Wind Energy Extraction by Pso Modelled Algorith in Energy Transmission

doi:10.21203/rs.3.rs-4874989/v1

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Wind Energy Extraction by Pso Modelled Algorith in Energy Transmission

https://doi.org/10.21203/rs.3.rs-4874989/v1

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The study presented a suggested technique for determining the best operating point of a proportional-integral-derivative (PID) controller in the context of a wind turbine. Additionally, the strategy aimed to find the stability zones within the parameter space. The proposed approach uses a Modified Particle Swarm Optimization (MPSO) algorithm for optimum PID controller design. The modification is carried out by integrating most effectual Genetic Algorithm (GA) with PSO. The aforementioned intelligent algorithms are artificial learning mechanisms capable of identifying the ideal operating points. They were used to derive a function that represents the most favourable operating parameters, namely kp and ki, for each value of kd inside the stability area of the PID controller. In fact, GA has slow convergence process with local convergence issue. These issues are resolved by PSO algorithm while GA takes the responsibility for new population generation. The solutions under consideration were shown by simulations of a drive train model without time delay and a pitch control model including time delay. Simulations are carried out in MATLAB Simulink tool which is most effective is designing and validating power systems with VSC HVDC transmissions.

Wind energy

Particle swarm optimization

HVDC

Dynamic simulation

PID controller.

In recent times, there has been a decline in the availability of fossil fuel resources, accompanied with a rise in average global temperatures. As a result of the significant impacts of these occurrences, a multitude of research investigations have been undertaken in the field of renewable energy. Wind energy has garnered significant interest and has had a notable expansion in its use. Traditional wind turbines use mechanisms designed to capture and harness wind energy. In order to maintain a consistent power production in wind turbines when wind speeds exceed the authorized wind speed, pitch control is used. This control mechanism involves modifying the pitch angle of the turbine blades to restrict the power output, hence safeguarding the gearbox and generator of the wind turbine. Hence, the significance of pitch control schemes for variable-speed turbines is growing substantially [1, 2].

The PID controller is widely recognized as the predominant closed-loop control system, and its use in designing the pitch control system is considered the most straightforward and uncomplicated approach [3]. In addition to conventional PID control systems, scholarly research has put forward additional approaches for regulating the pitch angle. Recent research endeavours have been undertaken to ascertain the area of stability for control systems using Proportional-Integral-Derivative (PID) control. The authors described a method for achieving stable synthesis of a pitch controller based on PI control. The stability zone and controller settings for achieving an arbitrary-order time delay system were determined using a graphical technique. In order to conduct system-stabilization studies and calculations, it is necessary to achieve a 3D stabilizing domain inside the parameter space of a PID controller [4]. The use of the 3D stability region approach for ascertaining the range of controller parameter values within which the operating point remains stable is characterized by its simplicity and intuitive nature. Hence, the design of the controller may be accurately represented and easily examined. One potential approach to controlling the pitch angle in wind turbine generators involves the establishment of several operating points for the control system. The researchers constructed a robust discrete-time control model, known as the R-S-T model, in order to construct an average model of the operational zone [5]. The robust control method discussed in references demonstrated enhanced output power performance throughout a broad range of operating conditions, even in the presence of unidentified disturbances.

The use of renewable energy saw a substantial surge in the aftermath of the first major oil crisis that transpired in the late 1970s. During that period, economic concerns emerged as the primary determinants, resulting in a decline in enthusiasm in these activities after the reduction in oil prices. The present upsurge of interest in the use of renewable energy sources is being driven by the phenomenon of global warming. It has a significant impact on several dimensions of human life, including but not limited to economy, public health, and the environment. Global warming is primarily attributed to the emission of greenhouse gases, which are mostly released by the combustion of fossil fuels, such as oil or coal [6]. Undoubtedly, one of the primary issues of our day pertains to the large-scale harvesting of energy. The future viability of energy sustainability is contingent upon the effective resolution of the renewable energy challenge during the next decades. The global energy demand is seeing a consistent upward trajectory, necessitating the exploration and development of alternative energy sources to meet future requirements, given the imminent depletion of existing sources. Wind energy is considered to be a crucial kind of renewable energy source (RES), with a distinct advantage over photovoltaic (PV) systems due to its lower installation cost. Consequently, wind energy is projected to contribute a much greater share of installed electrical power derived from renewable energy. Currently, wind power production is often regarded as the most favourable use of renewable energy sources, particularly in terms of financial investment and scholarly investigation. The field of wind energy is seeing significant growth, with an increasing number of wind farms being integrated into electrical power networks. However, this progress presents some issues that require careful consideration and resolution. This study introduces a hybrid power system, referred to as the Egyptian Power System (EPS), that addresses the challenge of frequency stability by including both conventional power plants and renewable energy sources (namely wind energy).

