Power System Controller Research Articles

Mobile base station site as a virtual power plant for grid stability

Energy grids and markets are in transition. Increased use of renewable energy sources (RES) introduces new stability challenges for power grids. Despite the substantial electrical consumption of mobile networks, they are yet to harness their inherent flexibility for aiding in the stability of the power grid. A noticeable research gap exists concerning measuring full activation time for fast frequency reserve (FFR) product while using batteries from mobile network base stations. Our objective is to demonstrate that mobile operators could use their existing infrastructure to participate in the reserve market of a contemporary power grid. Furthermore, it seeks to determine if the full activation time can meet the requirements of an FFR product. The system consists of a live mobile base station site with a mobile connection to the site, local controller, an existing battery, and a power system that, in combination, can function as part of a power grid balancing system. Our main finding indicates that the rectifier reaction time within an installed base station site infrastructure ranges from 5 to 8 s, and the time when the base station is entirely off from the grid varies from 7 to 10 s. This finding is significant since the activation time is too long for the base station power system controller to be used for FFR. The required full activation time for FFR is less than 1.3 s. In conclusion, power system vendors should investigate improvements for their equipment and software products to enable fast reserve market entry for their existing customers and stay competitive.

A novel artificial intelligence based multistage controller for load frequency control in power systems

The imbalance between generated power and load demand often causes unwanted fluctuations in the frequency and tie-line power changes within a power system. To address this issue, a control process known as load frequency control (LFC) is essential. This study aims to optimize the parameters of the LFC controller for a two-area power system that includes a reheat thermal generator and a photovoltaic (PV) power plant. An innovative multi-stage TDn(1 + PI) controller is introduced to reduce the oscillations in frequency and tie-line power changes. This controller combines a tilt-derivative with an N filter (TDn) with a proportional-integral (PI) controller, which improves the system’s response by correcting both steady-state errors and the rate of change. This design enhances the stability and speed of dynamic control systems. A new meta-heuristic optimization technique called bio-dynamic grasshopper optimization algorithm (BDGOA) is used for the first time to fine-tune the parameters of the proposed controller and improve its performance. The effectiveness of the controller is evaluated under various load demands, parameter variations, and nonlinearities. Comparisons with other controllers and optimization algorithms show that the BDGOA-TDn(1 + PI) controller significantly reduces overshoot in system frequency and tie-line power changes and achieves faster settling times for these oscillations. Simulation results demonstrate that the BDGOA-TDn(1 + PI) controller significantly outperforms conventional controllers, achieving a reduction in overshoot by 75%, faster settling times by 60%, and a lower integral of time-weighted absolute error by 50% under diverse operating conditions, including parameter variations and nonlinearities such as time delays and governor deadband effects.

Load frequency and virtual inertia control for power system using fuzzy self-tuned PID controller with high penetration of renewable energy

Reliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.

Optimization of dual-stage controllers in renewable energy sources-based interconnected power systems through refinement of the African Vultures Optimization Algorithm

With an emphasis on a three-region system, this paper explores automaticgeneration control within a conventional framework. Area 2 uses thermal-thermal sources, Area 3 uses gas-thermal sources, and Area 1 uses thermal-biodiesel sources. IPD(1 + I) is a unique cascade controller that combines integral-proportional-derivative (IPD) components with one-plus-integral (1 + I) components. To find the best gains and settings for the IPD(1 + I) controller, a meta-heuristic method called the African Vulture Optimization Algorithm (AVOA) is applied. Reducing the integral squared error (ISE), as determined by the performance index (Pi), is the main goal. The performance of the IPD(1 + I) controller is evaluated by contrasting it with a range of secondary controllers. Hybrid Peak Area-ISE (hPA-ISE), a novel performance metric, is also assessed in addition to the conventional ISE. The incorporation of renewable energy sources, such solar thermal energy and solid oxide fuel cells, significantly improves the system’s performance. Interestingly, under normal conditions, the IPD(1 + I) controller’s parameter settings continue to work for both sinusoidal and random load situations, obviating the need for additional tuning.

