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Due to natural randomness, partial shading conditions (PSCs) to photovoltaic (PV) power generation significantly drop the power generation. Metaheuristic based maximum power point tracking (MPPT) can handle PSCs by searching PV panels’ global maximum power point (GMPP). However, trapped at local maxima, sluggishness, continuous power oscillations around GMPP and inaccuracy are the main disadvantages of metaheuristic algorithm. Therefore, the development of algorithm under complex PSCs has been continuously attracting many researchers to yield more satisfying results. In this paper, several algorithms including conventional and metaheuristic are selected for candidate, such as perturb and observe (P&O), firefly (FF), differential evolution (DE), grey wolf optimizer (GWO) and Seagull optimizer (SO). From the preliminary study, SO has shown best performance among other candidates. Then, SO is improved for rapid global optimizer. Modified variable step sizes perturb and observe (MVSPO) is applied to enhance the accuracy tracking of SO. To evaluate the performances, high complexity multipeak partial shading is used to test the algorithms. Statistical results are also provided to analyze the trend of performances. The proposed method performances are shown better fast-tracking time and settling time, high accuracy, higher energy harvesting and low steady-state oscillations than other candidates.

The increasing development of renewable energy, especially photovoltaic (PV) more applicable in daily use. Unfortunately, PV has the disadvantage that it is vulnerable to shadow exposures that decrease power output depending on the scale of the shadow. The disadvantage includes the shadow of buildings, leaves, trees, etc. Shaded PV surface has two peak power conditions named Global Maximum Power Point (GMPP) and Local Maximum Power Point (LMPP). These conditions cause MPPT to be trapped in the LMPP so that the power obtained is not the actual power. The conventional method can be trapped in LMPP because it cannot distinguish GMPP and LMPP. These problems can be solved by using the Human Psychology Optimization (HPO) algorithm. This algorithm was chosen to overcome the effects of partial shading conditions so that MPPT can reach GMPP without getting stuck in LMPP. This algorithm is connected to ZETA Converter to produce real maximum power points. This research uses four shading patterns with different irradiation. HPO algorithm achieves the highest accuracy 99.99% with a tracking time 0.326 seconds occurring in the first shading pattern.

This paper shows that the design and implementation of a parallel full bridge DC-DC converter for a maximum power point tracker (MPPT) system with an incremental conductance method. This system consists of a parallel full-bridge DC-DC converter that will increase the output voltage of solar panels with a power of 3x200 WP. The output voltage of the converter will be converted into AC voltage by single phase inverter by using SPWM switching. The results of this system will be used to supply the water pump loads with a power is 125 watts and a frequency of 50 Hz. The system used is able to increase the output power of solar panels with an average value of 19.68% for a 100 watts incandescent lights arranged in series, the load using a water pump with no load conditions that is equal to 35.91%, while the load using a water pump with load conditions that is equal to 21.19%.

Photovoltaic is a device that can convert sun’s rays as a source of energy into electricity. However, the erratic intensity of sunlight can cause unstable electricity which generated by photovoltaic. Maximum Power Point Tracking (MPPT) is one of solution to maximize the output power of photovoltaic. Artificial Neural Network (ANN) method used to maximize the output power of photovoltaic. ANN is a computational method based on the neuron system. Short circuit current based ANN MPPT which applied for battery charging was proposed in this paper. The ANN will installed to the microcontroller in order to control the duty cycle for obtaining maximum power from the photovoltaic. Short circuit current from photovoltaic used as input of the ANN method while duty cycle used as the target. Based on the experiment, the MPPT ANN control is able to produce maximum power in various types of irradiance. When compared with the power produced by Non MPPT, MPPT ANN increased the power between 0,1 - 12,63 W on various kinds of irradiance data. When it used to charge the battery for 120 minutes (at 10:30 a.m. - 12:30 p.m.), MPPT ANN was able to increase the battery SOC by 20% while Non MPPT was only able to increase the battery SOC by 15%.

