Power production optimization of model-free wind farm using simulated annealing algorithm

This research set to appraise the potency of Simulated Annealing (SA) based method within enhancement of wind farms’ power generation performance. Horns Rev Offshore Wind Farm with a total magnitude of 80 wind turbines was hereby replicated to study the recommended SA based method. Core objective of the simulation then focused maximization of power output through parametric fine-tuning of individual wind turbine through the SA based method. Recorded findings on boosted convergence rate, elevated accuracy and magnified power production consequentially verified efficacy of the SA based method towards operational improvement of wind farms

Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm

This paper presents a preliminary study of a model-free approach based on spiral dynamic algorithm (SDA) for maximizing wind farms power production. The SDA based approach is utilized to find the optimal control parameter of each turbine to maximize the total power production of a wind farm. For simplicity, a single row wind farm model with turbulence interaction between turbines is used to validate the proposed approach. Simulation results demonstrate that the SDA based method produces higher total power production compared to the particle swarm optimization (PSO) and game theoretic (GT) based approaches

A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm

One of the main issues of the wind plant power generation nowadays is that the current stand alone controller of each turbine in the wind plant is not able to cope with chaotic nature of wake aerodynamic effect. Therefore, it is necessary to re-tune the controller of each turbine in the wind plant such that the total power generation is improved. This article presents an investigation of a data driven approach using moth-flame optimization algorithm (MFO) to the problem of improving wind plants power generation. The MFO based technique is applied to search the turbine’s optimum controller such that the aggregation power generation of a wind plant is maximized. The MFO is a population based optimization method that mimics the behavior of moths that navigate on specific angle with respect to the moon location. Here, it is expected that the MFO can solve the control accuracy problem in the existing algorithms for maximizing wind plant. A row of wind turbines plant with wake aerodynamic effect among turbines is adopted to demonstrate the effectiveness of the MFO based technique. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The performance of the proposed MFO algorithm is analyzed in terms of the statistical analysis of the total power generation. Numerical results show that the MFO based approach generates better total wind power generation than spiral dynamic algorithm (SDA) based approach and safe experimentation dynamics (SED) based approach

An improved marine predators algorithm-tuned fractional-order PID controller for automatic voltage regulator system

One of the most popular controllers for the automatic voltage regulator (AVR) in maintaining the voltage level of a synchronous generator is the fractional-order proportional–integral-derivative (FOPID) controller. Unfortunately, tuning the FOPID controller is challenging since there are five gains compared to the three gains of a conventional proportional–integral–derivative (PID) controller. Therefore, this research work presents a variant of the marine predators algorithm (MPA) for tuning the FOPID controller of the AVR system. Here, two modifications are applied to the existing MPA: the hybridization between MPA and the safe experimentation dynamics algorithm (SEDA) in the updating mechanism to solve the local optima issue, and the introduction of a tunable step size adaptive coefficient (CF) to improve the searching capability. The effectiveness of the proposed method in tuning the FOPID controller of the AVR system was assessed in terms of the convergence curve of the objective function, the statistical analysis of the objective function, Wilcoxon’s rank test, the step response analysis, stability analyses, and robustness analyses where the AVR system was subjected to noise, disturbance, and parameter uncertainties. We have shown that our proposed controller has improved the AVR system’s transient response and also produced about two times better results for objective function compared with other recent metaheuristic optimization-tuned FOPID controllers

Model-free wind farm control based on random search

This paper explores a model-free approach based on the random search (RS) algorithm for maximizing wind farms power production. The RS based approach is utilized to find the optimal control parameter of each turbine in maximizing the wind farm total power production. The Horns Rev wind farm model with turbulence interaction between turbines is used to validate the proposed approach. Simulation results demonstrate that the random search approach produces higher total power production as compared to the existing method

Model-free wind farm power production optimization using multi-resolution optimized relative step size random search

