Incremental Conductance Research Articles

Solar Power Plant with Constant Power Generation using Incremental Conductance MPPT Method

Abstract: In order to use solar energy as a possible power plant to be constructed, Indonesia, a tropical nation, has the advantage of receiving sunlight all year round. Power instability caused by the solar panels, which rely heavily on irradiance and have low energy conversion efficiency, is one of the issues with the solar power plant system. The Incremental Conductance approaches need the Maximum control of Power Point Tracking (MPPT) to tackle this issue. Solar PV is operated at the MPP point by this Incremental Conductance MPPT control, maximizing its output power

An unprecedented control of 3‐phase grid tied solar photovoltaic‐hydrogen/bromine‐supercapacitor composite storage microgrid for pulse power load regulation under nonideal grid conditions

AbstractPhotovoltaic (PV) power penetration into the distribution grid has increased. PV power fluctuations, abrupt load changes, nonlinear loading, growing usage of portable electronic devices, electric cars low average power and comparatively high pulse power needs, and other factors translate into severe grid instability. As a result, in these circumstances, power exchange between PV and the utility grid becomes a difficult task. Therefore, this article proposes a hydrogen/bromine (H2/Br2) redox flow battery and supercapacitor composite energy storage for a three phase grid tied PV system with multifunctional active power filter support to ensure grid codes. Further, the system is equipped with an unprecedented control technique that encompasses: an adaptive frequency tuned complex coefficient filter‐phase locked loop that provides grid frequency and phase angle information under adverse grid conditions, a modified sparrow search algorithm tuned tilt integral derivative with filter plus double integral controllers are employed to regulate the control errors under various system dynamic and disturbance conditions, fractional order incremental conductance maximum power point tracking is incorporated to efficiently track the PV peak power irrespective of atmospheric variations, a holistic fast‐acting power based energy management control employed for efficient regulation of load power and smooth out the PV power variation, and PV feedforward control dynamics to enhances system dynamics by reducing oscillations under various atmospheric conditions. The aim is to attain a smooth power transition with the grid at the unit power factor under normal and nonideal grid conditions. With reactive power injection, the control also maintains the voltage profile. During all these operations, no trade‐off occurs with the system power quality. Matlab simulations and the real time simulation test bench results under unfavorable grid circumstances demonstrate the effectiveness of the proposed method, and system harmonics are well within bounds in all the scenarios undertaken.

A novel hybrid image processing‐based reconfiguration with RBF neural network MPPT approach for improving global maximum power and effective tracking of PV system

SummaryThe solar photovoltaic (PV) array output is reduced significantly by the frequently occurring inevitable partial shading conditions. In consequence, the array exhibits multiple peaks in its characteristics that cause the conventional maximum power point tracking (MPPT) algorithms to get stuck at the local maximum. So, to track the global maximum power (GMP) among the multiple peaks, a novel radial basis function (RBF)‐based neural network approach has been proposed for predicting the optimal GMP. Additionally, a novel and intelligent encryption‐based ruler transform (RT) reconfiguration approach is proposed to disperse the shading effect enhancing the GMP and mitigating the multiple peaks. The effectiveness of the proposed RBF‐MPPT and novel RT‐reconfiguration strategies has been tested and analyzed for a 5 × 7 PV array under distinct dynamic, uniform, and nonuniform shading conditions. The results of the proposed RBF have been compared with the conventional incremental conductance (INC) algorithm before and after reconfiguration of the PV array. Further, the ease of GMP tracking by a simple conventional INC due to the reduction of peaks after the array reconfiguration under shading conditions has been demonstrated and discussed in detail. After reconfiguration, the GMP is enhanced by 37.35%, 31.41%, 30.86%, 21.46%, 13.69%, and 8.88%, using the proposed RBF for the considered five shading conditions. The steady‐state oscillations are also considerably mitigated by employing the proposed reconfiguration and RBF strategies.