The ongoing advancement in electrical loads, particularly within industrial facilities and human activities, has led to a rise in the construction of new transmission lines, power plants, and interconnections between various power systems. The interconnected power system can experience frequency and power oscillations, as well as tie-line power deviation, which can lead to various issues such as disconnection actions, loss of multiple lines, zone islanding, equipment damage, transmission line overload, and interference with system protection schemes [7]. Conversely, the integration of Renewable Energy Sources (RESs) into the system may have substantial effects on its inertia as their penetration level increases. Therefore, when the penetration level of renewable energy sources (RESs) increases, the system inertia constant will be less, resulting in an elevation of the system frequency deviation. Hence, the management of frequency regulation becomes challenging when there is a discrepancy between electricity supply and load demand, especially with the increasing integration of renewable energy sources (such as wind and solar energy) into the power grid. Therefore, load frequency control (LFC) is widely recognized as a crucial control mechanism in power systems. Its primary objective is to ensure the system frequency and power fluctuations remain within predefined standard limits. The frequency of a system is contingent upon the active power, whereas the system voltage is significantly influenced by the reactive power. Hence, the categorization of power system control may be delineated into two primary concerns. (a) The management of active power in conjunction with frequency control. (b) The management of reactive power in conjunction with voltage regulation [4]. Furthermore, it is important to assess the potential of Renewable Energy Sources (RESs) to regulate the system frequency, in addition to using Load Frequency Control (LFC) for the purpose of enhancing frequency stability [8].

1.1 WIND ENERGY SYSTEM

Currently, there exists a diverse array of operational wind turbine models. The horizontal axis wind turbine is widely recognized as the most prevalent device in the field [9], [10]. The turbine is comprised of a small number of rotor blades that have been aerodynamically optimized. These blades may typically be rotated about their longitudinal axis for the purpose of control, a process known as pitch-regulation. An alternative cost-effective approach for regulation involves the use of blade designs that induce turbulence in the airflow at a certain velocity, known as Stall-Regulation. The turbines have the capacity to generate electricity within a range of 10 kilowatts to several megawatts. The European market now offers turbines with a maximum power output of 8 MW, while ongoing testing is being conducted on larger units. The efficiency of this particular turbine is very good. Consequently, its only use is in the realm of power production, necessitating the utilization of "high-speed engines" to maintain compact and cost-effective gear transmission and generator systems. The multiblade wind turbine is an additional traditional variant of the horizontal axis rotor design. The initial construction of the structure took place around a century ago. Wind turbines with a substantial initial torque are well-suited for powering mechanical water pumps. The rotational count is rather small, and the blades are constructed from simple sheets with straightforward geometrical properties. In the context of water pumping, the implementation of a rotation controlling system is deemed unnecessary. However, a mechanical safety system has been added to safeguard the turbine from potential damage caused by storms. The rotor may be rotated in the direction of the wind by using a wind-sheet, also known as a wind deflector, in the lee direction. To enhance the rotational capacity of this turbine, it has been outfitted with blades that possess improved aerodynamic efficiency, hence aiding the generation of energy. This is achieved by reducing the surface area of each individual blade. The mechanical stability shown by "slow speed turbines" is very robust, as evidenced by their operational longevity exceeding fifty years in some cases. The power generated from wind turbines is modelled as follows,

$$\:P=\frac{E}{t}=\frac{1}{2}.A.\rho\:a.{v}^{3}$$

Here $\:E$ represents the kinetic energy while $\:A$ represents the area and $\:\rho\:a$ denotes the density of air with wind velocity of $\:v$. From this, the output power can be computed as follows,

$$\:{S}_{wt}=\frac{R.\phi\:}{v}$$

Speed of the wind turbine ($\:{S}_{wt}$) depends upon the radius of blades ($\:R$), wind velocity ($\:v$) and angular speed of the turbine ($\:\phi\:).$