This study investigates the performance of Multi-Resolution Optimize Relative Step Size Random Search (MR-ORSSRS) based method in maximizing the total power production of wind farms. The performance is investigated based on the Horns Rev wind farm layout which consists of 80 wind turbines under the case studies of different wind directions at 170°, 200°, 220°, 240°, 250° and 270°, five wind turbines failures and non-static wind variations. The implementation of Multi-Resolution (MR) function is used to improve the convergence speed of the Optimize Relative Step Size Random Search in the case of maximizing the total power production of a wind farm in real-time optimization. The MR function is significant in improving the convergence speed since this approach exploits the dimension of the design parameter using several optimization stages. In particular, it firstly adopts a small size of design parameter tuning followed by a bigger size of design parameter tuning in the following stages. Therefore, it is expected that less computation effort is required to obtain the optimal design parameter. Even though the Multi-Resolution Stochastic Perturbation Simultaneous Approximation (MR-SPSA) is developed to solve the real-time high-dimensional problem with faster convergence, the obtained total power production of the wind farm is still not optimum. This is because the SPSA is a memory-less structure type optimization that limits the storage of the best design parameter. Alternatively, ORSSRS based method is a memory type optimization structure. Hence, it can store the best design parameter value while producing consistent objective function. However, the ORSSRS based method alone does not have the sufficient convergence speed to optimize wind farm problem in real time. Therefore, the MR function is implemented to improve the convergence speed of the ORSSRS based method. In this study, the performance of MR-ORSSRS based method is compared with MR-SPSA based method in terms of the convergence speed, accuracy, and robustness in maximizing the total power production of Horns Rev wind farm. The results show that MR-ORSSRS based method outperforms the benchmark MR-SPSA based method in terms of the convergence speed of all the study cases. In particular, it can improve the convergence speed of incoming wind direction at 170°,200°,220°,240°,250° and 270° by 88.89%,88.89%,41.66%,88.89%,88.89% and 66.67%, respectively. However, in the case of the five wind turbine failures, the speed of the incoming wind direction is 66.67%. Moreover, the MR-ORSSRS based method produces better total power production for wind direction at 170°,200°,220°,240° and 270°, as well as wind turbines failures compared to the MR-SPSA based method. In term of the convergence speed, the MR-ORSSRS based method produces higher convergence speed for all the wind direction cases even in the wind turbines failure cases. Hence, it is proven that the proposed MR-ORSSRS based method is effective in producing better total power production with faster convergence speed even with turbines failure and time-varying wind compared to the benchmark MR-SPSA based method

Fast and optimal tuning of fractional order PID controller for AVR system based on memorizable-smoothed functional algorithm

Voltage regulation in automatic voltage regulator (AVR) system has been one of the most challenging engineering problem due to the uncertain load condition. Therefore, the control of AVR system by using PID based controller is one of the essential approach to maintain the performance of the AVR system. Subsequently, the application of FOPID controller in AVR system is gaining more attention recently. This is because the FOPID has additional control parameters at the derivative and integral parts than the PID controller, which has the advantage to improve the output response of AVR system while retaining the robustness and simple construction as the PID controller. Nevertheless, many existing optimization tools for tuning the FOPID controller, which are based on multi-agent based optimization, require large number of function evaluation in their algorithm that could lead to high computational burden. Therefore, this study proposes a modified smoothed function algorithm (MSFA) based method to tune the FOPID controller of AVR system since it requires fewer number of function evaluation per iteration. Moreover, the proposed MSFA based method also can solve the unstable convergence issue in the original smoothed function algorithm (SFA), thus able to provide better convergence accuracy. The simulations of step response analysis, Bode plot analysis, trajectory tracking analysis, disturbance rejection analysis, and parameter variation analysis are conducted to evaluate the effectiveness of the proposed MSFA-FOPID controller of AVR system. Consequently, the results obtained from the simulations revealed that the proposed method is highly effective and significantly improved as compared to the other existing FOPID controllers

Performance evaluation of random search based methods on model-free wind farm control

This paper investigates the performance of Sequential Random Search (SRS), Fixed Step Size Random search (FSSRS), Optimized Relative Step Size Random Search (ORSSRS) and Adaptive Step Size Random Search (ASSRS) methods on maximizing offshore wind farms power production. The RS based methods are used to tune the control parameter of each turbine to its optimum until the wind farm total power production is maximized. The validation of this investigation is performed using the Horns Rev wind farm model with turbulence interaction between turbines. Simulation results show that Optimized Relative Step Size Random Search (ORSSRS) produces higher total power production as compared to other types of RS based methods

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent’s preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other’s wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy