Artificial neural network based maximum power point tracking controller for photovoltaic standalone system

This article presents a two-stage maximum power point tracking (MPPT) controller using artificial neural network (ANN) for photovoltaic (PV) standalone system, under varying weather conditions of solar irradiation and module temperature. At the first-stage, the ANN algorithm locates the maximum power point (MPP) associated to solar irradiation and module temperature. Then, a simple controller at the second-step, by changing the duty cycle of a DC–DC boost converter, tracks the MPP. In this method, in addition to experimental data collection for training the ANN, a circuit is designed in MATLAB-Simulink to acquire data for whole ranges of weather condition. The whole system is simulated in Simulink. Simulation results show small transient response time, and low power oscillation in steady-state. Furthermore, dynamic response verifies that this method is very fast and precise at tracking the MPP under rapidly changing irradiation, and has very low power oscillation under slowly changing irradiation. Experimental results are provided to verify the simulation results as well

Evaluation of Fuzzy Logic Subsets Effects on Maximum Power Point Tracking for Photovoltaic System

Photovoltaic system (PV) has nonlinear characteristics which are affected by changing the climate conditions and, in these characteristics, there is an operating point in which the maximum available power of PV is obtained. Fuzzy logic controller (FLC) is the artificial intelligent based maximum power point tracking (MPPT) method for obtaining the maximum power point (MPP). In this method, defining the logical rule and specific range of membership function has the significant effect on achieving the best and desirable results. This paper presents a detailed comparative survey of five general and main fuzzy logic subsets used for FLC technique in DC-DC boost converter. These rules and specific range of membership functions are implemented in the same system and the best fuzzy subset is obtained from the simulation results carried out in MATLAB. The proposed subset is able to track the maximum power point in minimum time with small oscillations and the highest system efficiency (95.7%). This investigation provides valuable results for all users who want to implement the reliable fuzzy logic subset for their works

Integration of non-isolated converters in battery storage systems: Topology development, evaluation and optimisation

This thesis examines topological variations of non-isolated DC-DC converters and their implications on design parameters and configurations of battery-integrated-converter systems. Furthermore, the opportunity of increased reliability with battery-integrated-converter systems is discussed with examples for both DC-DC and DC-AC converter applications, by taking into account the module voltage, redundancy level, scheduled maintenance and converter topology factors. Moreover, the optimisation and other practical trade-offs associated with the selection of the voltage rating of battery power modules (BPMs) in a battery-integrated-converter-system from an efficiency perspective is investigated

Maximum power point tracking using artificial neural network for photovoltaic standalone system

Solar energy has drawn much attention in recent years because of high demand for green energy resources. Electrical power can be generated by using semiconductors in photovoltaic (PV) cells to convert solar irradiance into DC current. Each PV module has its own optimum point at which the power delivered from the PV is at its maximum value. Since the initial cost for using PV is high, it is essential to make the PV module to work at its maximum power point. Thus, an algorithm named as maximum power point tracking (MPPT) has been introduced. These algorithms by controlling the duty cycle of a converter which is inserted between the PV module and the load make the PV to work at its maximum power point (MPP). Since the characteristics of PV module are dependent on atmospheric conditions of solar irradiance and cell temperature, conventional MPPT methods fail to find the MPP under rapidly changing of solar irradiance. Artificial intelligence methods have drawn much attraction in recent years due to their capability of handling uncertainty and nonlinearity conditions. In this work, an improved MPPT using Artificial Neural Network (ANN) has been presented. The control unit is comprised of two stages where at the first stage, ANN finds the voltage and current at which the maximum power is delivered, and at the second stage, another algorithm by developing the mathematical equation in related to input impedance, output impedance and duty cycle of the boost converter, tracks the MPP independent from the load, under changing condition of solar irradiance and cell temperature.The overall system consists of a PV module, a DC-DC boost converter, a control system and a resistive load. Also, a digital signal processor is used to generate the pulse width modulation signals for the driver of the converter. The proposed MPPT system is simulated using MATLAB. The results are compared with the results of the perturbation and observation (P&O) method under low and high solar irradiances; and slowly and rapidly changing of solar irradiance. Furthermore, the results of the proposed method are compared with results of the previous ANN MPPTs in two aspects of ANN outputs, and PV MPPT performance. The simulation and experimental results show that for both high and low solar irradiances, the proposed ANN method has smaller trackingtime, less power oscillation at steady-state, and higher efficiency than P&O MPPT with different step- sizes. Simulation results for different loads of 20 Ω, 33 Ω, and 40 Ω show that the proposed MPPT has efficiency between 99.96-100%, for different irradiances between 300-1000 W/m2 . In term of ANN output, the percentage error between the expected power and power predicted from ANN in this work is 0-0.119 %, which is more accurate than the previous ANN MPPT works with error percentage of 0.05- 3.66 %. In term of MPPT performance, the proposed MPPT has efficiency of 99.97% for low irradiance of 200 W/m2 and temperature of 31.9OC, which shows better performance as compared to ANN MPPT using PI controller which has efficiency of about 84% for low irradiance. As conclusion, the proposed ANN MPPT has high precision in finding the optimum points, as compared to previous ANN works. Furthermore, it tracks the MPP independent from the load, with high efficiency as compared to P&O with differentstep sizes and ANN MPPT using PI controller