Nonlinear controllers have the potential to effectively achieve dynamic capabilities; yet, their design process has inherent complexities that are considered undesirable. Therefore, the pursuit of alternate linear equivalents is necessary in order to get optimal performance. The use of a fractional-order proportional-integral derivative (FOPID) controller is a suitable option for this objective, as it represents an enhanced iteration of the commonly employed linear controller, known as the proportional-integral derivative (PID) controller. Hence, the use of a Fractional Order Proportional Integral Derivative (FOPID) controller was employed in this investigation to get optimal operational efficiency for a buck converter. In order to achieve optimal performance, the researchers used a unique hybridized metaheuristic algorithm known as the Lévy flight distribution and simulated annealing method (LFDSA). The algorithm that was created has a well-balanced structure, including both explorative and exploitative phases. This assertion was substantiated by the execution of relevant analyses on unimodal and multimodal benchmark functions. The non-parametric statistical test has shown superior performance of the suggested technique. The suggested approach demonstrated improved performance in reaching optimal values for FOPID parameters, resulting in higher closed-loop output voltage control performance of the buck converter. This improvement was seen in both temporal and frequency domain responses, as well as in the system's capacity to reject disturbances.

This paper proposes a technique for designing an optimum and resilient fractional order PID (FOPID) controller for the creation of a Smith predictor based FOPID (SP-FOPID) control system. The present design methodology incorporates the use of continuing fraction expansion (CFE) based approximation models in the optimum design process of fractional order proportional-integral-derivative (FOPID) controllers. This technique ensures that the design optimality is maintained in various control applications. In order to achieve the desired outcome, a design methodology is employed to approximate the frequency response of a Bode's ideal loop reference model using an inverse controller loop shaping approach. Additionally, robust performance CFE-based FOPID controller models are derived by solving a multi-objective optimization problem through the utilization of a genetic algorithm. Therefore, the proposed approach effectively addresses many implementation challenges in the design process of FOPID controllers and effectively mitigates performance difficulties associated with model approximation errors and signal saturation limits of electronic hardware in practical controller applications.

The discussion focuses on the specific design steps, which involve the intelligent utilization of information obtained from a database containing control tracking and disturbance rejection step responses. These responses are evaluated using performance measures that consider the speed and shape characteristics of the input and output signals of the process. This evaluation is represented as a performance portrait (PP). The first phase involves using the performance portrait technique (PPM) as a means of verification to see whether the pilot analytical design of the parallel 2DoF PID controller has adequately considered relevant settings and to demonstrate the feasibility of extending the optimality analysis to the series 2DoF PID controller. The significance of this lies in the explicit observation of equivalent input disturbances in ultra-local DIPDT models, based on steady-state input values. In contrast, the parallel PID controller, which enables faster transient responses, necessitates the inclusion of a low-pass filter when reconstructed equivalent disturbances are needed. The following section demonstrates the use of an iterative approach to the Performance Parameter Mapping (PPM) technique in order to showcase the design efficiency and conciseness in examining the impact of various loop parameters on the modification of optimum processes. This is made possible via the visualization of the relationship between the closed-loop performance and the characteristics of the control signals.

The proposal is to use a Simplified Grey Wolf Optimizer (SGWO) as a means of addressing optimization problems. The original Grey Wolf Optimizer (GWO) approach incorporates simplicity by disregarding the wolves in the poorest category and emphasizing the wolves in the better categories throughout the search process. The superiority of the provided SGWO algorithm over the GWO algorithm is shown by its ability to produce more optimal solutions in a shorter amount of time. This is proved via the evaluation of several types of test functions, including unimodal, multimodal, and fixed dimension functions. The findings are also compared to the Gravitational Search Algorithm, Particle Swarm Optimization, and Sine Cosine Algorithm, highlighting the better performance of the proposed SGWO approach. The subsequent analysis involves the examination of the practical implementation of an Adaptive Fuzzy PID (AFPID) controller in a Distributed Power Generation System (DPGS) with energy storage. This implementation is achieved via the use of the recommended SGWO technique for the purpose of frequency regulation. The Distributed Power Generation System (DPGS) comprises many sources of renewable energy, including solar and wind technologies, as well as storage components like batteries and flywheels. Additionally, the system incorporates plug-in electric cars. The superiority of the SGWO approach over the GWO method in the problem of optimum controller design has been established.

