Two-area Power System Research Articles

Adaptive Neuro-Fuzzy Based Load Frequency Control in Presence of Energy Storage Devices

Energy storage technologies are utilized for improving the primary frequency control in complex electrical systems. In this paper, the modeling and simulation of a two-area power system is done to evaluate and compare the impact of three different energy storage applications on load frequency control performance. Capacitive energy storage (CES), battery energy storage (BES), and superconducting magnetic energy storage (SMES) are considered for the study. On the basis of peak overshoot and settling time, the performance of these energy storage devices is compared. The power system consists of thermal, wind, and solar resources. All nonlinearities are incorporated in the system model. Both conventional and artificial intelligence (AI) based controllers are tested for the case study. The simulations are carried out under multiple operating conditions and penetration levels of renewable sources. It is found that the primary frequency response of the test system is improved in the presence of the storage facilities and the neuro-fuzzy controller. The simulation results exhibit that the SMES displays the best performance indices. The prime contribution of the paper is to investigate and compare the response of the storage devices in a realistically modeled power system with renewable resources using both conventional and AI controllers.

Restructured LFC Scheme with Renewables and EV Penetration using Novel QOEA Optimized Parallel Fuzzy I-PID Controller

An effective controller configuration improves the action of the load frequency control (LFC) mechanism. In the present work, a novel parallel fuzzy integral - proportional-integral-derivative (FI-PID) controller is suggested for a two-area power system in the deregulated environment. The controller gains are optimized using novel quasi-oppositional equilibrium algorithm (QOEA). The proposed control scheme has been subjected to step and random load disturbances. Moreover, the suggested scheme is also compared with different prevalent controllers and optimizing techniques. EV and the distributed generation (DG) act as energy storing elements and improve system transients during peak demands. Hence, the integration of electric vehicle into the grid and its impact on frequency regulation is also studied. Simulation results establish the efficacy of the suggested controller over prevailing ones.

Maiden Application of Skill Optimization Algorithm on Cascaded Multi-Level Neuro-Fuzzy based Power system Stabilizers for Damping Oscillations

Power system oscillation is a significant threat among interconnected power systems that may lead to instability. The success of oscillation damping is primarily responsible for a modern power system's safe operation. However, the development of damping controllers is a multimodal optimization problem with constraints which challenges the traditional optimization algorithms. This paper critically examines damping schemes and controller stability analyses to find solutions to these issues and improve the performance of a multi-machine power system (MMPS) . This paper reveals the technique of improving power system stability by employing the Skill Optimization Algorithm (SOA) to optimize the gains of the conventional power system stabilizer (PSS) and the Neuro-Fuzzy inputs-output scaling parameters. This study aims to propose a multi-level Neuro-Fuzzy power system stabilizers (MLNFPSS ) to reduce the instability of a multi-machine two-area power system (MMTAPS) under fault conditions. The simulations were run on a MMTAPS considering a transmission line fault in the middle. The analysis leads to the deduction that the proposed MLNFPSS response is more efficient than conventional PSS under symmetrical three-phase faults. All control strategies have been executed, and the simulation results have been assessed using MATLAB 2016b/Simulink.

Load frequency control of time-delayed power system based on event-triggered communication scheme

In frequency regulation of power grids, conveying observations to controllers and obtaining control outputs depend greatly on communication and computation resources. Particularly for modern power system with an open communication network, the costs of communication and computation should not be ignored. This paper investigates an event-triggered based load frequency control for time-delayed power system with an open communication network. Based on the lifting technique in the sampled-data control theory, a new control scheme, using both a large sampling period and a maximized threshold parameter, is introduced to further lessen communication and computation costs while preserving a desired H∞ robust performance. The delay-dependent stability criterion of the proposed control scheme is less conservative, which takes fully the time delay, the sampling period, and the threshold parameter of event-triggered communication scheme into account. The proposed criterion is unified and can be transformed to the stability conditions of the existing research by setting different values of time delay, sampling and threshold parameter. Additionally, the usage of a large and aperiodic sampling period complies with the aperiodic updating characteristic of 2-4s in control signals in load frequency control scheme. Case studies based on a one-area power system, a two-area power system and an IEEE 39-bus benchmark test system are carried out. The simulation tests demonstrate that the proposed control scheme further reduces the communication and computation costs and ensures the load frequency control system stable operation with a preset H∞ robust performance.

