Development of Maximum Power Point Tracking for Doubly-Fed Induction Generators in Wind Energy Conversion Systems

This paper proposes a novel application of a runner-root (RR) algorithm for maximum power point tracking (MPPT) of a wind energy conversion system (WECS) direct-driven by a permanent magnet synchronous generator (PMSG). The WECS is required to operate at the MPP under varying wind conditions. The RR algorithm is the meta-heuristic algorithm, inspired by the function of runners and roots of some plants in nature. The plants are propagated through runners. They look for water resources and minerals by developing runners and roots. The RR algorithm is proposed to define maximum power points (MPP) corresponding to the optimal output voltage of the DC/DC boost converter of the PMSG-based WECS. The numerical results show that the RR algorithm based MPPT approach efficiently defines and tracks the MPPs of the PMSG-based WECS. A comparison with the hill-climb search (HCS) and particle swarm optimization (PSO) algorithms demonstrates the superiority of the RR algorithm and confirms its potential in the MPPT strategy of the PMSG-based WECS.

In view of the disadvantages of traditional global maximum power point tracking (GMPPT) algorithms such as particle swarm optimization and cuckoo search algorithm that the structure is complex, the tracking path is uncertain and the requirement on controller hardware is high, this paper adapts an improved cuckoo search algorithm to track the GMPP of series-parallel PV array. Compared with the traditional algorithm by analyzing the tracking process of maximum power point under uniform illumination and partial shading condition, the algorithm makes it faster and more accurate to track the GMPP of the series-parallel photovoltaic array connected to the DC distribution network under both conditions and finally provides the technical foundation for the safe and efficient connection of distributed photovoltaic power to the distribution network.

Solar Photovoltaic (PV) Systems are one of the most favorable and advantageous power generation frameworks used worldwide to fulfill the global requirements/needs for Renewable & Clean Energy. The output power characteristics generated by the PV Systems are non-linear and are strongly dependent upon the variable environmental parameters such as the Solar Irradiation (W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and Temperature (°C). Each PV System has its own ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$V_{MPP},I_{MPP}$</tex> ) point defined at a specific Irradiation (W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and Temperature (°C) value at which the power is optimal. Thus, in order to ensure the continuous operation of the PV System at the Maximum Power-Point (MPP), an intelligently smart Maximum Power-Point Tracking (MPPT) mechanism is mandatorily implemented along with the Photovoltaic (PV) System. This paper exhibits a comparative study on the behaviors of the four MPPT algorithms namely: Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA), Grey Wolf Optimization Algorithm (GWOA), and the Particle Swarm Optimization Algorithm (PSOA) under Dynamic Weather/Environmental conditions based on fluctuations in Temperature (°C) and Solar Irradiation (W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for a DC-DC Step-Up Converter based Photovoltaic (PV) System that is modeled & implemented under Matlab/Simulink Environment.

The cuckoo search (CS) algorithm, which is a new meta-heuristic optimization algorithm, is an efficient approach to solve continuous function optimization problems. Normally, the parameters of the CS are fixed constants. This may result in affecting the algorithm efficiency. To cope with this issue, we properly tune the parameters of the CS and propose an adaptive cuckoo search (ACS) algorithm to enhance the convergence rate and accuracy of the CS. Then, we validate the proposed ACS against test functions and compare its performance with those of particle swarm optimization (PSO), improved cuckoo search algorithm (ICS) and CS by simulation. The simulation results demonstrate that the ACS obtains some solutions better than those obtained by PSO, ICS and CS.

Since the performance of the Photo-Voltaic (PV) arrays is dependent on irradiation and temperature, the PV output power fluctuates with the ambient temperature and the solar irradiance. The achievement of the Maximum Power Point (MPP) under various shading patterns is, as a result, an important aspect in the enhancement of PV systems' overall performance. Because existing methodologies, such as perturb & observe, incremental conductance, etc., are likely to fail, it is necessary to develop an improved Maximum Power Point Tracking (MPPT) method to distinguish between the Global MPP (GMPP) and the Local MPP (LMPP). The characteristics of a PV array under shading conditions include several LMPPs and a single GMPP. To improve the MPPT method of shaded photovoltaic systems, this paper introduces an improved Particle Swarm Optimization (PSO) algorithm with Time-Varying Acceleration Coefficients (PSO-TVAC) that is fast and more efficient than the PSO. Firstly, the PSO-TVAC algorithm is mathematically modeled and applied to the MPPT application for solar PV systems. The primary objective of this paper is to analyze the performance of the PSO-TVAC for the MPPT application. Three shading patterns are considered to study the effectiveness of the PSO-TVAC in handling the MPPT application. The results and discussions prove that PSO-TV AC can be an alternative tool for MPPT application for PV systems.