A Novel Hybrid Maximum Power Point Tracking Technique for PV System under Complex Partial Shading Conditions in Campus Microgrid

Solar generation has become increasingly important in grid applications. In order to improve the energy efficiency of the photovoltaic array (PV), factors such as temperature, nonlinear characteristics, and partial shadow conditions (PSCs) of the PV must be fully considered. An excellent maximum power point tracking (MPPT) control strategy can effectively improve the energy utilization efficiency of photovoltaic cells and provide strong support for the construction of smart campuses in terms of environmental protection and energy saving. A traditional method such as Perturb & Observe (P&O) and incremental conductance (INC) will fall into the local maximum power point (LMPP). In the past decade, researchers have proposed many MPPT methods to solve the difficulties of the PV system. However, they have failed to fully consider dynamic changes in irradiance conditions. Changes in the irradiance of photovoltaic arrays can lead to an extension of the convergence time and an increase in the oscillation amplitude. Many current MPPT methods have shortcomings such as requiring a long convergence time, large oscillation amplitude, and being prone to falling into LMPP. In order to reduce the oscillation amplitude and improve the convergence speed, a novel Multi-strategy Improved Tuna Swarm Optimization hybrid INC (ITSO-INC) method is introduced in this article. This strategy involves improving the Tuna Swarm Optimization (TSO) through Levy Flight and a linear weight coefficient. In addition, the INC method is added in the later stage to improve the accuracy of MPPT tracking. The proposed algorithm can extract the global maximum power point under different partial shading. In order to verify the effectiveness of the proposed method, the proposed method was compared with other metaheuristic algorithms such as Cuckoo Search (CS) and TSO. The proposed ITSO-INC technique was tested over four different patterns of partial shading conditions. Modulation was performed by tracking the sudden change in the shadow pattern of the MPP. These simulation results confirm that the proposed method has fast convergence, high accuracy, zero steady state oscillation, and a rapid response to dynamic change.

Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system

This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimally selected using AOA. To accomplish this study, a 100-kW benchmark PV system connected to a medium distribution utility is constructed and analyzed employing MATLAB/SIMULINK. The optimization framework seeks to minimize four standard benchmark performance indices, then select the best of the best among them. To verify the efficacy of the recommended methodology, a comprehensive comparison is conducted between AOA-based PI-IC-MPPT, modified incremental conductance MPPT (MIC), grey wolf optimization (GWO), genetic algorithm (GA), and particle swarm optimization (PSO)-based MPPT. The proposed control approach has achieved a reduction of 61, 3, 4.5, and 26.9% in the rise time and a decrease of 94, 84.7, 86.6, and 79.3% in the settling time compared with MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively.

Rancang Bangun Maximum Power Point Tracking pada Panel Surya dengan Metode Incremental Conductance Menggunakan Zeta Konverter

Maximum Power Point Tracking (MPPT) is a method of maintaining the maximum output power of solar panels when the power of the solar panel changes over time due to the influence of the load or intensity of sunlight. This research proposed designing MPPT on photovoltaic using the Incremental Conductance method. The incremental Conductance method controls the duty cycle when the voltage and current change values ?I/?V>-I/V. This system uses one solar panel with a power capacity of 100 Wp, a ZETA converter as a DC-DC converter to lower the voltage, and used a load 12V/5W DC lamp. From the tests that have been carried out, the tracking time for MPPT Incremental Conductance is 4,5 seconds with a maximum power of 63,9015 Watt with irradiation of sunlight 813 W/m2. The average power without MPPT is 51,215 Watt, and by using MPPT Incremental Conductance, the average power is 57,274 Watt. So, there is an increase in power when using the MPPT Incremental Conductance control.

A novel robust maximum power extraction framework for sustainable PV system using incremental conductance based MRAC technique

AbstractThis study proposes a maximum power point tracking (MPPT) approach with two components: an Incremental Conductance (INC) MPPT for reference voltage regulation and a Model Reference Adaptive Controller (MRAC) for adjusting the duty cycle of the DC‐DC converter switch. A robustness test is performed on the system considering real‐world situations that involve an abrupt change in atmospheric conditions with load uncertainties. The probabilistic load distribution analysis is accomplished through levels of uncertainty (i.e., Probabilistic DOWN and UP) to guarantee the operation of the proposed INC‐MRAC controller while generating unexpected disturbances in the system. Using MATLAB/Simulink, the performances of the novel INC‐MRAC MPPT are comparatively analyzed with PO, INC, VSSPO, and ANN under realistic case studies in seven states. After comparative analysis, it is evident that the proposed MPPT offers less tracking time, i.e., 3.8 ms, to track maximum power point (MPP) with negligible steady‐state oscillation and ripples. It is about 3, 7, 9, and 10 times faster than ANN, VSSPO, INC and PO, respectively. Moreover, the tracking efficiency of the proposed controller is up to 99.63% as well as overall efficiency of the system is more than 98%. The tracking power loss and error rate in finding MPP for the proposed controller is the lowest among all state‐of‐the‐art MPPT approaches. Finally, the effectiveness of the proposed INC‐MRAC approach is experimentally validated by employing a real‐time simulator OPAL‐RT (OP4510). The study helps in environmental protection and participation in sustainable development.

