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1. 发明申请

US20140032187A1 STOCHASTIC STATE ESTIMATION FOR SMART GRIDS 审中-公开
标题翻译：智能网格的STOCHASTIC状态估计
公开(公告)号：US20140032187A1
公开(公告)日：2014-01-30
申请号：US13880449
申请日：2011-11-04
申请人： Motto Alexis Legbedji , Andrey Torzhkov , Amit Chakraborty
发明人： Motto Alexis Legbedji , Andrey Torzhkov , Amit Chakraborty
IPC分类号： G06F17/50
CPC分类号： G06F17/5009 , G05B13/042 , G05B17/02 , G06F17/10
摘要： A method of approximating a solution of a stochastic state estimation (SSE) model of an electric grid, includes choosing (70) starting anchor points in an SSE model of an electric grid, relaxing (71) constraints of an SSE objective function to solve for a feasible solution of the SSE model, calculating (72) updated dual variables and infeasibility reduction directions from the feasible solution, generating (73) a linear cut for the chosen starting anchor points, choosing (74) a step size toward the reduction directions, and updating (75) the anchor points through branching by making the chosen step, wherein each anchor point defines a rectangle that at least partially covers a feasible solution set of the SSE model and the set of rectangles covering the feasible solution set of the SSE model define an approximate solution of the SSE model of said electric grid.
摘要翻译：一种近似电网随机状态估计（SSE）模型解的方法包括选择（70）在电网SSE模型中启动锚点，放松（71）SSE目标函数的约束，以求解从可行解中计算（72）更新的双变量和不可行性减少方向，对所选择的起始锚点产生（73）线性切割，选择（74）向减小方向的步长，以及通过进行所选择的步骤来通过分支来更新（75）所述锚定点，其中每个锚点定义至少部分地覆盖所述SSE模型的可行解集合的矩形，并且覆盖所述SSE模型的可行解集的矩形集合定义所述电网的SSE模型的近似解。

2. 发明授权

US08977524B2 Interior point method for reformulated optimal power flow model 有权
标题翻译：重构最优潮流模型的内点法
公开(公告)号：US08977524B2
公开(公告)日：2015-03-10
申请号：US13413086
申请日：2012-03-06
申请人： Motto Alexis Legbedji , Ruken Duzgun , Ioannis Akrotirianakis , Amit Chakraborty
发明人： Motto Alexis Legbedji , Ruken Duzgun , Ioannis Akrotirianakis , Amit Chakraborty
IPC分类号： G06F7/60 , H02J3/14 , G06Q10/04 , H02J3/00
CPC分类号： H02J3/14 , G06Q10/04 , H02J2003/007 , H02J2003/146 , Y02B70/3225 , Y04S20/222 , Y04S20/224
摘要： A method for approximating an optimal power flow of a smart electric power grid includes providing a cost function that models a smart electric power grid having buses connected by branches, deriving a set of linear equations that minimize the cost function subject to constraints from an expression of an extremum of the cost function with respect to all arguments, reducing a dimension of the linear equations by solving for a subset of the linear equations, re-organizing the reduced dimension linear equations into primal and dual parts, and decomposing the re-organized reduced dimensional linear equations into two systems of block matrix equations which can be solved by a series of back substitutions. A solution of the two systems of block matrix equations yields conditions for a lowest cost per kilowatthour delivered through the smart electric power grid.
摘要翻译：一种用于近似智能电网的最佳功率流的方法包括提供一种成本函数，其对具有通过分支连接的总线的智能电力网进行建模，导出一组线性方程，其中所述线性方程使得成本函数最小化，关于所有参数的成本函数的极值，通过求解线性方程的子集来减少线性方程的维度，将缩减维线性方程重新组织成原始和双重部分，并且分解重新组织的减少二维线性方程组成两个系统的矩阵方程组，可以通过一系列的后置换算来求解。块矩阵方程的两个系统的解决方案产生通过智能电力网输送的每千瓦最低成本的条件。

