Superconducting Magnetic Energy Storage Controller Research Articles

The Regulation of Superconducting Magnetic Energy Storages with a Neural-Tuned Fractional Order PID Controller Based on Brain Emotional Learning

Intelligent control methodologies and artificial intelligence (AI) are essential components for the efficient management of energy storage modern systems, specifically those utilizing superconducting magnetic energy storage (SMES). Through the implementation of AI algorithms, SMES units are able to optimize their operations in real time, thereby maximizing energy efficiency. To have a more advanced understanding of this issue, DynamoMan is presented in this paper. For use with SMES systems, DynamoMan, an Artificial Neural Network (ANN)-tuned Fractional Order PID Brain Emotional Learning-Based Intelligent Controller (ANN-FOPID-BELBIC), has been developed. ANN tuning is employed to optimize the key settings of the reward/penalty generator of a BELBIC, which are important for its overall efficacy. Following this, DynamoMan is integrated into the SMES control system and compared to scenarios in which a BELBIC, PID, PI, and P are utilized. The findings indicate that DynamoMan performs considerably better than other models, demonstrating robust and control attributes alongside a considerably reduced period of settling time, especially when incorporated with the power grid.

Fractional order control strategy for superconducting magnetic energy storage to take part effectually in automatic generation control issue of a realistic restructured power system

Keeping frequency regulation (FR) while fulfilling bilateral contracts and pool-co transactions simultaneously in a competitive environment become a challenging task for solitary automatic generation control (AGC) owing to inherent nonlinear limitations like generation rate constraint (GRC), governor dead band (GDB), and communication time delay (CTD) in realistic power systems. An effective solution to help AGC is employing rapid-response energy storage devices (ESDs) like superconducting magnetic energy storage (SMES). It is an appropriate choice for applications like AGC which need to deliver a large amount of power within such little time. Although employing the SMES in the AGC issue has been addressed widely in literature, however, the considered SMES models have fundamental weak points which are explained thoroughly in this paper. A comprehensive literature review reveals that the lack of an efficient control strategy implemented on the real dynamics of the SMES unit remains unsolved and needs further investigation. In this paper, a maiden supplementary control approach based on fractional order controllers (FOCs) is put forward for the SMES per its real dynamics to take part effectually in the deregulated AGC. The concurrent design of the proposed SMES and AGC is treated as a nonlinear minimization issue wherein an appropriate performance index is minimized utilizing different meta-heuristic algorithms. To achieve realistic and reliable results, a fully competitive two-area various-GENCOs multiple-DISCOs power system is taken into consideration for examinations with taking into account the nonlinearity sources of GDB and GRC. Detailed investigations are conducted under different contract violation scenarios and different CTDs to assess the competency of the suggested SMES model. The robust behavior of the proposed SMES-AGC coordinated control is verified using sensitivity analyses. The simulation results confirm the superiority of the presented SMES model and control strategy over the former models to enhance AGC performance.

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.

Artificial electric field algorithm based superconducting magnetic energy storage system for damping inter area oscillations

This paper presents an artificial electric field algorithm (AEFA) based optimal control of superconducting magnetic energy storage (SMES) system for damping inter area oscillations. The artificial electric field algorithm is based upon Coulomb’s law of electrostatics and Newton’s law of motion. The proposed technique is used to optimize the SMES controller parameters for effective damping of inter area oscillations. Because of the potential for system damage and the subsequent constraints on power transmission along select lines, low frequency inter-area oscillations have been identified as a serious problem in utility systems. With the advancement in power electronics technologies, a new option for damping of power system oscillations has been created by integrating energy storage devices to it. The proposed scheme is tested on a two area interconnected power system which is suitably modelled and it’s modal analysis is done. The participation of each area towards the inter area mode is analyzed by using participation matrix and the suitable placement of SMES units is decided accordingly. MATLAB Simulations are carried out to prove the efficacy of proposed scheme. The simulation results show that proposed SMES controller along with the incorporation of SMES units to each generator bus effectively damps out the inter area oscillations.

