The first-order algorithm we propose is the regularized proximal primal-dual gradi- ent algorithm, and we develop a comprehensive theory on **its** tracking performance.
Specifically, we provide analytical results in terms of tracking a KKT point, and derive bounds for the tracking error defined as the distance between the algorithmic iterates and a KKT trajectory. We then provide sufficient conditions under which there exists a set of algorithmic parameters that guarantee that the tracking error bound holds. Qualitatively, the sufficient conditions for the existence of feasible parameters suggest that the problem should be “sufficiently convex” around a KKT trajectory to overcome the nonlinearity of the nonconvex constraints. The study of feasible algorithmic parameters motivates us to analyze the continuous-**time** limit of the discrete-**time** algorithm, which we formulate as a **system** of differential inclu- sions; results on **its** tracking performance as well as feasible and optimal algorithmic parameters are also derived. Finally, we derive conditions under which the KKT points for a given **time** instant will always be isolated so that bifurcations or merging of KKT trajectories do not happen.

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KEYWORDS: Distributed generation, grid connection, Kalman filter algorithm, load-concentration-bus, optimal location, optimal size, **power** loss
I.INTRODUCTION
IN response to the recently improved prices of oil and natural gas, it is expected that the electrical energy industry will go through significant and fast change with regard to **its** framework, function, preparing, and control. Moreover, because of new restrictions placed by cost-effective, governmental, and ecological factors, styles in **power** **system** preparing and function are being forced toward maximum usage of current **power** facilities with limited working edges[2]. Therefore, the electrical powered **power** companies are attempting to achieve this purpose via many different ways, one of which is to delay the distribution generation (DG) remedy by a separate energy manufacturer (IPP) to meet growing customer fill demand. In this case, deferral attributes obtained by the IPP rely on the step-by-step **system** stability enhancement made by the DG remedy[1]-[4]. The DG is based on the alternative energy such as energy cell, photovoltaic or PV and wind energy as well as mixed heat and energy gas generator, micro-turbine, etc. Now, it becomes an essential important component of the modern energy **system** recently for several reasons.

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* Correspondence: george.bollas@uconn.edu; Tel.: +1-860-486-6037
Abstract: The increasing variability in **power** plant load, in response to a wildly uncertain electricity market and the need to to mitigate CO 2 emissions, lead **power** plant operators to explore advanced options for efficiency **optimization**. Model-based, **system**-scale dynamic simulation and **optimization** are useful tools in this effort, and the subject of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical **power** plant was presented.

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JILIN CHEN, FENGQUAN ZHANG, ZHONGHUA GUO and WEIJIANG QIU
ABSTRACT
The collaborative **system** for **power** grid **operation** mode calculation implements data management of multi-level paralleling operated dispatching department’s indifferent places and joint **operation** mode calculation, which is based on the calculated data of **power** grid **operation** modes. The transient stability analysis monitors the components of electrical variation curve through the network transmission to the web page. When more users and more curves reaches a certain extent, the overall simulation **time** is longer and the curve is stuck because of the limitation of the transmission bandwidth and the refresh rate of the web page. This paper, The Douglas-Peucker algorithm is used in **system** to solve the data transmission of large amount of data delay problem and achieve the transmission of the large amount of data, the overall simulation **time** of curve in the web page display is decreased, and the curves are refreshed smoothly.

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𝑢 𝑡 𝑟 𝑛 = 𝐶 (2)
n–Index, 0.5∼0.9, decide by the **operation** condition.
It can be seen that the tangential velocity will increased, the hydrostatic pressure will be into the dynamic pres- sure with the reduced radius of gyration. In other words, the ﬂuid has had a rotating movement. The Serous gran- ules are disjoined two parts, one of it moves to the hy- drocyclone wall in the role of centrifugal force, at the same **time**, other part moves to the inside of the cyclone chamber by the role of radial velocity ﬂow. Coarse grain attains the higher speed by the centrifugal force. When the velocity is bigger than the radial velocity, the parti- cles transferred towards the wall, fell into the underﬂow region, taken by the underﬂow pipe from Chamber for the coarse-grained product. When the velocity is smaller than the radial velocity, the particles transferred to the inside of the chamber, entered into the overﬂow region, taken by the overﬂow tube from Chamber for the ﬁne product. And when the velocity is equal to the radial velocity, the particle will be swing at the radial of r. If the ﬂow region r on the decline, they will ﬂow into the underﬂow area, otherwise they will enter into the over- ﬂow. If the axial velocity is amount to zero, one half of the gypsum slurry will be into the overﬂow, and another will enter the Chamber. The size of particle is separa- tion grading, which is the basic principles of hydrocyclone classiﬁcation.