This study introduces an adaptive proportional-integral-derivative (PID) control algorithm that utilizes stochastic gradient descent with momentum (SGDM) for regulating the performance of a proton exchange membrane fuel cell (PEMFC) power system. The Proton Exchange Membrane Fuel Cell (PEMFC) is a complex system characterized by nonlinearity and susceptibility to external disturbances, including fluctuations in intake gas pressures and temperature. Consequently, it is necessary to develop an adaptive control rule to effectively manage these challenges. The SGDM method is used to minimize the cost function and adjust the PID settings in response to perturbation variations. The researchers choose to use the whale optimization algorithm (WOA) in order to improve the adaptation rates during offline operations. The suggested controller is compared with PID stochastic gradient descent (PIDSGD) and PID Ziegler Nichols tuning (PID-ZN). The resilience of the control techniques is evaluated by experimentation over a range of temperatures and loads. In contrast to the PIDSGD and PID-ZN controllers, the PIDSGDM controller demonstrates the capability to provide the desired control performance, including rapid convergence and strong resilience.

One method that is often adopted for portable robots is a modified pendulum based concept. The aim of this challenge is to develop a control framework for self-adjusting robots to achieve stability. In contrast to the subsequent mobile robot that maintains a stable standing posture, a balancing robot necessitates a control mechanism for balance adjustment, which relies on a certain angle value as an input. The adjusting control mechanism is responsible for regulating the movements of the robot, enabling it to maintain its upright posture. In addition to the adjusting mechanism itself, the creation of an adjusting robot necessitates the implementation of a dedicated control system to oversee the development process and ensure the robot remains properly adjusted. The task at hand may be achieved by adjusting the proportional-integral-derivative (PID) controller using the data from the gyroscopic and accelerometric sensors. The execution of the PID regulator computation would serve as a suitable test model for this robot. Implementing these types of robotic systems inside industries may have a positive impact on the organization's economics, as well as reduce the risk factor for those working in hazardous settings. Furthermore, this can serve as a foundational paradigm for self-adjusting applications.

Particle Swarm Optimization (PSO) algorithm is a population-based search method that involves particles, which are people that modify their location (state) over time. In a Particle Swarm Optimization (PSO) system, particles traverse a search space that is characterized by several dimensions. During the course of a flight, individual particles undergo positional adjustments based on their own experiences, referred to as Pbest, as well as the experiences of nearby particles, referred to as Gbest. These adjustments are performed by using the best positions experienced by the particles themselves and their respective neighbors. In the conventional PSO, we made two significant modifications as follows,

Improvement in Local Optima: The problem of local optima is resolved by updating velocity in both Worst Case as well as Best Case Directions (in conventional PSO the local optima is updated in best case direction only).

Improvement in Population Generation: As PSO lacks with generation of new population we incorporate GA operators (Crossover & Mutation) for new population generation.

With the above modifications, we present our MPSO algorithm for PID controller design in wind energy systems.

3.1 PARTICLE SWARM OPTIMIZATION (PSO)

SPSO algorithm involves the movement of a swarm of particles inside a search space of D dimensions, with the objective of finding the most optimum solution. Every individual particle, denoted as i, is characterized by a velocity vector Vi=[vi1,vi2,…,viD] and a location vector Xi=[xi1,xi2,…,xiD], where D is the number of dimensions. The SPSO procedure starts with the random initialization of Vi and Xi. In each iteration, particle iPbesti=[Pbesti1,Pbesti2,…,PbestiD], representing the best position discovered by particle i, and the overall best position found by the whole swarm Gbest=[Gbest1,Gbest2,…,GbestD], serve as guiding factors for particle i to update its velocity and position,

$$\:{v}_{id}\left(t+1\right)={v}_{id}\left(t\right)+{c}_{1}{r}_{1}\left(PBes{t}_{id}\left(t\right)-{x}_{id}\left(t\right)\right)+{c}_{2}{r}_{2}\left(GBes{t}_{d}\left(t\right)-{x}_{id}\left(t\right)\right)$$

$$\:{x}_{id}\left(t+1\right)={x}_{id}\left(t\right)+{v}_{id}\left(t+1\right)$$

The cognitive and social acceleration coefficients are denoted as c1 and c2, respectively. Additionally, r1 and r2 represent two uniform random variables that are created from the range [0,1]. The implementation of velocity clamping restricts the movement of particles inside a defined border in the search space by the establishment of an upper limit on their velocity, denoted as Vmax. If the velocity of a particle is determined to surpass the maximum velocity Vmax, it is adjusted to Vmax in the following manner:

$$\:{v}_{id}\left(t+1\right)=\text{min}({v}_{id}\left(t+1\right),{V}_{max})$$

While the use of velocity clamping helps to mitigate the issue of velocity explosion, determining an appropriate value for Vmax is a crucial but challenging endeavor. Inadequate selection of Vmax may lead to suboptimal performance. In instances when the value of Vmax is quite large, it is possible for particles to exhibit a highly erratic trajectory, perhaps leading to the avoidance of the optimum solution. In contrast, when Vmax is modest, the particles exhibit a limited exploration range, perhaps leading to confinement inside a local optimum. In order to address this crucial issue, it is recommended to establish the maximum velocity, denoted as Vmax, in the following manner,

$$\:{V}_{max}=\tau\:({x}_{max}-{x}_{min})$$

The variables xmax and xmin represent the upper and lower limits of the search space border, respectively. Additionally, the variable δ is a real number that is inside the interval (0,1]. In general, two commonly used stopping criteria are utilized to conclude the Particle Swarm Optimization (PSO) process. The initial halting condition for the execution of Particle Swarm Optimization (PSO) occurs after a certain number of iterations has been completed. The calculation of function evaluation is performed in the following manner,

$$\:FE=S\times\:T$$

where S is the swarm size and T is the maximum number of iterations.

3.2 GENETIC ALGORITHM & OPERATORS

The Genetic technique (GA) is an optimization technique that draws inspiration from the process of natural selection. The algorithm under consideration is a population-based search method that incorporates the principle of survival of the fittest. The generation of new populations is achieved by the repeated use of genetic operators on individuals that are already part of the population. The fundamental components of a Genetic Algorithm (GA) are the encoding of solutions into chromosomes, the process of selection, the application of crossover and mutation operators, and the computing of fitness functions. The process of genetic algorithm (GA) is outlined as follows. The population (Y) consists of n chromosomes that are randomly initialized. The fitness of every chromosome within the population Y is calculated. Two chromosomes, denoted as C1 and C2, are chosen from the population Y based on their respective fitness values. The use of the single-point crossover operator, with a certain crossover probability (Cp), results in the production of an offspring denoted as O, by crossing over the genetic material of C1 and C2. Subsequently, the uniform mutation operator is used on the generated offspring (O) using a mutation probability (Mp) in order to create O′. The newly generated offspring, denoted as O', is introduced into a separate population. The selection, crossover, and mutation processes will be iteratively applied to the present population until the new population is fully formed. The mathematical analysis of genetic algorithms (GA) may be described as follows. GA employs dynamic adjustments to the search process by manipulating the probability of crossing and mutation, ultimately leading to the identification of an optimum solution. Genetic algorithms (GA) have the capability to alter the encoded genes. Genetic algorithms (GA) provide the capability to assess several people and generate various ideal solutions. Therefore, it may be argued that genetic algorithms (GA) provide superior global search capabilities. The progeny resulting from the recombination of parental chromosomes is likely to disrupt the favourable genetic patterns. The concept of parent chromosomes and the crossover formula may be formally described as follows,

$$\:R=\frac{\left(G+2\sqrt{g}\right)}{3G}$$

In this context, the variable "g" represents the number of generations, whereas "G" denotes the total number of evolutionary generations created by the population. It can be noted from the aforementioned equation that the variable R undergoes dynamic changes and exhibits a rise as the number of evolutionary generations increases. During the first stage of a genetic algorithm (GA), the level of similarity between individuals is often very low. In order to preserve the exceptional genetic architecture of individuals, it is essential to maintain a low value for R, the parameter representing the reproductive rate of the new population. At the culmination of the evolutionary process, there is a notable increase in the degree of resemblance among individuals, accompanied by a correspondingly elevated value of the coefficient of relatedness, denoted as R.

a. Crossover Operator

Crossover operators are used in order to produce children via the amalgamation of genetic information from two or more parental individuals. The commonly recognized crossover operators in genetic algorithms are single-point, two-point, k-point, uniform, partly matched, order, precedence preserving crossover, shuffle, reduced surrogate, and cycle.

b. Mutation Operator

Mutation is a fundamental mechanism that facilitates the preservation of genetic variation as it is passed from one group to the subsequent generation. The well recognized mutation operators include displacement, simple inversion, and scramble mutation. The Displacement Mutation (DM) operator is used to shift a specific substring of an individual solution inside itself. The location is selected at random from the provided substring in order to achieve a legitimate solution, as well as to introduce a random displacement mutation. There are two types of variations in the context of DM, namely exchange mutation and insertion mutation. The Exchange mutation and Insertion mutation operators involve the manipulation of individual solutions by either exchanging a portion with another component or inserting it into a different position, respectively.