Research on M-SMES Temperature Equilibrium Control Strategies Considering State Assessment

In the future, superconducting magnetic energy storage (SMES) technology will be developed toward large capacity. Among them, a single magnet is expensive and technically challenging. The integration of multiple SMES (M-SMES) is a feasible technical route. But there is coordination among multimodule energy storage magnets, so the formulation of its control strategy is very important. In this article, based on the toroidal magnet, a state evaluation method of toroidal magnet considering the withstand voltage and temperature of the magnet is proposed. To solve the problem of power distribution caused by the difference in initial capacity, initial temperature, and maximum output power during the cooperative control of multiple SMES, a temperature equilibrium control strategy for energy storage magnets combined with state assessment is proposed. System simulation results showed that this strategy could effectively balance the working state of each module and maximize the power output capability of the energy storage magnet by ensuring the safety of the magnet.

PV/Wind-Integrated Low-Inertia System Frequency Control: PSO-Optimized Fractional-Order PI-Based SMES Approach

A paradigm shift in power engineering transforms conventional fossil fuel-based power systems gradually into more sustainable and environmentally friendly systems due to more renewable energy source (RES) integration. However, the control structure of high-level RES integrated system becomes complex, and the total system inertia is reduced due to the removal of conventional synchronous generators. Thus, such a system poses serious frequency instabilities due to the high rate of change of frequency (RoCoF). To handle this frequency instability issue, this work proposes an optimized fractional-order proportional integral (FOPI) controller-based superconducting magnetic energy storage (SMES) approach. The proposed FOPI-based SMES technique to support virtual inertia is superior to and more robust than the conventional technique. The FOPI parameters are optimized using the particle swarm optimization (PSO) technique. The SMES is modeled and integrated into the optimally designed FOPI to support the virtual inertia of the system. Fluctuating RESs are considered to show the effectiveness of the proposed approach. Extensive time-domain simulations were carried out in MATLAB Simulink with different load and generation mismatch levels. Systems with different inertia levels were simulated to guarantee the frequency stability of the system with the proposed FOPI-based SMES control technique. Several performance indices, such as overshoot, undershoot, and settling time, were considered in the analysis.

Voltage and Frequency Control of Balanced/Unbalanced Distribution System Using the SMES System in the Presence of Wind Energy

This paper presents an effective solution to overcome the problem caused by intermittent energy sources that are connected to a balanced/unbalanced distribution system using a superconducting magnetic energy storage (SMES) system by mitigating the voltage and frequency fluctuations during wind gusts. The fuzzy logic control technique (FLC) is used with SMES to improve the voltage and frequency. A squirrel cage induction generator (SCIG) is applied as the wind energy generator. The IEEE 33-bus distribution system is used to validate the proposed method. Buses 18 33 are the weakest points in this system; thus, the wind and SMES systems are connected to the system at these buses. We used MATLAB/Simulink to simulate the performance of the IEEE 33-bus system (balanced/unbalanced) considering the SMES, wind system, and fuzzy logic control (FLC). The simulation results show the high performance of the proposed control method to alleviate the voltage and frequency fluctuation and achieve the power leveling strategy of the studied system.

High-Level Renewable Energy Integrated System Frequency Control with SMES-Based Optimized Fractional Order Controller

The high-level penetration of renewable energy sources (RESs) is the main reason for shifting the conventional centralized power system control paradigm into distributed power system control. This massive integration of RESs faces two main problems: complex controller structure and reduced inertia. Since the system frequency stability is directly linked to the system’s total inertia, the renewable integrated system frequency control is badly affected. Thus, a fractional order controller (FOC)-based superconducting magnetic energy storage (SMES) is proposed in this work. The detailed modeling of SMES, FOC, wind, and solar systems, along with the power network, is introduced to facilitate analysis. The FOC-based SMES virtually augments the inertia to stabilize the system frequency in generation and load mismatches. Since the tuning of FOC and SMES controller parameters is challenging due to nonlinearities, the whale optimization algorithm (WOA) is used to optimize the parameters. The optimized FOC-based SMES is tested under fluctuating wind and solar powers. The extensive simulations are carried out using MATLAB Simulink environment considering different scenarios, such as light and high load profile variations, multiple load profile variations, and reduced system inertia. It is observed that the proposed FOC-based SMES improves several performance indices, such as settling time, overshoot, undershoot compared to the conventional technique.

Constrained neural adaptive predictive control of SMES for dynamic performance improvement of power systems

For dynamic performance improvement of modern power systems, the use of fast acting energy systems like superconducting magnetic energy storage (SMES) is imperative. In this paper, incorporation of a small rating SMES in a solar and wind power penetrated multi-area power system is proposed. A non-linear neural adaptive predictive controller is used to generate an optimal power command taking into account the converter rating and energy level constraints of SMES unit. The SMES is represented by a control relevant model comprising of a first order lag compensator cascaded by an integrator to translate the hardware constraints pertaining to its coil current into modified power constraints. Moreover for avoiding the sudden SMES outage, the power thresholds are forcibly varied as the SMES current reaches near its maximum and minimum values. The uprightness of the designed scheme is illustrated by simulation studies performed on a three area power system.

