Superconducting Magnetic Energy Storage Unit 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.

Optimal design and cost of superconducting magnetic energy storage for voltage sag mitigation in a real distribution network

One of the most important aspects of power quality for a distribution network's operation is the voltage sag issue. Simultaneous starting of irrigation motors fed from a distribution network leads to a voltage drop, which degrades the network's power quality. Mitigation of the voltage sag was carried out before by using superconducting magnetic energy storage (SMES) with a pre-defined capacity. The innovation of the present research work is optimal design of SMES including optimal sizing of SMES and its controller parameters with the consideration of its optimal cost for mitigating voltage sag resulting from simultaneous starting of irrigation motors in a real Egyptian distribution network. This is made by minimizing a multi-objective function formulated by a weighted-sum voltage sag and SMES cost. A new optimization technique called Mountain Gazelle Optimizer (MGO) is used to optimize the sizing of fuzzy logic controller (FLC)-SMES as well as the weight factors of multi-objective function. These factors are not free for the decision maker to choose. An actual Egyptian electric distribution network named Karot feeding 16 induction-driven irrigation motors and residential loads, is implemented as a test case study to exhibit the performance of the suggested optimal FLC-SMES strategy in mitigating the voltage sag. For validating the performance of the MGO technique, the obtained results are compared with those determined by particle swarm optimization (PSO). The present results show that the MGO-optimized SMES unit with a capacity of 0.135 MJ and actual cost of 0.2483 M$ successfully mitigated the voltage-sag in the investigated network due to simultaneous starting of the irrigation motors where the voltage never drops below 0.9 p.u. against 0.625 MJ capacity and 0.6934 M$ actual cost for the non-optimized SMES. This corresponds to about 78 % and 64 % reduction in capacity and actual cost of the SMES unit.

Virtual synchronous generator based superconducting magnetic energy storage unit for load frequency control of micro-grid using African vulture optimization algorithm

An isolated microgrid has significant frequency stability issues due to the erratic nature of renewable energy sources, stochastic load behaviour, and low system inertia. Consequently, this paper presents a Virtual Synchronous Generator (VSG) based Superconducting Magnetic Energy Storage (SMES) unit for frequency stability enhancement of microgrid. A newly developed metaheuristic algorithm named African Vulture Optimization Algorithm (AVOA) is used to optimize the proportional-integral controller parameters with the help of the Integral Time Absolute Error criterion. The proposed Load Frequency Control (LFC) model of an isolated microgrid is evaluated against numerous scenarios and well-known algorithms like Genetic Algorithm, Particle Swarm Optimization, Grey Wolf Optimization, and Harris Hawks Optimization. The MATLAB simulation results of the proposed system demonstrate a significant improvement in the dynamic response pertaining to maximum overshoot, settling duration, and frequency oscillations when contrasted to the other algorithms. Also, observed that the LFC system with VSG-based SMES unit improves the frequency response of 77.37 % and 86.56 % in peak overshoot and settling time respectively as compared to the system without energy storage units. Finally, real-time simulations are carried out in OPAL-RT (OP-4500) to validate the proposed system.

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.

Mitigation of voltage sag in a distribution system during start-up of water-pumping motors using superconducting magnetic energy storage: A case study

Voltage sag represents one of the most significant features of power quality in operation of distribution systems. The voltage sag throughout the distribution system caused by simultaneous start-up of water-pumping motors represents a problem, which negatively affects the power quality of the distribution system. The present research work investigates mitigation of voltage sag in a real Egyptian distribution system during simultaneous start-up of many connected water-pumping motors by using superconducting magnetic energy storage (SMES) unit. This calls for a transient analysis of the real distribution system using MATLAB package in order to follow up the temporal variation of the system voltage-profile during motors' start-up. A fuzzy-logic (FL) control strategy of SMES unit is adopted to compose of cascaded control a six-pulse pulse-width modulation (PWM) voltage source converter (VSC) and two-quadrant DC-DC chopper. The proposed fuzzy logic controller (FLC) of SMES unit employs the bus voltage variation and the variation of SMES current as input signals to control the power transfer between the distribution system and SMES. The effectiveness of the proposed FL-controlled SMES unit is validated in a case study of a real distribution feeder named Karot distribution feeder loaded by 16 water-pumps driven by induction motors as well as residential loads. The feeder is part of a distribution system serving the Unified Egyptian Electrical Network. The obtained results indicate that the SMES unit successfully mitigated the voltage sag in Karot distribution feeder due to simultaneous start-up of connected water-pumping motors where the voltage never drops below 0.9 pu.

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.

Enhanced block-sparse adaptive Bayesian algorithm based control strategy of superconducting magnetic energy storage units for wind farms power ripple minimization

This article presents a novel enhanced block-sparse adaptive Bayesian algorithm (EBSABA) to fully control proportional-integral (PI) controllers of superconducting magnetic energy storage (SMES) units. The main goal is to smooth the real power output from grid-tied wind farms (WFs) and enhance its power quality, which represents a significant concern in the industry. In this regard, two WFs are tied to the network and each one is equipped with a SMES unit. The proposed algorithm takes into consideration the effect of actuating error signal and its magnitude to online update the PI controller gains. The proposed control strategy is applied to all power electronic circuit converters. To obtain a realistic work, practical measured data of wind speed that recorded from Hokkaido Island are incorporated into the analyses. Moreover, a three-drive train model represents a turbine model. A practical 10 MW SMES unit is connected at the WFs terminals. The effectiveness of proposed SMES units is verified by comparing its results with those obtained by using the least mean square-PI SMES units and optimal PI SMES units by genetic algorithm under wind speed variability and uncertainty. The real power can be smoothened by more than 10% using the proposed system at some intervals. The validity of the study is tested by the simulation results that are carried out by PSCAD environment. The controlled SMES units can further improve the power quality of WFs.

Application of Fractional Order-PID Control Scheme in Automatic Generation Control of a Deregulated Power System in the Presence of SMES Unit

A fractional order PID (FOPID) control technique for automatic generation control (AGC) in a multi-area power system is presented in this study. To create a reliable controller, a variety of control strategies were used. The load frequency control (LFC) problem in a power system implementing different power transactions, such as bilateral and Poolco transactions, are investigated here. Because any control scheme’s performance is only as good as its parameters, the parameters of the designed control scheme were determined using the big bang big crunch (BBBC) algorithm. Furthermore, in this work, the effect of a superconductive magnetic energy storage (SMES) unit is addressed in the given test (two and four area) systems. When confronted with a fluctuation in immediate load, the SMES unit is thought to follow the initial drop in frequency and tie-line power in order to increase LFC. It is evident that the performance of an FOPID control scheme is improved in the presence of an SMES unit and it provides frequency, tie-line power, change in generation with reduced oscillations and settling time.

Coordination of mode switching control and SMES unit for low‐voltage ride‐through fulfillment of power electronic transformer

AbstractPower electronic transformer (PET) should have a satisfying low‐voltage ride‐through (LVRT) ability to cope with different kinds and severities of voltage sags. To solve the LVRT issue, this paper proposes to coordinate mode switching control (MSC) and superconducting magnetic energy storage (SMES), and it is to realize the targets of promoting voltage recovery, keeping DC‐link stabilization, and alleviating current inrush. First, the theoretical modeling of a PET incorporating with a SMES unit is implemented, and the PET’s fault transient characterizes as well as the SMES’s potential supportability is analyzed. Then, the coordination philosophy of the MSC and the SMES is presented. For that the voltage sag reaches a certain threshold, the MSC is enabled to remove the PET’s normal control, and the SMES collaboratively operates to lower the DC‐link overvoltage, while avoiding the PET damage. In the process of the coordination control handling the voltage transients, a proper reactive current injection is carried out at the PET’s output stage to assist the voltage recovery, and the PET can possess a better LVRT margin. Using MATLAB, a 3 MW PET with an 8 H/1.2 MJ SMES unit is modeled, and different voltage sag scenarios and utilization ways of the MSC and the SMES are simulated. The comparative analysis results show that the proposed approach realizes a satisfying LVRT for the PET.