A REVIEW ON MPPT ALGORITHMS FOR SOLAR PV SYSTEMS

In past few decades, solar energy plays a vital role in energy production among the different renewable energy resources. In shaded/unshaded photovoltaic (PV) systems, tracking of maximum power under different environmental conditions is provided by maximum power point tracking (MPPT). In recent years many works available on different types of MPPT techniques to track maximum power from PV systems with own pros and cons. This article comprehensively reviews the different traditional methods like perturb and observation (P&O), open circuit voltage (OCV), short circuit current (SCC), hill climbing (HC), incremental conductance (IC). Also recall the advanced MPPT techniques like particle swarm optimization (PSO), grey wolf optimization (GWO), cuckoo search (CS), artificial neural networks (ANN), fuzzy logic controller (FLC) available in literature. This review is conducted based on implementation, accuracy, tracking speed, cost, merits, and demerits of each technique. Traditional MPPT methods can’t able to track global maximum power point under partial shaded conditions and exhibits less efficiency when compared with advanced soft computing methods. Hybrid methods provide good efficiency and performance than traditional and advanced methods. Authors powerfully confirm that this article offers convenient information’s to enthusiastic engineers and new researchers those who are all working in solar PV systems.

A QUASI-Z-SOURCE Converter Using a Single-Stage Switched-Inductor Boost DC-DC Converter

Abstract: This paper introduces a new topology, yet simple and efficient, for a grid-connected wind-solar cogeneration system. A permanent magnet synchronous generator-based full-scale wind turbine is interconnected to the utility grid. The dc-link capacitor has been utilized to directly interface a photovoltaic solar generator. No dc/dc conversion stages are required, and hence, the hybrid system is simple and efficient. Moreover, the proposed topology features an independent maximum power point tracking for both the wind and the solar generators to maximize the extraction of renewable energy. This paper presents a quasiZ-source inverter (qZSI) that is a new topology derived from the traditional Z-source inverter (ZSI). The qZSI inherits all the advantages of the ZSI, which can realize buck/boost, inversion and power conditioning in a single stage with improved reliability. In addition, the proposed qZSI has the unique advantages of lower component ratings and constant dc current from the source. All of the boost control methods that have been developed for the ZSI can be used by the qZSI. One of the more commonly used techniques is the incremental conductance method. In this paper, an improved particle swarm optimization- (IPSO-) based MPPT technique for photovoltaic system operating under varying environmental conditions is proposed. Basically, an Artificial Neural Network (ANN) based approach is utilized to automatically detect the global maximum power point of the PV array by using a preselected number of power measurements of the PV system. The approach of linearly decreasing scheme for weighting factors and cognitive and social parameters is modified. The proposed control scheme can overcome deficiency and accelerate convergence of the IPSO-based MPPT algorithm. Detailed small-signal models for the system components are developed to characterize the overall stability. The influence of the utility-grid faults on the performance of the proposed system is also investigated. Nonlinear time-domain simulation results under different operating conditions are presented to validate the effectiveness of the proposed topology.