The partial shading of a photovoltaic array repeatedly occurs in the natural environment, which can cause a failure of a conventional maximum power point tracking (MPPT) algorithm. In this paper, the convergence conditions of the standard particle swarm optimization (PSO) algorithm are deduced by the functional analysis, and then the influence of the random variables and inertia factor of the algorithm on the trajectory in the particle swarm optimization is analyzed. Based on the analysis results, an improved particle swarm optimization (IPSO) algorithm, which adopts both global and local modes to locate the maximum power point, is proposed. Compared to the standard PSO algorithm, in the improved PSO algorithm, many random and interfered variables are removed, and the structure is optimized significantly. The proposed algorithm is first simulated in MATLAB to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the global maximum power point within 3.3 s with an accuracy of 99%. Compared with five recently developed Global MPPT algorithms, the proposed IPSO algorithm achieved better performance in the maximum power tracking in the partial shading conditions.

Solar modules under partial shading (PS) conditions will result in power and voltage characteristic curves (P-VCC) having multiple peaks. If the maximum power point cannot be obtained, the output power of the solar modules will be greatly reduced. Hence, there have been various maximum power point tracking (MPPT) control methods developed to address this problem. One alternative is to employ the meta-heuristic approach (MHA) to track the global maximum power point (GMPP). Recently, a new MHA called Bat Algorithm (BA) has performed well in the MPPT. Nevertheless, BA may fail to track the GMPP when there are some local maximum power points (LMPPs) close to the GMPP. Also, the tracking time needs to be further reduced to accommodate rapidly changing irradiance. Therefore, a combination of BA with the abandonment mechanism of Cuckoo Search (CS) is proposed to improve the tracking performance of the BA. Both simulation and experimental results show that the proposed method, as compared to BA, yields better accuracy and an improvement of convergence speed of about 35% for various P-VCCs can be achieved. Moreover, the MBA has also been tested against some of the state-of-the-art MPPT algorithms such as Particle Swarm Optimization and Grey Wolf Optimization (GWO), and the results showed the superiority of the proposed method.

The maximum power point tracking (MPPT) system ensures that the solar cell operates at its maximum power point, which is the point at which it produces the most power for a given set of conditions. This study uses an artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithms to optimize the boost converter. The ABC and PSO algorithms help to optimize the boost converter by finding the best possible duty cycle and other parameters that maximize efficiency. The resulting duty cycle of 20% produced the highest efficiency of 78.26% obtained from the boost converter. A system-wide test was conducted between 9:00 and 11:10 WIB. In MPPT systems using boost converters with the PSO algorithm, the average power is 16.80 W, while in MPPT systems using boost converters with the ABC algorithm, the average power is 14.53 W. The novelty of the study discusses a system designed to optimize power by using a boost converter in order to increase power output from solar panels. The algorithms PSO and ABC are used to analyze the data from the PV and adjust the converter parameters to optimize the output in order to increase the efficiency of the system.

In this paper, a hybrid algorithm based on Particle Swarm Optimization (PSO) and Cuckoo Search (CS) is detailed with the application of Optimal Reactive Power Dispatch (ORPD). The Levy flight and the elimination mechanism are introduced to the traditional PSO algorithm. Benchmark tests show that the hybrid algorithm has better optimization efficiency than original PSO and CS algorithms. In the application of the ORPD, the fitness function is the total real loss of the distribution network. Inequality constrains are handled by the limitation of searching space. The simulation results on the IEEE-14 bus standard system and a real distribution network illustrate the feasibility and effectiveness of the algorithm. The total real power losses decrease 7.1% and 8% in two applications. The mathematical tests and engineering applications demonstrate that the hybrid algorithm has better searching ability than the standard PSO algorithm, and it is suitable for the solution of ORPD problem.

Due to the nature of unpredicted wind speed, determining the optimal generator speed to extract the maximum available wind power at any wind speed is essential. Therefore, it is significant to include an intelligent controller that can track the maximum peak regardless of wind speed. This paper describes the design and development of particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to variable-speed fixed-pitch wind turbines. Other than the electrical power subjected to maximization, the proposed algorithm does not need any additional sensor. In addition, the MPPT algorithm does not require any prior knowledge of the wind energy system. Unlike the conventional search optimization method, PSO-based MPPT algorithm produces almost negligible oscillations at the maximum power once the true peak is located. In short, the proposed MPPT is simple, flexible, accurate and efficient in maximum wind power tracking. In this work, MATLAB/Simulink simulation package is used to simulate the performance of the proposed MPPT algorithm.

Variable step size P&O MPPT algorithm for optimal power extraction of multi-phase PMSG based wind generation system

Wind energy systems have become a major commodity in the use of renewable energy resources, but its implementation requires great technical challenges in power extraction. Giligenting Island, Madura, East Java, is a prospective area for wind energy development. An algorithm applied to obtain optimal power from wind power generation. This paper presents a performance evaluation of the methods that can be applied as the MPPT (Maximum Power Point Tracking) algorithms. P&O (Perturbation and Observation), IC (Incremental Conductance), and PSO (Particle Swarm Optimization) are studied and discussed as the MPPT techniques in this research. All three methods are implemented on systems that have the same characteristics, they are compared to the simulation results. At the base wind turbine speed of 12 m/s, the P&O method obtained MPPT power of 295.82 W with settling time at 1.02 s. The IC method obtained MPPT power of 297.08 W with settling time at 1 s. Then, The PSO method achieved an MPPT power of 297.45 W with time settling at 0.88 s. According to the simulation results, the PSO is the algorithm that provided the best performance among all MPPT algorithms studied. Beside good response to wind speed variations, PSO also has a simple structure and easy to implement.