Fuzzy logic-based integral controller for load frequency control in an isolated micro-grid with superconducting magnetic energy storage unit

In a Micro-Grid (MG), substantial-frequency fluctuations are caused by the sporadic nature of Renewable Energy Sources (RESs). Conventional Integral controllers are incapable of delivering adequate performance against the widely varied operating conditions. Hence, in this paper, the Fuzzy Logic-based Integral controller & Superconducting Magnetic Energy Storage (SMES) unit are incorporated in isolated MG to mitigate the frequency control issues. As a result of rapid changes in load and the discontinuous nature of RESs, frequency oscillations are produced, which are dampened by SMES units. The simulations are done in MATLAB / Simulink software. The results of the simulation reveal that the LFC system with a Fuzzy logic-based Integral controller and SMES unit has greater dynamic performance when compared to other LFC systems (with & without SMES unit using conventional Integral controller).

Automatic Load Frequency Control in an Isolated Micro-grid with Superconducting Magnetic Energy Storage Unit using Integral Controller

Due to depletion of fossil fuels and increasing power demand, employing Renewable Energy Sources (RES) in the form of micro-grid has become very essential. However, the reliable operation and control relies on the intermittent nature of RES and maintaining the frequency within the acceptable limit is the challenging task in isolated micro-grid. This paper presents the Automatic Load Frequency Control (ALFC) in an isolated Micro-Grid (MG) with Superconducting Magnetic Energy Storage (SMES) unit using Integral controller, to enhance the stability of the system frequency. The simulation result shows that the dynamic performance of the proposed system is significantly improved while incorporating the SMES Unit. The simulations are carried out in MATLAB – Simulink.

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.

Design of a 1 MJ/100 kW high temperature superconducting magnet for energy storage

With significant progress in the manufacturing of second-generation (2G) high temperature superconducting (HTS) tape, applications such as superconducting magnetic energy storage (SMES) have become promising for implementation in the electricity grid. Compared to Li-ion batteries, SMES can provide higher power levels at a lower capital cost (down to $200/kW), they have a longer lifetime (over 20 years) and a comparable cycle efficiency of over 95%. Nevertheless, SMES technology is still underdeveloped, which is reflected in the lack of large-scale 2G HTS SMES units. One of the main challenges is designing an optimal magnet that can persistently store energy while withstanding the forces arising from the magnetic field and maintaining a temperature below the critical transition value. This paper outlines a methodology of designing a 2G HTS SMES, using Yttrium-Barium-Copper-Oxide (YBCO) tapes operating at 22 K. The target storage capacity is set at 1 MJ, with a maximum output power of 100 kW. The magnet consists of a stack of double pancake coils designed for maximum storage capacity, using the minimum tape length. The properties of a commercial YBCO tape published in the literature are used to derive the equations for scaling the critical current with magnetic field intensity and direction, as well as operating temperature. The inductance of the resulting coil configuration is calculated analytically, and is used for estimating the total storage capacity of the magnet. Finally, the properties of the superconducting magnet are summarised in a table.

Effect of Superconducting Magnetic Energy Storage on Two Agent Deregulated Power System Under Open Market

Abstract This article addresses the impact of SMES (superconducting magnetic energy storage) on two agent restructured power system under open market. In order to have better analysis regarding LFC problems inside restructured power systems, the proposed system has been chosen as a two agent hydro-thermal restructured system. Proposed deregulated power system with different contract scenarios such as unilateral, bilateral & violation have been studied. An UPFC unit is employed along with the SMES unit to lessen several LFC problems. Further a bat optimization technique has been proposed to tune the fractional order PID controller.

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.

Output power smoothing of wind power plants using self-tuned controlled SMES units

