The renewed interest in nuclear energy can be ascribed to an urgent necessity to reduce the number of greenhouse gases and the ever-growing energy demand of the global community. Nuclear plants supply base-load power that is steady and reliable. This vegetation is characterized by its high energy levels and low carbon emissions in operations. Nuclear energy contributes to meeting the sustainable energy goals, and it reduces the reliance of the world on fossil fuels, says the International Atomic Energy Agency (IAEA) [1]. The traditional power generation systems using fossil fuels have a number of issues, including pollution, climate change, and scarcity of fuel. However, when the reaction is carried out by using uranium-235 or plutonium-239, the amount of heat energy produced is very much, and this can be converted to electricity with minimal fuel requirements [2]. At this, nuclear power will take a significant place in renewable energy schemes and in those nations where economic growth is very rapid.

Nuclear energy systems deployment is subject to certain barriers, despite its advantages. These involve complex waste disposal strategies, fears of people for safety, and high capital investment. The catastrophes of the past, such as those in Chernobyl and Fukushima, have had immense influence on the public mind and the regulatory frameworks by which the modern plant design is played out [3]. All of these demonstrate just how essential it is to incorporate high-tech simulation tools during the initial phases of designing and assessing nuclear facilities. Such advances in computational modeling, particularly in the use of MATLAB/Simulink, allow engineers to simulate nuclear power systems in detail and, as they are able to do so, evaluate the thermal behavior, electrical load flow, and harmonic characteristics of systems under different operating situations [4]. By going through a simulation-based design process, you can conserve cash, make your system safer, and tune it to perfection before rolling it out physically. This project has a focus on the 159 MW nuclear power plant that rides on uranium and employs MATLAB/Simulink as its design and simulating tool. When the load flow is at the steady-state, we would like to calculate the dynamics of the load flow, the behavior of the current and voltage, and the Total Harmonic Distortion (THD). Wide analysis of the system's quality and its reliability can be carried out with the help of applying FFT analysis and modeling of real-world electrical components. This contribution to the growing body of knowledge on simulation-driven development of nuclear power system modeling reveals the importance of simulation-driven development in the transition to renewable energy infrastructure.

The chosen 159 MW size reflects the typical size of a medium-capacity nuclear generating unit that fits well into the typical range of regional or modular power plants and is easy to consider simulation-based grid integration applications for in the studies.

