Coherency-based fast voltage contingency ranking employing counterpropagation neural network

Power system security is one of the major concerns in competitive electricity markets driven by trade demands and regulations. If the system is found to be insecure, timely corrective measures need to be taken to prevent system collapse. This paper presents an approach based on a counterpropagation neural network (CPNN) to identify and rank the contingencies expected to reduce or eliminate the steady-state loadability margin of the system, making it prone to voltage collapse. It has been shown that unlike other artificial neural networks (ANN) paradigms, which start with random weights, CPNN is very sensitive to initial weights. To reduce the dimension and training time, a novel feature selection method, based on the coherency existing between load buses with respect to voltage dynamics, is employed to select significant input features for the CPNN. Once trained, the CPNN is found to rank voltage contingencies accurately for previously unknown system conditions very fast. Due to its fast training, the proposed CPNN will be particularly useful for power system planning studies, as a number of combinations can be tried within a small time frame. The effectiveness of the proposed approach has been demonstrated on IEEE 30-bus test system and a 75-bus practical Indian system.     

Modified water cycle algorithm for optimal direction overcurrent relays coordination

The optimization model of Directional Over Current Relays (DOCRs) coordination is considered non-linear optimization problem with a large number of operating constraints. This paper proposes a modified version for Water Cycle Algorithm (WCA), referred to as MWCA to effectively solve the optimal coordination problem of DOCRs. The main goal is to minimize the summation of operating times of all relays when they act as primary protective devices. The operating time of a relay depends on time dial setting and pickup current setting or plug setting, which they are considered as decision variables. In the proposed technique, the search space has been reduced by increasing the C-value of traditional WCA, which effects on the balance between explorative and exploitative phases, gradually during the iterative process in order to find the global minimum. The performance of proposed algorithm is assessed using standard test systems; 8-bus, 9-bus, 15-bus, and 30-bus. The obtained results by the proposed algorithm are compared with those obtained by other well-known optimization techniques. In addition, the proposed algorithm has been validated using benchmark DIgSILENT PowerFactory. The results show the effectiveness and superiority of the proposed algorithm to solve DOCRs coordination problem, compared with traditional WCA and other optimization techniques.     

Voltage stability assessment in power network using self organizing feature map and radial basis function

Self-organizing feature map (SOFM) in conjunction with radial basis function (RBF) has been applied in this paper to determine and classify the voltage stability states of a multi-bus power network. Simulations were carried out on a real 203-bus system of an Indian power utility considering load changes and contingencies. The data collected from simulations are then used as inputs to the SOFM which acts as a classifier to classify the voltage stability states of the system under test. To augment the effectiveness of the proposed method, the initial classification results were improved with the application of RBF technique. Studies show that the SOFM-RBF combination delivers high classification accuracy in the order of almost 100% and can be considered an effective soft-computing tool to ease the operation of large-multi bus power network under variable operating conditions.     

Multiobjective optimal power flow using interior search algorithm: A case study on a real-time electrical network

Optimal power flow (OPF) is a vital concern in an electrical network. In consequence of the intricacy of the power systems, the conventional formulations are not adequate for current situation. Hence, in this study, the multiobjective OPF (MOOPF) problem has been modeled to diminish the production cost, environmental emission, and losses and to enhance the voltage stability and voltage profile simultaneously. This study proposes the application of interior search algorithm (ISA) for resolving MOOPF problem. The simulations have been carried out on three various test systems such as IEEE 30-bus system, IEEE 57-bus system, and Tamil Nadu Generation and Distribution Corporation Limited, as a real part of 62 bus Indian utility system (IUS) to infer the efficacy of ISA in solving the OPF problems. The simulation results have been compared with other techniques. The comparison shows that ISA is used in resolving MOOPF problems.     

Parallel self-organising hierarchical neural network based estimation of degree of voltage insecurity

Increased interconnections and loading of power systems, sometimes, lead to insecure operation. Since insecure cases often represent the most severe threats to secure system operation, it is important that the user be provided with a measure for quantifying the severity of the cases both in planning and operational stages of a power system. The Euclidean distance to the closest secure operating point has been used as a measure of the degree of insecurity. Recently, artificial neural networks are proposed increasingly for complex and time-consuming problems of power system. This paper presents a parallel self-organised hierarchical neural network based approach for estimation of the degree of voltage insecurity. Angular distance based clustering is used to select the input features. The proposed method has been tested on IEEE 30-bus system and a practical 75-bus Indian system and found to be suitable for real time implementation in Energy management centre.     

Krill herd algorithm for optimal location of distributed generator in radial distribution system

Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.     