Deep Stackelberg heuristic dynamic programming for frequency regulation of interconnected power systems considering flexible energy sources

The flexible energy sources (FESs) could refuse to execute the generation commands (GCs) if the benefits fell short of their expectations when applying traditional automatic generation control methods, this problem leads to frequency instability and the waste of scheduling resources. Accordingly, a deep Stackelberg heuristic dynamic programming (DSHDP) is proposed for the frequency regulation and power dispatch of smart generation control (SGC). Firstly, the heuristic deep dynamic programming, whose networks are replaced by three deep neural networks, can be trained offline by historical data to fit the optimal performance index. Then, the GCs can be optimized through an online updating strategy for stabilizing the frequency of interconnected power systems. To maximize the benefits of FESs further, an improved SGC framework based on the Stackelberg game is built to let FESs play games with conventional power plants in a fair environment. Finally, the optimal GCs can be distributed by the Nash equilibrium to obtain higher profits than traditional methods, and the control accuracy is increased indirectly. Two cases, i.e., IEEE two-area power system and six-area power system based on China Southern Power Grid (CSPG), are provided to verify the superiority of the DSHDP. The results indicate that the excellent flexibility and stability of the proposed algorithm in complex power systems containing renewable energy and FESs. Totally, the frequency deviation obtained by DSHDP is reduced by up to 59.38%, while the total profit of FESs rises by up to 67.47% when compared with the other seven representative algorithms.

Trajectory sensitivity analysis-based systematic Q-matrix of DFIG with LQR auxiliary voltage and power compensation for oscillation damping

This study proposes a systematic method to define the weighting Q-matrix of the linear quadratic regulator (LQR) for doubly fed induction generators (DFIGs) through auxiliary voltage and power compensation for oscillation damping. Trajectory sensitivity analysis (TSA) is applied to define the Q-matrix. The TSA solves the system dynamics to obtain the sensitivity of states to DFIG primary voltage control reference and primary wind power reference. The Q-matrix is determined as the infinity norm of TSA sensitivity. Stability criteria of the LQR are analyzed with the proposed Q-matrix. The combined LQR voltage and power compensation loops are designed, and the LQR compensation gains are applied to a modified two-area power system and then tested at different disturbances and wind power penetration levels (WPLs). Simulation results show the capability and robustness of the designed process in damping the oscillation with designing the Q-matrix under major fault conditions. Moreover, the proposed method is robust at different WPLs.

The Bees Algorithm Tuned Sliding Mode Control for Load Frequency Control in Two-Area Power System

This paper proposes a design of Sliding Mode Control (SMC) for Load Frequency Control (LFC) in a two-area electrical power system. The mathematical model design of the SMC is derived based on the parameters of the investigated system. In order to achieve the optimal use of the proposed controller, an optimisation tool called the Bees Algorithm (BA) is suggested in this work to tune the parameters of the SMC. The dynamic performance of the power system with SMC employed for LFC is studied by applying a load disturbance of 0.2 pu in area one. To validate the supremacy of the proposed controller, the results are compared with those of recently published works based on Fuzzy Logic Control (FLC) tuned by Teaching–Learning-Based Optimisation (TLBO) algorithm and the traditional PID optimised by Lozi map-based Chaotic Optimisation Algorithm (LCOA). Furthermore, the robustness of SMC-based BA is examined against parametric uncertainties of the electrical power system by simultaneous changes in certain parameters of the testbed system with 40% of their nominal values. Simulation results prove the superiority and the robustness of the proposed SMC as an LFC system for the investigated power system.