Literature Review

In the case of satisfying the escalating demand for sustainable and, above all, scalable energy across the globe, nuclear energy has proved to be quite an essential tool. It emits much thermal energy due to nuclear fission, typically involving such isotopes as uranium-235 or plutonium-239. This energy is converted into mechanical and then electrical by the use of turbines and generators. One of the significant advantages of nuclear power is the ability to produce large volumes of base-load power with minimal or no release of greenhouse gases [5]. There is a huge range of nuclear reactors with different advantages. Pressurized water reactors (PWRs) and boiling water reactors (BWRs) are the most popular, and the other emerging classes are the small modular reactors (SMRs) and fast breeder reactors (FBRs) [6]. These designs aim to reduce the radioactive waste, increase efficiency, and increase the efficiency of the fuel usage. When it comes to the safety of nuclear power plants, such tragic events as the Chernobyl disaster of 1986 and the 2011 Fukushima Daiichi disaster, the topic has always been of much concern. These events resulted in the transformation of the nuclear policy and principles guiding the world. Thus, contemporary nuclear plants possess better above-ground as well as underground containment systems, passive cooling systems, redundant systems, the defense-in-depth (DiD) approach, and various stringent safety measures [7]. With the aim of minimizing risks that may lead to dangers, today isolation barriers, automated shutdown systems, and Emergency Core Cooling Systems (ECCS) have become a necessity [8]. To ensure that the nuclear power plant does not overheat and make the system stable, the heat removal must be efficient. Three of the most frequently used cooling systems are primary, secondary, and tertiary. Together, they make sure that the heat within the reactor core is safely released into the environment. Passive cooling systems, which have been introduced more recently, use natural convection, gravity, and heat sinks to maintain systems in a highly available state even during a power outage or blackout situation [9]. Other factors include the fact that the simulation tools allow engineers to be certain that the systems are functioning well without having to conduct a physical examination, therefore making them an indispensable part of any modern design in nuclear power. MATLAB/Simulink is also gaining popularity, especially in simulations of harmonic distortion, fault conditions, and load flows within a power system. Two of its advanced features that can model complex dynamics are Simulink Control Design and Simscape Power Systems [10]. Which is why, due to their versatility and capacity to be accommodated in mechanical, electrical, and thermal subsystems, these tools are almost always used in both academic and industrial research. The replication of the transient and steady-state behaviors of both the nuclear thermal power plant and the control systems of the power plant was shown by Smith and Lee to be accurately represented by MATLAB/Simulink without leaving the electrical response of the generators out of the equation [10]. A nonlinear dynamic model of the full scale of pressurized water reactors is also developed by Vajpayee et al. [11]. This model incorporated the process of feedback control that was used to maximize the process of optimization of the stability of the reactor. These models can be used to obtain important information about reactor operation, as well as integration of the systems and their monitoring in real time. According to advanced study, the models of the nuclear power plant have also begun to be implemented with AI and ML. As an example, to do better defect identification and predictive maintenance, Zhang et al. [12] predicted the operational states of nuclear systems using methods of deep learning. Meanwhile, Hossain et al. explored the potentiality of the association of renewable energy sources with normal simulation models by creating a 100 MW solar thermal power plant in MATLAB, and this study indirectly supports using the nuclear energy-related hybrid system modeling techniques [13]. Scarcely any works dwell on medium-scale nuclear plants (100-200 MW), which are integrated into nationwide grids with the help of harmonic distortion analysis based on FFT, even though reactor dynamics and control systems are well documented. Both macro-scale commercial plants and more modest-scale SMR-based designs have been the main area of previous research. Moreover, a lack of research on Total Harmonic Distortion (THD) has been applied to nuclear simulations on MATLAB. This research paper is a gap-filler in the literature, as it simulates a nuclear power plant of the size of 159 MW in MATLAB/Simulink, and the plant is evaluated on its operation under steady-state conditions with respect to load flow, profiles of voltages and the current, and the harmonic distortion indices [14]. Future nuclear power plants need cutting-edge simulation-based design and testing to ensure that plants operate safely and securely through a combination of high-fidelity models that are interconnected to reality using near real-time power quality evaluation.

Overview of the Research Design

This research utilizes a MATLAB/Simulink-based simulation model to analyze the steady-state and dynamic behavior of a nuclear power plant. The methodology follows a step-by-step approach: system modeling, simulation configuration, component connection, result collection, and performance analysis.

Simulink Model Design

Figure 1: Simulink Schematic of the 159 MW Nuclear Power System.

And here, we can observe the entire nuclear power system, which has been modelled in MATLAB/Simulink. The graphic has all the modular connections comprising generation, transformation, load, fault, and monitoring. The model is able to simulate both temporary and pseudo-steady-state activity during changing conditions.

Overall System Architecture

Figure 2: Simplified Power Flow Diagram of the Nuclear Plant System.

This high-level schematic shows the logical energy flow from the generator to the load, outlining core components of the system. It helps readers grasp the functional architecture before analyzing simulation details.

Protection Logic and Signal Flow

 Figure 3: Workflow Diagram of Load Monitoring and Protection Mechanism.

Figure 3 illustrates a signal-level workflow of a power distribution and fault protection system that monitors the dynamic heavy loads by current and protection relays and finally measures the final wave at the scope. The flow began at the power source and moved into the transformer, then the bus, and then split into the feeder and the grounding transformer upper branches, which saw action whenever the 4-protection relay operated.

System Configuration and Simulation Parameters

Table 1: System Configuration & Simulation Parameters.

Electrical Modeling Equations

The solution comes in the form of the standard synchronous machine model adapted by Kundur [15] and implemented on Simulink through dynamic blocks.

Stator Voltage Equation (d – Axis)

Stator Voltage Equation (q – Axis)

