A Feed forward Neural Network MPPT Control Strategy Applied to a Modified Cuk Converter

This paper presents an intelligent control strategy that uses a feedforward artificial neural network in order to improve the performance of the MPPT (Maximum Power Point Tracker) MPPT photovoltaic (PV) power system based on a modified Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characteristic of the Cuk converter due to the switching technique is difficult to be handled by conventional controller. To overcome this problem, a neural network controller with online learning back propagation algorithm is developed. The NNC designed tracked the converter voltage output and improve the dynamic performance regardless load disturbances and supply variations. The proposed controller effectiveness during dynamic transient response is then analyze and verified using MATLAB-Simulink. Simulation results confirm the excellent performance of the proposed NNC

Učinkoviti optimalni regulator uzlaznog DC/DC pretvarača za sustav fotonaponskih ćelija za praćenje točke maksimalne snage temeljen na umjetnim neuronskim mrežama

In this paper, a simulation study of the maximum power point tracking (MPPT) for a photovoltaic system using an artificial neural network is presented. Maximum power point tracking (MPPT) plays an important role in photovoltaic systems because it maximizes the power output from a PV solar system for all temperature and irradiation conditions, and therefore maximizes the power efficiency. Since the maximum power point (MPP) varies, based on the PV irradiation and temperature, appropriate algorithms must be utilized to track it in order maintain the optimal operation of the system. The software Matlab/Simulink is used to develop the model of PV solar system MPPT controller. The system simulation is elaborated by combining the models established of solar PV module and a DC/DC Boost converter. The system is studied using various irradiance shading conditions. Simulation results show that the photovoltaic simulation system tracks optimally the maximum power point even under severe disturbances conditions.U ovom je radu prikazana simulacijska studija postupka traženja točke maksimalne snage za sustav fotonaponski ćelija korištenjem umjetne neuronske mreže. Traženje točke maksimalne snage važno je za sustave fotonaponskih ćelija zato što maksimizira izlaznu snagu fotonaponskih ćelija za sve temperature i osunčanosti te na taj način maksimizira učinkovitost. Točka maksimalne snage varira s promjenom osunčanosti i temperature te je potrebno koristiti prikladne algoritme za praćenje kako bi se osiguralo optimalno funkcioniranje sustava. Za razvoj modela regulatora za praćenje točke maksimalne snage, koji upravlja sustavom fotonaponskih čelija, koristio se Matlab/Simulink. Simulacija je provedena kombinacijom modela modula solarnih fotonaponskih ćelija i DC/DC uzlaznog pretvarača. Sustav se promatrao tijekom različitih uvjeta osunčanosti i zasjenjenosti. Simulacijski rezultati pokazuju da simulacijski sustav fotonaponskih ćelija optimalno prati točku maksimalne snage i u uvjetima većih poremećaja

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

In this paper, the behavior of a grid-connected hybrid ac/dc microgrid has been investigated. Different renewable energy sources - photovoltaics modules and a wind turbine generator - have been considered together with a solid oxide fuel cell and a battery energy storage system. The main contribution of this paper is the design and the validation of an innovative online-trained artificial neural network-based control system for a hybrid microgrid. Adaptive neural networks are used to track the maximum power point of renewable energy generators and to control the power exchanged between the front-end converter and the electrical grid. Moreover, a fuzzy logic-based power management system is proposed in order to minimize the energy purchased from the electrical grid. The operation of the hybrid microgrid has been tested in the MATLAB/Simulink environment under different operating conditions. The obtained results demonstrate the effectiveness, the high robustness and the self-adaptation ability of the proposed control system

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

Photovoltaic (PV) energy, representing a renewable source of energy, plays a key role in the reduction of greenhouse gas emissions and the achievement of a sustainable mix of energy generation. To achieve the maximum solar energy harvest, PV power systems require the implementation of Maximum Power Point Tracking (MPPT). Traditional MPPT controllers, such as P&O, are easy to implement, but they are by nature slow and oscillate around the MPP losing efficiency. This work presents a Reinforcement learning (RL)-based control to increase the speed and the efficiency of the controller. Deep Deterministic Policy Gradient (DDPG), the selected RL algorithm, works with continuous actions and space state to achieve a stable output at MPP. A Digital Twin (DT) enables simulation training, which accelerates the process and allows it to operate independent of weather conditions. In addition, we use the maximum power achieved in the DT to adjust the reward function, making the training more efficient. The RL control is compared with a traditional P&O controller to validate the speed and efficiency increase both in simulations and real implementations. The results show an improvement of 10.45% in total power output and a settling time 24.54 times faster in simulations. Moreover, in real-time tests, an improvement of 51.45% in total power output and a 0.25 s settling time of the DDPG compared with 4.26 s of the P&O is obtained

The design, management and testing of a solar vehicle's energy strategy

In recent years the interest in implementing solar energy on vehicles (electrical and hybrid) has grown significantly [1]. There are currently limitations in this sector, such as the low energy density (efficiency of conversion) of this source, but it is still a renewable resource and as such, there is a growing interest [1]. A “smart” energy strategy implemented on a solar/electrical vehicle, in order to increase its energy harvesting volume, could enhance the growth of this sector. A tracking algorithm for a solar vehicle’s MPPT (Maximum Power Point Tracker) can be designed to source solar energy very effectively and to increase the speed of finding (tracking) this optimal sourcing point (solar panel voltage and current). Even though there are many different MPPT algorithms, it was decided that most of them were designed for stationary MPPT applications and the dynamics of implementing a MPPT on a vehicle create some unique scenarios. These include: Shadow flicker. This is rhythmic, rapid moving shadows across a solar panel, such as shadows from a line of trees: Rapid changes in solar panel orientation due to the road surface/relief; Rapid changes in panel temperature due to the location of the vehicle. The aim of the research can be divided into three outcomes: 1 Creating a “Smart” energy strategy/control, 2 Implement the new control system on a solar vehicle’s MPPT, and 3 Harvesting maximum energy from solar panels using the new energy strategy. The term “smart” is used to indicate the ability of the MPPT algorithm to be updated and improved based on previous results. A MPPT and scaled solar vehicle is designed and manufactured in order to test the MPPT algorithm. The purpose of using a self-developed experimental setup is to have more control over the system variables as well as having the maximum freedom in setting up the system parameters

Performance Analysis Of Hybrid Ai-Based Technique For Maximum Power Point Tracking In Solar Energy System Applications

Demand is increasing for a system based on renewable energy sources that can be employed to both fulfill rising electricity needs and mitigate climate change. Solar energy is the most prominent renewable energy option. However, only 30%-40% of the solar irradiance or sunlight intensity is converted into electrical energy by the solar panel system, which is low compared to other sources. This is because the solar power system\u27s output curve for power versus voltage has just one Global Maximum Power Point (GMPP) and several local Maximum Power Points (MPPs). For a long time, substantial research in Artificial Intelligence (AI) has been undertaken to build algorithms that can track the MPP more efficiently to acquire the most output from a Photovoltaic (PV) panel system because traditional Maximum Power Point Tracking (MPPT) techniques such as Incremental Conductance (INC) and Perturb and Observe (P&Q) are unable to track the GMPP under varying weather conditions. Literature (K. Y. Yap et al., 2020) has shown that most AIbased MPPT algorithms have a faster convergence time, reduced steady-state oscillation, and higher efficiency but need a lot of processing and are expensive to implement. However, hybrid MPPT has been shown to have a good performance-to-complexity ratio. It incorporates the benefits of traditional and AI-based MPPT methodologies but choosing the appropriate hybrid MPPT techniques is still a challenge since each has advantages and disadvantages. In this research work, we proposed a suitable hybrid AI-based MPPT technique that exhibited the right balance between performance and complexity when utilizing AI in MPPT for solar power system optimization. To achieve this, we looked at the basic concept of maximum power point tracking and compared some AI-based MPPT algorithms for GMPP estimation. After evaluating and comparing these approaches, the most practical and effective ones were chosen, modeled, and simulated in MATLAB Simulink to demonstrate the method\u27s correctness and dependability in estimating GMPP under various solar irradiation and PV cell temperature values. The AI-based MPPT techniques evaluated include Particle Swarm Optimization (PSO) trained Adaptive Neural Fuzzy Inference System (ANFIS) and PSO trained Neural Network (NN) MPPT. We compared these methods with Genetic Algorithm (GA)-trained ANFIS method. Simulation results demonstrated that the investigated technique could track the GMPP of the PV system and has a faster convergence time and more excellent stability. Lastly, we investigated the suitability of Buck, Boost, and Buck-Boost converter topologies for hybrid AI-based MPPT in solar energy systems under varying solar irradiance and temperature conditions. The simulation results provided valuable insights into the efficiency and performance of the different converter topologies in solar energy systems employing hybrid AI-based MPPT techniques. The Boost converter was identified as the optimal topology based on the results, surpassing the Buck and Buck-Boost converters in terms of efficiency and performance. Keywords—Maximum Power Point Tracking (MPPT), Genetic Algorithm, Adaptive Neural-Fuzzy Interference System (ANFIS), Particle Swarm Optimization (PSO

