Magnetic Energy Storage Unit Research Articles

Firefly algorithm optimized fuzzy PID controller for AGC of multi-area multi-source power systems with UPFC and SMES

In this paper, a Firefly Algorithm (FA) optimized fuzzy PID controller is proposed for Automatic Generation Control (AGC) of multi-area multi-source power system. Initially, a two area six units power system is used and the gains of the fuzzy PID controller are optimized employing FA optimization technique using an ITAE criterion. The superiority of the proposed FA optimized fuzzy PID controller has been demonstrated by comparing the results with some recently published approaches such as optimal control and Differential Evolution (DE) optimized PID controller for the identical interconnected power system. Then, physical constraints such as Time Delay (TD), reheat turbine and Generation Rate Constraint (GRC) are included in the system model and the superiority of FA is demonstrated by comparing the results over DE, Gravitational Search Algorithm (GSA) and Genetic Algorithm (GA) optimization techniques for the same interconnected power system. Additionally, a Unified Power Flow Controller (UPFC) is placed in the tie-line and Superconducting Magnetic Energy Storage (SMES) units are considered in both areas. Simulation results show that the system performances are improved significantly with the proposed UPFC and SMES units. Sensitivity analysis of the system is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Finally, the effectiveness of the proposed controller design is verified by considering different types of load patterns.

A new design of dual-mode Type-II fuzzy logic load frequency controller for interconnected power systems with parallel AC–DC tie-lines and superconducting magnetic energy storage unit

Abstract: LFC (Load frequency control) in power systems is very important to supply reliable electric power with good quality. In general, proportional PI (plus integral) controller is used for the LFC, which is incapable of obtaining good dynamic performance for a wide range of operating conditions. Type-I fuzzy logic controller (T-I FLC) is a sophisticated technique to use, but the designing of membership functions and control rules is an important function.. To achieve satisfactory membership function and control rules, a designer's experience is necessary. This conflict may be checked up byapplying the principle of Type-II fuzzy logic controller (T-II FLC) to use expert knowledge and being adaptive in nature. The dual-mode concept is incorporated in the proposed controller to improve the system performance. In this work, SMES (superconducting magnetic energy storage) unit and parallel alternating current – direct current (AC–DC) tie-lines are included with base model to study their performance under different operating conditions. The simulation results were analyzed using MATLAB 2014, proving that the superior performance of the proposed controller provides very good transient and steady state response when compared to PI and T-I FLC under different operating conditions and parameter variations.

Particle Swarm Optimization-based Superconducting Magnetic Energy Storage for Low-voltage Ride-through Capability Enhancement in Wind Energy Conversion System

— This article presents a novel application of the particle swarm optimization technique to optimally design all the proportional–integral controllers required to control both the real and reactive powers of the superconducting magnetic energy storage unit for enhancing the low-voltage ride-through capability of a grid-connected wind farm. The control strategy of the superconducting magnetic energy storage system is based on a sinusoidal pulse-width modulation voltage source converter and proportional–integral-controlled DC-DC converter. Control of the voltage source converter depends on the cascaded proportional–integral control scheme. All proportional–integral controllers in the superconducting magnetic energy storage system are optimally designed by the particle swarm optimization technique. The statistical response surface methodology is used to build the mathematical model of the voltage responses at the point of common coupling in terms of the proportional–integral controller parameters. The effectiveness of the proportional–integral-controlled superconducting magnetic energy storage optimized by the proposed particle swarm optimization technique is then compared to that optimized by a genetic algorithm technique, taking into consideration symmetrical and unsymmetrical fault conditions. A two-mass drive train model is used for the wind turbine generator system because of its large influence on the fault analyses. The systemic design approach is demonstrated in determining the controller parameters of the superconducting magnetic energy storage unit, and its effectiveness is validated in augmenting the low-voltage ride-through of a grid-connected wind farm.