A multi-objective AVR-LFC optimization scheme for multi-area power systems

The AVR and LFC schemes are essential power system tools, which are used to support the operation, stability, and security of the generation systems in the main power networks. These control units are designed using numerical as well as analytical approaches. The numerical solutions that are based on heuristics for instance, vary in quality and procedure of the underlying optimization processes leading to the desired outcomes. These processes are mostly used to optimize a single objective criterion that compromises between different performance characteristics like, steady state and transient responses. This work introduces a nonlinear threshold accepting heuristic with an innovative multi-objective optimization architecture. This method is employed to select the control gains of a coupled AVR-LFC scheme for a two-area power system to ensure stable and fast AC generator bus voltage-frequency regulation in react to dynamic disturbances. This approach decides a set of control laws that guarantee fast dynamic response while optimizing conflicting objective criteria. It is validated against other standard heuristics, namely artificial bee colony, differential evolution, and particle swarm optimization in addition to other analytical approaches like internal model-control schemes.

Cybersecurity Analysis of Load Frequency Control in Power Systems: A Survey

Today, power systems have transformed considerably and taken a new shape of geographically distributed systems from the locally centralized systems thereby leading to a new infrastructure in the framework of networked control cyber-physical system (CPS). Among the different important operations to be performed for smooth generation, transmission, and distribution of power, maintaining the scheduled frequency, against any perturbations, is an important one. The load frequency control (LFC) operation actually governs this frequency regulation activity after the primary control. Due to CPS nature, the LFC operation is vulnerable to attacks, both from physical and cyber standpoints. The cyber-attack strategies ranges from a variety of attacks such as jamming the network communication, time-delay attack, and false data injection. Motivated by these perspectives, this paper studies the cybersecurity issues of the power systems during the LFC operation, and a survey is conducted on the security analysis of LFC. Various cyber-attack strategies, their mathematical models, and vulnerability assessments are performed to understand the possible threats and sources causing failure of frequency regulation. The LFC operation of two-area power systems is considered as a tutorial example to quantify the vulnerabilities. Mitigation strategies through control theoretic approaches are then reviewed and highlighted for LFC operation under cyber-attack.

Model order reduction using logarithmic assembly technique and second cauer form for power system models

Power system is broad, wide and complex area to do research therefore, for reducing complexity; there is an enormous possibility of simplification of large system in the field of science and technology. Hence, this paper presents a new vital advance methodology a mixed-demand reduction that uses logarithmic assembly technique and the second Cauer form to minimize the order of multivariate feed systems on a large scale power system. Model order reduction technique helps to reduce the large scale power system into an approximate small scale model without changing the inherent property of the actual system. A multi-boundary model of the low-order model is achieved by utilizing new methodology for the aggregation of the pound called the logarithmic mass of the poles, while the numeral coefficients are obtained in the second form of Cauer. The usefulness of the anticipated technique has been tested on two-area power system models.

Lyapunov modal analysis and participation factors applied to small-signal stability of power systems

In large dynamical systems subjected to noise or forced oscillations, disturbances constantly arise over time. In this case, the small-signal stability is determined by the energy of perturbations accumulated in the system. To analyze this perturbation energy, this study proposes a novel physically motivated Lyapunov modal analysis (LMA) framework that combines selective modal analysis with the spectral decompositions of specially chosen Lyapunov functions. Based on this approach, we propose new modal indicators that characterize individual modes and modal interactions in connection with specific state variables. Conventional participation factors characterize the relative contributions of the system modes to the evolution of states. In contrast, the proposed Lyapunov participation factors make similar contributions to the Lyapunov functions that estimate the integral energy associated with the states and signals on an infinite or finite time interval. The Lyapunov modal interaction factors characterize the pairwise interaction between modes in terms of their mutual actions produced in states over time. We prove that the proposed modal indicators characterize the stability of individual modes and resonant modal interactions in linear parameter-varying systems and demonstrate the application of these indicators for the small-signal stability analysis of a two-area power system model. New indicators can be calculated independently for a selected part of the system spectrum and used to quickly assess the behavior of critical modes in large-scale dynamical systems.