3.3 PROPOSED MODIFIED PSO ALGORITHM

Worst particles selection: The subsequent procedure involves the identification of the most unfavorable particles for the purpose of updating velocity in the event of the worst-case scenario. In the context of our research, we designate a subset of particles as the worst particles (WP) based on their least fitness value. These WP particles are subject to distinct velocity functions. Particles other than WP are regarded as superior particles ($\:BP$).
Velocity updation: In the WC-PSO algorithm, the optimum solution is monitored in both the best search space and the worst search space to enhance the algorithm's capability to determine the global solution. Therefore, in order to improve particle behavior, the velocity is adjusted by taking into account both the particle's best position (pbest) and the global best position (gbest). This adjustment is carried out in the following manner,

$$\:v\left[t+1\right]=v\left[t\right]+{C}_{1}*rand*\left(pbest-present\right)+{C}_{2}*rand(gbest-present)$$

Similarly, for WP velocity is updated based on particle worst ($\:pworst$) and global worst ($\:gworst$). Velocity updation for WP is performed as follows,

$$\:v\left[t+1\right]=v\left[t\right]+{C}_{1}*rand*\left(pworst-present\right)+{C}_{2}*rand(gworst-present)$$

Therefore, the Wikipedia (WP) algorithm seeks the optimum solution within the worst area, whereas the Backtracking (BP) algorithm seeks the ideal solution within the best zone. By using this approach, we effectively mitigate the issue of local optima in our research endeavors.

3. Position updation: Based on computed velocity, the position is updated for all particles as follows,

$$\:Pos\left[t+1\right]=Pos\left[t\right]+V\{T+1]$$

Current position of the particle is computed based on previous position ($\:Pos\left[t\right]$), and current velocity ($\:v[t+1]$).

4. New population generation: The two chromosomes are chosen by the mechanism of tournament selection. The genetic algorithm (GA) employs a metric called crossing fraction, represented as XoverFrac, to signify the percentage of offspring produced by the crossover operation, omitting the elite individuals that have already been chosen from the present population being assessed. The variable XoverFrac, as previously stated, is subject to the constraint 0 ≤ XoverFrac < 1. In the present investigation, the variable XoverFrac is given a value of 0.8, and the chosen crossover function is of the arithmetic type. In the given context, the XOR operation is performed on the two parental chromosomes owing to their binary characteristics. The phrase is stated in the following way,

$$\:Cross={P}_{1}\oplus\:{P}_{2}$$

Mutation: Mutation is a genetic anomaly that impacts individuals within a certain population. Mutation plays a pivotal function in the preservation of genetic diversity and the facilitation of the study of a broader spectrum of viable solutions. The strategy of uniform mutation was chosen as the optimal approach for our investigation. Within the domain of genetic algorithms, the procedure of uniform mutation encompasses the development of a collection of random numbers (RDs) derived from a uniform distribution. The quantity of random numbers produced is equivalent to the length of the genome. The assignment of numerical values to individual genes (bits) inside the chromosome is decided by the associated random number.

$$\:NewGen=\#\:of\:ElitKids+\#\:of\:Crossover+\#\:of\:Mutation$$

Wind energy system generation using modified particle swarm optimization algorithm generating the power of 500MW connected to 400KV ac grid. The wind generating system integrated with VSC HVDC transmission link. The DC side voltage is +/- 320KV and DC Power is 500MW. Table.1 summarizes the parameters used in simulation.