Krill herd algorithm for automatic generation control with flexible AC transmission system controller including superconducting magnetic energy storage units

This study presents an optimisation technique, called krill herd algorithm (KHA), for the effectiveness and performance analysis of an interconnected automatic generation control (AGC) system. A two-area multi-unit hydro–hydro (HH–HH) power system equipped with classical I-controller and two other test systems: namely, thermal–thermal, thermal–hydro which are widely available in literature are considered for design and analysis purpose. Eigenvalues analysis assesses that HH–HH power system is highly unstable under small load perturbation. To stabilise this power system, different frequency stabilisers such as superconducting magnetic energy storage, thyristor control phase shifter, static synchronous series compensator etc. are proposed in AGC system. Optimum gains of the controller and frequency stabiliser are evaluated using KHA. Integral square error criterion is used to minimise the area control error, which is considered as an objective function. The superiority of the proposed algorithm is checked by means of an extensive comparative analysis with the results published in recent research algorithms such as craziness-based-particle swarm optimisation and real coded genetic algorithm etc. for the same test system.

An Adaptive-controlled Superconducting Magnetic Energy Storage Unit for Stabilizing a Grid-connected Wind Generator

This article introduces an adaptive artificial neural network controlled superconducting magnetic energy storage with the purpose of enhancing the dynamic stability of a wind generator that is connected to the electric grid. The control strategy of the superconducting magnetic energy storage unit depends on the cascaded control scheme of a voltage source converter and a two-quadrant DC-DC chopper using insulated-gate bipolar transistors. The proposed controller is used to control the duty cycle of the DC-DC chopper. The modeling of the system and its control techniques are presented. The effectiveness of the proposed adaptive artificial neural network controlled superconducting magnetic energy storage is then compared with that of a conventional proportional-integral controlled superconducting magnetic energy storage. This study concentrates on symmetrical and unsymmetrical faults. A two-mass drive train model of the wind generator is utilized in the analyses for realistic responses as it has a huge impact on fault analysis. An over-voltage protection scheme is connected across the DC-link capacitor for over-voltage protection. The validity of the proposed control scheme is verified with the simulation results which are performed using the standard PSCAD/EMTDC environment.

An Action Dependent Heuristic Dynamic Programming-controlled Superconducting Magnetic Energy Storage for Transient Stability Augmentation

To enhance the stability of power system, the active power and reactive power can be absorbed from or released to Superconducting magnetic energy storage (SMES) unit according to system power requirements. This paper proposes a control strategy based on action dependent heuristic dynamic programing (ADHDP) which can control SMES to improve the stability of electric power system with on-line learning ability. Based on back propagation (BP) neural network, ADHDP approximates the optimal control solution of nonlinear system through iteration step by step. This on-line learning ability improves its performance by learning from its own mistakes through reinforcement signal from external environment, so that it can adjust the neural network weights according to the back propagation error to achieve optimal control performance. To investigate the effectiveness of the proposed control strategy, simulation tests are carried out in Matlab/Simulink. And a conventional Proportional-Integral (PI) controlled method is used to compare the performance of ADHDP. Simulation results show that the proposed controller demonstrates superior damping performance on power system oscillation caused by three-phase fault and wind power fluctuation over the PI controller.

Superconducting Magnetic Energy Storage Unit for Damping Enhancement of a Wind Farm Generation System

Superconducting Magnetic Energy Storage Unit for Damping Enhancement of a Wind Farm Generation System

Power–frequency balance with superconducting magnetic energy storage using optimized intelligent controller

This paper presents an optimized design of the fuzzy logic controller (FLC) for the two-area interconnected power system to regulate the frequency deviation and power deviations when subjected to change in load. The developed power system model apart from having conventional sources also has a superconducting magnetic energy storage (SMES) unit which is capable of storing electric energy and releases it as per system requirements. SMES is used to support short-term active power in order to maintain power–frequency balance in the system under test. This two-area power system considered comprises reheat thermal and hydro units of the same capacity in each area. In the proposed control scheme, optimization of the FLC is carried out in four different steps: the first step is for optimization of the range of input and output variables, the second one for membership functions, the third and fourth steps for rule base and rule weight optimization, respectively. Genetic algorithm (GA) is used for the proposed step-by-step optimization process. Comparing the other methods available in literature, the proposed method is found more effective in achieving optimal results. Simulation results are also tabulated as a comparative performance in view of the main performance indices, i. e. settling time and peak undershoot.

