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The escalating global demand for electricity is driving a significant expansion in the size and complexity of electric power systems. Explosive growth strains power distribution, creating challenges for operators. Among these challenges, the imperative to minimise power losses is crucial. To enhance power efficiency and quality, Distributed Generation (DG) technology, particularly wind energy, is being integrated into distribution systems. However, the variable nature of wind speed poses a significant challenge to the seamless integration of wind energy into grids. To address this challenge, wind speed prediction methods are explored. In this work, a Machine Learning framework is employed for multivariate wind speed forecasting in Tamil Nadu, India $\left[{{{8.0883}^ \circ }N,{{77.5385}^ \circ }E} \right]$ 8.0883 ∘ N , 77.5385 ∘ E . Key performance metrics, such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error values (MAPE), are used to assess the accuracy of the wind speed predictions. The predicted wind speed data is then utilised to estimate wind farm power output which is seamlessly integrated into the distribution system. Load Flow analysis is conducted using the robust and straightforward current injection method, focusing on IEEE-33 and IEEE-69 bus-balanced Mesh distribution systems. The aim is to determine the impact of wind-powered Distributed Generation on power losses within these systems. [ABSTRACT FROM AUTHOR]

A machine learning based generalized neural network estimator (GRNNE) and Takagi‐Sugeno (T‐S) fuzzy control system is implemented to accelerate the functional performance indices of dynamic voltage restorer (DVR). The GRNNE predictive model is recommended for the fast estimation of the reference load voltage under the distorted power supply. The fruit fly optimization learning strategy is employed to optimize the weights and smoothing parameters for the extraction of the reference voltage signals as well as unit vectors, resulting in the required sinusoidal load component. The dynamics in the DC‐link voltage are optimized by the metaheuristic‐based gray wolf optimization algorithm. The coefficients of the adaptive controller are updated automatically to achieve the best fitted adaptive neuro‐fuzzy inference system predictive model with the least tracking voltage deviation error even in the presence of voltage disturbances. In comparison to classical techniques, the recommended neural‐based approach offers a faster convergence speed with fewer parameters to tune, shorter training time, and lower risks of local entrapment. The performance metrics such as mean square error, root mean square error, mean absolute error, mean absolute percent error, coefficients of correlational and determination (R and R2) are used to evaluate the efficacy of the proposed controller and hence enhance the DVR performance. Finally, the simulation results of the hybrid approach confirm that the NN‐based DVR estimator has proven its ability to alleviate voltage sensible issues at critical loads and outperform others in reducing power quality issues. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to develop a hybrid predictor based on intelligence techniques for dynamic voltage restorer (DVR) to estimate the reference load voltage and self‐tuned voltage regulation to enhance voltage power quality issues. The best fitted predictor model is obtained by using the potential merits of metaheuristic algorithms coupled with Antlion Optimization (ALO) and Genetic Algorithm (GA). This article proposes ALO optimized multilayer perceptron (MLP) neural network (NN) control algorithm for appropriate selection of weights and biases to generate the subsequent switching signal with lower error rates for load voltage estimation. The ANFIS‐GA control algorithm is implemented for automatic tuning of fuzzy rules with fewer membership functions (MFs) to obtain the perfect ANFIS (Takagi–Sugeno) predictive models for effective DC and AC link voltage regulation. The proposed hybrid controller counteracts the drawbacks of traditional ANN such as early convergence, slow learning mechanism, less probability of local entrapment, and difficulties to achieve the estimated target under parametric uncertainties, namely, sag, swell, unbalance, and distortion. The accuracy of the prediction model is computed by considering the error indices for the training and testing data set. The result confirms that the hybrid system ALO‐MLP NN establishes satisfactory performance in extracting the reference with the least error values. [ABSTRACT FROM AUTHOR]

