Modeling and Optimization Control for Aircraft AC Generator Brushless Excitation System Based on Improved Adaptive PSO

In this article we describe a novel Adaptive particle swarm optimization (APSO) algorithm based on population diversity information. It is presented to solve the precocious convergence problem of particle swarm optimization algorithm. The APSO algorithm uses the information of the population diversity to adjust nonlinearly inertia weight. Velocity mutation factor and position interchange factor are both introduced and the global performance is clearly improved. The APSO algorithm is applied to optimization of parameters in the optimal model based on support vector machine (SVM). SVM is a popular classification method with many diverse applications. A novel Adaptive particle swarm optimization (APSO) based approach for parameter determination and feature selection of the SVM, termed APSO+SVM is developed. The illustrating example shows that the classification accuracy of APSO+SVM is higher than other traditional methods of classification, so using APSO+SVM method to classify is feasible and effective.

Synchronous generators (SG) are key electromechanical converters widely used in power plants, where precise excitation control is crucial for maintaining voltage regulation and stability. The main limitation of the conventional brushless excitation (CBE) system is its inability to supply negative voltages to the field winding, which limits fast de-excitation during sudden load rejection and causes terminal-voltage overshoots. To address this issue, this paper proposes an advanced fuzzy brushless excitation (FABE) system integrating fuzzy logic control (FLC) with H∞ control to enhance the transient and steady-state performance of synchronous generators. The H∞ controller ensures robustness against disturbances, parameter uncertainties, and nonlinearities, while the FLC improves adaptability and dynamic response. The proposed hybrid approach enables faster de-excitation, reduced voltage peaks, and improved damping of oscillations. Simulation results demonstrate that the FABE system clearly outperforms the conventional excitation method in terms of voltage stability, dynamic response, and overall control performance under various operating conditions.

Aiming at the control variables of reactive power optimization are discrete, and some parameters in the standard particle swarm optimization (PSO) algorithm need to be predefined by test, so the algorithm's practicability is restricted. For these reasons, an adaptive particle swarm optimization (APSO) algorithm is proposed by the authors. APSO introduces the self-adaptive tuning strategy and boundary constraint conditions can find the global optimal solution and solve the discrete variables. The reactive power optimization results of the standard IEEE-30-bus power system show that APSO is efficient than standard PSO. The global convergence accuracy and convergence stability is obviously improved compared with that of PSO.

Fault diagnosis for wireless sensor is very important to ensure signal acquisition precision. Radial basis function (RBF) neural network has strong classification ability. However, the selection of the connection weights, the hidden centers and the widths has an important influence on the classification performance of the RBF neural network in the learning process of RBF neural network. Thus, ant colony optimization is employ to gain the parameters of radial basis function neural network. Therefore, a novel method for fault diagnosis of wireless sensor based on RBF neural network and ant colony optimization is presented. The results of computational experiments show that ACO- RBF neural network has a great higher than RBF neural network.

The integration of power electronics devices into on-board electrical power distribution systems (EPDS) has steadily increased due to the trend toward more electric aircraft (MEA) and all electric aircraft (AEA). Digital control and pulse width modulation (PWM) of these devices give rise to the electrical harmonics, which have been extensively discussed for utility grids. However, the variable fundamental frequency and relatively lower switching-to-fundamental (S2F) frequency ratio in the EPDS result in more severe harmonic distortion and coupling between different subsystems. Onboard power electronic converters (PECs) exhibit greater transparency in compacted environments compared to utility power systems. Therefore, their relative PWM phase-shifted carrier (PSC) angles can be harnessed to cancel switching harmonics. In this work, a 3-bus system with an electric ground power unit (eGPU) converter and two active front end (AFE) converters is used to restore the situation of ground powering of an aircraft. The effect of PSC angles of the PECs on the system harmonics distortion is studied with both balanced and unbalanced load situation. On this basis, an online total harmonic distortion (THD) monitor unit is proposed which could calculate the real-time THD level of the EPDS ac bus within one fundamental period. The accuracy of the proposed THD monitor is verified by PLECS simulation.