A Set-Membership Affine Projection Algorithm-Based Adaptive-Controlled SMES Units for Wind Farms Output Power Smoothing

This paper presents a novel adaptive control scheme of the superconducting magnetic energy storage (SMES) units with the purpose of smoothing the wind farms' output power. The adaptive control scheme is based on the set-membership affine projection algorithm (SMAPA), which provides a faster convergence and less computational complexity than the normalized least-mean-square algorithm. In this study, two grid-connected fixed-speed wind farms are considered. The control strategy of the SMES units is based on a pulse width modulation (PWM) voltage source converter (VSC) and a dc-dc converter. The VSC and dc-dc converter is used to control the reactive and active power exchange with the power system, respectively. The SMAPA-based adaptive proportional-integral (PI) controllers are utilized to control both converters. For realistic responses, real wind speed data extracted from Hokkaido Island, Japan, and two-mass drive train model of the wind turbine generator system are used in the analyses. Also, a real 10 MVA SMES unit that was installed at a power plant in Kameyama, Japan, is connected to the point of common coupling of the wind farms. The validity of the proposed control scheme is verified by the simulation results, which are performed using PSCAD/EMTDC environment. With the SMAPA-based adaptive-controlled SMES units, the wind farms' output power can be smoothed easily avoiding huge effort for fine tuning the controllers' parameters.

Computation of Ancillary Service Requirement Assessment Indices for Load Frequency Control in a Restructured Power System using SMES Unit and SCES Unit

To ensure a quality power supply the power system should not only match the total generation with total load and the associated system losses but also should emphasis better Ancillary Services. Even small disturbances to the power system can result in wide deviation in system frequency and quick restoration process are of prime importance not only based on the time of restoration and also should ensure stability limits. This paper proposes various design procedures for computing Power System Ancillary Service Requirement Assessment Indices (PSASRAI) for a Two-Area Thermal Reheat Interconnected Power System (TATRIPS) in a restructured environment. As simple conventional Proportional plus Integral (PI) controllers are still popular in power industry for frequency regulation as in case of any change in system operating conditions new gain values can be computed easily even for multi-area power systems, this paper focus on the computation of various PSASRAI for Two Area Thermal Reheat Interconnected Power System in restructured environment based on the settling time and peak undershoot concepts of control input deviations of each area. Energy storage is an attractive option to augment demand side management implementation, so storage devices like Super Capacitor Energy Storage (SCES) and Superconducting Magnetic Energy Storage (SMES) unit can be efficiently utilized to meet the peak demand. So the design of the Proportional plus Integral (PI) controller gains for the restructured power system without and with the storage units are carried out using Bacterial Foraging Optimization (BFO) algorithm. These controllers are implemented to achieve a faster restoration time in the output responses of the system when the system experiences with various step load perturbations. In this paper the PSASRAI are calculated for different types of possible transactions and the necessary remedial measures to be adopted are also suggested. If PSARAI based on settling time lies between 1 to 1.5 and if PSARAI based on peak undershoot is less than 0.2 distributed generation has to be incorporated and if the limit exceeds then the system becomes vulnerable and may result to black outs.

Load Frequency Control of Multi-source Multi-area Hydro Thermal System Using Flexible Alternating Current Transmission System Devices

This article describes the load frequency control of a multi-area system. Each control area contains both a hydro and thermal power plant to form a multi-source multi-area hydro thermal system. The secondary proportional-integral controller has been tuned using Ziegler–Nichols, genetic algorithm, and fuzzy gain scheduling methods. On comparing the controller performance based on various performance indices, it is found that a fuzzy gain scheduling tuned proportional-integral controller is suitable for a multi-source multi-area hydro thermal system. Further improvement on the load frequency dynamics has been achieved by connecting superconducting magnetic energy storage unit in each control area and a static synchronous series compensator unit on a tie-line.

