Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

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

The Improved MPPT Controller For Photovoltaic System Under Partial Shading Condition

The performance of a photovoltaic (PV) array is affected by temperature, solar insolation, shading and array configuration. The PV system exhibits a non-linear I-V characteristic and its unique maximum power point on the P-V curve varies with insolation and temperature. Often the PV array gets shadowed, completely or partially, by passing clouds, neighbouring buildings and trees. This situation is of particular interest in case of large PV installations such as those used in a distributed power generation scheme and residential PV systems. Under partially shaded conditions, the P-V characteristic becomes more complex with multiple peaks. Conventional Maximum Power Point Tracking (MPPT) techniques fail to reach global peak power point and tend to stay in local peak power point which significantly reduces the efficiency of the PV system. This paper mainly focuses on extracting the maximum power from PV array under partially shaded conditions by executing improved hill climbing algorithm to identify the global maximum power point (GMPP)

Адаптивная система обеспечения максимальной выходной мощности фотоэлектрической станции на основе робастного прогнозного управления

Photovoltaic power plants have non-linear voltage-current characteristic, with specific maximum power point, which depends on operating conditions, viz. irradiation and temperature. In targeting the maximum power, it is by far known that the photovoltaic arrays have to operate at the maximum power point despite unpredicted weather changes. For this reason the controllers of all photovoltaic power electronic converters employ some method for maximum power point tracking. This paper makes an emphasis on model predictive controller as a control method for controlling the maximum power point tracking through the utilization of the well-known algorithm namely the Perturb and Observe technique. Further, during the advanced stages of this research study, the model will compare the results obtained for tracking the maximum power point from model predictive controller and a PID-controller as they are integrated Perturb and Observe algorithm. The obtained results will verify that the adaptive PID-controller Perturb and Observe algorithm has limitation for tracking the precise MPP during the transient insulation conditions. As compared to the proposed adaptive/modified model predictive controller for Perturb and Observe algorithm we illustrate that by adopting this method we will get to establish more accurate and efficient results of the obtained power in any dynamic environmental conditions: advantages as on regulation time (six times under the accepted experimental conditions) and by the number of fluctuations.Фотоэлектрические станции (ФЭС) характеризуются нелинейной зависимостью выходного тока и напряжения с уникальной точкой  максимальной  выходной мощности (МВМ), зависящей от условий эксплуатации – температуры и уровня солнечной радиации. Поэтому для повышения эффективности фотоэлектрического преобразования необходимо обеспечить работу ФЭС в точке МВМ. Это достигается применением соответствующих алгоритмов управления, наиболее известными из которых являются P&O («возмущение и наблюдение»). Эти алгоритмы основаны на изменении напряжения постоянного тока ФЭС с помощью преобразователя постоянного тока (регулятора), выходное напряжение которого должно изменяться по определенному закону с изменением условий эксплуатации. При этом используются регуляторы: пропорциональные (П), интегральные (И), дифференциальные (Д) или чаще всего их комбинации ПИД. В статье исследуется эффективность применения регулятора с прогнозным адаптивным управлением (MPC). Посредством численных экспериментов на разработанной имитационной модели показано, что ПИД-регуляторы в интеграции с P&O и INC алгоритмами не обеспечивают достаточно быстрой реакции при изменении условий внешней среды. В то же время МРС в сочетании с P&O имеет преимущества как по времени регулирования (в шесть раз при принятых условиях эксперимента), так и по количеству колебаний

CONTROL STRATEGIES OF DC MICROGRID TO ENABLE A MORE WIDE-SCALE ADOPTION

Microgrids are gaining popularity in part for their ability to support increased penetration of distributed renewable energy sources, aiming to meet energy demand and overcome global warming concerns. DC microgrid, though appears promising, introduces many challenges in the design of control systems in order to ensure a reliable, secure and economical operation. To enable a wider adoption of DC microgrid, this dissertation examines to combine the characteristics and advantages of model predictive control (MPC) and distributed droop control into a hierarchy and fully autonomous control of the DC microgrid. In addition, new maximum power point tracking technique (MPPT) for solar power and active power decoupling technique for the inverter are presented to improve the efficiency and reliability of the DC microgrid. With the purpose of eliminating the oscillation around the maximum power point (MPP), an improved MPPT technique was proposed by adding a steady state MPP determination algorithm after the adaptive perturb and observe method. This control method is proved independent with the environmental conditions and has much smaller oscillations around the MPP compared to existing ones. Therefore, it helps increase the energy harvest efficiency of the DC microgrid with less continuous DC power ripple. A novel hierarchy strategy consisting of two control loops is proposed to the DC microgrid in study, which is composed of two PV boost converters, two battery bi-directional converters and one multi-level packed-u-cell inverter with grid connected. The primary loop task is the control of each energy unit in the DC microgrid based on model predictive current control. Compared with traditional PI controllers, MPC speeds up the control loop since it predicts error before the switching signal is applied to the converter. It is also free of tuning through the minimization of a flexible user-defined cost function. Thus, the proposed primary loop enables the system to be expandable by adding additional energy generation units without affecting the existing ones. Moreover, the maximum power point tracking and battery energy management of each energy unit are included in this loop. The proposed MPC also achieves unity power factor, low grid current total harmonics distortion. The secondary loop based on the proposed autonomous droop control identifies the operation modes for each converter: current source converter (CSC) or voltage source converter (VSC). To reduce the dependence on the high bandwidth communication line, the DC bus voltage is utilized as the trigger signal to the change of operation modes. With the sacrifice of small variations of bus voltage, a fully autonomous control can be realized. The proposed distributed droop control of different unit converters also eliminates the potential conflicts when more than two converters compete for the VSC mode. Single-phase inverter systems in the DC microgrid have low frequency power ripple, which adversely affects the system reliability and performance. A power decoupling circuit based on the proposed dual buck converters are proposed to address the challenges. The topology is free of shoot-through and deadtime concern and the control is independent with that of the main power stage circuit, which makes the design simpler and more reliable. Moreover, the design of both PI and MPC controllers are discussed and compared. While, both methods present satisfied decoupling performances on the system, the proposed MPC is simpler to be implemented. In conclusion, the DC microgrid may be more widely adopted in the future with the proposed control strategies to address the current challenges that hinder its further development

