Solar energy is the radiant heat and light energy harvested by ultra violet rays to convert into electrical Direct Current (DC). The solar energy stood ahead of other renewable energy as it can produce a constant level of alternating current over the year with minimal harmonic distortions. The renewable energy attracts the energy harvesters as there is rise of deficiency of carbon and reduction of efficiency in thermal energy generation. The concerns associated with the solar power generation are the fluctuation in the generated direct current due to the displacement of sun and deviation in the quantity of solar rays from place to place. This apprehension is overcome by following the technical methods of employing latest technology is determining the optimal position to harvest the solar power at the high rate and forecasting the power generation effectively. This paper proposes a novel hybrid methodology of employing fuzzy based controller to determine the Maximum Power Point Tracking (MPPT) in solar power generation and employing Artificial Intelligence (AI) technology to perform high precision forecasting of power generation. The K-Nearest Neighbor algorithm is a least assumption algorithm is employed in predicting the energy level harvested in the solar Photovoltaic cells. The Artificial Intelligence considers the vital parameters of displacement direction of the sun, temperature, clearness index and humidity in the air. The performance analysis of the proposed methodology is compared with the IEEE standard bus and the prediction is proved to be more precision with a maximum standard deviation of 0.06%.

Renewable energy [

The database to record the quantity of solar radiation for tenure of 5 years is performed and to obtain this database the week has been divided into 24/7 pattern and the data of solar radiation is represented as the Probability Density Function (PDF). The output power of the solar photo voltaic cell measured every hour is represented as in

This research work concentrates on efficient forecasting of the quantity of solar radiation using the Artificial Intelligence (AI) algorithm of K-Nearest Neighbor so that to determine the optimal direction of the maximum intensity of solar radiation. Proceeded by the fuzzy based controller for obtaining the Maximum Power Point Tracking [

The demand for power and the search for non-polluting power generation mechanism had attracted various countries, governments to concentrate on efficient renewable power generation system. This motivates more researchers to actively contribute in designing and identifying solutions for the challenges associated with the solar power generation using photo voltaic cells. Some of the highly notable research results were considered for study which acts as the motivation for this proposed research work. Liu et al. [

Sangrody et al. [

Doubleday et al. [

Ramakrishna et al. [

Lu et al. [

Research gaps in Existing methodologies: From the aforementioned recent research results, certain challenges in the solar power generation and forecasting process remains unaddressed. The identified research gaps are listed as follows:

The Machine Learning model of solar power forecasting process experiences prediction error particularly during the cloudy and rainy days.

The existing Machine Learning algorithms are highly generic and incorporate varieties of model set ups.

The Deep Learning algorithms, the measured influence the performance of the forecasting model, thus reducing the efficiency of the forecasting process yielding high value of standard deviation among the actual and measured values.

Most of the existing models concentrate only in the forecasting and the solution part remains unaddressed. The Maximum Power Point Tracking process is lacking is most forecasting models, thus proving to be more generic models.

The existing Maximum Power Point Tracking (MPPT) models are inefficient in tracking the maximum power point due to the variation of sun shine.

To design a probabilistic forecasting of solar power generated by photovoltaic cells using K-Nearest Neighbor algorithm categorized under Artificial Intelligence (AI) technique.

To design a fuzzy based controller to control the Maximum Power Point Tracking process in the solar power generation forecasting process.

The forecasting process is performed for a period of one week ahead with reference to current weather condition and cloud conditions.

The existence of Artificial Intelligence (AI) technology and its adoption to multiple technologies had developed multiple domains in an automated manner. The algorithms of the Artificial Intelligence (AI) like Support Vector Machine (SVM) [

The PV inverter in the solar power generation system converts the variable Direct Current (DC) generated from the solar photovoltaic cells into Alternating Current (AC) at a utilization frequency. The converted alternating current can be employed for commercial and industrial applications using electric grid. The proposed solar photovoltaic cell is composed of cascaded multilevel inverter which accepts voltages at a minimum of three different levels and the full bridge topology inverter constructed with four switches is employed to synthesize the three level output voltage waveform. The three level output voltage obtained at the inverter is mathematically represented in

If the input dc voltage of all the solar PV sources is equal to the V_{dc} then the inverter is considered to be in symmetric level. In case of non-attainment of symmetrical level, the fuzzy system adjusts the photovoltaic cell in such a way to attain the symmetric level. The number of output voltage depends on the number of full bridges constructed in the inverter circuit and is represented as (2n + 1), where “n” is the number of bridges. The inverter output voltage of the proposed system is controlled by the fuzzy based controller which controls the position of the solar panel to obtain the maximum voltage from the solar PV systems.

The detailed process performed in the fuzzy based MPPT controller in the solar photovoltaic cell power generation depends on the fuzzification and de-fuzzification process as explained. The set of rules to be followed in fuzzy based MPPT controller in mentioned in

The fuzzy controller depicted in

_{C} is the input variable change in Error, ΔP is the change in power generated and ΔE is the change in energy generated.

The input variable change of Error (E_{C}) is defined on achieving the predefined condition of displacement of operating point towards the direction of Maximum Power Point. The output function increases with respective to the change in the duty cycle ΔD, whose value varies from negative range to the positive range of values. The output of this fuzzy based controller is fed as the input to the DC-DC converter in the solar photo voltaic cell power generation which is operated to drive the load at the constant voltage level. The accumulator placed in the PV system prepares to receive the value of duty cycle ΔD using the

The inference process is performed on the basis of set of rules defined in the fuzzy based MPPT controlling process and the rule table for fuzzy based controlling process is illustrated in

The

WP - WP yields Weak Power point “WP”

WP - RAP yields Rising Average Point “RAP”

WP – PP yields Peak Power point “PP”

WP - DAP yields Decreasing Power Point “DAP”

RAP - RAP yields Peak Power point “PP”

The set of rules were defined from the feedback of the output process and the rows were represented by the change of Error (EC) while the columns were defined by the values of error (E).

The de-fuzzification process is the core function of the fuzzy based MPPT controlling process, in which the output of the defuzzification process is fed to the Pulse Width modulation process to generate the pulse for driving the semiconductor MOSFET switch in the DC-DC converter. The defuzzification process is performed by two methodologies namely Centre of Area and Maximum Criterion Methodology. The Centre of Area method determines the controller output which acts as the centre of gravity for the previous processed set of fuzzy process. The cumulative fuzzy process is determined by the performing sampling process of the incoming data and is computed by the

The proposed fuzzy based MPPT controller drives the DC-DC converter of the solar photovoltaic cell power generation system. The proposed method is portrayed in

The MPPT controller which experiences maximum deviation will drive the Pulse Width Modulator (PWM) more effectively resulting in a high width output pulse given to the driver circuit. The width of the pulse generated by the Pulse Width Modulator (PWM) controls the MOSFET switch; the greater the deviation between the generated output and the MPPT point with generated more voltage (approximately 5 V) which drives the MOSFET to ON status. The lesser the difference generates reduced width of pulse (approximately less than 5 V) which is not sufficient to drive the MOSFET. The driver circuit accepts the pulses of all MPPT controllers and based on the width voltage, the driver circuit converts the MOSFET from switch OFF status to the switch ON status by driving the gate terminal. The MOSFET on switching ON, will allows the current to flow through from source to drain resulting in rotating of solar photovoltaic panel. This process is repeated until the MPPT controller produce voltage less than the threshold voltage such that the PWM output width will be minimum. The derivative control frequency is mathematically expressed in

K_{C} is the derivative coefficient

n represents the switching period

The input voltage and the input current are supplied to the fuzzy based MPPT controller to determine the power generated by the solar photovoltaic cells. The derivative control frequency DC(n) and the change in switching on frequency ΔDTON(n) controls the MPPT time period. The switching On time of the pulse width modulator (PWM) is determined by the ΔDTON(n) in the Differential Pulse Width Modulator. Artificial Intelligence in Solar power forecasting process: This section describes the forecasting process of solar power generation by the photovoltaic cell for the upcoming week based on the datasets collected from the present week and the data related to the weather condition. The different data collected as processed using the K-Nearest Neighbor algorithm of Artificial Intelligence which is diagrammatically described in

The

The

Step by step process of KNN algorithm in solar irradiation forecasting process |
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_{i})_{i}) • Defining rules to process the inputs and to determine the nearest relative data. • The data processing is performed by the KNN structure. • Determine the adaptive nodes using the • Determine the irradiation (μ The irradiation values are forecasted using member functions 16 and17. where, o, p, and q are the precision parameters. The total irradiation is determined using the |

The

The weighting of the test data is performed by employing the data validity in both training data and measured test data. The mathematical expression for determining the weightage of the test data is expressed in

The Artificial Intelligence incorporated in this proposed system is superior to the existing state of art technologies in such a way that it automatically generates the difference between the generated power and Maximum power point and adjusts the Solar Photovoltaic cells accordingly which is absent is existing methods.

The proposed methodology of forecasting the solar irradiation from the solar photovoltaic cells using K-Nearest Neighbor algorithm of Artificial Intelligence and the design of fuzzy based Maximum Power Point Tracking controller is designed and executed using MATLAB 2021a. The result section is two folded with the analysis of multiple waveforms in first fold followed by the quantitative analysis of the proposed method in the second fold.

The

The grid voltage and the grid current exhibits fluctuations at constant time intervals due to the variations in the power generated in individual solar photovoltaic cells. The accumulated voltage and current will experience constant fluctuations due to the variation of quantity of irradiation incident on the solar photovoltaic cells. The grid voltage and the current were analyzed for harmonic distortion and the Total Harmonic Distortion (THD) of the current generated by the solar photo voltaic cell is measured and is graphically represented in

The Total Harmonic Distortion in the solar grid current is measured by the considering the entire current component of the solar grid and it is defined as the ratio of the additive of entire current harmonic components to a fundamental frequency. The fundamental frequency considered in the proposed system is 50 Hz and the measured Total Harmonic Distortion is 1.01% shown in

The comparison of fuzzy based controller and the PI controller is depicted in afore portrayed figures which yields that the proposed fuzzy controller possess a Total Harmonic Distortion of 2.68% while the existing PI controller exhibits a Total Harmonic Distortion of 3.79%. The output power encompassed of both real power and reactive power is measured after performing the fuzzy based MPPT controlling process and is diagrammatically displayed in

The

The proposed method of solar irradiation forecasting mechanism in solar power generation using photovoltaic cells has been designed in multiple models with reference to the three IEEE bus models like IEEE 14 bus system, 23 bus system, IEEE 30 bus system and IEEE 57 bus systems. The measurement and analysis of the aforementioned bus systems using the parameters defined in

KNN model | MBE | MAPE | RMSE |
---|---|---|---|

Model 1 | 1.1086 | 1.1156 | 0.613 |

Model 2 | 1.1121 | 1.1269 | 0.569 |

Model 3 | 1.1978 | 1.2021 | 0.591 |

Model 4 | 1.215 | 1.2021 | 0.531 |

The statistical analysis of the proposed methodology with different IEEE models has been analyzed and the mean RMSE value is identified to be 0.576% which is considered to be more efficient than the existing forecasting methodologies. The necessity of using different models for analysis of the proposed methodology is to test the proposed model against different complexion types of circuits. Here four different models with different level of complexity have been designated as Model 1 to Model 4 to analyze the performance metrics of proposed model.

The

IEEE bus model | ML | DL | Proposed AI (KNN) |
---|---|---|---|

Model 1 | 0.653 | 0.627 | 0.613 |

Model 2 | 0.619 | 0.599 | 0.569 |

Model 3 | 0.603 | 0.599 | 0.591 |

Model 4 | 0.579 | 0.561 | 0.531 |

The proposed methodology of employing K-Nearest Neighbor of Artificial Intelligence (AI) technique for forecasting the solar radiation and the fuzzy based Maximum Power Point Tracker controller is presented and is analyzed quantitatively. The proposed model outperforms well than the existing methodologies and provides an high level of accuracy with Root mean square error of 0.576% and thus provides an accuracy of above 99.424% which is considered to be the more efficient and accurate forecasting methodology. The analysis was performed for period of 6 months and with pre-defined data sets and using IEEE bus models for designing the solar power generation systems. Besides, the fuzzy based MPPT controller has been compared with the existing PI controller for the critical parameter of Total Harmonic Distortion (THD). The THD of the PI controller is 3.79% while the THD of the proposed fuzzy based MPPT controller is 1.01% which is considered to be more efficient.