Using Artificial Neural Network Algorithm for Voltage Stability Improvement

An innovative technique to evaluate network equivalent for voltage stability assessment in a widespread sub-grid system

This article is aimed at justifying the need to modernize the existing systems that ensure voltage stability in the Unified Power System of Russia under the conditions of its permanent development, structural complication and active implementation of distributed generation. To this end, an analysis of the existing systems of emergency control used in the Unified Power System of Russia was conducted by considering their efficiency during major system accidents, including those in 2005 and 2017. In addition, specific features of operating power system facilities in the northern part of the Irkutsk Oblast were investigated from the standpoint of voltage stability. The conducted analysis of the operation of emergency control systems in the context of system accidents, including possible incorrect actions, identified their following disadvantages: insufficient fault tolerance, weak adaptability to emergency disturbances (including abnormal situations), as well as a lack of coordination between local devices and the discreteness of regulation. These shortcomings can be eliminated by introducing intelligent automation systems based on artificial neural networks and machine learning, as well as high-performance multi-agent systems into the structure of the emergency management of the Unified Power System of Russia. The results obtained indicate the need to modernize the existing voltage stability systems operated in the Unified Power System of Russia, both their software and hardware components. The proposed implementation of intelligent systems is expected to improve the existing systems at the same time as maintaining the current effective hierarchical principles of emergency management.

Gerilim kararlılığı, güç sistemlerinde incelenmesi gereken önemli bir konudur. Sürekli artan elektrik enerjisi talebi, artan talebi karşılamak için yeni elektrik üretim santrallerinin kurulumu ve güç sistem arızaları gerilim kararlılığını olumsuz yönde etkilemektedir. Günümüzde gerilim kararlılığını iyileştirmek için güç sistemlerinde Esnek Alternatif Akım İletim Sistemleri (FACTS cihazları) kullanılmakta olup, ayrıca enerji talebini karşılamak ve gerilim kararlılığını iyileştirmek için güç sistemlerine Dağıtık Üretim Kaynakları (DGs) entegre edilmektedir. Bu çalışmada, IEEE 30 baralı güç sistemi ile Kuzeybatı Anadolu (KBA) güç sistemi, gerilim kararlılığı bakımından temel (nominal), aşırı yüklenme ve hat kopması durumlarında incelenmiştir. İncelenen güç sistemlerine FACTS cihazlarından Statik Var Kompanzatör (SVC) ve Tristör Kontrollü Seri Kapasitör (TCSC), DGs’lerden ise Yakıt Hücresi (YH), Güneş Enerji Santrali (GES) ve Rüzgâr Enerji Santrali (RES) ayrı ayrı ve birlikte bağlanmıştır. Güç sistemlerinin bara gerilim değerlerinde ve yüklenme parametresinde meydana gelen değişimler analiz edilmiştir.

STATCOM is used to adjust bus voltage by injecting or absorbing the reactive power, therefore it is capable of improving power system voltage stability. However, it is important to find the optimal location and sizing of the device in a power system to support bus voltage in steady-state and design a controller to enhance transient voltage stability. In this paper, the optimal location and sizing of STATCOM are used based on the PSO method and the online ANFIS controller is designed for STATCOM to improve transient voltage stability under large disturbance. Online training of ANFIS is done using a neural identifier. And based on this identification, the weights and coefficients are adjusted timely. To demonstrate the performance of the proposed controllers, simulation results of the voltage response in time-domain are performed in MATLAB environment to evaluate the effectiveness of the designed controller for STATCOM.

This paper investigates on-load tap changer OLTC effect on voltage stability. Chaos theory used in this analysis is via the calculation of the maximum power transmission. Resulted chaotic behavior is exhibited by singular induced bifurcation which clearly shows the voltage stability of the power system with OLTC from a singular induced bifurcation point of view. The paper is organized as follows: 1) increasing in the maximum reactive power via increasing the ratio of OLTC and the possibility of voltage collapse decrease under the same load command. 2) Noting that the voltage instability is directly affected by the load characteristics, shown via the effect of OLTC on the voltage instability under different static load models. 3) Studying voltage stability in state space and the effects of the generator excitation current limit, the OLTC and load dynamics on voltage collapse. In addition, a small-disturbance voltage stability region of a power system is constructed. The voltage stability of the system is determined by the dynamic characteristics of both the OLTC and the load.