Optimization of shafting and excitation dual damping controller for combined pumped storage and wind power system

Optimization of shafting and excitation dual damping controller for combined pumped storage and wind power system

Dandelion Optimizer-Based Frequency Controller for a Hybrid power systems Considering Renewables

Dandelion Optimizer-Based Frequency Controller for a Hybrid power systems Considering Renewables

Observer-based single phase robustness load frequency sliding mode controller for multi-area interconnected power systems

Observer-based single phase robustness load frequency sliding mode controller for multi-area interconnected power systems

Novel hybrid arithmetic-equilibrium optimizer as robust tuner for power system stabilizer (PSS) parameters in interconnected electrical power system

Nowadays, the metaheuristic approaches become popular in the optimal tuning parameters of power system controllers, especially Power System Stabilizers (PSS). Besides the easiness, the stochastic behavior in metaheuristic approaches needs further investigation to increase the quality of the offered optimal parameters. Thus, this paper proposes A Novel Hybrid Arithmetic-Equilibrium Optimizer (HAEO) as a robust tuner for PSS in the interconnected electrical power system. The algorithms are hybridized in unique ways to utilize the features in both Arithmetic Optimizer (AO) and Equilibrium Optimizer (EO). Hybridization is performed to improve the exploration ability by providing the widest optimal parameters possible. In benchmarking by the Friedman Ranking Test, the proposed algorithm shows a significant improvement in consistency and accuracy over the basic AO and EO. A wider-distributed optimal solution and better convergence are also offered by the proposed algorithm. Besides that, the application in the optimal tuning shows that the proposed algorithm can provide the best PSS parameters in different objective functions with better average and standard deviation values. The HAEO-PSS gives a significant overshoot suppression of 35 % to 57 %, while the settling time of the system is also improved by 7 % to 13 %. Overall, the HAEO-PSS is proven to improve the stability response of the interconnected electrical power system both in local and interarea responses.

Innovative design for enhancing transient stability with an ATFOPID controller in hybrid power systems

In order to make multi-area power systems more efficient and stable, this study aims to create a new intelligent controller design based on adaptive property. The integrated power grid becomes more unstable as renewable energy sources become more prevalent, and system inertia decreases. Resultant, the penetration of renewable and load variance causes frequency changes to be more pronounced. Keeping frequency stable is essential for the electrical energy transmission system to work reliably and efficiently. This paper presents an adaptive design based on the tilt fractional order proportional integral derivative (TFOPID) controller by using an optimized property of bio inspired tunicate search algorithm (TSA). The gains of the TFOPID controller are continuously updated by utilizing the ancillary TPID controller, which is dependent on changes in area frequency. The suggested control system is implemented in the northern and southern regions, which consist of photovoltaic (PV), wind, redox flow battery (RFB), hydro, and thermal power plants. Therefore, this study contributes in the creation of more resilient and efficient power grids, which are critical for incorporating renewable energy sources and satisfying the increasing need for environmentally friendly power solutions.

Power Control Based on a Proportional-dual Integral Controller for Multi-rotor Wind Power Systems Using Genetic Algorithm

Power Control Based on a Proportional-dual Integral Controller for Multi-rotor Wind Power Systems Using Genetic Algorithm

Improving load frequency controller tuning with rat swarm optimization and porpoising feature detection for enhanced power system stability

Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as “Porpoising”. This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems.