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However, theoretical as well as experimental results have shown that this simplifying assumption is generally far from reality [4]. Thus, it appears that sky radiance should be treated as anisotropic, particularly because of the strong forward scattering effect of aerosols [5–8]. Reviews on transforming data recorded by horizontal pyranometers to data that would have been received by tilted surfaces are given by many researches [9–14]. The best way to collect maximum daily energy is to use tracking systems. A tracker is a mechanical device that follows the direction of the sun on **its** daily sweep across the sky. The trackers are expensive, need energy for their **operation** and are not always applicable. Therefore, it is often practicable to orient the solar collector at an optimum tilt angle, and to correct the tilt from **time** to **time**. Several interesting articles have been devoted to this problem. Most of these articles treat the problem qualitatively and quantitatively [15–17], while others articles give an analytical treatment [18–21]. It is reported in the literature that in the northern hemisphere, the optimum orientation is south facing and the optimum tilt angle depends only on the latitude. No definite value is given by researchers for the optimum tilt angle. Further review of literature shows that there is a wide range of optimum tilt angle as recommended by different authors, and they are mostly for specific locations.

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This work presents an efficient and scalable method to pro- duce scenarios for a large **system** of wind farms. This method utilizes probabilistic forecasts using a conditional heteroscedas- tic model, and spatio-temporal dependencies are captured using a Gaussian copula. The literature on scenario generation is largely focused on the day-ahead **time** horizon, whereas this work considers the real-**time** applications. Although there is much overlap between the two problems, there are major dif- ferences that arise from the change in **time** scale. Most notably, real-**time** applications operate under much tighter **time** con- straints. Consider that the MISO LAC tool executes every 15 minutes, and the UC **optimization** problem must be solved within 5 minutes. The scenarios that correspond to the present

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3 Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
Received: 25.07.2013 • Accepted/Published Online: 30.09.2013 • Printed: 31.12.2015
Abstract: This paper proposes a modified particle swarm **optimization** considering **time**-**varying** acceleration coeﬃcients for the economic-emission load dispatch (EELD) problem. The new adaptive parameter is introduced to update the particle movements through the modification of the velocity equation of the classical particle swarm **optimization** (PSO) algorithm. The idea is to enhance the performance and robustness of classical PSO. The price penalty factor method is used to transform the multiobjective EELD problem into a single-objective problem. Then the weighted sum method is applied for finding the Pareto front solution. The best compromise solution for this problem is determined based on the fuzzy ranking approach. The IEEE 30-bus **system** has been used to validate the eﬀectiveness of the proposed algorithm.

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a.) We employ the DC **power** flow approximation [3] to express all network constraints.
b.) We model an “acceptable **system** trajectory”, denoted as (x 0 , x c , x b c ) ∈ X a in (7), as the exis- tence of a steady-state equilibrium with no loss of load throughout: (i) the pre-contingency **operation**, (ii) the short-term interval after the occurrence of any contingency c ∈ C c and before the **application** of the respective corrective control actions, and, (iii) the final state reached by following the **application** of corrective control actions while taking into account their possible failures. It follows that any case under which the mathematical constraints expressing the **system** operational limits would be violated for at least one of these three regimes is regarded in our implementation as an “unaccaptable **system** trajectory”.

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The deregulated market model is progressively taking place in **power** industries all over the world. With such liberalization, a third-party access is provided by separating the generation & transmission of **power**, and the consumers are empowered to pick from the private utilities as per their own choices for the electricity-buying purpose. However, in order to grab a larger customer-base and survive in the market, these suppliers might cause the commercial rivalry to take an unhealthy turn and resort to the unplanned **power** exchange through transmission lines. Parallel to restructuring of market, the industry also faces the challenge of satisfying the ever-increasing growth of load demand. All these force to operate some of the transmission lines close to their thermal limits which could result in being overstressed and eventually in congestion. On the other hand, the up gradation of **power** network by building new infrastructures like transmission lines, substations etc, most of the **time**, turns out to be not practical due to political, economical & environmental constraints.

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In this paper, we study the landscape of an online nonconvex **optimization** problem, for which the input data vary over **time** and the solution is a trajectory rather than a single point. To understand the complexity of finding a global solution of this problem, we introduce the notion of spurious (i.e., non-global) local trajectory as a generalization to the notion of spurious local solution in nonconvex (**time**-invariant) **optimization**. We develop an ordinary differential equation (ODE) associated with a **time**-**varying** nonlinear dynamical **system** which, at limit, characterizes the spurious local solutions of the **time**-**varying** **optimization** problem. We prove that the absence of spurious local trajectory is closely related to the transient behavior of the developed **system**. In particular, we show that if the problem is **time**-**varying**, the data variation may force all of the ODE trajectories initialized at arbitrary local minima at the initial **time** to gradually converge to the global solution trajectory. We study the Jacobian of the dynamical **system** along a local minimum trajectory and show how **its** eigenvalues are manipulated by the natural data variation in the problem, which may consequently trigger escaping poor local minima over **time**.