Field winding Dynamic Equation

Damper winding (d-axis) Equation

Damper winding (q-axis) Equation

Electromagnetic Torque Equation

Explanation of Mathematical Modeling

The equations in this simulation are important to understanding the inner electricity of the nuclear power plant. They are founded on the identical notions of the power system analysis-synchronous machine modeling. You can understand how the voltage of the synchronous generator behaves during a transient and a steady state by substituting the Vd and Vq into the d-q frames of the stator voltages. To model the response of the generator during loading scenarios, the above equations consider stator resistance, the stator and rotor flux linkages, and the rotor speed. These are based on the patterns of machine modelling according to Kundur [15]. The effect of rotor excitation and damping effects affects system dynamic stability; the equations of field and damper windings shed light on these effects. These are critical in capturing the spikes of electromagnetic energy that occur in the switching or in case of failure. This interaction between current and magnetic field can be used to define the torque of the rotor in terms of the electromagnetic torque equation. It is the driver of the mechanical rotation that leads to the production of power. The percentage by which a voltage or current waveform consists of harmonics of its frequency can be calculated using the Total Harmonic Distortion (THD) relationship. The use of this metric to determine the power quality is recognized as a standard practice adopted by IEEE Std 519-2014 [16]. To model the behavior of the generator, its voltage/current responses, and power quality, this study used Simulink blocks to demonstrate it under both normal and disturbed conditions. The dynamics simulations were performed.

Load Flow Analysis

This section presents the results of the load flow analysis for the 159 MW nuclear power plant. The simulation evaluates voltage levels, power angles, and the impact of reactive compensation on system performance [17].

Bus Data Summary

Table 2: Bus Data Summary Table.

Voltage Profile per Bus

Figure 4: Voltage Magnitudes in per Unit (P.U.) Across Key Buses, Showing Minor Voltage Drop under Load.

Figure 5: Voltage Phase Angles per Bus. A Negative Angle at Load Buses Indicates Power Flow Direction Away from the Source.

Power Angle and Load Balance

The power angle difference between Bus 120 and Bus 13.8 kV is -23.91, which means that the power is flowing forward. Active and reactive loads are shared by two main nodes, and the dynamic load on the two nodes is 1 MW and 3 MVAR, respectively, and the RLC load is 3 MW and 2 MVAR, respectively, with a total active and reactive power of 4 MW and 5 MVAR [18]. This is easily manageable by the generator, and the voltage issued does not exceed the permissible limits.

Reactive Power Compensation using Capacitor Bank

A bank of 1.2 MVAR capacitors is located near the dynamic load in order to compensate for the reactive power. This means less strain is put on the upstream transformers, there is higher voltage at the load buses, and there is a higher power factor. The adjustment enhances stability by reducing the amount of reactivity that the generator requires [19].

Load Flow Summary per Bus

This table presents the complete bus-wise load flow results, including voltage magnitude, phase angle, active/reactive power, and the calculated load flow outputs. It helps assess network stability, detect voltage drops, and evaluate reactive power demand [20-22].

Table 3: Detailed Load Flow Results per Bus with Voltage Profile, Angle, and Power Flow.

Description

Table 4.2 summarizes the details of the electrical characteristic of the nuclear power system by bus, having a total capacity of 159 MW. It includes the voltage angles, the base voltage, and the reference voltage [23]. The quantities of estimated voltages are presented in the per unit (p.u.). To determine the generation-load matching, we provide the active (P) and reactive (Q) power figures, both the reference and load flow calculated values. Voltage dips and cross-bus angle deviations are a dynamic illustration of how generators, loads, and transmission components interact. The PV bus (B13.8) is set to 0.98 p.u. and supplies 120 MW, whereas the swing bus (120) maintains stability of the system, bearing a 0° reference angle and 1.02 p.u. voltage [24-26]. The normal voltage drops and reactive demand impact are the kinds of things you would expect to happen on PQ buses such as B25_1 and B575. The Z bus is one of the typical grounding conditions of low-impedance tests.

Voltage and Current Behavior

The voltage and current waveforms of the nuclear power plant system are observed using measurement blocks at key locations. The following figures demonstrate waveform characteristics under steady-state conditions [27].

Figure 6: Voltage and Lagging Current Waveform at Dynamic Load (1 MW, 3 MVAR). The 45° Lag Illustrates Inductive Load Behavior.

Figure 7: Voltage and In-Phase Current Waveform at RLC Load (3 MW, 2 MVAR). The Current Waveform Aligns with Voltage, Confirming Balanced Power Draw.

Table 4: Voltage and Current Observations.

The waveform and tabular results confirm the electrical behavior of both loads. Reactive demand at the dynamic load introduces lagging current, whereas the RLC load demonstrates balanced power consumption.

All voltage and current magnitudes fall within operational thresholds and comply with IEEE standards for power quality. This validates the system’s load support capability and ensures reliability during steady-state operation [28,29].