This paper exhibits a new application of the continuous mixed p-norm (CMPN) algorithm to self-tune all controlled superconducting magnetic energy storage (SMES) units to smooth wind power plants output power. The proposed algorithm is applied to self-tune all PI controllers of these storage units. In the present article, two wind farms are connected with the power grid. Each wind farm is equipped with a self-tuned (ST) controlled SMES unit. The control strategy of this unit is based on a voltage source converter (VSC) and a DC chopper. The VSC is implemented to control the reactive power and the DC chopper is utilized to control the real power exchange with the power grid. These aforementioned power electronic circuits are fully controlled by the proposed CMPN-ST-PI controllers. To achieve more realistic studies, measured values of wind speed that captured from Hokkaido wind power plants are implemented in system investigation. In addition, the turbine model depends on a two-mass structure, which highly affects system dynamics. Moreover, a practical SMES device of a rated capacity of 10 MVA, which is established in Kameyama, is joined the PCC of wind power plants. The proposed self-tuned controller is validated by using the simulation results, which are extensively performed on PSCAD program. The effectiveness of proposed adaptive controlled SMES devices is compared with that obtained by using optimal genetic algorithm-based PI controlled SMES devices under wind speed uncertainty conditions. With these CMPN-ST-PI controllers, the output power of wind power plants can be further smoothed and its fluctuations can be depressed.

Impact of Superconducting Magnetic Energy Storage Unit on Doubly Fed Induction Generator Performance During Various Levels of Grid Faults

Since 2004, Doubly Fed Induction Generator (DFIG) has become the most admired wind turbine generator type that is widely installed worldwide. The popularity of DFIG is due to its capability to extract more energy than fixed speed wind turbine generator type. Moreover, a DFIG requires only one-third of the full converter size used by type-4 wind turbine generator which in turn reduces the total cost of DFIG installation. The main weakness of DFIG is its vulnerability to grid faults such as voltage dip and swell. Grid faults may have adverse impacts on the overall performance of the DFIG including the voltage at the point of common coupling that, at certain fault levels, may violate the permissible margins of the grid codes established by the transmission line operators. This paper investigates the application of Superconducting Magnetic Energy Storage (SMES) unit to improve the fault ride-through capability of a DFIG during grid faults. Results show that the connection of a SMES unit at the point of common coupling between the DFIG and the grid can enhance the overall performance of the DFIG during such faults events.

Optimal fuzzy-PID controller with derivative filter for load frequency control including UPFC and SMES

A newly adopted optimization technique known as sine-cosine algorithm (SCA) is suggested in this research article to tune the gains of Fuzzy-PID controller along with a derivative filter (Fuzzy-PIDF) of a hybrid interconnected system for the Load Frequency Control (LFC). The scrutinized multi-generation system considers hydro, gas and thermal sources in all areas of the dual area power system integrated with UPFC (unified power flow controller) and SMES (Super-conducting magnetic energy storage) units. The preeminence of the offered Fuzzy-PIDF controller is recognized over Fuzzy-PID controller by comparing their dynamic performance indices concerning minimum undershoot, settling time and also peak overshoot. Finally, the sensitiveness and sturdiness of the recommended control method are proved by altering the parameters of the system from their nominal values and by the implementation of random loading in the system.

Enhancing FRT performance and smoothing output power of DFIG wind farm equipped by SFCL and SMES in a fuzzy framework

Wind farms equipped by Doubly-Fed Induction Generators (DFIG) should have two capabilities so that system operators can efficiently utilizes them. These capabilities include Fault-Ride-Through (FRT) and smoothing the output active power fluctuations, especially when these generators provide significant electrical power. The Superconducting Fault Current Limiter (SFCL) and also the Superconducting Magnetic Energy Storage (SMES) are supplementary devices which are used to enhance these capabilities and reduce electrical power swings. In the present work, improvement of FRT capability and smoothing the output power of wind farms are formulated as a multi-objective problem in a fuzzy framework. The variables of optimization are the Proportional-Integral (PI) gains of DFIG and SMES controllers and also SMES and SFCL parameters. Minimization of the initial energy stored in the SMES unit, the energy losses of SFCL, deviations of the DC-link voltage of DFIG, deviations of output active power of DFIG, deviations of output voltage of DFIG, and DFIG speed deviations are six objective functions of the problem. These objective functions are scaled by a fuzzy operator, then the scaled objective functions are aggregated by the “max-geometric mean” operator to obtain the multi-objective function. For optimizing this multi-objective function, the Hybrid Big Bang Big Crunch (HBB-BC) as a meta-heuristic optimization algorithm is used. The proposed algorithm is implemented on a case study to numerically evaluate its efficiency. Simulation results show that the proposed algorithm is more effective to enhance FRT capability and smooth output power of wind farm in comparison with the other algorithms.