Performance of static synchronous series compensator and superconducting magnetic energy storage controllers for frequency regulation in two area hybrid wind‐thermal power system using Cuckoo Search Algorithm

AbstractTo improve the management of limited real power resources, the controller parameters of flexible alternating current transmission systems (FACTs) and energy storage systems (ESS) may be simultaneously tuned to optimize the frequency regulation in a two‐area thermal power system penetrated by wind power. In this regard, superconducting magnetic energy storage (SMES) and static synchronous series compensator (SSSC), are operated together in different combinations in a two‐area power system. The controller parameters of SMES and SSSC placed in the system are optimized by means of Cuckoo Search Algorithm (CSA). Among the two, SMES is found to provide the best dynamic performance. The performance of coordinated controllers of SMES in both the areas supported by wind power from a double‐fed induction generator (DFIG), is also tested. In the event of any load perturbation, the dynamic performance of the controllers is analyzed with the sole participation of the DFIG and with the simultaneous operation of DFIG and SMES in both areas. Finally, time and frequency domain performance indices are reported and discussed.

Modeling and Control of Power System Containing PV System and SMES using Sliding Mode and Field Control Strategy

Although the great advance in power system production and operation, storage energy technologies and its control techniques can be considered as one of the most important and critical topics of power companies , government and consumers, especially when the power system containing renewable source and storage system simultaneously. In this paper, a novel electrical grid structure including photovoltaic system and storage system based on Superconducting Magnetic Energy Storage (SMES) has been proposed and investigated. The SMES produced power is injected in power system during specific time or when it required. Two control strategies for exchanged power Grid- SMES have been proposed and analyzed, the first uses sliding mode and the second uses field oriented control based on PI controller, also the injected SMES power is controlled by PID controller. In addition, the photovoltaic system operates at the MPP employing PID MPPT method. The proposed control strategies have been tested successfully in which many scenarios have been studied: standby and discharging of SMES, injection of SMES storage energy for variable and constant load and control of grid containing PV system. In addition, a comparative study of exchanged power Grid- SMES control using Sliding mode and field oriented control based on PI controller has been presented and discussed.

Design modeling, and control of multi-stage SMES integrated with PV system

Abstract Superconducting Magnetic Energy Storage (SMES) is an electrical storage device. It stores the available energy in the form of electromagnetic fields. The features of SMES include relatively high-power density, fast response time, very quick full discharge time, high cycle efficiency, and long lifetime. SMES is suitable for short-term storage in power and energy system applications so that it can play an important role in the increased use of intermittent renewable energy. This paper presents modeling and design of a high-efficiency multi-stage SMES system supplied by either a photovoltaic (PV) source or Constant Voltage Source (CVS). The multi-staging process is a new concept for extending the SMES discharging time and improving their efficiency. The system is proposed as a standalone hybrid system that stores the PV energy in the form of an electromagnetic field. At power interruptions, the system can supply the connected loads for a specified period depending on the number of stages. The proposed model is simulated in Matlab/Simulink environment. The paper presents a comparative study between single and multi-stage SMES systems supplied by either PV or CVS source. The obtained results show that: increasing the number of stages leads to an increase in the system efficiency with a high increase in discharging time. The proposed system can be used as Uninterruptable Power Supply (UPS) for isolated or grid-connected applications.

SFCL-SMES Control for Power System Transient Stability Enhancement Including SCIG-based Wind Generators

The resolution of the environment pollution depends on renewable energy sources, such as wind energy systems. These systems face transient and voltage stability issues with wind energy employing fixed-speed induction generators to be augmented with resistive type Superconducting Fault Current Limiter (SFCL) and Superconducting Magnetic Energy Storage (SMES) devices. The use of a combined model based on SFCL and SMES for promoting transient and voltage stability of a multi-machine power system considering the fixed-speed induction generators is the primary focus of this study. Our contribution is the development of a new model that combines the advantages of SFCL and SMES. The proposed model functions assure flexible control of reactive power using SMES controller while reducing fault current using superconducting technology-based SFCL. The effectiveness of the proposed combined model is tested on the IEEE11-bus test system applied to the case of a three-phase short circuit fault in one transmission line.