AloA Optimized RLNN Controller for LFC of Deregulated Two-Area Power System

AloA Optimized RLNN Controller for LFC of Deregulated Two-Area Power System

Design of combine load frequency and voltage control of power system using optimisation algorithm

Engineers' main concern is the stability of nominal frequency and voltage levels in an electric power system. If these two characteristics deteriorate, the performance and life expectancy of the power system's related equipment would suffer. Because of this, control devices must be set up and installed specifically for a given working environment in order to maintain the frequency and terminal voltage magnitude within acceptable ranges. An effort has been made to develop a fractional-order PID (FOPID) controller for combined frequency and voltage control issues, since the system performance may be enhanced by choosing an appropriate controller. Utilizing moth flame optimization method, this study examines the plan and execution of the FOPID controller for simultaneous load frequency and voltage management of the power system. An AVR-based excitation voltage control system is used in the first portion of the study to illustrate the suggested method for frequency stabilization of isolated power systems. Comparison of dynamic responsiveness of the system with PID controllers improved by other intelligent methods reveals the superiority and efficacy of the suggested methodology. Also included in this project are multi-unit two-area power systems. The algorithm's capacity to be tuned is examined in great detail and compared to other algorithms.

Intelligent monitoring of small sized supercapacitor in wind-penetrated power system

This work pertains to integration of supercapacitor energy storage system with wind-penetrated two-area power system. A small-sized SCES device is incorporated in automatic generation control of each area. For robust and optimum usage of SCES, a customized neuro-adaptive predictive control model is developed using level 1 S-function coding. This model feeds reference power command to the converter of storage system with its voltage used as feedback loop. A step load disturbance reflecting load outage is introduced back to back in the power system. It is seen through simulation studies carried out in MATLAB Simulink Environment that there is significant improvement in frequency response and tie power deviations. SCES converter power limits are fully maintained; besides, SCES voltage and power response is also depicted.

An intelligent Hybrid Wind–PV farm as a static compensator for overall stability and control of multimachine power system

The paper demonstrates the potential of a hybrid Wind–PV farm as STATCOM (Static Synchronous Compensator) for damping and control of overall chaotic oscillations in a two-area power system. Kundur’s modified system associated with a hybrid Wind–PV farm is simulated in MATLAB to demonstrate the detailed performance assessment of the hybrid farm. A unique controller is deployed to regulate the AC and DC currents of the STATCOM using two PI controllers. A swarm-based hybrid metaheuristic optimizer PSO-BFOA optimally tunes and controls the PI controller parameters. The system is compensated to an optimum level of 85% and exposed to a 3-phase fault. Zero dampings are accompanied by additional disturbances of a 20% change in electric torque and the reference voltage. The disturbances are made to model the worst conditions to examine the rigorous performance of the hybrid Wind–PV​ farm in alleviating the overall chaotic oscillations. The simulation results for the performance assessment in different cases, i.e., without control, with Wind-STATCOM, with PV-STATCOM, and with the hybrid Wind–PV-STATCOM, reveal the potential of the hybrid Wind–PV Farm as STATCOM in alleviating the overall chaotic oscillations.

Novel load frequency control scheme for an interconnected two-area power system including wind turbine generation and redox flow battery

This paper presents a novel load frequency control (LFC) model for an interconnected thermal two-area power system in the presence of wind turbine generation and redox flow battery (RFB). The study model includes frequency and voltage excitation loops with needed interactions between them along with the power system stabilizer. A two-degree of freedom (2DOF)-based controller called 2DOF-Hybrid controller is developed as secondary controller in automatic generation control (AGC) to adjust the power outputs of generator and RFB. Also, the dynamic performance of the proposed controller in the RFB loop is evaluated. The Hybrid controller comprises a fractional-order proportional-integral-derivative (FOPID) controller and a tilt-integral-derivative (TID) controller. In order to obtain accurate and realistic results, the outputs of thermal power plants are restricted by considering the limitations of the governor dead-band and generation rate constraint. Since the controller performance depends on its parameters, these parameters are optimized using a modified sine–cosine algorithm (MSCA). The dynamic performance of the proposed 2DOF-Hybrid controller as secondary controller of the AGC loop is compared with integral-double-derivative (IDD), integral-tilt-derivative, proportion-integral–derivative (PID)-DD, 2DOF-PID, 2DOF-TID, and 2DOF-FOPID ones under different scenarios. In addition, the superiority of the MSCA is compared with benchmark metaheuristic methods including an SCA, a genetic algorithm, a particle swarm optimization, and a differential evolution. The sensitivity analysis is also carried out to show the robustness of the proposed controller versus the changes of the parameters. The simulation studies on two-area and New England 39-bus power systems are carried out to examine the advantage of the presented LFC scheme. A range of power system signals such as frequencies of areas, terminal voltages, and tie-line power flow is demonstrated to compare the controllers. The results disclose that the proposed LFC scheme provides better dynamic performance compared to other ones. Moreover, RFB modeling based on the proposed controller is superior to conventional RFB modeling in reducing the amplitude of the oscillations.