Comprehensive learning particle swarm optimization for reactive power dispatch

Reactive power dispatch (RPD) is an optimization problem that reduces grid congestion by minimizing the active power losses for a fixed economic power dispatch. RPD reduces power system losses by adjusting the reactive power control variables such as generator voltages, transformer tap-settings and other sources of reactive power such as capacitor banks and provides better system voltage control, resulting in an improved voltage profile, system security, power transfer capability and over all system operation. In this paper, RPD problem is solved using particle swarm optimization (PSO). To overcome the drawback of premature convergence in PSO, a learning strategy is introduced in PSO, and this approach called, comprehensive learning particle swarm optimization (CLPSO) is also applied to this problem and a comparison of results is made between these two. Three different test cases have been studied such as minimization of real power losses, improvement of voltage profile and enhancement of voltage stability through a standard IEEE 30-bus and 118-bus test systems and their results have been reported. The study results show that the approaches developed are feasible and efficient.     

Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization

This paper describes teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal power flow (MOOPF) problems while satisfying various operational constraints. To improve the convergence speed and quality of solution, quasi-oppositional based learning (QOBL) is incorporated in original TLBO algorithm. The proposed quasi-oppositional teaching learning based optimization (QOTLBO) approach is implemented on IEEE 30-bus system, Indian utility 62-bus system and IEEE 118-bus system to solve four different single objectives, namely fuel cost minimization, system power loss minimization and voltage stability index minimization and emission minimization; three bi-objectives optimization namely minimization of fuel cost and transmission loss; minimization of fuel cost and L-index and minimization of fuel cost and emission and one tri-objective optimization namely fuel cost, minimization of transmission losses and improvement of voltage stability simultaneously. In this article, the results obtained using the QOTLBO algorithm, is comparable with those of TLBO and other algorithms reported in the literature. The numerical results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal non-dominated solutions of the multi-objective OPF problem. The simulation results also show that the proposed approach produces better quality of the individual as well as compromising solutions than other algorithms.     

基于QPSO-SVM模型的电力系统稳定性评估

随着我国电力系统的快速发展,超高电压输变电已经开始应用,电网变的更加复杂,其电力系统的稳定性和安全性问题更显得突出.电压的稳定性一直是系统可靠性的重要指标,而其电压质量的在线实时评估一直是研究的难题.本文采用支持向量机（SVM）模型来提高运算精度和效率,并通过量子行为粒子群算法（QPSO）优化并计算其参数,提出一种基于QPSO-SVM的模型,可用于实时在线评估电力系统的稳定性.此外,为了提高机器学习的评估指标的精准度,采用先进的潮流计算Jacobian的切向量分量来作为VSI,可以保证评估值的绝对性,并可以适用于各种网络结构.最后在WSCC9-bus标准系统上实验证明,该方法比GA-SVM、一般的SVM和BP神经网络在学习时间分别提高23.2%、63%、77.9%,测试时间分别加快26.2%、56.9%、72.56%,在精度上分别提高28.9%、42.19%、82.34%.另外,通过在IEEE14总线上做实验,可以找到系统崩塌前的关键总线,并与潮流计算的结果基本一致,因此该方法是一种可以作为实时在线电力系统稳定性评估的理想方法.     

Reactive power planning using a new hybrid technique

Voltage deviation and stability constrained VAr planning or reactive power planning (RPP) is an important challenging issue in power systems. This paper presents a new hybrid technique for modeling and solving RPP problem taking into account the static voltage stability constraint. First, the uncertain fuzzy clustering theory is employed to select new candidate VAr source locations. Then, modified gray code is applied and used to represent a series of non-uniform VAr capacity intervals at different candidate buses. Based on the new ordering of the VAr capacity intervals, a simplified piecewise linear function between the total transfer capability and new VAr capacity is derived and applied as static voltage stability constraint in RPP problem. Last, the RPP optimization problem is solved by a self adaptive fuzzy chaotic interactive honey bee mating optimization (FCIHBMO) technique taking advantage of the modified gray code. In the FCIHBMO algorithm, a modified definition of the updating factors on generation solution is proposed. In the case study, uncertain fuzzy clustering mechanism, the modified gray code, and the modified HBMO are applied to the IEEE 118-bus and IEEE 300-bus systems. Test results conclude that the proposed hybrid technique is a simplified and effective approach for voltage stability constrained VAr planning with contingency considered.     

Prediction of transmission line overloading using intelligent technique

With the worldwide deregulation of power system, fast line flows or real power (MW) security assessment has become a challenging task for which fast and accurate prediction of line flows is essential. Since last few years, limit violation of voltage and line loading has been responsible for undesirable incidents of power system collapse leading to partial or even complete blackouts. Accurate prediction and alleviation of line overloads is the suitable corrective action to avoid network collapse. The control action strategies to limit the transmission line loading to the security limits are generation rescheduling/load shedding. In this paper, an intelligent technique based on cascade neural network (CNN) is presented for identification of the overloaded transmission lines in a power system and for prediction of overloading amount in the identified overloaded lines. The effectiveness of the proposed CNN based approach is demonstrated by identification and prediction of line overloading for different generation/loading conditions in IEEE 14-bus system. Once the cascade neural network is trained properly, it provides accurate and quick results for previously unseen loading scenarios during testing phase.     

