Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation

Elvira-Ortiz, David A.; Jaen-Cuéllar, Arturo Yosimar; Moriñigo-Sotelo, Daniel; Morales-Velazquez, Luis; Osornio-Ríos, Roque Alfredo; Romero-Troncoso, René de Jesús

doi:10.3390/app10020542

Cited by 15 publications

(7 citation statements)

References 30 publications

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“…To overcome these drawbacks, the artificial intelligence techniques, the heuristic techniques, and deep learning are being used every time more frequently. The reason is very simple, these techniques are more suitable for treating problems where the prior knowledge of the system is not required, a big amount of data need to be processed, high accuracy is required, data with non-linear behavior, between other advantages [107][108][109]. Several works in the state of the art that address the tasks of detecting and clasifying power disturbances mention that methodologies based on data-driven could be considered to provide excellent results for the PQ analysis [110].…”

Section: Techniques For Power Quality Detection Identification and Mi...mentioning

confidence: 99%

Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review

et al. 2022

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The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.

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Section: Techniques For Power Quality Detection Identification and Mi...mentioning

confidence: 99%

Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review

et al. 2022

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“…At each time stamp, hourly forecasts are made for the next 24 h. Due to daily periodic characteristics of load curves, forecasts are made using a multiplicative seasonal auto-regressive integrated moving average model (ARIMA) [19,20]. With consideration of daily periodicity as well as short-term disturbance, ARIMA (0,1,1) with seasonal moving average part MA (24) are applied, i.e., ARIMA (0,1,1) × (0,1,1) 24 .…”

Section: Optimization In An Online Modementioning

confidence: 99%

“…The vertical dashed lines indicate boundaries between segments. There is a slight difference in segmentation results by As for optimization method, a genetic algorithm (GA) [23,24], which uses chromosomes instead of real parameter to imitate natural selection process, is an efficient method for the optimization of complex and non-linear problems, thus is carried out to produce optimal reactive power control strategies.…”

Section: Case Study In An Offline Modementioning

confidence: 99%

An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation

Wang

Deng

2020

Applied Sciences

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Due to fluctuating characteristics of loads, dynamic reactive power optimization over a certain time period is essential to provide effective strategies to maintain the security and economic operation of distribution systems. In operation, reactive power compensation devices cannot be adjusted too frequently due to their lifetime constraints. Thus, in this paper, an online reactive power optimization strategy based on the segmentation of multiple predicted load curves is proposed to address this issue, aiming to minimize network losses and at the same time to minimize reactive power-compensation device adjustment times. Based on forecasted time series of loads, the strategy first segments each load curve into several sections by means of thresholding a filtered signal, and then optimizes reactive power dispatch based on average load in each section. Through case studies using a modified IEEE 34-bus system and field measurement of loads, the merits of the proposed strategy is verified in terms of both optimization performance and computational efficiency compared with state-of-the-art methods. optimization is of importance, which optimizes for a time period and makes the adjustment times of devices one of the optimization objectives.Dynamic reactive power optimization is a large-scale multi-period mix-integer non-linear programming problem. The classical methods such as non-linear programming [4] require differentiable objective function and are thus not applicable for dynamic optimization.A number of attempts have been made to conquer the problem of dynamic optimization. Reference [13] made daily reactive power optimization considering network losses, adjustment times of on-load tap changer (OLTC) and switchable capacitor banks, using an improved multi-population ant colony algorithm. Reference [14] used mixed integer non-linear programming to optimize transmission losses only, considering constraints of reactive power equipment operation limits and power grid security. Using a filter collaborative state transition algorithm (FCSTA), reference [15] built a two-objective dynamic reactive power optimization model, with one objective to minimize the actual power losses and voltage deviations, the other to minimize incremental system loss compared with the last time stamp. The objectives can be exchanged during dynamic optimization.Generally, state-of-the-art attempts include reducing scales of objectives or constraints, optimizing for increments at continuous time stamp, and using AI methods. However, much computational burden will be introduced when using AI methodology.Aiming to maintain objectives and constraints, as well as to reduce computational complexity, this paper proposes to segment load curves in a duty cycle into several stable time series first, and then conduct optimization for each segment. Based on online load predictions, the method can be applied in an online mode. Since the whole time series of loads need to be segmented into parts with lowest inner deviation, the methods of wind ramp extraction ...

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“…They developed, trained, and validated distinct models using meteorological data and power quality measurements recorded over a year by considering different numbers of hidden layers and input parameters. Elvira‐Ortiz et al 13 proposed a genetic algorithm to estimate the generated power and total harmonic voltage distortion (THD v ) of a photovoltaic (PV) plant. Experiments were conducted over a year, and a sample of 8 days was selected to evaluate the model.…”

Section: Introductionmentioning

confidence: 99%

Novel time‐domain average model for harmonic current prediction in photovoltaic and wind power units

Moraes

Reis

Filho

2021

Int Trans Electr Energ Syst

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Computer models designed to assess harmonic levels created by renewable energy sources (RESs) need to reproduce the converter operation effect. However, only a few simplified time‐domain models have been developed to predict harmonics while reducing simulation time, which is one of the main limitations of detailed models. This paper presents a novel time‐domain model, called the harmonic average model (HM), to estimate harmonic currents generated by inverters. The superposition principle is applied to include dead time and switching effects in the average model. In addition, the snubber effect is considered by introducing snubber circuits in the HM. Time‐domain simulations of a type‐IV wind turbine are executed with existing models and then with the HM to evaluate the proposed model. Field measurements in a wind power plant and in a photovoltaic plant are used to validate the HM under different power injection scenarios. The results indicate that the HM follows reference currents while accurately estimating the harmonic spectra. Further, the HM is relatively simple to deploy and reduces computation time. A comparison between the HM results and field measurements suggests that the simulated spectra are an adequate approximation of the measured spectra. These results confirm that the HM can be used by transmission system operators to support the prediction of harmonic currents generated by inverters and to estimate the impact of RESs on voltage quality.

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Genetic Algorithm Methodology for the Estimation of Generated Power and Harmonic Content in Photovoltaic Generation

Cited by 15 publications

References 30 publications

Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review

Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review

An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation

Novel time‐domain average model for harmonic current prediction in photovoltaic and wind power units

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