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The generalized model of these problems is a hybrid **optimization** problem involving state delay with characteristic **time** and parameter control. In this paper, we ﬁrst present a general question on state delays, then design **optimization** algorithms. Finally, two ex- amples in the ﬁeld of pest control are given to exhibit the eﬀectiveness of the proposed **optimization** algorithm.

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With regard to the wide spectrum of Administration of health turnover, in order to have control and surveillance on the Services and costs of treatment, the BI center has been settled in year 1390 to achieve high quality information, precise decisions and exact data. It will improve the **system** **operation** and prevent **time** wasting. With respect to the issues that have been mentioned and after working more than three years with this **system**, the following questions will be posed:

Index Terms—multi-objective **optimization**, particle swarm **optimization** algorithm, daily generation scheduling
I. I NTRODUCTION
The hydropower stations are important economic and kinetic energy unit in **power** **system**, which, on the one hand, can gain capacity benefits through peak load regulation, frequency modulation and undertaking spinning reserve capacity; and, on the other hand, can gain electricity quantity benefit by replacing the thermal **power** stations [1]. The traditional economic benefits of hydropower systems generally only consider the generation benefit, and rarely include dynamic benefits, such as peak-energy capacity benefits, spinning reserve capacity benefits, etc [2], which is disadvantageous to optimize energy structure and resource allocation, and economically dispatch and safety operate of grid. With the development of **power** market in our country, the capacity benefits of hydropower **system** have played a more and more important role. It's an important issue worthy of deep study to change the optimal scheduling model which includes capacity and electricity quantity benefits from the traditional scheduling model which

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B. Simulation Results with Real Speech
A similar simulation is carried out by using a speech segment “tea” from [13] with a length of 0.1 second, which is shown in the top left panel of Fig. 2. It was downsampled to 2 kHz in order to focus on low frequency formants. A 12-order model is applied to both algorithms and **power** series with q=4 is used for TVLP. Other settings are identical to that in the previous example, except λ e = 0.9 are used to track fast **power** changing in real data and the regularization parameter needs to be multiplied with a constant factor around 5 to reflect the increase in noise **power**. Results are presented in Fig. 2. The spectrogram only shows the first and second tracts. The estimated tracts of the proposed algorithm agree well with the real one while the conventional TVLP algorithm suffers from slow tracking and significant deviation. As for the pole trajectories, the TVLP have many poles near or even outside the unit circle, which may cause instability.

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Index Terms: Adaptive cotroller, Self-tuning controller, dynamic performance, **power** **system**, Kalman filtering.
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1 I NTRODUCTION
SELF-TUNING controller was originally proposed by Kalman in 1958. However, because of the unavailability of high-speed computers and inadequately developed theory, this technique was not taken up seriously at that **time**. The breakthrough came with the work reported by Astrom and Wittenmark in 1973. Since then this technique has become popular, especially due to the advent of microprocessors, which make it feasible to implement the STC algorithms [1]. Self-tuning controller (STC) is one of the techniques of adaptive control.

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The GPS Earth Observation Network **system** (GEONET) has been playing an important role in monitoring the crustal deformation of Japan. Since **its** start of **operation**, the requirements for accuracy and timeliness have become higher and higher. On the other hand, recent broadband communication infrastructure has had capability to realize real-**time** crust monitoring and to aid the development of a location-based service. In early 2003, the Geographical Survey Institute (GSI) upgraded the GEONET **system** to meet new requirements. The number of stations became 1200 in total by March, 2003. The antennas were uniﬁed to the choke ring antennas of Dorne Margolin T-type and the receivers were replaced with new ones that are capable of real-**time** observation and data transfer. The new **system** uses IP-connection through IP-VPN (Internet Protocol Virtual Private Network) for data transfer, which is provided by communication companies. The Data Processing **System**, which manages the observation data and analyses in GEONET, has 7 units. GEONET carries out three kinds of routine analyses and an analysis of RTK-type for emergencies. The new **system** has shown **its** capability for real-**time** crust monitoring, for example, the precise and rapid detection of coseismic (and post-seismic) motion caused by 2003 Tokachi-Oki earthquake.

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Autocorrelation is a fundamental tool in signal processing for finding repeated patterns and characterizing signals [1]. The autocorrelation of a random process with lag k is the expectation of the product of **its** value at **time** t and the conjugate of **its** value at **time** t-k, i.e. r ( t , t − k ) = E [ x ( t ) x H ( t − k )] . Therefore, the autocorrelation is a function of both **time** t and the lag k. Due to this complication, it is usually assumed that the random process is wise-sense stationary (WSS), i.e. independent of t above, or quasi-WSS so that the estimation process can be considerably simplified since the autocorrelation is now a function of lag only. However, many practical signals encountered in biomedical engineering and other physical phenomena are non-stationary in nature due to the **time**-**varying** information they carry.

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