During its operation, a photovoltaic system may encounter many practical issues such as receiving uniform or non-uniform irradiance caused mainly by partial shading. Under uniform irradiance a photovoltaic panel has a single maximum power point. Conversely under non-uniform irradiance, a photovoltaic panel has several local maximum power points and a single global maximum power point. To maximize energy production, a maximum power point tracker algorithm is commonly implemented to achieve the maximum power operating point of the photovoltaic panel. However, the performance of the algorithm will depend on operating conditions such as variation in irradiance. Presently, most of existing maximum power point tracker algorithms work only in a single condition: either uniform or non-uniform irradiance. This paper proposes a new maximum power point tracker algorithm for photovoltaic power generation that is designed to work under uniform and partial shading irradiance conditions. Additionally, the proposed maximum power point tracker algorithm aims to provide: (1) a simple math algorithm to reduce computational load, (2) fast tracking by evaluating progress for every single executed duty cycle, (3) without random steps to prevent jumping duty cycle, and (4) smooth variable steps to increase accuracy. The performances of the proposed algorithm are evaluated by three conditions of uniform and partial shading irradiance where a targeted maximum power point is located: (1) far from, (2) near, and (3) laid between initial positions of particles. The simulation shows that the proposed algorithm successfully tracks the maximum power point by resulting in similar power values in those three conditions. The proposed algorithm could handle the partial shading condition by avoiding the local maxima power point and finding the global maxima power point. Comparisons of the proposed algorithm and other well-known algorithms such as differential evolution, firefly, particle swarm optimization, and grey wolf optimization are provided to show the superiority of the proposed algorithm. The results show the proposed algorithm has better performance by providing faster tracking, faster settling time, higher accuracy, minimum oscillation and jumping duty cycle, and higher energy harvesting.

Electronic equipment in household loads in the form of non-linear loads that require a circuit that requires a power supply in the form of a direct voltage (DC) that requires an AC to DC rectifier in the form of a full-wave rectifier. However, the installation of capacitor values in the circuit can cause the waveforms of the input current to become uneven or distorted so that the emergence of harmonics which results in a low power factor with the improvement of the power factor. It affects the output voltage to the load so we need control for the voltage regulator. The solutions to overcome the problem of input current defects can be overcome using a power converter used as a PFC. In this paper, CUK Converter used as PFC is expected to get close to 1 (one) results in increasing power factor and the need for a voltage regulator to regulate the voltage requirements for loads that are utilized by the Moth Flame Optimization algorithm method - PI method. This CUK Converter functions in DCM (Discontinuous Conduction Mode) which makes the system resistive. In conclusion from the results of simulations that have been done, the results obtained from the use of CUK Converter as PFC can improve the power factor from pf value 0.731 to 0.994 and with MFO-PI control it can maintain a constant output voltage of 12 volts with a current of 5 amperes.

This paper presents the implementation of a power converter to improve power factor for LED lamp driver. The power converter which used in this system is the integration of boost and flyback converter (boost integrated flyback rectifier energy storage DC-DC/BIFRED). The boost converter as power factor correction (PFC) works on discontinuous conduction mode (DCM) operation to make the resistive converter. Thus, when a rectifier circuit supplies a resistive load, the load current that flows back to the source will have the same waveform as the voltage and it makes the power factor value next to 1 (unity). According to experiment results, the BIFRED converter as LED lamp driver can improve power factor from 0.84 to become 0.98 and this driver circuit also meets the line-current harmonic limits set by IEC61000-3-2 class C.

Renewable energy is needed to reduce sources of electrical energy utilization derived from coal and fossil fuel. Photovoltaic is a component of renewable energy that used sunlight to produce electrical energy. The problem that occurs in photovoltaic is the output power that changes depending of light intensity. That problem causes the output power in the Photovoltaic to supply the load to be unstable due to changes in sunlight The solution of the problem is using the bidirectional converters. The bidirectional converter are currently widely used to store excess power in energy storage system and keep output voltage of the system on stable condition. Therefore this problem requires a good control to be applied to the bidirectional converter. The PID controller can be applied to the bidirectional converter to keep the voltage on the DC bus steady at the set point. Therefore, this paper proposed the performance of bidirectional SEPIC/Zeta converters using PID controller to solve that problem. The experiment result shows the PID controller can keep the DC bus voltage in stable condition. Average time speed to reach set point when charging mode is 1,024 ms with average error is 0,08%. On the other side average time speed to reach set point when discharging is 1,288 ms with average error is 0,6012%.

Solar panel is widely used as an alternative source to generate solar power plant. One of the ways to store solar panel power is by saving the power on a battery. Generally, the efficiency of solar panel is about 15% - 24.7%. It is quite small because the use of solar panel without control causes the power plant not maximum. In this paper, Modified Particle Swarm Optimization (Modified PSO) was proposed for MPPT control to optimize solar panel power. The algorithm of this method was used to increase efficiency of solar cell so that the power can reach maximum point. Furthermore, the power was used to optimize battery charging process. To verify the performance of Modified PSO algorithm, it was used SEPIC Converter as interfacing from solar panel to battery. Based on the experiment result on three shading patterns for 3 hours charging process, the increase of State of Charge (SOC) of the battery using Modified PSO algorithm was superior than uncontrolled and conventional method (Perturb and Observe).