In this article, a hybrid approach is implemented namely, neural network training (NNT) based machine learning (ML) estimator inspired by artificial neural network (ANN) and self-adaptive neuro-fuzzy inference system (ANFIS) to tackle the voltage aggravations in the power distribution network (DN). In this work, potential of swarm intelligence technique namely particle swam optimization (PSO) is analysed to obtain an optimum prediction model with certain modifications in training algorithm parameters. In practice, when the systems are continuously subjected to parametric changes or external disturbances, then ample time is dedicated to tune the system to regain its stable performance. To improve the dynamic performance of the system intelligence-based techniques are proposed to overcome the shortcomings of conventional controllers. So, gain tuning process based on the intelligence system is a desirable choice. The statistical tools are used to proclaim the effectiveness of the controllers. The obtained MSE, RMSE, ME, SD and R were evaluated as 0.0015959, 0.039949, −0.00089838, 0.039941 and 1 in the training phase and 0.0015372, 0.039207, −0.0005657, 0.039203 and 1 in the testing phase, respectively. The results revealed that the ANFIS-PSO network model could accomplish a better DC voltage regulation performance when it is compared to the conventional PI. The proposed intelligence strategies confirm that the predicted DVR model based on NNT-ML and ANFIS has faster convergence speed and reliable prediction rate. Moreover, the simulation results show that the dynamic response is improved with proposed PSO based NNT based ML and ANFIS (Takagi-Sugeno) that significantly compensates the voltage based PQ issues. The proposed DVR is actualized in MATLAB/SIMULINK platform. [ABSTRACT FROM AUTHOR]

Power generation from a hybrid wind–solar farm depends on several parameters such as farm location (solar radiation, wind direction, wind speed), layout of farm, shadowing effect and ambient temperature. Based on the location, the hourly uncertain wind speeds and solar radiation are estimated using Kernel density estimation. An irregular crossed wind farm layout is considered for study as it reduces the effective wake effect, produced due to upstream wind turbines. Further solar panels are also fitted onto the wind farm in order to increase the power output using the same land. In this work, the wake analysis includes single as well as multi‐wake model. The output of the solar farm is affected by many parameters like irradiance, wind speed, atmospheric temperature, self‐shadowing of consecutive photovoltaic (PV) panels and turbine shadowing on PV panels. The power output from solar is calculated considering the temperature effect on PV cell, effect of wind speed on solar panel, self‐shadowing of PV panel and shadowing effect of wind turbine on solar panel. [ABSTRACT FROM AUTHOR]

Environmental parameter consideration has always prompted wind power to be used as renewable energy. However, the biggest challenge lies in wind energy integration to the power grid due to wind intermittency. Wind speed or wind power forecasting is one of the approach to manage this intermittency. Numerous prediction methods have been reported in previous literatures over few years. In this work, multivariate wind speed forecasting using Machine Learning framework in a python environment is executed. Several statistical models and neural network models are examined to best predict the wind speed of Surat, India [22.2587° N, 71.1924° E]. The model efficiency is tested in terms of measurements of correlation factors and Mean Absolute Error values. The predicted wind speed value is further considered for the power generation from the wind farm and integrated to the distribution system. Load Impedance Matrix method is implemented for Distribution System Load Flow analysis for being robust and simple with single-step computation. IEEE-19 bus and IEEE-25 bus-unbalanced radial distribution systems are considered for finding the power losses in the branches with wind power as Distributed Generation. An efficient and effective optimization technique, Teaching Learning-Based Optimization, is used to obtain the optimal location and capacity of Distributed Generation to minimize the power loss in the distribution lines. [ABSTRACT FROM AUTHOR]

This article presents implementation of a solar energy-based DG (Distribution Generation) system, which is optimally designed by taking combined effect of solar irradiation and temperature into consideration to minimize the power loss in the Radial Distribution Systems (RDS). A power gap of 35 W/panel is reduced when the solar panel is considered under ambient temperature effect. Novelty of this work lies in the implementation of the obstructed solar astronomical model to improve the accuracy in the prediction of radiation level. In this work, the considered radiation level is determined to be 14.86% of the global radiation data. Consideration of self-shadowing losses for maximum efficiency from PV panels is another significant aspect of this work. Two different test systems are designed by integrating the proposed DG system to IEEE-15 and IEEE-28 RDS. Load Impedance Matrix (LIM) method is adopted to perform load flow in above mentioned test systems due to its single step analysis with faster results unlike traditional Backward Forward Sweep (BFS) method. Grey Wolf Optimization (GWO) technique is used to obtain optimal location of DG for both the test systems for its simple evaluation, good flexibility, avoidance of local optimal point and derivation free tool, which makes this technique popular among the other heuristic methods. Following DG allocation in IEEE-15 bus RDS, it is observed that active power loss is reduced by 39.69%. Similarly, for IEEE-28 bus RDS, reduction in active power loss is found to be 41.20% after integration of a DG unit. There is also a remarkable improvement in voltage profile in both systems after integration of DG units. [ABSTRACT FROM AUTHOR]