Enhancement of DFIG LVRT Capability During Extreme Short-Wind Gust Events Using SMES Technology

Wind energy is one of the premier renewable energy sources that have gained popularity during the last decade. Among the currently available wind energy conversion systems (WECS), doubly fed induction generator-based technology has been widely employed due to its superior advantageous. The key features of the DFIG-based WECS include its ability to capture more wind energy and support the grid with large reactive power during short disturbance events. On the other hand, DFIG is very sensitive to grid faults which affect its fault ride through (FRT) capability. Furthermore, extreme wind gust even for short durations may lead to the violation of the low voltage ride through (LVRT) threshold limits set by worldwide transmission line operators. This situation cannot be mitigated by the turbine blades pitch controller since the response of the pitch mechanical control is much slower than the rapid dynamic change in the wind speed during short duration of wind gust events. While DFIG FRT capability has been discussed in several papers in the literature, not much attention was given to the mitigation of the effects of extreme wind gust of short duration on the DFIG performance. In this paper, the effect of various levels of wind gust on the performance of a DFIG-based wind energy conversion grid-connected system is investigated and mitigated using a new controller for superconducting magnetic energy storage (SMES) unit. A combination of hysteresis current and fuzzy logic controllers is employed to control the voltage source converter and the DC-DC chopper interfacing the SMES coil with the investigated system. Simulation results reveal the effectiveness of the proposed SMES controller that can be easily implemented within existing as well as new WECS installations.

Moth-Flame Algorithm for TCSC- and SMES-Based Controller Design in Automatic Generation Control of a Two-Area Multi-unit Hydro-power System

An analysis on automatic generation control (AGC) performance of an interconnected two-area hydro–hydro-power system model is presented in this paper subjected to dynamic control of damped oscillations in the presence of thyristor-controlled series compensation (TCSC) and the superconducting magnetic energy storage (SMES) unit. In real time, the load profile characteristics are un-deterministic in nature. Therefore, the current article studies a diverse prospective of area load profiles [such as step load perturbation (SLP), random SLP and sinusoidal load perturbation] in view of AGC performance analysis. The present work is to improve the dynamic responses and to pursue their significances in damping oscillation after the addition of a fast-acting TCSC (as a damping controller) in area-1, whereas SMES unit is installed in area-2 (to provide large values of energy instantaneously). In the present prospect, a new control strategy based on Taylor theorem is implemented to modify the TCSC controller as well as the two-stage phase-compensating blocks are cascaded to both the TCSC and the SMES to improve the phase lag of the system. In this paper, a nature-inspired optimization paradigm [called moth-flame optimization (MFO) algorithm] is utilized to design the controller gains. Additionally, the robustness of the designed controller is investigated in the event of loaded condition and model parameter uncertainties through sensitivity analysis. Analytically, eigenvalues, performance indices values and transient details are presented in support of the designed MFO–TCSC–SMES controller. The obtained simulation results are compared to genetic algorithm (GA)-based designed GA–TCSC–SMES controller to show the optimizing performance of the MFO algorithm in the controller design. The simulation results showed that after the addition of a TCSC–SMES unit in the studied power system model, in addition to eliminating damped oscillations, the settling times of frequency and tie-line power flow are considerably reduced.

A robust SMES control for enhancing stability of distribution systems fed from intermittent wind power generation

The voltage and frequency stability issues of power systems are the main challenges that arise from high penetration levels of wind energy systems. This paper presents an effective solution for voltage and frequency stability problems by using a superconducting magnetic energy storage (SMES) system controlled with a fuzzy logic controller (FLC). The proposed control system can suppress the voltage and frequency fluctuations due to the high variations of wind speed. In addition, the proposed control system is suitable for both balanced and unbalanced distribution systems with high penetration levels of wind turbines (WTs). A squirrel cage induction generator (SCIG) is selected in the case study, which represents the worst scenario of WT generation from the voltage and frequency stability aspects. This is due to the high reactive power consumed by the SCIG from the utility grid during steady state and voltage fluctuations that may lead to harmful consequences for power system components. The proposed control method is validated using the IEEE 33-bus radial distribution network (RDN), wherein the SMES and WT systems are connected to the weakest points of the RDN. The obtained results demonstrate the superior performance of the proposed FLC-SMES system for alleviating the voltage and frequency fluctuations of the distribution power system during high variations of wind power.