Utilization and performance comparison of several HESSs with cascade optimal-FOD controller for multi-area multi-source power system under deregulated environment

Electric power system network is evolving rapidly towards a more flexible, robust and secured interconnected network which can provide better power quality within a competitive environment commonly known as deregulated power system (DPS). For safeguarding DPS, energy storage systems (ESSs) and an efficient control method are required to maintain equilibrium between load demand and generated power known as automatic generation control (AGC). This study highlights an attempt of performance comparison of several hybrid ESSs (HESSs) for AGC of multi-area multi-source power system under deregulated scenario. The considered system comprises of thermal, hydro and gas (THG) generating units in each area of a two-area DPS. A cascade optimal controller-fractional order derivative (COC-FOD) is suggested for AGC performance advancement and its performance is compared with optimal controller (OC). A nature inspired salp swarm algorithm (SSA) is utilized to optimise secondary FOD controller gains while primary OC is designed via full state feedback control approach. The time domain analysis and bode plot reveal the eminence of COC-FOD over OC by returning 66 % lower value of cost function, 24 % lower undershoot, 98 % lower overshoot, at least 24 % lesser rise time for area frequency deviation, and 30 % greater stability margins with comparable settling time and lesser number of oscillations. The performances of various HESSs like ultra-capacitor (UC) + redox flow battery (RFB), superconducting magnetic energy storage (SMES) + RFB and flywheel energy storage (FES) + RFB in the attendance of OC and COC-FOD controller are compared and performance of COC-FOD in the presence of SMES+RFB is found superior to other HESS combinations by giving 75 % lesser value of cost function and at least 43 % lesser undershoot for area frequency deviation compared to the scenario where COC-FOD is simulated without ESSs. To present a more realistic scenario, system nonlinearities are considered. Finally, COC-FOD controller proved its resilience and efficacy by providing satisfactory stabilized performance under wide variations in the system parameters.

A hybrid SCSO-QNN approach based load frequency control of three area power system with renewable sources using FOPID controller

ABSTRACT This research proposes a hybrid SCSO-QNN method for designing a FOPID controller in a three-area power system to regulate load frequency. The proposed hybrid approach combines quantum neural networks with sand cat swarm optimization, and it is commonly named as the SCSO-QNN method. The proposed method’s primary goal is to overcome load disturbances and attain the intended output of the interconnected system. The SCSO generates controller parameters tuned by the FOPID controller, providing a significant improvement in performance, while the QNN is employed to forecast the optimal control signal of the converter. Reducing steady-state errors and improving system stability are areas where the FOPID performs better than a traditional integer-order PID controller. Moreover, SCSO-QNN optimization technique exhibits excellent convergence properties, resulting in improved control performance. The proposed method ensures system frequency control by controlling frequency deviation and power fluctuations in the connect line during load disturbances. The proposed method is executed on the MATLAB platform and contrasted with current techniques. The proposed technique outperforms all current techniques, including ABC-FOPID, Genetic Algorithm, and Artificial Bee Colony PID. The proposed method Area control error is 1.8%, which is less than other existing methods.

A Novel Sea Horse Optimizer Based Load Frequency Controller for Two-Area Power System with PV and Thermal Units

This study introduces the Sea Horse Optimizer (SHO), a novel optimization algorithm designed for Load Frequency Control (LFC) in two-area power systems including photovoltaic and thermal units. Inspired by the interactive behaviors of seahorses, this population-based metaheuristic algorithm leverages strategies like Brownian motion and Levy flights to efficiently search for optimal solutions, demonstrating quicker and more stable identification of global and local optima than traditional algorithms. The proposed SHO algorithm was tested in a two-region power system containing a photovoltaic system and a reheat thermal unit under three different scenarios. In the first scenario, the frequency response of the algorithm to a 0.1 p.u. load change in both regions was examined. In the second scenario, the algorithm's frequency response to sudden load changes from 0.1 p.u. to 0.4 p.u. was tested. Finally, the algorithm's frequency response was examined against different levels of solar irradiance for sensitivity analysis. This study compared the performance of the SHO-optimized controller with the optimization algorithms reported in the literature, including the Genetic Algorithm (GA), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Modified Whale Optimization Algorithm (MWOA). In this context, the optimization of PI controller gain parameters based on the ITAE metric resulted in SHO algorithm achieving the best performance with values of 2.5308, followed by WOA at 4.1211, FA at 7.4259, and GA at 12.1244. In tests, SHO significantly outperformed these algorithms in key performance metrics, such as Settling Time, Overshoot (M+), and Undershoot (M-). Specifically, SHO achieved 98.94% better overshoot and 85.25% reduced undershoot than GA, and concluded settling times 52.79% faster than GA in the first scenario. Similar superior outcomes were noted in subsequent tests. These results underline SHO's efficacy in enhancing system stability and control performance, marking it as a significant advancement over conventional LFC methods.