Automatic generation control with thyristor controlled series compensator including superconducting magnetic energy storage units

In the present work, an attempt has been made to understand the dynamic performance of Automatic Generation Control (AGC) of multi-area multi-units thermal–thermal power system with the consideration of Reheat turbine, Generation Rate Constraint (GRC) and Time delay. Initially, the gains of the fuzzy PID controller are optimized using Differential Evolution (DE) algorithm. The superiority of DE is demonstrated by comparing the results with Genetic Algorithm (GA). After that performance of Thyristor Controlled Series Compensator (TCSC) has been investigated. Further, a TCSC is placed in the tie-line and Superconducting Magnetic Energy Storage (SMES) units are considered in both areas. Finally, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters.

Improving performance of DF Induction Generator SMES

Using of renewable energy sources are became very popular in the decade. One of the important renewable energy sources wind power generation has increased widely. Wind generators consist of double fed induction generator (DFIG). In this paper DFIG uses superconducting magnetic energy storage (SMES) unit for improving its performance. The converter and the chopper of the SMES unit are controlled using a hysteresis current controller and a fuzzy logic controller, respectively. Simulation carried out using MATLAB/SIMULINK software to track SMES unit in improving performance of DFIG under voltage sag and swell conditions.

Application of SMES Unit to Improve DFIG Power Dispatch and Dynamic Performance During Intermittent Misfire and Fire-Through Faults

The number of wind turbines connected to power grids has significantly increased during the last decade. This is mainly due to the convincing revolution in power electronic technology and the growing concern about greenhouse effect that is intensified due to the burning of fossil fuels. Variable-speed wind energy conversion systems (WECSs) such as doubly fed induction generators (DFIGs) are dominating the wind energy market due to their superior advantages over fixed-speed-based WECS which include more captured energy, less mechanical stress, and acoustical noise. DFIG is interfaced to the ac network through the grid-side voltage source converter (VSC) and rotor-side VSC to enable the variable-speed operation of the wind turbine and to provide reactive power support to the ac grid during disturbance events. Converter switching malfunction such as misfire and fire-through may influence the power dispatch capability of the DFIG. In this paper, a superconducting magnetic energy storage (SMES) unit is utilized to improve the power dispatch and dynamic performance of DFIG-based WECS during internal converter switching malfunctions such as misfire and fire-through faults. Simulation results without and with SMES connected to the system are presented, compared, and analyzed.

Application of superconducting magnet energy storage to a multi-area automatic generation control scheme suitable in a restructured power system

The application of superconducting magnetic energy storage (SMES) unit to the stabilization of genetic algorithm (GA) based multi-area automatic generation control scheme is presented. In this paper, SMES units have been used to the power systems to inject or absorb active power. Developed scheme utilizes a proportional, integral and derivative (PID) controller to control the output of the generators. The parameters of PID controller have been tuned according to GA based performance indices. The developed G A based PID (GAPID) controller has been tested on a practical Indian power system network representing 75- bus system. A deregulated electricity market scenario has been assumed in the 75- bus system, which has been divided into four control areas. Mixed transaction (Poolco and bilateral) has been considered in the frequency regulation. The effect of generator rate constraint (GRC) has also been included in developing the multi area AGC model.

Design of Load Frequency Controllers for Interconnected Power Systems with Superconducting Magnetic Energy Storage Units using Bat Algorithm

Design of Load Frequency Controllers for Interconnected Power Systems with Superconducting Magnetic Energy Storage Units using Bat Algorithm

Application of SMES to Enhance the Dynamic Performance of DFIG During Voltage Sag and Swell

The integration of wind turbines into modern power grids has significantly increased during the last decade. Wind turbines equipped with doubly fed induction generators (DFIGs) have been dominating wind power installation worldwide since 2002. In this paper, a superconducting magnetic energy storage (SMES) unit is proposed to improve the dynamic performance of a wind energy conversion system equipped with DFIG during voltage sag and voltage swell events. The converter and the chopper of the SMES unit are controlled using a hysteresis current controller and a fuzzy logic controller, respectively. Detailed simulation is carried out using MATLAB/SIMULINK software to highlight the impact of the SMES unit in improving the overall system performance under voltage sag and voltage swell conditions.

Load Frequency Control of an Interconnected Reheat Thermal system using Type-2 fuzzy system including SMES units

The transient behavior of many large scale systems is heavily influenced by perturbations and in particular, usually, due to changes in operating points. Load Frequency Control (LFC) in power systems is very important in order to supply reliable electric power with good quality. The goal of LFC is to maintain zero steady state errors for frequency deviations in each control area. Several control strategies, such as classical control, optimal control, suboptimal control, adaptive control, variable structure control etc. have been employed in the past to explore an optimum controller for LFC. In practice LFC systems use simple Proportional Integral (PI) controllers, Proportional Integral Derivative (PID) controllers etc. This paper presents a method based on Type-2 Fuzzy System (T2FS) for Load frequency control (LFC) of power systems including Superconducting magnetic energy storage (SMES) units of a two-area interconnected reheat thermal system. This paper proposes a Type-2 (T2) fuzzy approach for load frequency control of two-area interconnected reheat thermal power system with the consideration of Generation Rate Constraint (GRC), Boiler Dynamics (BD) and SMES. The salient advantage of this controller is its high insensitivity to large load changes and plant parameter variations even in the presence of non-linearities. The proposed method is tested on a two-area power system to illustrate its robust performance with various area load changes. The performance of the Type-2 (T2) fuzzy controller is compared with optimal PID (Khamsum’s optimal PID) controller and Fuzzy PI Controller (Type-1 Fuzzy) controller in the presence of GRC, BD and SMES. Simulation results confirm the high robustness of the proposed SMES controller with small power capacity against various disturbances and system uncertainties in comparison with SMES in the previous research.

A new control strategy of SMES for mitigating subsynchronous oscillations

This paper proposes a new strategy to mitigate the subsynchronous oscillations in power systems compensated by series capacitors via control of active power of superconducting magnetic energy storage (SMES) unit. The strategy is based on the generator acceleration signal. So, the SMES absorbs or generates the energy imbalance caused by different disturbances in the power system and suppresses the subsynchronous oscillations. The chaotic optimization algorithm (COA) is used to achieve the optimal parameter of the proposed controller. To validate the capability of the SMES in damping oscillations, some simulations with different disturbances are performed on the first model of IEEE second benchmark model. All the simulation results show that the subsynchronous resonance as well as low frequency oscillation (LFO) is satisfactorily mitigated by the SMES controlled by the proposed strategy.

Improvement Of Load Frequency Control By Using Self–Tuning Controller

Since a Superconducting Magnetic Energy Storage (SMES) unit with a self–commutated converter is capable of controlling both the active and reactive powers simultaneously and quickly, increasing attention has been focused recently on power system stabilization by SMES control. This paper presents a novel control method of SMES with a self-tuned Fuzzy Proportional Integral (FPI) controller associated with the Automatic Generation Control (AGC) for improving Load Frequency Control (LFC) in a single area power system. Boiler dynamics and nonlinearities such as governor dead band (DB) and generator rate constraints (GRC) are considered in the developed comprehensive mathematical model of a single area isolated power system. The effects of the self–tuning configuration of FPI controller in AGC on SMES control is compared with that of fixed gain PI controlled AGC. It is seen that with addition of FPI controller, SMES can perform a more effective primary frequency control for single area power system. Key words: Load frequency control; single area power system; automatic generation control; superconducting magnetic energy storage unit; fuzzy proportional integral controller