TLBO‐based approach to optimally place and sizing of energy storage system for reliability enhancement of radial distribution system

Summary Fault detection units (FDUs) used for power system are based on the time‐domain, frequency‐domain, or both time‐ and frequency‐domain signal information. These days, the demand for renewable sources is increasing and in such a condition the complete review of the detection algorithm is essential. The penetration of large‐scale wind farms in the transmission system introduces a dynamic change in the time‐domain signal. Under such a condition, phasor estimation will be challenging. Again, any threshold value selected for the normal condition of the power system will not be valid for wind‐integrated systems. To mitigate this issue, a new approach is proposed in this work through which the threshold value can be set optimally for any operating condition of the power network. Teaching learning‐based optimization (TLBO) algorithm is employed to introduce the optimal threshold selection concept in FDU. First, a simple current sampled mean error concept‐based FDU is implemented, and later TLBO is applied for setting an optimal threshold for better decision making under various conditions of the wind‐integrated transmission system. The method is tested for different critical fault conditions as well as variations in source and wind parameters. A comparative analysis is also performed to highlight the superiority of the proposed method.

Section: Proposed Methodsmentioning

confidence: 99%

Enhancing fault detection function in wind farm‐integrated power network using Teaching Learning‐Based Optimization technique

Prasad

Biswal

Ray

2020

“…35 After the inclusive literature review and simulations, TLBO has been decided to apply for the optimal controller parameters selection. 38 TLBO is a recent and self-regulating algorithm that is independent of algorithmspecific tuning constraints and hence, becomes a standard tuning algorithm for WECS. After tuning the controller gains using TLBO, PSO is also applied for tuning purposes and to compare with the effective performance offered by TLBO.…”

Section: Tuning Of the Proposed Mppt Methodsmentioning

confidence: 99%

“…It is necessary to apply a new, powerful, and suitable tuning algorithm to select the proposed MPPT controller gains in an optimal manner 35 . After the inclusive literature review and simulations, TLBO has been decided to apply for the optimal controller parameters selection 38 . TLBO is a recent and self‐regulating algorithm that is independent of algorithm‐specific tuning constraints and hence, becomes a standard tuning algorithm for WECS.…”

Section: Tuning Of the Proposed Mppt Methodsmentioning

confidence: 99%

“…The inertia weight w should be chosen carefully for obtaining the optimum result within fewer iterations. Algorithm-specific constants for PSO algorithm are taken similar to those in 38 with bounds of velocity restricted by v min = 0.1, v max = 1, w = 1 and Δw = 0.99, generation population, G = 30 and maximum iteration allowed, N = 80. In this paper, the fitness function is selected as a weighted sum of the integral of squared error (ISE) and integral of time-weighted absolute error (ITAE).…”

Section: Teaching Learning Based Optimizationmentioning

confidence: 99%

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Fractional‐order nonlinear PID controller based maximum power extraction method for a direct‐driven wind energy system

Pathak

Bhati

Gaur

2020

Objective To maintain the operation of a wind turbine at its maximum efficiency and for the extraction of maximum power from the wind. Method In this paper, at first, a novel application of fractional‐order nonlinear proportional‐integral‐derivative (FONPID) controller is proposed in the machine side converter (MSC) control loop of a 2 MW grid‐connected wind energy system. Then, the grid side converter (GSC) is controlled to ensure the unity power factor of the understudy system. To extract the maximum power from the wind, the application of the proposed controller is applied to a well‐established TSR MPPT control method, which compares optimal and actual values of rotor speed, and generated error is given to the proposed controller to vary the rotor speed accordingly. Moreover, improved MPPT performance offered by the proposed FONPID controller has also been compared to well‐established existing controllers based TSR MPPT methods. Furthermore, parameters of the proposed and existing controllers in the TSR MPPT control loop are also tuned using teaching‐learning based optimization (TLBO) and obtained performance is compared with the performance of particle swarm optimization (PSO) to show effectiveness. Results The generator speed offered by the proposed FONPID based TSR MPPT method effectively tracks its reference values far better than the extant controller based TSR MPPT method, whereas extant NPID has less overshoot as compared to the extant PID based TSR MPPT method. Conclusion To perform the effective operation of achieving MPP, q‐axis current of the stator has been controlled by the proposed FONPID based TSR MPPT method in such a style that rotor speed can be operated at its optimal value where the generator has the maximum power for given wind speed. DC‐link voltage is controlled at its rated value to guarantee the unity power factor operation using the GSC controller. FONPID based TSR MPPT method provides a better and robust MPPT operation than the PID, NPID, and FOPID controllers based TSR MPPT method. The performance has been assessed in terms of minimized fitness function value, steady‐state error, settling time, and percentage overshoot.

“…Various metaheauristic optimization algorithms such as genetic algorithm (GA), 26 crow search optimization, 27 teacher learning-based optimization, 28 shuffled bat algorithm 29 and wolf optimization algorithm 11 have been used in optimal sizing of power systems. Particle swarm optimisation (PSO) algorithm has superior advantages like simplicity, ease of use, high convergence rate, minimal storage requirements, and less dependency on initial points.…”

Section: Literature Reviewmentioning

confidence: 99%

AC‐coupledhybrid power system optimisation for an Australian remote community

Combe

Mahmoudi

Haque

et al. 2020

Summary This paper investigates the operation of various optimised AC‐coupled hybrid power systems for an isolated community in South Australia. In addition to diesel generators, various combinations of distributed generators and storage technologies are considered to form different hybrid power systems. Optimal design of the hybrid power systems is implemented using particle swarm optimisation (PSO) algorithm. The performance of the PSO in optimizing the above hybrid systems is discussed in detail. Whilst meeting the power system operational constraints, the minimum levelised cost of electricity (LCOE) of all systems, with various combinations of system components, are calculated. Simulation results obtained for each optimized system are carefully described and analysed. The annual operations of various components in each system are then compared and discussed in detail. A sensitivity analysis by varying the cost of wind turbines and the photovoltaic is also conducted for all hybrid systems. It has been found that the inclusion of energy storage systems (battery and flywheel) can reduce the LCOE and CO2 emission significantly compared to the inclusion of renewable generations (solar and wind) alone. The diesel‐wind‐solar‐battery system is the eco‐friendliest with an annual CO2 emission of 451 t compared to the reference diesel system (1064 t). In addition, the hybrid system consisting of diesel, wind, solar, battery and flywheel is found to be the most economic with a LCOE of 58.94 ¢/kWh compared to the diesel system with a LCOE of 67.17 ¢/kWh.