More Electric Aircraft (MEA) and All Electric Aircraft (AEA) require advanced autonomous electric Energy Management Systems (EMS) onboard the aircraft. The aircraft electric network can be considered as an islanded microgrid, and as such some approaches typical of the microgrid management can be used onboard the aircraft to design an effective EMS. In particular, distributed control with consensus techniques represents a promising approach due to the advantages in terms of reliability, computational simplicity and low-bandwidth requirement which are of great interest for implementation onboard. A consensus-based solution to the problem of coordinating and balancing several Energy Storage Systems (ESSs) coexisting in a generic aircraft architecture is proposed and analyzed. The proposed algorithm selects the current setpoints for each ESS according to their state of charge while ensuring safety of operations. Theoretical results and detailed simulations show the effectiveness of the proposed approach.

In this thesis a detailed study on the battery technologies used in aircrafts in the last five decades is done. A general background of the battery system is studied and the performance of the batteries based on energy densities and low temperature capabilities is evaluated for the deployment in the more electric aircraft (MEA). Evolution of MEA with its power system architecture and load profile is analysed to understand the requirements of the battery system. The electrical load profile of the MEA “Aircraft X” is designed and modeled. Different types of electrical loads such as primary/conventional loads, the environmental control system (ECS) load, anti-icing (wing ice protection) loads, various electrical actuators (aileron, spoilers, rudder) loads and signal communication (RADAR etc.) loads are designed and modeled. Furthermore, starting profile of engine starter generator (ESG), transient stability profile of military standard (MIL-STD-704F) and emergency load profile of the MEA “Aircraft X” are also studied and analysed. Thus, all these electrical loads are combined and an algorithm is successfully obtained to separate the constant and transient loads. An effective battery system is designed for supplying the transient loads by considering the weight saving and cost analysis factors between Li-ion and Ni-Cd batteries. Based on the analysis, Li-ion battery with Lithium ferrous phosphate (LFP) chemistry is designed and proposed for the MEA “Aircraft X”. The designed LFP based battery system is also modeled using the modified shepherd equation wherein the voltage polarization term is added to have a lower complexity and more proximity with the battery model block in MATLAB®. After the design and modeling of the proposed Li-ion battery system, the battery charger (phase shifted high power bidirectional dc-dc “PSHPBD” converter) is designed and modeled. The design of the components of PSHPBD converter is done keeping in view the requirement of maximum power transfer in the MEA environment. Zero voltage switching (ZVS) operation in wide range is also considered for design of the PSHPBD converter for MEA operating parameters. The PSHPBD converter is modeled using the Fourier series based harmonic analysis wherein an optimal harmonic number (h=7) is chosen by considering the proximity and complexity between the modeled converter and switch model. The battery energy storage system “BESS” (consisting of the battery system and the PSHPBD converter) is successfully integrated with the power distribution 270V dc bus architecture of the MEA and tested under different dynamic conditions using three different control techniques. Firstly, a direct estimation based technique is proposed for determining the phase shift of the PSHPBD converter (used as a charger for the batteries). Secondly, a current mode control for the single phase shift control technique for the PSHPBD converter is proposed. The proposed control technique has an inherent capability of current limit. Thirdly, a peak current controller is proposed which provides a limit on the peak current and provides a fast transient response. The advantages of using the proposed peak current control in avoiding the saturation of the transformer core is also reported in the work. In integration, finally the study and analysis of zero current switching (ZCS) inverter for integration of BESS to ESG set in MEA “Aircraft X” is discussed. Furthermore, a control method which can improve the life of the BESS in MEA is discussed in which three different modes of control based on the load estimation are given. Depending upon the load on the BESS, the corresponding control structure is implemented/activated leading to reduction in RMS current flow in the charger circuit and average current flow in the battery system thereby reducing the losses and temperature rise in the BESS, which eventually leads to an increase in the life of the BESS. Finally, a multi-functional battery charger (MFBC) is proposed for integration of BESS to the power distribution network in MEA. A novel control method is devised for suitable transfer and sharing of power between different buses. Based on the placement and applicability of the MFBC, many new dc power distribution architectures for MEA are proposed wherein energy storage system can be integrated into aircraft network leading towards fault tolerant operation and weight optimization in the MEA.

In the process of moisture content's measurement with microwave resonant cavity based on RBF (radial basis function) neural network, the relationship among resonant frequency, quality factor and environmental temperature (the inputs of network) and moisture content (the output of network) is multiple non-linear. The performance of multiple non-linear regression algorithm is the major factor that determines the accuracy of measurement. A regression algorithm based on a RBF neural network is put forward to improve the measurement result in this paper. The RBF neural network regression algorithm can effectively avoid the BP algorithm disadvantages such as getting into local minimum and the low efficiency, and has the advantages such as high generalization precision and rapid convergence. Consequently, the measurement accuracy is enhanced. In this paper, experimental results show that the high-precision measurement value of moisture content can be obtained with the multiple non-linear regression algorithm based on RBF neural network.