In the power system, voltage instability is one of the important issues and it may cause voltage sag, voltage swell, change in load and generation of harmonics. To tackle all these problems, the transmission power system makes use of an intelligent control model to improve the stability of power system. In this paper, a new adaptive neuro fuzzy inference system (ANFIS) based power system stabiliser (PSS) for the enhancement of voltage stability in power systems is proposed. A hybrid learning algorithm, which combines the least squares method and the back propagation algorithm, is used to train the ANFIS. The performance of the power system by using fuzzy based PSS is about 60.1% when compared to that of the conventional PSS. To evaluate the effectiveness of the proposed technique the performance of the proposed technique was compared with the results obtained by using fuzzy PSS model and its performance was analysed. Simulation results described in the paper demonstrate that the adaptive neuro-fuzzy based PSS can give 33.43% of improvement in voltage stability when compared to that of fuzzy based PSS over a wide range of operating conditions.

Many technological advancements in the modern era have made actual use of electrical power and the constrained operating of power systems within stability limits. Some expeditious load variations and rising power demands initiate complications in voltage stability and can put stress on performance, leading to voltage instability. Voltage Stability Indices can be used to perform voltage stability assessment. This review evaluates various VSIs based on mathematical derivations, assumptions, critical values, and methodology. VSIs determine the maximum loadability, voltage collapse proximity, stability margin, weak areas, and contingency ranking. Stability indices can also specify the optimal placing and sizing of Distributed Generators. Thus, VSIs play a vital role in power system voltage stability. This review is a comprehensive survey of various indices and analyses their accuracy in determining the instability of power systems. Voltage stability is a crucial concern in operating a reliable power system, and the systematic evaluation of voltage stability is essential in a power system. This review considered and analyzed 34 indices from 138 articles from the literature for their significant performance in various power system stability problems. Of 33 indices, were 22 derived from transmission line parameters, referred to as line indices, and 12 from bus and line parameters, referred to as bus indices.

This study presents an analysis of voltage stability of the electrical power system with the integration of renewable energy and the improvement of the conventional analysis using the artificial neural network (ANN), which is mainly focused on the effect of distributed generator-solar (DG-Solar) towards the electric power system. Real power and reactive power margins of both voltage stability are expressed as VSM (P) and VSM (Q), respectively. They are obtained from the actual power-voltage (PV) and reactive-power (QV) curve, which is created for each series by a series of load flows by incrementing P and Q load. Then, the system will integrate with 5MW, 50MW and 100MW of DG-Solar to compare the electric power system before and after the integration. The results are observed and compared for both situations. After that, an analysis improvement is made by using ANN-based model to predict the values of VSM (P) and VSM (Q) without having to perform PV QV Curve and calculate VSM (P) and VSM (Q). IEEE 30-bus was chosen as the electrical power system. The load flow analysis and ANN-based model are simulated and developed by using MATLAB software.

This paper presents voltage stability in power systems when connected by wind farm generators. Two models of wind generator, namely squirrel cage induction generator and doubly fed induction generator are used and based on steady-state model. P-V curve is used to express maximum loading factor in power system when wind farm was installed. The test IEEE 14 bus system is selected to show proposed performance. The results of this paper can be shown that maximum loading factor before install wind farm generator is 0.70398 p.u. The weakest bus is bus no.14. Therefore, loading factor of system that connects two types of wind turbine with 7 m/s wind speed are 0.8688 p.u and 0.7997 p.u respectively. To improve the voltage stability, FACTS device is selected. The results show that STATCOM can improve maximum loading factor better SVC for both types of wind farm generator. Maximum loading factor increased when setting STATCOM up to 56.33% and 53.28% in systems that connected squirrel cage induction generator and doubly fed induction generator wind turbine respectively. Therefore, this methodology can give direct advantage for organization that are responsible to built convincing of power system in study the respond of setting wind turbine generator on voltage stability.