Solar PV Combined Efficient Torque Control of BLDC Motor Using Salp Swarm Optimization

This study provides an efficient torque and speed control method for brushless DC (BLDC) motor utilizing a fractional order proportional integral derivative (FOPID) controller based on the multiverse optimization algorithm and salp swarm optimization (SSO). When combined with a solar photovoltaic (PV) system and a voltage boosting architecture, standard PID controllers regulated the BLDC drive parameters due to their simplicity and improved steady-state performances. A solar PV system with a specific power range is chosen for the best output solution on the output side. The MPPT method employed is the incremental conductance approach for tracking maximum power from the source. However, it has a problem with unpredictability owing to load changes. The PID controller tuning also relates to the structure’s parametric uncertainties. Therefore, precise control methods may be offered with the aid of the FOPID controller to address the aforementioned issues. Simulation results of the proposed MVO and SSO-based FOPID controller for BLDC speeds run in the Matlab Simulink platform. To confirm the adaptability of the suggested control scheme for BLDC motor, the outcomes are interrelated using genetic algorithm, gray wolf, and PSO-based FOPID controllers.

Design and Implementation of a Low-Voltage Photovoltaic System Integrated with Battery Energy Storage

In this paper, the simulation and design of a power converter suitable for a low-voltage photovoltaic (PV) battery energy storage converter was investigated. The converter was suitable for sources and loads with near voltage levels and were aimed at efficiency improvement. The converter was called a series partial power converter (SPPC). A continuous current and a boost SPPC topology based on an isolated Cûk converter was constructed. The operation modes of the converter were analyzed. The efficiency verification test of the SPPC in a laboratory environment was completed using an outdoor start-up test, a photovoltaic full cover test and a steady-state operation test under different power levels. The results show that the SPPC can achieve 95–98% converter efficiency within the 87 W–242 W power range. The incremental conductance method based on the SPPC can realize the dynamic MPPT under constant illumination and changing meteorological conditions.

Synthesis of Voltage PID Controller to Improve INC- MPPT Algorithm for Cascade Regulation of KC200GT Panel-Based Solar System

This paper proposes the Maximum Power Point Tracking MPPT method based on the Incremental Conductance INC algorithm. The primary goal is to prevent the ripple problem that is occurred in the output responses of the solar panel system. This last includes the KC200GT panel system, while using it to supply a resistive load throughout the DC-DC boost converter. This goal is reached through respecting the two following steps. First, the INC algorithm modified so that it produces an optimal reference voltage rather than a duty cycle control, which is often utilized in the standard INC-MPPT control strategy for controlling the switch button of the DC-DC boost converter. Second, the solar system behavior is modeled by the linear small-signal model for the design of the voltage PID controller. At each sample, a voltage discrepancy is generated as a result of comparing the previous reference voltage to the one delivered by the KC200GT panel where the introduction of the voltage PID controller becoming indispensable. These two steps create the development of a novel strategy that is afterwards known as improved INC-MPPT. The performance assessments of the proposed strategy are carried out by simulation using MATLAB®/SIMULINK software, and the obtained results reveal the crucial importance of including the voltage PID controller to overcome the ripple issue, occurred when applying the standard INC-MPPT control strategy.

Novel Incremental Conductance Feedback Method with Integral Compensator for Maximum Power Point Tracking: A Comparison Using Hardware in the Loop

Research on renewable energy sources and power electronic converters has been increasing due to environmental concerns. Many countries have established targets to decrease CO2 emissions and boost the proportion of renewable energy, with solar power being a prominent area of investigation in the recent literature. Techniques are being developed to optimize the energy recovered from PV cells and increase system efficiency, including modeling PV cells, the use of converter topologies to connect PV systems to high-power inverters, and the use of MPPT methods. Certain MPPT algorithms are intricate and demand high processing power. The literature describes several MPPT methods; however, the number of hardware resources required by MPPT algorithms is typically not disclosed. This work proposes a novel MPPT technique based on integral feedback conductance and incremental conductance error, considering the current dynamics of the boost converter. This MPPT algorithm is compared to the most widely used techniques in the literature and evaluates each method’s efficiency, performance, and computational needs using an HIL system. Comparisons are made with well-known MPPT algorithms, such as perturb and observe, incremental conductance, and newer techniques based on fuzzy logic and neural networks (NNs). As the NN that is most widely used in the literature depends on irradiation and temperature, an additional NN that is trained using the proposed method is also investigated.