A modified particle swarm optimization based maximum power point tracking for photovoltaic converter system

This thesis presents a modified Particle Swarm Optimization based Maximum Power Point Tracking for Photovoltaic Converter system. All over the world, many governments are striving to exploit the vast potential of renewable energy to meet the growing energy requirements mainly when the price of oil is high. Maximum Power Point Tracking (MPPT) is a method that ensures power generated in Photovoltaic (PV) systems is optimized under various conditions. Due to partial shading or change in irradiance and temperature conditions in PV, the power-voltage characteristics exhibit multiple local peaks; one such phenomenon is the global peak. These conditions make it very challenging for MPPT to locate the global maximum power point. Many MPPT algorithms have been proposed for this purpose. In this thesis, a modified Particle Swarm Optimisation (PSO)-based MPPT method for PV systems is proposed. Unlike the conventional PSO-based MPPT methods, the proposed method accelerates convergence of the PSO algorithm by consistently decreasing weighting factor, cognitive and social parameters thus reducing the steps of iterations and improved the tracking response time. The advantage of the proposed method is that it requires fewer search steps (converges to the desired solution in a reasonable time) compared to other MPPT methods. It requires only the idea of series cells; thus, it is system independent. The control scheme was first created in MATLAB/Simulink and compared with other MPPT methods and then validated using hardware implementation. The TMS320F28335 eZDSP board was used for implementing the developed control algorithm. The results show good performance in terms of speed of convergence and also guaranteed convergence to global MPP with faster time response compared to the other MPPT methods under typical conditions (partial shading, change in irradiance and temperature, load profile). This demonstrates the effectiveness of the proposed method

Solar Irradiance Forecasting and Implications for Domestic Electric Water Heating

As the effects of burning fossil fuels continues to present its prevalence, the interests in alternative forms of energy is expanding. Within the home, the domestic electrical water heater accounts for approximately 17% of its energy consumption. Reducing the amount of energy required to produce hot water from this thermal system alone can have a significant effect on reducing its carbon footprint. In this presented work, a modeled domestic electrical water heater was supplied photovoltaic and on-grid electrical power to increase its energy efficiency. As photovoltaic (PV) energy is directly related to solar irradiation, it is important to receive accurate solar irradiance data for the area and to forecast future solar irradiance outputs to determine optimal energy input. A Kipp & Zonen solar tracker, capable of Baseline Surface Radiation Network (BSRN) level data collection, was installed on Georgia Southern University’s campus to determine extremely accurate solar irradiance. Future irradiance data based on the historical data was then predicted by using artificial neural network (ANN) methods and those results were used to determine future PV output. The system was evaluated strictly by modeling the PV system and domestic electric water heater (DEWH), and then the PV system was integrated into the operation of the DEWH. In comparison to the typical operation of the DEWH, a PV inclusive DEWH produced a significant decrease in the on-grid energy dependency of the entire system