According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

Voltage stability has recently become a challenging problem for many power systems. Voltage instability can be ascribed to the lack of reactive power support needed to maintain the voltage profile at a specified value. It has been responsible for severe system disturbances including major blackouts. A key concept in the restructuring of the electric power industry is the ability to accurately and rapidly quantify the capabilities of transmission systems. Computation of available transmission capability (ATC) must take into account adequate static, dynamic, and voltage stability margins. Online voltage stability assessment tools will be required for the secure operation of interconnected power systems. This paper presents the application of artificial neural networks (ANN) for online voltage stability assessment. A key feature of the proposed method is the use of principal component analysis to project the input patterns in the original pattern space into a new subspace having fewer dimensions than the original pattern space. This enhances the efficiency of ANN training. The proposed neural network is used to determine the voltage stability margin of the IEEE 118 bus power system for different operating conditions. The possibility of including topology information in a single ANN is also investigated.

The present study aims to apply artificial neural networks (ANNs) for reference evapotranspiration (ETo) prediction. Three different feed-forward artifical neural network (ANN) models, each using varied input combinations of previous months ETo, have been trained and tested. The output of the network was the one-month-ahead ETo. The networks learned to forecast one-month-ahead ETo for Mahanadi reservoir project area using the three learning methods namely quasi-Newton algorithm, Levenberg–Marquardt algorithm and backpropagation with adaptive learning rate algorithm. The training results were compared with each other, and performance evaluations were done for untrained data. The performance evaluations measured were standard error of estimates (SEE), raw standard error of estimates (RSEE), and model efficiency. The best ANN architecture for prediction of ETo was obtained for Mahanadi reservoir project area. The monthly reference evapotranspiration data were estimated by the Penman–Monteith method and used for training and testing of the ANN models. Further ANNs predicted results were compared with those obtained using the statistical multiple regression technique. Based on results obtained, the ANN model with architecture of 3–9-1 (three, nine, and one neuron(s) in the input, hidden, and output layers, respectively) trained using quasi-Newton algorithm was found to be the best amongst all the models with minimum SEE and RSEE of 0.45 and 0.45 mm/d respectively and maximum model efficiency of 93%. It is concluded that ANN can be used to predict ETo.

This paper presents an application of Artificial Neural Network (ANN) for monitoring power system voltage stability. The training of ANN is accomplished by adapting information received from local and remote measurements as inputs and fast indicators providing voltage stability information of the whole power system and one at each particular bus as outputs. The use of feature reduction techniques can decrease the number of features required and thus reduce the number of system quantities needed to be measured and transmitted. In this paper, the effectiveness of the proposed algorithm is tested under a large number of random operating conditions on the standard IEEE 14-bus system and the results are encouraging. Fast performance and accurate evaluation of voltage stability indicators have been obtained. Finally, the idea of applying load shedding based on voltage stability indicator as one of potential countermeasures is described.

Voltage stability analysis is the major concern in order to operate any power system safely. In this context considerable research work has been carried out to improve the voltage stability. FACTS devices can used for controlling power flow, transmission voltage, improving voltage profile, and reducing reactive losses. In this paper two different FACTS devices have been selected to place in suitable location to improve voltage stability in power system. The mathematical model of the power system equipped with a Static VAR Compensator (SVC) and Static synchronous compensator (STATCOM) separately is systematically derived and the parameters of the shunt FACTS are modeled into the power flow equations and used in the control strategy. The two models of FACTS are developed for the IEEE 30-bus system and implemented in Newton-Raphson load flow algorithm in order to control the voltage of the bus to which the shunt FACTS is connected. In this study a MATLAB written program is used for the solution of power flow problems and calculate voltages, active and reactive power at each bus of IEEE 30-bus system.

Recently, much effort has been taken to apply learning algorithms for voltage stability in power systems. Even though these algorithms obtained remarkable performance, an evident disadvantage is that they generally depend on a fixed-length inputting data, where even slight data loss will cause them completely invalid. To overcome this shortcoming, this paper proposes an adaptive neural network based approach which is tolerant for missing data. In this approach, the temporal measurement sequence from each PMU is treated equally by the identical multi-layer neural network to generate abstract representations. Then multiple abstract representations of different PMUs are aggregated by a symmetrical function, followed by an output block to conduct either regression or classification tasks of voltage stability. Massive experiments using different testing systems show that our method is almost unaffected by missing data as well as maintains a comparable accuracy with previous learning algorithms.