A robust SMES controller strategy for mitigating power and voltage fluctuations of grid-connected hybrid PV–wind generation systems

Recently, hybrid generation systems (HGSs) are considered to be the optimal solution for supplying remote areas with the required electrical power. HGSs contain two or more renewable energy sources (RESs), such as wind power generators (WPGs), photovoltaic (PV) generation systems and the energy of the tides. Due to the intermittent nature of RESs, power and voltage fluctuations have appeared accordingly at the connection point with the utility power grid. Hence, energy storage technologies can play an effective role in mitigating power and voltage fluctuations caused by hybrid PV–wind generation systems. This paper proposes a robust control strategy for superconducting magnetic energy storage (SMES) combined with HGS. Fuzzy logic control (FLC) is used to control the chopper circuit of SMES in order to charge/discharge the active power from/to HGS. The proposed FLC strategy considers the fluctuations due to PV and wind power generation systems and the random load power variation. SMES with the proposed FLC method can effectively mitigate the fluctuation of active and reactive powers transfer between HGS and grid, and it can also regulate the voltage profile of the common connection point of SMES with HGS. Moreover, the system line power loss is clearly reduced by using the SMES system. The complete power system including loads is built in MATLAB/Simulink platform. The simulation results indicate that the proposed FLC–SMES can smooth both power and voltage fluctuations due to uncertain climate changes of PV and WPG systems.

Improvement in Frequency Regulation of Interconnected Thermal Power System with Optimal SSSC and SMES Controllers

The level of real and reactive power reserve with only contribution from rotor inertia of generators provides a very small margin of maneuverability in system operation. The use of energy storage device and “Flexible AC Transmission systems (FACTs)” devices solves the problem to a large extent particularly in larger interconnected system. In this work energy storage devices like “Superconducting Magnetic Energy Storage (SMES)” system and “Static Synchronous Series Compensator (SSSC)”, are operated together in different combined two-area thermal system.

Performance Enhancement of Micro Grid System with SMES Storage System Based on Mine Blast Optimization Algorithm

Frequency control represents a critically significant issue for the enhancement of the dynamic performance of isolated micro grids. The micro grid system studied here was a wind–diesel system. A new and robust optimization technique called the mine blast algorithm (MBA) was designed for tuning the PID (proportional–integral–differential) gains of the blade pitch controller of the wind turbine side and the gains of the superconducting magnetic energy storage (SMES) controller. SMES was implemented to release and absorb active power quickly in order to achieve a balance between generation and load power, and thereby control system frequency. The minimization of frequency and output wind power deviations were considered as objective functions for the PID controller of the wind turbine, and the diesel frequency and power deviations were used as objective functions for optimizing the SMES controller gains. Different case studies were considered by applying disturbances in input wind, load power, and wind gust, and sensitivity analysis was conducted by applying harsh conditions with varying fluid coupling parameter of the wind–diesel hybrid system. The proposed MBA–SMES was compared with MBA (tuned PID pitch controller) and classical PI control systems in the Matlab environment. Simulation results showed that the MBA–SMES scheme damped the oscillations in the system output responses and improved the system performance by reducing the overshoot by 75% and 36% from classical and MBA-based systems, respectively, reduced the settling time by 45% compared to other systems, and set the final steady-state error of the frequency deviation to zero compared to other systems. The proposed scheme was extremely robust to disturbances and parameter variations.

Decentralized robust mixed [formula omitted] reactive power control of DFIG cluster using SMES

This paper proposes a method for developing a decentralized robust mixed H2/H∞ output feedback reactive power control using superconducting magnetic energy storage (SMES) which improves transient stability of the DFIG cluster. The decentralized mixed H2/H∞ output feedback optimal control design provides robustness via optimization of the worst-case quadratic performance of the underlying uncertain cluster system and guarantees that the obtained controller is a locally optimal or on the boundary of the H∞ norm constraint. The proposed controller takes into account the uncertainty of model parameters, which have a significant effect on the stability of the critical modes, which are identified from the critical parameter analysis. The proposed controller also tackles local uncertainties as well as interconnection effects of decentralized systems. The iterative optimization method to synthesize the controller and the steps involved in the controller design for the test system are presented. The performance of the proposed SMES controller is evaluated and the superiority of the designed control method over the conventional PI-based SMES controller is demonstrated.