivity analysis is carried out by considering ±25 % variation in HPS parameters and the real-time applicability is tested with Malaysian meteorological data of solar radiation and wind speed variation. These analysis indicates that the transient oscillations are damped out with minimum settling time and the system regains to stable operating conditions. Further, the evaluation of transient and steady-state performance indices shows that the tent map-based IASO is found to be more efficient for obtaining the optimal solution in solving the ALFC problems. In addition, the stability of the system is analysed by approximating the fractional-order transfer function based on the oustaloup filter in frequency domain.

Performance Analysis of Diverse Energy Storage on Combined ALFC and AVR Control of Multiarea Multiunit System with AC/HVDC interconnection

This paper discusses the significance of various energy storage devices like redox flow battery (RFB), capacitive energy storage (CES), superconducting magnetic energy storage (SMES), and flywheel energy storage system (FESS) impact on the combined control of automatic load frequency control (ALFC) and automatic voltage regulator (AVR) of three areas interconnected power systems having thermal, hydro, gas, and geothermal plants in presence of HVDC link. A novel PD with filter coefficient (PDN) controller cascade with fractional-order PID with filter coefficient (FOPIDN), CPDN-FOPIDN is proposed as a secondary controller for combined ALFC-AVR loop. An artificial flora algorithm is utilized to obtain the various controller parameters under numerous system conditions. The proposed CPDN-FOPIDN controller provides less settling time, overshoots, undershoots, and reduces oscillation compare to the FOPIDN controller. The study also reveals that the RFB provides better system performance than SMES, CES, and FESS. Integration of HVDC link improves the system dynamic response. Furthermore, the sturdiness of the proposed CPDN-FOPIDN controller is tested against the changes in system loading conditions via sensitivity analysis.

Efficient Load Frequency Control of Renewable Integrated Power System: A Twin Delayed DDPG-Based Deep Reinforcement Learning Approach

Power systems have been evolving dynamically due to the integration of renewable energy sources, making it more challenging for power grids to control the frequency and tie-line power variations. In this context, this paper proposes an efficient automatic load frequency control of hybrid power system based on deep reinforcement learning. By incorporating intermittent renewable energy sources, variable loads and electric vehicles, the complexity of the interconnected power system is escalated for a more realistic approach. The proposed method tunes the proportional-integral-derivative (PID) controller parameters using an improved twin delayed deep deterministic policy gradient (TD3) based reinforcement learning (RL) agent, where a non-negative fully connected layer is added with absolute function to avoid negative gain values. Multi deep reinforcement learning agents are trained to obtain the optimal controller gains for the given two-area interconnected system, and each agent uses the local area control error information to minimize the deviations in frequency and tie-line power. The integral absolute error (IAE) of area control error is used as a reward function to derive the controller gains. The proposed approach is tested under random load-generation disturbances along with nonlinear generation behaviors. The simulation results demonstrate the superiority of the proposed approach compared to other techniques presented in the literature and show that it can effectively cope with nonlinearities caused by load-generation variations.

Analysis of tilt integral derivative controller-based automatic load frequency control of multi-area multi-source system

Analysis of tilt integral derivative controller-based automatic load frequency control of multi-area multi-source system

Analysis of tilt integral derivative controller-based automatic load frequency control of multi-area multi-source system

Analysis of tilt integral derivative controller-based automatic load frequency control of multi-area multi-source system

Design of single- and multi-loop self-adaptive PID controller using heuristic based recurrent neural network for ALFC of hybrid power system

This paper presents a novel heuristic based recurrent Hopfield neural network (HNN) designed self-adaptive proportional-integral-derivative (PID) controller for automatic load frequency control of interconnected hybrid power system (HPS). The control problem is conceptualized as an optimization problem and solved using a heuristic optimization technique with the aim of minimizing the Lyapunov function. Initially, the energy function is formulated and the differential equations governing the dynamics of HNN are derived. Then, these dynamics are solved using hybrid particle swarm optimization-gravitational search algorithm (PSO-GSA) to obtain the initial solution. The effectiveness of the controller is tested for two-area system considering the system non-linearities and integration of plug-in-electric vehicle (PEV). Further, to improve the speed of response of the system, the cascade control scheme is proposed using the presented approach of heuristic based HNN (h-HNN). The efficacy of the method is examined in single- and multi-loop PID control of three-area HPS. The performance of propounded control schemes is compared with PSO-GSA and generalized HNN based PID controller. The results obtained show that the response of proposed controller is superior in terms of transient and steady state performance indices measured. In addition, the control effort of suggested cascade controller is much reduced compared with other controllers presented. Furthermore, the self-adaptive property of the controller is analyzed for random change in load demand and their corresponding change in gain parameters are recorded. This reveals that the proposed controller is more suitable for stable operation of modern power network with green energy technologies and PEV efficiently.