Real-Time Dynamic Performance Enhancement for Solar-Powered Pumping Systems Using PI-Based MPPT Techniques

Abstract The integration of solar-powered pumping systems (SPPS) into agriculture and potable and wastewater sectors becomes mandatory to provide water in remote regions. The broad use of SPPS with classical maximum power point tracking controllers (MPPTCs) showed moderated voltage and power response deterioration. Therefore, the necessity for overcoming these performance degradations and pre-testing of MPPTCs is not an option for the proper operation of such systems. This paper presents a new simple, cost-effective real-time hardware-in-the-loop (RT-HIL) framework to enhance the dynamic performance of SPPS. To accomplish this study, a real pumping station was modeled and equipped with MPPTCs through matlab/simulink. Besides, a practical SPPS was implemented to evaluate the effectiveness of the proposed RT-HIL on system performance. The practical SPPS includes designing a DC-DC buck converter circuit equipped with metaheuristic optimization-based real-time MPPTCs. The tuned PI/FOPI-based MPPTCs are adopted in this work to gain the maximum power from the PV generator under measured real environmental conditions. The proposed real-time MPPTCs techniques are perturb and observe, and incremental conductance (IC) with I, PI, and fractional-order PI (FOPI) controllers. The simulation and the experimental results prove the superiority of the developed real-time FOPI-based MPPTCs in enhancing the system performance in terms of the gained power, module output current, pump flowrate, and pump efficiency. The paper’s novelty lies behind the relatively low-cost real-time execution of PI/FOPI-based MPPT techniques on SPPS. This work was simulated using matlab/simulink in conjunction with Arduino-based RT-HIL and the experimental validation was implemented at the National Water Research Center (NWRC) in Egypt.

A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization

In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The proposed algorithm was compared with other well-known methods viz. Perturb & Observer (P&O), Incremental Conductance (INC), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), Flower Pollen Algorithm (FPA), Gray Wolf Optimization (GWO), Neural-Network-trained Machine Learning (NN_ML), Genetic Algorithm (GA), and PSO-trained Machine Learning. The proposed algorithm was modelled in the MATLAB/Simulink environment under different operating conditions, for example, with step changes in temperature, solar irradiance, and partial shading. The proposed algorithm improved the efficiency up to 0.72% and reduced the settling time up to 76.4%. The findings of the research highlight that PSO_ML-FSSO is a potential approach that outperforms all other well-known algorithms tested herein for solar PV systems.

Optimal Parameter Identification of Single-Sensor Fractional Maximum Power Point Tracker for Thermoelectric Generator

A thermoelectric generator (TEG) is used for converting temperature difference and into DC directly to electric energy based on the Seebeck effect. This new technology has attracted researchers of sustainable energy. The energy obtained from the TEG depends on the temperature difference between the two sides of the TEG. A reliable MPP “maximum power point” tracker (MPPT) is mandatory to guarantee that the TEG is working close to the MPP under different operational conditions. There are two common methods that have been widely used to track the MPP: hill climbing (HC) and incremental conductance (INR). The HC method is very fast in tracking the MPP; however, oscillation can occur under a high steady state. On the contrary, the INR method needs more time to track the MPP but does not oscillate around the MPP. To overcome these issues, fractional control is adopted. Furthermore, the proposed MPPT requires only a single current sensor, as opposed to conventional MPPTs, which require at least two sensors: current and voltage sensors. The cost of the control system is reduced when the number of sensors is reduced. Hunger games search optimization is used to estimate the parameters of a single sensor optimized fractional MPPT (OFMPPT). During the optimization process, three parameters were assigned as decision variables: proportional gain, integral gain, and order, with the objective function being the TEG’s energy. The results demonstrated the superiority of OFMPPT in both transient and steady state compared to HC and INR.

A Novel MPPT Technique Based on Combination between the Incremental Conductance and Hysteresis Control Applied in a Standalone PV System

A new Maximum Power Point Tracking (MPPT) method, consisting in combining the Incremental Conductance (INC) algorithm with the Hysteresis control, was developed and applied to a standalone photovoltaic (PV) system to generate the maximum power of the PV array. The INC allows one to search for the Maximum Power Point (MPP). The hysteresis improves the accuracy of tracking the MPP very fast even after severe changes in weather conditions and has no oscillations around the MPP. The five-level S-Packed U Cells (SPUC5) inverter is used to transform the produced DC voltage to AC voltage; it generates five-level output voltage with a small number of switches and only DC source voltage. The capacitors of the SPUC5 are controlled by the Pulse Width Modulation (PWM) in order to balance their voltages. The proposed PV system was established and trained in the MATLAB/Simulink environment under various irradiation conditions. A comparison between different MPPT methods, INC-PWM and INC-PI, was investigated in order to examine the effectiveness of the developed MPPT technique in particular, and of all the PV system components. The results of the simulation validate the effectiveness of the suggested MPPT algorithm as well as the used SPUC5 inverter.

A new maximum power point tracking method for PEM fuel cell power system based on ANFIS with modified manta ray foraging algorithm

The proton exchange membrane fuel cell (PEMFC), which is accounted as a reliable fuel cell (FC) technology, is mainly famous for its low operating temperature and expanded lifespan while also being highly efficient. Such merits have turned the PEMFC into an interesting option to be employed in a wide range of applications, comprising those for backup power, portable power, and vehicle power sources. Due to the output power’s dependence on temperature and membrane water content, the PEMFC, however, faces a number of difficulties. The FC system’s maximum output power can only be achieved at one specific operating point under varying operating circumstances. Therefore, maximizing the power obtained from the PEMFC is essential for improved operation and effective exploitation. It is noteworthy that a combinatorial technique using the developed modified manta ray foraging optimization (DMRFO) method and enhanced adaptive neuro-fuzzy inference system (ANFIS)-based incremental conductance (INC) has been deployed in this paper for the maximum power point tracking (MPPT) to optimally increase the power extraction capacity of an FC-resourced energy system. This method consists of two steps. In the first stage, the DMRFO algorithm is used to specify the best power levels while taking temperature and membrane water content fluctuations into account. This information would be utilized as the input–output training dataset in the ANFIS. The INC approach would initialize at this value in the second stage and seek the highest maximum power point (MPP). The dataset with the shrunk size given by the proposed DMRFO method for training the ANFIS in contrast to prevalent ANFIS is one of the benefits of adopting the ANFIS-based INC technique. This approach is also very quick, stable, and efficient. By taking into account a PMFC with a capacity of 7,000 W, the recommended technique is simulated and the results are compared with other methods to validate the performance.

Power quality performance enhancement by PV-based distribution static compensator under incremental conductance maximum power point tracking algorithm

In this work, a PV-based Distribution Static Compensator (DSTATCOM) system has been integrated into the three-phase grid having linear/non-linear loading conditions. The complete DSTATCOM system has two stage converters i.e., DC-DC converter and voltage source converter (VSC). DC-DC converter has been controlled by the Incremental Conductance based MPPT algorithm while the voltage source converter has been controlled by the power balance theory (PBT)/ unit vector template (UVT) technique. These two converters are coupled with a DC link and connected at the common intersection point of the grid. The complete PV-based DSTATCOM system along with the grid has been developed and executed in the MATLAB/Simulink platform. The power quality performances have been observed in terms of DC voltage regulation and grid current harmonics mitigation for linear/non-linear loading conditions. Moreover, the THD regulation for the grid current has been verified by the IEEE 519–2014 and IEC 6100–3–2 standard.

Maximum power point tracker using an intelligent sliding mode controller of a photovoltaic system

<span lang="EN-US">The operating performance of a PV module/array is extremely reliant on the weather (temperature/irradiation) and non-linear. Thus, to ensure that the PV array produces the maximum possible power at any time and regardless of the external conditions, maximum power point tracking (MPPT) techniques are required. The solution suggested in this paper involves taking into account two cascaded controllers as follows; the incremental conductance (INC) controller, which is intended to provide a reference proportional to the PV array's optimal power P<sub>MPP</sub>, and the sliding mode control (SMC), which is in charge of controlling the GPV voltage. The strategy of the SMC is to design a sliding surface that defines the operating point. The SMC combined with the INC aims to achieve fast MPPT action on PV systems using cascade control. The proposed controller is robust to changing weather conditions. In order to evaluate what is done, the results are compared with the INC+PI controller. When an abrupt change occurs, the SMC has a low transient and arrives to equilibrium sooner than the INC+PI controller. the results are presented by the PSIM software, and demonstrate the SMC controller's performance while confirming that the new approach has increased both production and energy efficiency.</span>