In power plant process, a stable generator output voltage is needed. However, if the load on generator changes, it can cause unstable generator output voltage. This condition is not desirable in power plant because it can cause problem in electrical devices. To overcome this problem, an AVR (Automatic Voltage Regulator) is connected to the generator. In this paper, the AVR has been designed and implemented using PID-Cuckoo Search Algorithm method. This method is applied to adjust a firing angle of three phase semi-converter in order to stabilize the generator output voltage. From some experiment, it was shown the result of PID-CSA control can return generator output voltage response back to 380 Volt point with 0.26% of error. PID-CSA control could generate rise time and settling time faster than PID control. At the initial condition, PID-CSA control could produce 0.28 seconds of rise time and 0.4 seconds of settling time while PID control required 0.45 seconds of rise time and 0.6 seconds of settling time. On the 2 A of load, PID-CSA required settling time 0.2 seconds faster than PID control as well as when the load released condition from 2 A to 0 A, CSA PID required settling time 0.2 seconds faster than PID control.

PV system (Photovoltaic) is a system that many discussed and researched by researchers in the world. Unfortunately, when converting solar energy into electrical energy, the Maximum Power Point Tracking (MPPT) is required to obtain maximum power output in PV. In addition, the problems that occur are when the PV is partially shaded resulting in the emergence of Local Maximum Power Point (LMPP) and Global Maximum Power Point (GMPP). The last problem is when the power of PV is maximized, the voltage changes rapidly at the output of the converter while the voltage required to enter the DC bus is a constant and stable voltage. Therefore, this paper proposes the modeling and simulation of MPPT-SEPIC converter using modified PSO series constant voltage Boost converter using FLC (Fuzzy Logic Controller) under dynamic partial shading condition in DC microgrid system. MPSO algorithm is used to maximize power on PV accurately through Single Ended Primary Inductance converter (SEPIC), while FLC is used to make the voltage to DC bus becomes constant and stable through Boost converter.

Photovoltaic (PV) module is used to convert solar energy to electrical energy. The output power of PV module is affected by the amount of irradiation of sun and temperature of PV module. The problem that may occur is partial shading caused by some objects such as trees, building, clouds, and so on. The impact of this condition is the decrease of output power of PV and the maximum power point (MPP) become more than one. Maximum power point of P-V characteristic curve will be more than one such as GMPP (Global Maximum Power Point) and LMPP (Local Maximum Power Point). This case cannot be solved using the ordinary MPPT (Maximum Power Point Tracking) method because the maximum power is trapped on wrong point, it is LMPP. Therefore, to overcome this problem, Modified P&O (Perturb and Observe) method is proposed as a MPPT method under partial shading condition. The proposed method is implemented on SEPIC converter to get actual maximum power point (GMPP). Experiment results prove the Modified P&O method can generate greater power and faster charging process on battery. The State of Charge of battery increases about 30% if the Modified P&O method, which is applied on battery charging system.Photovoltaic (PV) module is used to convert solar energy to electrical energy. The output power of PV module is affected by the amount of irradiation of sun and temperature of PV module. The problem that may occur is partial shading caused by some objects such as trees, building, clouds, and so on. The impact of this condition is the decrease of output power of PV and the maximum power point (MPP) become more than one. Maximum power point of P-V characteristic curve will be more than one such as GMPP (Global Maximum Power Point) and LMPP (Local Maximum Power Point). This case cannot be solved using the ordinary MPPT (Maximum Power Point Tracking) method because the maximum power is trapped on wrong point, it is LMPP. Therefore, to overcome this problem, Modified P&O (Perturb and Observe) method is proposed as a MPPT method under partial shading condition. The proposed method is implemented on SEPIC converter to get actual maximum power point (GMPP). Experiment results prove the Modified P&O method can gen...

Solar energy can be utilized through photovoltaic (PV) into electrical energy. The output power of PV can reach maximum point when the irradiation of the sun is exposed to the surface without any object being obstructed. Unfortunately, In the actual implementation of irradiation received by PV it is often blocked by a moving object or a shadow of a non-moving object. This problem resulted in the emergence of more than one maximum power point on the P-V characteristic curve. The condition can't be solved by using the usual MPPT Algorithm so that MPPT Partial shading algorithm is required. Partial shading algorithm has been developed from conventional methods to artificial intelligence methods. Hence, in this paper proposed comparison method of MPSO, FPA, and GWO in MPPT SEPIC converter under dynamic partial shading condition. The level of accuracy of all three methods is validated through simulation. The simulation results show that the three methods are able to overcome the problem of partial shading very well with a high level of accuracy. The simulation results also show that the FPA method has advantages compared to other proposed methods. The simulation results also show that the MPSO method has more advantages compared to other proposed methods in tracking accuracy and the FPA method is faster to reach convergence situation by other proposed method.