Considering the slow convergence speed and unstable transmission capacity of photovoltaic systems in maximum power point tracking (MPPT), this paper proposes a MPPT control strategy based on the fine-layered population algorithm. This strategy improves the search efficiency of the algorithm by fine-layered sequence reconstruction of the sparrow search algorithm (SSA) and particle swarm optimization algorithm (PSO) populations. Firstly, a PSO population pause mechanism is introduced to avoid the premature convergence problem of PSO. Secondly, a fine-layered sequence is used to generate a uniformly distributed initial population, and the cooperative optimization of the sparrow group and the particle swarm is designed to improve the compatibility of the algorithm. The two main populations are decomposed, the number of individuals in the population is optimized, and the individual optimal fitness value of the population is updated. At the same time, the population position is reorganized in each layer to enable each layer to optimize independently, making the entire population easier to collaborate, increasing population diversity, and thereby improving search efficiency. Finally, strategies such as filling functions and sign matrices are introduced into the speed update position of the improved hierarchical population algorithm (PSSSA) to reduce the adverse effects of power fluctuations caused by boundary effects, improve the convergence and speed of the algorithm, and ensure that the optimal solution is obtained after each update. To verify the effectiveness of the PSSSA algorithm, a simulation model under the weather conditions of a certain laboratory in Northeast China was constructed using Matlab. The simulation results show that when it is applied to the maximum power tracking problem, in the case of rapid changes in external environmental illumination, it can accurately track the maximum power point within 0.2 seconds. Its tracking stability is significantly better than the Particle Swarm Optimization (PSO) algorithm, the Seagull Search Algorithm (SSA), and the Perturbation Observation (P&O) algorithm. The size of the maximum power point is approximately 64% larger than that of the PSO algorithm. At the same time, it can also ensure the stability and rapidity of the tracking effect.

As a sustainable energy source, the use of solar photovoltaic (PV) systems has significantly increased. Environmental elements, especially partial shadowing circumstances, have a considerable impact on how well solar PV systems function. In such cases, the various local maxima in the power-voltage curve make it difficult for conventional Maximum Power Point Tracking (MPPT) methods to maintain peak efficiency. Heuristic Maximum Power Point Tracking (MPPT) system for solar photovoltaic (PV) applications, employing the cuckoo search algorithm. The primary objective of the research is to enhance the efficiency and reliability of solar PV systems by optimizing the MPPT process, which is crucial for maximizing energy extraction under varying environmental conditions. The model is used to simulate the performance of the system under various environmental conditions, such as changes in temperature and irradiance levels. The simulation results are then statistically analyzed to evaluate the effectiveness of the cuckoo search algorithm in tracking the maximum power point accurately and rapidly. The algorithm demonstrates a robust ability to converge to the maximum power point efficiently, thereby enhancing the overall energy yield of the solar PV system.. The performance of the hybrid PSO-CSA MPPT algorithm in contrast to traditional MPPT techniques is assessed through simulations and tests under various shading circumstances. The findings show that the hybrid strategy regularly outperforms conventional methods by enhancing the total energy production of partially shadowed solar PV installations through faster convergence, less oscillations, and greater tracking accuracy. The proposed hybrid algorithm also demonstrates stability and flexibility in real-world settings, making it a potential option for boosting the dependability and efficiency of solar PV systems when shade is present. This study advances the field of renewable energy and prepares the path for the use of sophisticated optimization methods to the problems of solar PV power generation under changing environmental circumstances.

Due to the growing demand for renewable energy solutions, the integration of photovoltaic-thermal (PV/T) systems and photovoltaic (PV) systems is attracting more and more attention. High temperatures have a substantial impact on the efficiency of photovoltaic (PV) power panels. As the temperature of a photovoltaic (PV) panel rises, the efficiency and power production generally decline. The main reason for this is that photovoltaic cells are highly responsive to variations in temperature, which directly impact the choice of semiconductor materials employed in their fabrication. An experimental set up has employed for a cooled and non-cooled panel and the cooled panel has been cooled with a back surface cooling technology with Cu tubes. An important obstacle in improving the efficiency of these systems during high temperature conditions is the efficient use of PV panel cooling system along with Maximum Power Point Tracking (MPPT) techniques. These approaches assure that the systems function at their highest power output even when faced with changing environmental constraints. This work provides a thorough assessment of PV/T and PV systems using distinct MPPT approaches, such as Perturb and Observe (P&amp;O), Incremental Conductance (IC) Particle Swarm Optimization (PSO) and Cuckoo Search (CS) algorithm. Simulation and experimental analysis are used to examine the combined effect of panel cooling &amp; MPPT. The combine effect has been observed in the output power of the system by increasing electricity production efficiency by over 16%.