A new method for optimal location and sizing of capacitors in distorted distribution networks using PSO algorithm

This paper presents an optimization algorithm for simultaneous improvement of power quality (PQ), optimal placement and sizing of fixed capacitor banks in radial distribution networks in the presence of voltage and current harmonics. The algorithm is based on particle swarm optimization (PSO). The objective function includes the cost of power losses, energy losses and those of the capacitor banks. Constraints include voltage limits, number/size of installed capacitors at each bus, and PQ limits of standard IEEE-519. Using a newly proposed fitness function, a suitable combination of the objective function and relevant constraints is defined as a criterion to select a set of the most suitable buses for capacitor placement. This method is also capable of improving particles in several steps for both converging more readily to the near global solution as well as improving satisfaction of the power quality constraints. Simulation results for the 18-bus and 33-bus IEEE distorted networks using the proposed method are presented and compared with those of previous works. In the 18-bus IEEE distorted network, this indicated an improvement of 3.29% saving compared with other methods. Using the proposed optimization method and simulation performed on the 33-bus IEEE distorted network an annual cost reduction of 31.16% was obtained.     

On-line voltage security assessment of power systems using core vector machines

This paper presents a core vector machine (CVM)-based algorithm for on-line voltage security assessment of power systems. To classify the system security status, a CVM has been trained for each contingency. The proposed CVM-based security assessment has very small training time and space in comparison with support vector machines (SVM) and artificial neural networks (ANNs)-based algorithms. The proposed algorithm produces less support vectors (SV) and therefore is faster than existing algorithms. In this paper, a new decision tree (DT)-based feature selection technique has been presented, too. The proposed CVM algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line voltage security assessment procedure of large-scale power system.     

Stability of current-mode control for DC–DC power converters

DC–DC power converters are switched devices whose averaged dynamics are described by a bilinear second-order system with saturated input. In some cases (e.g., boost and buck–boost converters), the input output dynamics can be of nonminimum-phase nature. Current-mode control is the standard strategy for output voltage regulation in high dynamic performance industrial DC–DC power converters. It is basically composed by a saturated linear state feedback (inductor current and output voltage) plus an output voltage integral feedback to remove steady-state offset. Despite its widespread usage, there is a lack of rigorous results to back up its stabilization capability and to systematize its design. In this paper, we prove that current-mode control yields semiglobal stability with asymptotic regulation of the output voltage.     

Optimal reactive power dispatch problem using a two-archive multi-objective grey wolf optimizer

In this paper, a novel two-archive Multi-Objective Grey Wolf Optimizer (2ArchMGWO) is proposed for solving Multi-Objective Optimal Reactive Power Dispatch (MORPD) problems. The optimizer has been improved from its original Multi-Objective Grey Wolf Optimizer (MGWO) by modifying the reproduction operator and adding the 2-archive concept to the algorithm. It is then implemented on solving MORPD with objective functions being active power loss minimization and voltage profile improvement (voltage deviation minimization). The generator bus voltages, tap setting transformers and shunt reactive power sources or flexible alternating current transmission systems are set as design variables. The proposed algorithm along with other existing multiobjective optimizers are applied to solve three test problems with the standard IEEE 30-bus, IEEE 57-bus, and the IEEE 118-bus power systems. The optimum results obtained from the various optimizers performance are compared based on the hypervolume indicator and they reveal that 2ArchMGWO is clearly superior to the others.     

A Nonlinear Coordinated Approach to Enhance the Transient Stability of Wind Energy-Based Power Systems

This paper proposes a novel framework that enables the simultaneous coordination of the controllers of doubly fed induction generators (DFIGs) and synchronous generators (SGs). The proposed coordination approach is based on the zero dynamics method aims at enhancing the transient stability of multi-machine power systems under a wide range of operating conditions. The proposed approach was implemented to the IEEE 39-bus power systems. Transient stability margin measured in terms of critical clearing time along with eigenvalue analysis and time domain simulations were considered in the performance assessment. The obtained results were also compared to those achieved using a conventional power system stabilizer/power oscillation (PSS/POD) technique and the interconnection and damping assignment passivity-based controller (IDA-PBC). The performance analysis confirmed the ability of the proposed approach to enhance damping and improve system’s transient stability margin under a wide range of operating conditions.      