Solar energy source can be utilized into electric energy using Photovoltaic (PV) panel. Maximum power in PV can be obtained in sunny weather conditions, optimum temperature, and unshaded surface. When PV is blocked by an object, the resulting power will decrease depending on the size of the barrier. Barriers of the sun intensity are shadows of trees, buildings, peoples, and others. These conditions cause PV can't absorb the maximum intensity of sunlight. Unfortunately, an obstructed PV surface condition may affect the resulting characteristic curves. These conditions can produce different characteristic curves. The difference is the position of the maximum power point that should be only one that is GMPP (Global Maximum Power Point) but this is divided into two there are GMPP and LMPP (Local Maximum Power Point). These conditions make a simple MPPT trapped in LMPP or an unreal maximum power point. To solve this problem, in this paper proposed MPPT Partial Shading using Modified Particle Swarm Optimization (MPSO) method. The MPSO method is chosen to solve partial shading conditions so the MPPT can reach GMPP without being trapped in LMPP. The MPSO method is applied to the Sepic converter to generate maximum power. According to simulation result, the MPSO method has a good accuracy greater than 95% and a fast time convergence is about 0.5 until 1 second to obtain a MPP depend on the shading pattern.

This paper presents a combination of power factor correction converter using Flyback converter and Full-bridge dc-dc converter in series connection. Flyback converter is operated in discontinuous conduction mode so that it can serve as a power factor correction converter and meanwhile Full-bridge dc-dc converter is used for dc regulator. This converter system is designed to produce a 86 Volt of output voltage and 2 A of output current. Both simulation and experiment results show that the power factor of this converter achieves up to 0.99 and meets harmonic standard of IEC61000-3-2.Keywords: Flyback Converter, Full-bridge DC-DC Converter, Power Factor Correction.

The electrical energy generated by the photovoltaic (PV) as a renewable energy source highly affected by environmental conditions such as intensity of sunlight irradiance, temperature, geographic location and tilt angle of PV itself. How much power should be generated by the PV for every times and anywhere will be discussed in this paper. This paper are implemented models of clear sky solar irradiance, solar position and PV temperature to predict the power output should be generated by PV. The model is implemented on the ARM Cortex M4F microcontroller STM32F407 which is a 32bit microcontroller and equipped with DSP, so the prediction of PV power output can be done online and in real time. To be able to predict the PV power output online, at any time and wherever they are, in this study microcontroller equipped with temperature sensors and input geographical information (latitude-longitude) and also equipped with a memory card for data logger between the predictions and field measurement. Results have been obtained by field experiments, measurements test for PV is very close to predictions with an average error 4.72% and computation time for all models by microcontroller with DSP instruction 33.64% faster compare to without DSP instruction. Keywords : Photovoltaic, Real time power prediction and Microcontroller. Abstrak —Energi listrik yang dihasilkan oleh photovoltaic (PV) sebagai sumber energi terbarukan sangat terpengaruh oleh kondisi lingkungan seperti besar kecilnya intensitas iradiasi sinar matahari, suhu, letak geografis dan orientasi kemiringan dari PV itu sendiri. Berapa daya yang seharusnya dihasilkan oleh PV untuk setiap saat dan dimana saja akan dibahas pada makalah ini. Pada makalah ini mengimplementasi clear sky solar irradiance , solar position dan PV temperatur mode l untuk memprediksi daya output yang seharusnya dihasilkan oleh PV. Model tersebut diimplemantasikan pada mikrokontroller ARM Cortex M4F STM32F407 yang merupakan mikrokontroller 32bit dan dilengkapi dengan DSP, sehingga prediksi daya ou tput PV dapat dilakukan secara online dan real time . Untuk dapat memprediksi daya output PV secara online , setiap saat dan dimana saja berada, maka pada makalah ini mikrokontroler dilengkapi dengan sensor suhu dan input informasi geografis berupa lintang-bujur dan dilengkapi juga dengan memory card untuk data logger antara daya hasil prediksi dan daya hasil pengukurang dilapangan. Hasil yang telah diperoleh dari percobaan lapangan menunjukkan bahwa daya hasil pengukuran PV terhadap prediksi daya melalui model sangat mendekati dengan rata-rata error 4.72% dan penggunaan instruksi DSP pada mikrokontroler untuk perhitungan model waktu komputasinya 33.64% lebih cepat dibandingkan tanpa instruksi DSP Kata Kunci : Photovoltaic, Prediksi daya secara real time dan Mikrokontroler