This paper proposes to solve the dynamic optimization problem (DOP) by using an adaptive particle swarm optimization (APSO) algorithm with an variable relocation strategy (VRS). The VRS based APSO algorithm (APSO/VRS) has the following two advantages when solving DOP. Firstly, by using the APSO optimizing framework, the algorithm benefits from the fast optimization speed due to the adaptive parameter control. More importantly, the adaptive parameter and operator in APSO make the algorithm fast respond to the environment changes of DOP. Secondly, VRS was reported in the literature to help dynamic evolutionary algorithm (DEA) to relocate the individual position in promising region when environment changes. Therefore, the modified VRS used in APSO can collect historical information in the stability stage and use such information to guide the particle variable relocation in the change stage. We evaluated both APSO and APSO/VRS on several dynamic benchmark problems and compared with two state-of-the-art DEAs and DEA that also used the VRS. The results show that both APSO and APSO/VRS can obtain very competitive results on these problems, and APSO/VRS outperforms others on most of the test cases.

Aerospace industry is moving towards a more electric aircraft (MEA) or even an all-electric aircraft (AEA), which will incite the focus on reducing the total harness mass and complexity, thus employing powerline communication (PLC) technique seems to be a potential solution to be implemented for airborne uses. This paper explores the feasibility to implement PLC technology over aircraft DC powerline, fed by switched-mode power supply. First, a channel characterization and analysis were conducted, also an adaptive stage was discussed and designed to transmit the data over an already existing powerline. Furthermore, the channel capacity was estimated along with the required data signal level. This study investigates also a real-case context with the use of switched-mode dc/dc converter which effect the PLC performance and signal integrity. Eventually, PLC implementation and tests were conducted as well as discussed from latency, bit error rate (BER) and EMC compliance perspective with the RTCA DO-160G standard requirements.

Energy optimization strategy of vehicle DCS system based on APSO algorithm

This paper presents a novel optimal H∞ tracking-based adaptive fuzzy neural network controller (HAFNNC) for a variable speed wind turbine. The main objective of the controller is to optimise the energy captured from the wind. In the presence of large uncertainties, H∞ control approach produces oscillatory phenomenon due to the higher needed gain. In order to reduce this gain, fuzzy neural network (FNN) with online adaptation of the parameters is used to estimate the uncertain parts of the system plant and hence enable a lower gain to be used. To eliminate the trade-offs between the H∞ tracking performance and the high gain at the control input, we have introduced a new method based on the modification of the output tracking error through the use of both the exponential function and the adaptive particle swarm optimisation (APSO) algorithm. The stability and effectiveness of the proposed method are proved by Lyapunov method and the simulations are given to demonstrate the performance of the proposed approach.

Recent developments in aircraft electrical technology, such as the design and production of more electric aircraft (MEA) and major steps in the development of all-electric aircraft (AEA), have had a significant impact on aircraft’s electrical power systems (EPSs). However, the EPSs of the latest aircraft produced by the main players in the market, Airbus with the Neo series and Boeing with the NG and MAX series are still completely traditional and based on the constant speed constant frequency (CSCF) configuration. For alternating current ones, the EPS is composed of the following: prime movers, namely the aircraft turbofan engine (TE); the electrical power source, i.e., the integrated drive generator (IDG); the command and control system, the generator control unit (GCU); the transmission and the system distribution system; the protection system, i.e., the CBs (circuit breakers); and the electrical loads. This paper presents the analysis of this system using the Simscape package from Simulink v 8.7, a MATLAB v 9.0 program, which is actually the development of some systems designed in two previous personal papers. For the first time in the literature, a complete MATLAB modelled EPS system was presented, i.e., the aircraft turbofan engine model, driving the constant speed drive system (CSD) (model presented in the first reference as a standalone type and with different parameters), linked to the synchronous generator (SG) (model presented in second reference for lower power and rotational speed) in the so-called integrated drive generator (IDG) and electrical loads.

Compensation for target speckle phase by use of the combination of the adaptive particle swarm optimization algorithm and the array detector method in heterodyne detection

An adaptive Particle Swarm Optimization algorithm for fire source identification of the utility tunnel fire