Neural network based switching control of AC–AC converter with DC–AC inverter for voltage sags, harmonics and EMI reduction using hybrid filter topology

A neural network based AC–AC voltage restorer is designed for voltage sags and PWM type active power filter with compound trap passive filter as a new hybrid filter are simultaneously used for voltage harmonics compensation and electromagnetic interference (EMI) reduction. First objective is to apply the neural network based switching control technique for the AC–AC voltage restorer to reduce time delays during the switching conditions and switching losses. The aim of the IGBTs used in the AC–AC voltage restorer is to test and to find the best switching frequency–power combination in the steps of the simulation. Thus, the proposed AC–AC voltage restorer has important advantages such as fast switching response, simplicity and more intelligent structure, better output waveform. The transient condition of the AC–AC voltage restorer is improved via the neural network based control technique. The second objective is the proposed strategy for elimination of voltage harmonics using PWM type DC–AC inverter part of the system as an active power filter. The last objective of the system is EMI reduction with using hybrid filter and voltage restorer together. Three problems which are voltage sags, harmonics and EMI are solved with the proposed system simultaneously.     

A computing method-based rearrangement of network protocols to improvise quality factors of FACTS devices and sensors using Newton-Raphson technique

The load flow analysis project was carried out using the Newton-Raphson's iteration technique and a multiobjective method was suggested to minimize power loss, increase bus voltage, reduce operating costs, and controlling the flexible AC transmission system (FACTS) controllers. The key focus is to improvise the load sustainability subjected to controlling of system safety, integrity, and stability margins within specified limits by acquiring optimum place, installation expenses for FACTS controllers. It is important to analyze the benefits and architect the FACTS devices for the power steady state analysis. For effective modeling, the five bus standard is analyzed without the FACTS end devices and with the FACTS controllers. Transient voltage is critical which requires accurate and quick response to avoid the voltage collapse and instability issues. The Newton-Raphson's method of load flow analysis is an iterative method which approximates the set of nonlinear simultaneous load flow equations to a set of linear simultaneous load flow equations using Taylor's series expansion and the terms are limited to first order approximation. The variations in voltage are within 5% for a well designated power system. If it exceeds the specified limit then the performance of equipment will be poor and the life of equipment will reduce. Hence the voltage control is very important to improvise the quality factor of the FACTS controllers and devices in power system. The voltage variations in a bus or node are related to reactive power. If the reactive power is injected to a bus is less than reactive power drawn from the FACTS devices, the voltage instability becomes infinite issue causes damage to the controllers and devices. In a load flow problem, two quantities are specified for each bus and the remaining quantities are obtained by the load flow equation analysis using Newton-Raphson method. This method has been tested for IEEE 30 bus system and then the values are compared and analyzed with MATLAB.     

Sensing properties of organised films based on a bithiophene derivative

Highly reproducible optic and electrochemical sensors have been developed using organised films from a polar bithiophene derivative, the 5-(dimethylamino)-5′-nitro-2,2′-bithiophene (Me₂N–T₂–NO₂). The strength of the molecular dipole moment of this push–pull end-capped bithiophene has permitted to obtain highly ordered, homogeneous and reproducible films by using both the Langmuir–Blodgett and the casting techniques. The organisation of the molecules in LB films and cast films has been established by means of UV–vis, infrared and Raman spectroscopy and by AFM.Me₂N–T₂–NO₂ thin films possess appealing optical and electrochemical sensing capabilities. UV–vis spectra can be modified in the presence of a variety of volatile organic compounds and the sensitivity is related to the polarity of the gas analysed. Films can also be used as electrochemical sensors because the characteristics of the current/potential curves are sensitive to the nature of the electrolytic solution. The spectral changes accompanying the applied voltage could be used to produce ionochromic sensor electrodes.The structure of the films has an important impact in the sensing properties of the films and in their stability. The optical and electrochemical sensing properties of Langmuir–Blodgett films are more reproducible than those observed in cast films. This makes films prepared using the LB technique to be preferred as sensing devices. However the casting technique provides a fast method to obtain cheap and highly ordered sensors.     

Suboptimal mean controllers for bounded and dynamic stochastic distributions

Based on the recently developed algorithms for the modelling and control of bounded dynamic stochastic systems (H. Wang, J. Zhang, Bounded stochastic distributions control for pseudo ARMAX stochastic systems, IEEE Transactions on Automatic control, 486–490), this paper presents the design of a subotpimal nonlinear mean controller for bounded dynamic stochastic systems with guaranteed stability. The B-spline functional expansion based square root model is used to represent the output probability density function of the system. This is then followed by the design of a mean controller of the output distribution of the system using nonlinear output tracking concept. A nonlinear quadratic optimization is performed using the well known Hamilton–Jacobi–Bellman equation. This leads to a controller which consists of a static unit, a state feedback part and an equivalent output feedback loop. In order to achieve high precision for the output tracking, the output feedback gain is determined by a learning process, where the Lyapunov stability analysis is performed to show the asymptotic stability of the closed loop system under some conditions. A simulation example is included to demonstrate the use of the algorithm and encouraging results have been obtained.