3. 发明申请

US20130035885A1 TOPOLOGY IDENTIFICATION IN DISTRIBUTION NETWORK WITH LIMITED MEASUREMENTS 有权
标题翻译：配电网的拓扑识别与有限的测量
公开(公告)号：US20130035885A1
公开(公告)日：2013-02-07
申请号：US13558711
申请日：2012-07-26
申请人： Yoav Sharon , Anuradha Annaswamy , Motto Alexis Legbedji , Amit Chakraborty
发明人： Yoav Sharon , Anuradha Annaswamy , Motto Alexis Legbedji , Amit Chakraborty
IPC分类号： G06F19/00 , G06F17/50 , G01R21/00
CPC分类号： H02J3/46 , G06F17/509 , G06F2217/10 , H02J13/0079 , H02J2003/003 , Y04S10/54 , Y04S20/48
摘要： A statistical technique is used to estimate the status of switching devices (such as circuit breakers, isolator switches and fuses) in distribution networks, using scares (i.e., limited or non-redundant) measurements. Using expected values of power consumption, and their variance, the confidence level of identifying the correct topology, or the current status of switching devices, is calculated using any given configuration of real time measurements. Different topologies are then compared in order to select the most likely topology at the prevailing time. The measurements are assumed as normally distributed random variables, and the maximum likelihood principle or a support vector machine is applied.
摘要翻译：使用统计技术来估计配电网络中的开关设备（如断路器，隔离开关和保险丝）的状态，使用惊吓（即有限或非冗余）测量。使用预期的功耗值及其方差，使用任何给定的实时测量配置来计算识别正确拓扑的置信水平或开关设备的当前状态。然后比较不同的拓扑结构，以便在当前的时间选择最可能的拓扑。将测量值假定为正态分布随机变量，并应用最大似然原理或支持向量机。

4. 发明授权

US09336338B2 Global solutions of smart building-grid energy management models 有权
标题翻译：智能建筑电网能源管理模式的全球解决方案
公开(公告)号：US09336338B2
公开(公告)日：2016-05-10
申请号：US13760420
申请日：2013-02-06
申请人： Motto Alexis Legbedji , Yu Sun , Amit Chakraborty
发明人： Motto Alexis Legbedji , Yu Sun , Amit Chakraborty
IPC分类号： G06F7/60 , G06F17/10 , G06F17/50 , G06Q50/06
CPC分类号： G06F17/5004 , G06F2217/78 , G06Q50/06 , Y02E60/76 , Y04S40/22
摘要： According to an aspect of the invention, there is provided a method for optimizing a cost of electric power generation in a smart site energy management model, including providing a cost function that models a smart building-grid energy system of a plurality of buildings on a site interconnected with electric power grid energy resources and constraints due to a building model, an electric grid model, and a building-grid interface model, where decision variables for each of the building model, the electric grid model, and the building-grid interface model are box-constrained, and minimizing the cost function subject to the building model constraints, the electric grid model constraints, and building-grid interface model constraints.
摘要翻译：根据本发明的一个方面，提供了一种用于优化智能场所能量管理模型中的发电成本的方法，包括提供对多个建筑物中的智能建筑物 - 电网能量系统进行建模的成本函数由于建筑模型，电网模型和建筑物 - 电网接口模型，电力网络能源资源和制约因素相互联系，其中建筑模型，电网模型和建筑物 - 电网接口各自的决策变量模型是框限制的，并且最小化构建模型约束，电网模型约束和建筑 - 电网接口模型约束的成本函数。

5. 发明申请

US20130238148A1 INTERIOR POINT METHOD FOR REFORMULATED OPTIMAL POWER FLOW MODEL 有权
标题翻译：改进的最优动力流量模型的内部点方法
公开(公告)号：US20130238148A1
公开(公告)日：2013-09-12
申请号：US13413086
申请日：2012-03-06
申请人： Motto Alexis Legbedji , Ruken Duzgun , Ioannis Akrotirianakis , Amit Chakraborty
发明人： Motto Alexis Legbedji , Ruken Duzgun , Ioannis Akrotirianakis , Amit Chakraborty
IPC分类号： G05F5/00
CPC分类号： H02J3/14 , G06Q10/04 , H02J2003/007 , H02J2003/146 , Y02B70/3225 , Y04S20/222 , Y04S20/224
摘要： A method for approximating an optimal power flow of a smart electric power grid includes providing a cost function that models a smart electric power grid having buses connected by branches, deriving a set of linear equations that minimize the cost function subject to constraints from an expression of an extremum of the cost function with respect to all arguments, reducing a dimension of the linear equations by solving for a subset of the linear equations, re-organizing the reduced dimension linear equations into primal and dual parts, and decomposing the re-organized reduced dimensional linear equations into two systems of block matrix equations which can be solved by a series of back substitutions. A solution of the two systems of block matrix equations yields conditions for a lowest cost per kilowatthour delivered through the smart electric power grid.
摘要翻译：一种用于近似智能电网的最佳功率流的方法包括提供一种成本函数，其对具有通过分支连接的总线的智能电力网进行建模，导出一组线性方程，其中所述线性方程使得成本函数最小化，关于所有参数的成本函数的极值，通过求解线性方程的子集来减少线性方程的维度，将缩减维线性方程重新组织成原始和双重部分，并且分解重新组织的减少二维线性方程组成两个系统的矩阵方程组，可以通过一系列的后置换算来求解。块矩阵方程的两个系统的解决方案产生通过智能电力网输送的每千瓦最低成本的条件。