Table 1

Simulation settings.
S.No	Parameter	Ratings/Topology
1	Rated Power Flow	1000 MW
2	MMC Levels	331
3	Sub-module	Half bridge
4	Rated Pole Voltage [U_dc] (Positive to Negative)	640 KV (+/- 320KV)
5	Pugalur AC Grid	50 Hz, 400 kV, 36.4 GVA, X/R = 10
6	Thrissur AC Grid	50 Hz, 400 kV, 36.4 GVA, X/R = 10
7	Converter Transformer	400/380 kV, 367 MVA,
8	Leakage Impedance	17.5%
9	Neutral Grounding Reactor	Lg = 6 kH, Rg = 2.5 kΩ,
MMC Sub-module Parameters
10	Nominal Switching Frequency	100 Hz
11	Capacitor	8.5 ± 2% mF
12	Peak Turn-off Current	3300 A
13	DC Reactor per arm	90 mH
Power Transmission Parameters
14	Conductor size	2500 mm2
15	Conductor	Copper
16	Insulation	XLPE insulation
17	OHL length	143 km
18	Cable Length	32 km
19	OHL DC Resistance	23.27 mΩ/km
20	Cable DC Resistance	9.2 mΩ/km

Based on above configuration, the simulation is performed. The MATLAB Simulink model designed for our work is shown in Fig. 3 and Fig. 4.

Figure 5. shows the output results obtained from Simulink model. We presented the output for angular power, AC grid power and PID controller. The Wind Energy converter station module (station-1: Pugalur station) control station responses are illustrated in Fig. 6. The input control voltage, active power, reactive power, three phase control voltages and three phase control currents in per units respectively are shown in Fig. 6. The conventional AC grid station module (station-2: Thrissur station) control station responses are illustrated in Fig. 7. The input control voltage, active power, reactive power, three phase control voltages and three phase control currents in per units respectively are shown in Fig. 7. The Wind Energy converter station module (station-1: Pugalur station) filter performance is illustrated in Fig. 8. The direct axis current, quadrature axis current, modulation index and the reference voltage generated for VSC gating respectively are shown in Fig. 8. The conventional AC grid station module (station-2: Thrissur station) filter performances are illustrated in Fig. 9. The direct axis current, quadrature axis current, modulation index and the reference voltage generated for VSC gating respectively are shown in Fig. 9.

The velocity, current position and fitness value of each optimal particle during the simulation are presented in Table 2, after this table the best fitness value is 3.943e + 005 appeared in iteration number 9, and the optimal gains are the fitness function variations and the variation of optimal Ki et Kp gain during the simulation are presented in Fig. 10.

paper proposed a methodology for identifying the optimal operating point of a proportional-integral-derivative (PID) controller within the specific domain of wind turbine systems. Furthermore, the technique was designed to identify the regions of stability within the parameter space. The present study employs a Modified Particle Swarm Optimization (MPSO) method as a means to achieve optimal design of a Proportional-Integral-Derivative (PID) controller. The update entails the integration of a highly efficient Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). The aforementioned intelligent algorithms refer to artificial learning processes that possess the capability to find optimal operating locations. A function was used to determine the optimal operating parameters, namely kp and ki, for each kd value inside the stability region of the PID controller. Indeed, it has been shown that genetic algorithms (GA) exhibit a rather sluggish convergence process, sometimes accompanied by challenges related to local convergence. The PSO method is used to address these challenges, whilst the GA algorithm is responsible for generating a new population. The potential solutions were shown using simulations of a drive train model that did not include time delay, as well as a pitch control model that accounted for time delay. Simulations are conducted using the MATLAB Simulink tool, which is very efficient in the design and validation of power systems using VSC HVDC transmissions.

Author Contribution

A - author is main contributor for the article, woks in HVDC industry and studying part time research scholar, Used his experience and analysis in Realtime HVDC grid. B - Supported in analyzing the values and relate with other technologies.C- Supported in technically in preparing the documnet.D- The reviewed artilce and suggestions have been made.

Acknowledgement

Hi, I don't have any funding support from institution or other for this article.

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Wind Energy Extraction by Pso Modelled Algorith in Energy Transmission

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Abstract

1. INTRODUCTION

1.1 WIND ENERGY SYSTEM

2. RELATED WORKS

3. PROPOSED WORK

3.1 PARTICLE SWARM OPTIMIZATION (PSO)

3.2 GENETIC ALGORITHM & OPERATORS

3.3 PROPOSED MODIFIED PSO ALGORITHM

4. EXPERIENCE RESULTS

5. CONCLUSION

Declarations

Author Contribution

Acknowledgement

References

Additional Declarations

Status:

Version 1