A novel sine augmented scaled sine cosine algorithm for frequency control issues of a hybrid distributed two-area power system

In this research article, a novel approach is proposed by considering the sine augmented scaled sine cosine (SAS-SCA) Algorithm for the load frequency control of multi-area hybrid distributed power system. The said modified algorithm is tested by comparing it with the standard SCA algorithm by considering various standard benchmark test functions. Further, the genuine utilization of the SAS-SCA algorithm is tried by planning a TID controller for frequency regulation of the distributed system which consists of various renewal energy sources. The supremacy of the SAS-SCA-based TID controller is introduced by comparing its outcomes and other modern soft computing techniques, and it is observed that the SAS-SCA-based TID controller is more effective compared to the conventional controller.

A novel Quasi Opposition based controller design for hybrid AGC considering renewable energy and excitation cross coupling effect

ABSTRACT The paper addresses the Automatic Generation Control (AGC) of the interconnected two-area power system under deregulated environment. Area 1 includes thermal system, Distributed Generation (DG), and aggregate Electric Vehicle (EV) wheras area 2 contains thermal system, gas system, and aggregate electric vehicle. Nonlinearities such as Generation Rate Constraint (GRC), Governor Dead Band (GDB), Boiler Dynamics (BD), and Communication Delay (CD) are explored in the proposed test system to achieve a realistic approach. For considering cross coupling effect between excitation system and AGC, an exact model has been investigated. A novel cascade controller has been proposed to achieve the desired goal. For optimum values of controller, new novel Quasi Opposition Lion Optimization Algorithm (QOLOA) has been suggested and implemented for the studied system. The sensitivity analysis study was performed to assess the robustness of the proposed controller by varying system parameters. System parameters are varied by ±25%, ±35%, and ±50%, and the result shows the robustness of the controller and algorithm For the proposed controller, Figure of Demerit (FOD) is compared with various algorithms, and the Integral square Error (ISE) is taken as objective function. The optimistic results shows the effectiveness & superiority of proposed AGC in different scenario of deregulation.

Hybrid Gravitational–Firefly Algorithm-Based Load Frequency Control for Hydrothermal Two-Area System

The load frequency control (LFC) and tie-line power are the key deciding factors to evaluate the performance of a multiarea power system. In this paper, the performance analysis of a two-area power system is presented. This analysis is based on two performance metrics: LFC and tie-line power. The power system consists of a thermal plant generation system and a hydro plant generation system. The performance is evaluated by designing a proportional plus integral (PI) controller. The hybrid gravitational search with firefly algorithm (hGFA) has been devised to achieve proper tuning of the controller parameter. The designed algorithm involves integral time absolute error (ITAE) as an objective function. For two-area hydrothermal power systems, the load frequency and tie-line power are correlated with the system generation capacity and the load. Any deviation in the generation and in the load capacity causes variations in the load frequencies, as well as in the tie-line power. Variations from the nominal value may hamper the operation of the power system with adverse consequences. Hence, performance of the hydrothermal power system is analyzed using the simulations based on the step load change. To elucidate the efficacy of the hGFA, the performance is compared with some of the well-known optimization techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), gravitational search algorithm (GSA) and the firefly algorithm (FA).

Load Frequency Control Using Hybrid Intelligent Optimization Technique for Multi-Source Power Systems

The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.