A NASA high-power space-based laser research and applications program

Applications of high power lasers are discussed which might fulfill the needs of NASA missions, and the technology characteristics of laser research programs are outlined. The status of the NASA programs or lasers, laser receivers, and laser propulsion is discussed, and recommendations are presented for a proposed expanded NASA program in these areas. Program elements that are critical are discussed in detail

Investigation of Some Self-Optimizing Control Problems for Net-Zero Energy Buildings

Green buildings are sustainable buildings designed to be environmentally responsible and resource efficient. The Net-Zero Energy Building (NZEB) concept is anchored on two pillars: reducing the energy consumption and enhancing the local energy generation. In other words, efficient operation of the existing building equipment and efficient power generation of building integrated renewable energy sources are two important factors of NZEB development. The heating, ventilation and air conditioning (HVAC) systems are an important class of building equipment that is responsible for large portion of building energy usage, while the building integrated photovoltaic (BIPV) system is well received as the key technology for local generation of clean power. Building system operation is a low-investment practice that aims low operation and maintenance cost. However, building HVAC and BIPV are systems subject to complicated intrinsic processes and highly variable environmental conditions and occupant behavior. Control, optimization and monitoring of such systems desire simple and effective approaches that require the least amount of model information and the use of smallest number but most robust sensor measurements. Self-optimizing control strategies promise a competitive platform for control, optimization and control integrated monitoring for building systems, and especially for the development of cost-effective NZEB. This dissertation study endorses this statement with three aspects of work relevant to building HVAC and BIPV, which could contribute several small steps towards the ramification of the self-optimizing control paradigm. This dissertation study applies self-optimizing control techniques to improve the energy efficiency of NZEB from two aspects. First, regarding the building HVAC efficiency, the dither based extremum seeking control (DESC) scheme is proposed for energy efficient operation of the chilled-water system typically used in the commercial building ventilation and air conditioning (VAC) systems. To evaluate the effectiveness of the proposed control strategy, Modelica based dynamic simulation model of chilled water chiller-tower plant is developed, which consists of a screw chiller and a mechanical-draft counter-flow wet cooling tower. The steady-state performance of the cooling tower model is validated with the experimental data in a classic paper and good agreement is observed. The DESC scheme takes the total power consumption of the chiller compressor and the tower fan as feedback, and uses the fan speed setting as the control input. The inner loop controllers for the chiller operation include two proportional-integral (PI) control loops for regulating the evaporator superheat and the chilled water temperature. Simulation was conducted on the whole dynamic simulation model with different environment conditions. The simulation results demonstrated the effectiveness of the proposed ESC strategy under abrupt changes of ambient conditions and load changes. The potential for energy savings of these cases are also evaluated. The back-calculation based anti-windup ESC is also simulated for handling the integral windup problem due to actuator saturation. Second, both maximum power point tracking (MPPT) and control integrated diagnostics are investigated for BIPV with two different extremum seeking control strategies, which both would contribute to the reduction of the cost of energy (COE). In particular, the Adaptive Extremum Seeking Control (AESC) is applied for PV MPPT, which is based on a PV model with known model structure but unknown nonlinear characteristics for the current-voltage relation. The nonlinear uncertainty is approximated by a radial basis function neural network (RBFNN). A Lyapunov based inverse optimal design technique is applied to achieve parameter estimation and gradient based extremum seeking. Simulation study is performed for scenarios of temperature change, irradiance change and combined change of temperature and irradiance. Successful results are observed for all cases. Furthermore, the AESC simulation is compared to the DESC simulation, and AESC demonstrates much faster transient responses under various scenarios of ambient changes. Many of the PV degradation mechanisms are reflected as the change of the internal resistance. A scheme of detecting the change of PV internal shunt resistance is proposed using the available signals in the DESC based MPPT with square-wave dither. The impact of the internal resistance on the transient characteristics of step responses is justified by using the small-signal transfer function analysis. Simulation study is performed for both the single-string and multi-string PV examples, and both cases have demonstrated successful results. Monotonic relationship between integral error indices and the shunt internal resistance is clearly observed. In particular, for the multi-string, the inter-channel coupling is weak, which indicates consistent monitoring for multi-string operation. The proposed scheme provides the online monitoring ability of the internal resistance condition without any additional sensor, which benefits further development of PV degradation detection techniques