6. 发明授权

US09287713B2 Topology identification in distribution network with limited measurements 有权
标题翻译：配电网拓扑识别测量有限
公开(公告)号：US09287713B2
公开(公告)日：2016-03-15
申请号：US13558711
申请日：2012-07-26
申请人： Yoav Sharon , Anuradha Annaswamy , Motto Alexis Legbedji , Amit Chakraborty
发明人： Yoav Sharon , Anuradha Annaswamy , Motto Alexis Legbedji , Amit Chakraborty
IPC分类号： H02J3/46 , G06F17/50 , H02J13/00 , H02J3/00
CPC分类号： H02J3/46 , G06F17/509 , G06F2217/10 , H02J13/0079 , H02J2003/003 , Y04S10/54 , Y04S20/48
摘要： A statistical technique is used to estimate the status of switching devices (such as circuit breakers, isolator switches and fuses) in distribution networks, using scares (i.e., limited or non-redundant) measurements. Using expected values of power consumption, and their variance, the confidence level of identifying the correct topology, or the current status of switching devices, is calculated using any given configuration of real time measurements. Different topologies are then compared in order to select the most likely topology at the prevailing time. The measurements are assumed as normally distributed random variables, and the maximum likelihood principle or a support vector machine is applied.
摘要翻译：使用统计技术来估计配电网络中的开关设备（如断路器，隔离开关和保险丝）的状态，使用惊吓（即有限或非冗余）测量。使用预期的功耗值及其方差，使用任何给定的实时测量配置来计算识别正确拓扑的置信水平或开关设备的当前状态。然后比较不同的拓扑结构，以便在当前的时间选择最可能的拓扑。将测量值假定为正态分布随机变量，并应用最大似然原理或支持向量机。

7. 发明申请

US20130238294A1 GLOBAL SOLUTIONS OF SMART BUILDING-GRID ENERGY MANAGEMENT MODELS 有权
标题翻译：智能建筑能源管理模式的全球解决方案
公开(公告)号：US20130238294A1
公开(公告)日：2013-09-12
申请号：US13760420
申请日：2013-02-06
申请人： Motto Alexis Legbedji , Yu Sun , Amit Chakraborty
发明人： Motto Alexis Legbedji , Yu Sun , Amit Chakraborty
IPC分类号： G06F17/50
CPC分类号： G06F17/5004 , G06F2217/78 , G06Q50/06 , Y02E60/76 , Y04S40/22
摘要： According to an aspect of the invention, there is provided a method for optimizing a cost of electric power generation in a smart site energy management model, including providing a cost function that models a smart building-grid energy system of a plurality of buildings on a site interconnected with electric power grid energy resources and constraints due to a building model, an electric grid model, and a building-grid interface model, where decision variables for each of the building model, the electric grid model, and the building-grid interface model are box-constrained, and minimizing the cost function subject to the building model constraints, the electric grid model constraints, and building-grid interface model constraints.
摘要翻译：根据本发明的一个方面，提供了一种用于优化智能场所能量管理模型中的发电成本的方法，包括提供对多个建筑物中的智能建筑物 - 电网能量系统进行建模的成本函数由于建筑模型，电网模型和建筑物 - 电网接口模型，电力网络能源资源和制约因素相互联系，其中建筑模型，电网模型和建筑物 - 电网接口各自的决策变量模型是框限制的，并且最小化构建模型约束，电网模型约束和建筑 - 电网接口模型约束的成本函数。

8. 发明授权

US08396572B2 System and method for energy plant optimization using mixed integer-linear programming 有权
标题翻译：使用混合整数线性规划的能源工厂优化的系统和方法
公开(公告)号：US08396572B2
公开(公告)日：2013-03-12
申请号：US12691909
申请日：2010-01-22
申请人： Andrey Torzhkov , Amit Chakraborty
发明人： Andrey Torzhkov , Amit Chakraborty
IPC分类号： G05B13/02
CPC分类号： G05B17/02 , G05B13/042
摘要： A method for optimizing operational settings for a plurality of energy devices includes representing each of the plurality of energy devices in terms of a set of decision variables and operational parameters. The decision variables and operational parameters are constrained based on operational conditions and interrelationship within the plurality of energy devices. A two-tiered model of the plurality of energy devices is generated wherein a top tier of the model represents interaction of various sub-models and a bottom tier of the model includes a set of the sub-models that form the top tier, each sub-model representing detailed operation of the plurality of energy devices. The two-tiered model is optimized to provide either a schedule of operation for the plurality of energy devices or real-time control for the plurality of energy devices.
摘要翻译：用于优化多个能量设备的操作设置的方法包括根据一组决策变量和操作参数来表示多个能量设备中的每一个。决定变量和操作参数基于多个能量设备内的操作条件和相互关系来约束。生成多个能量装置的两层模型，其中模型的顶层表示各种子模型的交互作用，模型的底层包括形成顶层的子模型集合，每个子模型 - 表示多个能量装置的详细操作的模型。双层模型被优化以提供多个能量装置的操作计划或者用于多个能量装置的实时控制。