Development of a novel model matching decentralized controller design algorithm and its experimental validation through load frequency controller implementation in restructured power system using TMS320F28379D controlCARD

A three-stage decentralized controller design algorithm is developed to achieve setpoint tracking and disturbance rejection in MIMO systems with communication time delay, and nonlinearities such as saturation and dead band. The first stage involves reference model formulation. The second stage comprises equating approximate generalized time moments/approximate generalized Markov parameters of closed-loop system model with reference model at certain expansion points in s-plane to obtain synthesis-like equation, concerning products of unknown controller numerator and denominator polynomials. This process yields simultaneous linear equations, whose solution provides the coefficients of the product polynomials. In the third stage, the controller parameters are extracted from the product polynomials using exact model matching. The proposed method is illustrated by designing load frequency controller in traditional and restructured power systems under scenarios like system parameter uncertainties, random load variation, and time-varying communication delays. Simulation studies reveal the efficacy of the proposed technique over existing techniques. Furthermore, the practical implementation feasibility of the decentralized controller designed for the restructured power system is validated using the TMS320F28379D controlCARD.

Intermittent data communication loss resistant state feedback wide area damping controller for interconnected power system

Intermittent data communication loss resistant state feedback wide area damping controller for interconnected power system

Extended state observer-based primary load frequency controller for power systems with ultra-high wind-energy penetration

In this paper, a novel extended state observer-based (ESO) load frequency control is implemented. Specifically, the proposed control law focuses on the incorporation of wind energy injection as one of the disturbances, treating it as an additional state within the system. The proposed ESO is designed to estimate both the system states and the net disturbance, thereby enhancing its ability to regulate the overall load frequency performance. The proposed control strategy hinges on the judicious selection of control gains and disturbance gain. The estimated disturbance is then effectively compensated to regulate the load frequency. To evaluate the efficacy of the proposed controller, tests are conducted on both single and three area systems. The results demonstrate superior performance, even under conditions involving load and parameter variations.

Fractional-Order Sliding Mode Load Frequency Control and Stability Analysis for Interconnected Power Systems With Time-Varying Delay

The universal use of open communication networks in interconnected power systems has changed the constant delay in the formerly dedicated channels into time-varying delay. The delay will deteriorate the dynamic performance of the load frequency control and lead to system frequency instability. This paper presents a fractional-order sliding mode controller for interconnected power systems with time-varying delay and performs stability analysis based on the Lyapunov direct method. Firstly, fractional-order integral sliding mode control is used to improve the stability of interconnected power systems containing time-varying delay. This expands the delay margin by taking advantage of the scalability of the fractional-order to the sliding mode surface. Then, the Riemann-Liouville fractional-order Lyapunov functional is introduced to stability analysis. This reduced the conservatism of the stability analysis by adjusting the upper limit of the functional integral. Lastly, combining Lyapunov's theory and integral inequality to obtain the stability conditions of the time-varying delay power system described by linear matrix inequality, and bring the stability margin of the system. The simulation results show that the designed controller can guarantee that the maximum delay time of the system is less than a predefined upper limit. The proposed control strategy obtains a larger stability margin than the other four scenarios. Additional simulation study was also conducted to verify its robustness to load variations and wind fluctuations.

Robust design of damping controller for power system using a combination of snake optimisation algorithm and optimal control theory

Robust design of damping controller for power system using a combination of snake optimisation algorithm and optimal control theory

Robust design of damping controller for power system using a combination of snake optimization algorithm and optimal control theory

Robust design of damping controller for power system using a combination of snake optimization algorithm and optimal control theory