Fast Fourier Transform (FFT) Analysis

As per Fourier transform (FFT) analysis, the main frequency of the system is 60 Hz, and there are negligible harmonic components under normal operation of the system. However, due to load imbalance and oscillations in the generators, distortion can be sensed at 120 Hz and 180 Hz. The THD should be met even though it is less than the allowable figures (<5%). PID tuning, filter design, and proper balance of the load can ensure the quality of the waveform since deviations of the rotor and active power signify the mechanical faults [30,31].

Figure 8: Ideal Sinusoidal Voltage Waveform (60 Hz).

Figure 9: Harmonic Spectrum Showing 3rd Harmonic (180 Hz).

Figure 10: Harmonic Spectrum – Combined 3rd & 5th Components.

Figure 11: Distorted Voltage Signal Due to Load Imbalance.

Figure 12: Waveform Distortion from Rotor Oscillation.

Figure 13: Signal Variation under Partial Load Conditions.

Figure 14: Rotor Speed Oscillation Signal.

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Figure 15: Active Power Fluctuation Pattern.

FFT Observation Report

Table 5: FFT Observation Summary.

Total Harmonic Distortion (THD) Evaluation

Total Harmonic Distortion (THD) is a vital statistic in determining the extent or magnitude to which harmonics and non-linear components can have an impact on a power system. As part of this project, the output voltage waveform of the 159 MW nuclear power plant model was put in analysis with regard to THD using the FFT.

In general practice, harmonic loads of a third and fifth kind are used to source dynamic and RLC loads. THD is one of the calculated measures of system quality and distortion of the waveform.

The THD is computed using the following formula:

Figure 16: Line Plot of Harmonic Components Extracted from FFT Data.

Harmonic Components Used in THD Evaluation

Table 6: Summary of Harmonic Components Used in THD Evaluation.

According to the calculations, the total harmonic distortion (THD) is 50 percent, a very high percentage far more than the recommended 5 percent that IEEE 519 requires on the distribution system and voltage THD at point of common connections. As a result, it might be deemed essential to use a mitigation technique, including passive filtering, optimal design of transforms, and harmonic filters to reduce the effects of harmonics. When measured under the ideal circumstances, FFT exhibited less than 5 percent THD; when it was exposed to the effect of dynamic load imbalance and rotor oscillations, the maximum amount of THD came about at about 50 percent.

Voltage Waveform (Ideal vs Distorted)

Figure 17: Voltage Waveform.

In this figure (17), you can see the distorted waveform of the voltage both in the simulation of the nuclear power plant (left-most) compared to conventional ideal sinusoidal signals (right-most). One can find some slight amount of distortion, which is caused by the harmonic components. This graphical evidence supports the THD (Total Harmonic Distortion) test. This assists in generating evidence on why we need harmonic mitigation strategies, some of them being the filters or the invigoration of capacitor banks.

Limitations

As detailed as it was, this study has limitations due to the 159 MW nuclear power system modeling and simulation. It is a modeling of steady-state and simplified dynamic performance, but not a transient during fault conditions, start-up, or shutdown. Second, hardware-in-the-loop (HIL) testing in real-time and physical controller integration was not deployed, which constrained real-world verification. No switching effects, noise, or multi-phase asymmetry was the subject of consideration in the harmonic distortion analysis on an idealized condition. There are a number of factors that might interfere with the accuracy of HD analysis. Lastly, thermal-hydraulic safety systems and radiation shielding dynamics have not been simulated since the primary goal was to simulate electrical performance and power quality.

In this work, the mock draft of a nuclear power station with the use of uranium that is capable of 159 MW and dynamically simulated with MATLAB/Simulink is demonstrated. The operating characteristics of the system were evaluated through FFT-based harmonic analysis, observation of voltage-current waveforms, and full load flow analysis. One can easily notice that harmonic mitigation methods, such as active or passive filters, are needed since the effects produced high Total Harmonic Distortion (THD) with a maximum of 50 percent in dynamic conditions, which is much more than the prescribed restriction as provided by IEEE 519. Despite this, the system was able to illustrate the reliability of the nuclear system simulation based on driven modeling in terms of voltage stability and balanced power flow. To maximize power quality, enhance design safety, and facilitate the future adoption of nuclear energy in the modern grids, the current study discusses the importance of virtual test environments. The extension of this concept may be developed into research into the integration of hybrid renewable-nuclear power egamills with different load profiles into real-time hardware controllers and advanced fault protection logic.

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