Conventional protection scheme of power transformer maloperation during abnormalities such as inrush condition, current transformer (CT) saturation during external fault and high resistance internal fault conditions. This article presents inrush detection based on an average angle of 2nd order derivative of differential current. The magnitude and phase angle of primary and secondary currents are estimated using a Modified Full Cycle Discrete Fourier Transform (MFCDFT) algorithm. Normally, during an internal fault, the differential current is well above the restraining current; however, the same condition arises during external fault with heavy CT saturation. Hence, a phase angle comparison based scheme is combined with percentage biased differential protection. During an internal fault, the phase angle difference of primary and secondary current is <90°, whereas it differs >90° for any external fault conditions. In the proposed scheme, 315 MVA, 400/220 kV transformer is considered for validation of various fault conditions simulated in PSCAD™ software. Moreover, the proposed scheme is validated on developed laboratory prototype of 2 kVA, 230/115 V single phase transformer using ATmega328 microcontroller. The proposed algorithm is effectively validated on both, simulation and hardware to discriminate inrush, internal and external fault by considering various system and fault parameters. [ABSTRACT FROM AUTHOR]

Identification of faults within power transformers is the means of ensuring unit transformer protection. Existing relay maloperates during abnormalities such as magnetising inrush, CT saturation and high resistance internal fault condition. Therefore, it is essential to categorise the internal fault and external abnormality/fault in case of transformer protection. This study presents a new scheme, based on relevance vector machine (RVM) as a fault classifier. The developed algorithm is assessed by simulating various disorders on 345 MVA, 400/220 kV transformer in PSCAD/EMTDC software and also on prototype model with 2 kVA, 230/110 V multi‐tapping transformer. One cycle post fault current signals are captured from primary and secondary to form feature vectors. These feature vectors are used as an input to RVM for classification of various test cases. Wide variation in system parameters and fault conditions are considered for test data generation and validation. The proposed scheme is compared with the support vector machine (SVM) and probabilistic neural network (PNN)‐based techniques. The proposed scheme successfully discriminates various types of internal faults and external abnormalities in power transformer within a short time. The fault classification accuracy obtained by proposed RVM technique is more than 99% in comparison to SVM and PNN‐based schemes. [ABSTRACT FROM AUTHOR]

• Total Harmonic Distortion (THD) based inrush and fault identification scheme for power transformer is proposed in this work. • Extensive PSCAD simulation is carried out with FFT analysis to estimate the THD during different operating condition of transformer. • Suitable THD limit is proposed for normal, inrush and fault condition of power transformer. • Validation of the suggested scheme has been carried out by software algorithm and displayed results shows satisfactory performance. • Hardware prototype has been developed and various inrush and fault cases are performed on the transformer to validate the proposed technique. The non linear characteristic of transformer magnetizing core leads more current/voltage signal distortion in the event of system disturbance/fault. The harmonic penetration in the current signal depends of the transformer core, characteristic of load and abnormal conditions. Different situations or abnormalities have different harmonics content and consequently different waveform signature. In this article, authors have utilized the harmonics content to identify normal, inrush condition and fault condition in power transformer. Total Harmonic Distortion (THD) of the current waveform for one cycle, is measured by performing Fast Fourier Transformation (FFT) analysis. Authors observed that the percentage THD is generally high in the case of inrush condition due to initial flux setup in magnetic core. Conversely, during fault condition, the %THD will be low (within a particular range) due to symmetric waveform. The magnitude of %THD below predefined range will be considered as normal condition and excess THD will be considered as inrush condition. On the other hand, if THD remains within a particular range then, it is considered as fault condition. In this article, authenticity of the presented scheme is checked by performing various test cases like initial inrush, internal fault, switching of considered transformer in faulty condition and CT saturation during fault condition. The hardware validation proves the competency of the proposed THD based discrimination scheme for transformer protection. The scheme presented here is simple and capable to solve complex problem of classification of inrush condition and fault condition. [ABSTRACT FROM AUTHOR]