9. 发明申请

US20110066258A1 System and Method for Energy Plant Optimization Using Mixed Integer-Linear Programming 有权
标题翻译：使用混合整数线性规划的能源工厂优化的系统和方法
公开(公告)号：US20110066258A1
公开(公告)日：2011-03-17
申请号：US12691909
申请日：2010-01-22
申请人： Andrey Torzhkov , Amit Chakraborty
发明人： Andrey Torzhkov , Amit Chakraborty
IPC分类号： G05B13/02
CPC分类号： G05B17/02 , G05B13/042
摘要： A method for optimizing operational settings for a plurality of energy devices includes representing each of the plurality of energy devices in terms of a set of decision variables and operational parameters. The decision variables and operational parameters are constrained based on operational conditions and interrelationship within the plurality of energy devices. A two-tiered model of the plurality of energy devices is generated wherein a top tier of the model represents interaction of various sub-models and a bottom tier of the model includes a set of the sub-models that form the top tier, each sub-model representing detailed operation of the plurality of energy devices. The two-tiered model is optimized to provide either a schedule of operation for the plurality of energy devices or real-time control for the plurality of energy devices.
摘要翻译：用于优化多个能量设备的操作设置的方法包括根据一组决策变量和操作参数来表示多个能量设备中的每一个。决定变量和操作参数基于多个能量设备内的操作条件和相互关系来约束。生成多个能量装置的两层模型，其中模型的顶层表示各种子模型的交互作用，模型的底层包括形成顶层的子模型集合，每个子模型 - 表示多个能量装置的详细操作的模型。双层模型被优化以提供多个能量装置的操作计划或者用于多个能量装置的实时控制。

10. 发明授权

US08719194B2 Hybrid interior-point alternating directions algorithm for support vector machines and feature selection 有权
标题翻译：用于支持向量机和特征选择的混合内点交替方向算法
公开(公告)号：US08719194B2
公开(公告)日：2014-05-06
申请号：US13611528
申请日：2012-09-12
申请人： Zhiwei Qin , Xiaocheng Tang , Ioannis Akrotirianakis , Amit Chakraborty
发明人： Zhiwei Qin , Xiaocheng Tang , Ioannis Akrotirianakis , Amit Chakraborty
IPC分类号： G06N99/00
CPC分类号： G06N99/005
摘要： A method for training a classifier for selecting features in sparse data sets with high feature dimensionality includes providing a set of data items x and labels y, minimizing a functional of the data items x and associated labels y L ⁡ ( w , b , a , c , γ 1 , γ 2 ) := 1 N ⁢ ∑ i = 1 N ⁢ a i + λ 1 ⁢  c  1 + λ 2 2 ⁢  w  2 2 + γ 1 T ⁡ ( e - Y ⁡ ( Xw + be ) - a ) + γ 2 T ⁡ ( w - c ) + μ 1 2 ⁢  e - Y ⁡ ( Xw + be ) - a  2 2 + μ 2 2 ⁢  w - c  2 2 to solve for hyperplane w and offset b of a classifier by successively iteratively approximating w and b, auxiliary variables a and c, and multiplier vectors γ1 and γ2, wherein λ1, λ2, μ1, and μ2 are predetermined constants, e is a unit vector, and X and Y are respective matrix representations of the data items x and labels y; providing non-zero elements of the hyperplane vector w and corresponding components of X and Y as arguments to an interior point method solver to solve for hyperplane vector w and offset b, wherein w and b define a classifier than can associate each data item x with the correct label y.
摘要翻译：用于训练用于在具有高特征维度的稀疏数据集中选择特征的分类器的方法包括提供一组数据项x和标号y，使数据项x和相关标签y L⁡（w，b，a， c，γ1，γ2）：= 1NΣi = 1 N ai +λ1c1 +λ2 2w2 2 +γ1 T⁡（e-Y⁡（Xw + be）-a）+γ2 T⁡（w-c）+μ1 2e -Y⁡（Xw + be）-a22 +μ2 2w -c2 2求解对于分类器的超平面w和偏移量b，通过连续迭代地近似w和b，辅助变量a和c以及乘数向量γ1和γ2，其中λ1，λ2，μ1和μ2是预定常数，e是单位矢量， X和Y是数据项x和标签y的相应矩阵表示; 提供超平面矢量w的非零元素和X和Y的对应分量作为内点方法求解器的参数来求解超平面矢量w和偏移b，其中w和b定义分类器，可以将每个数据项x与正确的标签y。

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