Integration of non-isolated DC-DC converters in battery storage systems - a topological exploration

This paper presents a topological exploration and evaluation of modularised battery systems with integrated non-isolated DC-DC converters (battery-integrated-converters). Converter requirements for battery operation in charging/discharging, charge balance, bypass and pass-through modes have been considered. Topological variations have been derived to permit all battery operation modes. Design considerations and equations for each topology have been presented. The topological requirements have been summarised in tabular form with a 380 V bus storage system case study presented to demonstrate application of the design considerations discusses to a typical battery system specification. The comparison shows the buck, boost and non-inverting buck-boost based converters are all similarly suitable, and other performance metrics are likely to determine the best choice for a given set of circumstances

Comparison of ANN and P&O MPPT methods for PV applications under changing solar irradiation

This paper presents an artificial neural network (ANN) maximum power point tracking (MPPT) method which is fast and precise in finding and tracking the maximum power point (MPP) in photovoltaic (PV) applications, under rapidly changing of solar irradiation, and is stable under slowly changing of solar irradiation. ANN and P&O MPPT algorithms, and other components of the MPPT control system which are PV module and DC-DC boost converter, are simulated in MATLABSimulink, and their performances under rapidly and slowly changing of solar irradiation are compared as well. Simulation results show that ANN method has very fast and more precise response under fast changes of solar irradiation. In addition, this method performs with less power oscillation under constant or slow changes of solar irradiation

Impact of DC-DC converter distribution and redundancy on reliability of battery-integrated-converter systems

Battery-integrated-converter systems were first proposed as a solution for charge balancing in series connected battery systems. Modularizing the battery cells/blocks with individual converters also provides the opportunity for redundancy and thus increased reliability. By adding additional series modules, modules with failed battery cells/blocks can be bypassed by their associated converter while maintaining system operation. In this paper, we assess the effect of converter distribution on system reliability. The results show that for low battery cell and converter failure rates, by proper redundancy choice, it is possible to design a highly reliable system. However, there is a compromise between the module failure rate (based on the cell and the converter failure rates); and redundancy level. For the modules with higher failure rates, the system reliability can be improved by applying annual scheduled replacement of failed modules. Using this approach, the six-sigma criterion can be achieved for a system of single/two strings of 30 V rated modules and the system of two parallel strings of 40 V rated modules. Further reliability-cost assessment is suggested to choose the optimum design for a specific application based on the influential factors such as: module voltage rating, module reliability, redundancy level, and scheduled-maintenance intervals.</p

Impact of module voltage on efficiency of battery-integrated-converter systems

The concept of modularizing batteries with multiple dedicated DC-DC or DC-AC converters brings several benefits as compared to a single battery pack and converter. These include charge balancing control, enhanced reliability, improved safety, and lower investment risk. Lower voltage and power ratings of power electronic converter elements may also be beneficial, but require optimization. The system design might favour fewer battery power modules (BPMs) with a high number of battery cells and higher voltage power electronic switches, or many BPMs with fewer cells with low voltage switches. This paper examines the optimization and other practical trade-offs associated with the selection of the voltage rating of battery power modules (BPMs) in a battery-integrated-converter system from an efficiency perspective. A nominal 3.8 kW battery system with LiFePO4 battery cells is taken as an example, and modularized with integrated buck converters for a regulated 380 Vdc bus. Based on MOSFET and thus module voltage rating (30, 40, 60, 80, 100 and 150 V), different configurations are derived. The MOSFET and inductor losses of different configurations are examined for high and low working frequencies. The total system losses of all scenarios does not exceed 52 W, for both high and low working frequencies. However, configurations with a high number of lower voltage modules have the advantage of lower MOSFET loss, which should simplify cooling.</p

Impact of inverter distribution and redundancy on reliability of battery-integrated-converter systems

The concept of modularizing battery storage systems by distributing DC-DC or DC-AC converters among battery module in the battery string brings several benefits. This paper compares the reliability of a DC-AC battery-integrated-converter system to a conventional single inverter battery energy storage system. To provide a comparative evaluation, an existing real 100 kWh energy storage system with a single-phase 25 kVA DC-AC inverter has been considered as a case-study for modularisation. The system is modularized by distributing and integrating individual DC-AC inverters into the associated extra low voltage (ELV) battery modules. Two different battery structures are proposed; one based on individual 24 V modules, a second on 48 V battery module pairs. The reliability of the original system and proposed systems are evaluated and further compared for different redundancy levels. The results show that for low failure rates of battery cells and inverters, the redundancy provided by multiple modules ensures a high reliability battery system. Furthermore, when designing the battery system with battery cells and inverters of higher failure rates, other approaches such as scheduled preventive maintenance is suggested as a means to maintain acceptable reliability. However, cost effectiveness of designing such systems should be taken into account as compared to the battery systems with low failure rates of components.</p